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Department of Statistics

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The department encourages research in both theoretical and applied statistics. Faculty members of the department have been leaders in research on a multitude of topics that include statistical inference, statistical computing and Monte-Carlo methods, analysis of missing data, causal inference, stochastic processes, multilevel models, experimental design, network models and the interface of statistics and the social, physical, and biological sciences. A unique feature of the department lies in the fact that apart from methodological research, all the faculty members are also heavily involved in applied research, developing novel methodology that can be applied to a wide array of fields like astrophysics, biology, chemistry, economics, engineering, public policy, sociology, education and many others.

Two carefully designed special courses offered to Ph.D. students form a unique feature of our program. Among these, Stat 303 equips students with the  basic skills necessary to teach statistics , as well as to be better overall statistics communicators. Stat 399 equips them with generic skills necessary for problem solving abilities.

Our Ph.D. students often receive substantial guidance from several faculty members, not just from their primary advisors, and in several settings. For example, every Ph.D. candidate who passes the qualifying exam gives a 30 minute presentation each semester (in Stat 300 ), in which the faculty ask questions and make comments. The Department recently introduced an award for Best Post-Qualifying Talk (up to two per semester), to further encourage and reward inspired research and presentations.

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Department of Statistics and Data Science

Ph.d. program.

Fields of study include the main areas of statistical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory (stochastic processes, asymptotics, weak convergence), information theory, bioinformatics and genetics, classification, data mining and machine learning, neural nets, network science, optimization, statistical computing, and graphical models and methods.

With this background, graduates of the program have found excellent positions in universities, industry, and government. See the list of alumni for examples.

A Short Guide for Students Interested in a Statistics PhD Program

This summer I had several conversations with undergraduate students seeking career advice. All were interested in data analysis and were considering graduate school. I also frequently receive requests for advice via email. We have posted on this topic before, for example here and here , but I thought it would be useful to share this short guide I put together based on my recent interactions.

It’s OK to be confused

When I was a college senior I didn’t really understand what Applied Statistics was nor did I understand what one does as a researcher in academia. Now I love being an academic doing research in applied statistics. But it is hard to understand what being a researcher is like until you do it for a while. Things become clearer as you gain more experience. One important piece of advice is to carefully consider advice from those with more experience than you. It might not make sense at first, but I can tell today that I knew much less than I thought I did when I was 22.

Should I even go to graduate school?

Yes. An undergraduate degree in mathematics, statistics, engineering, or computer science provides a great background, but some more training greatly increases your career options. You may be able to learn on the job, but note that a masters can be as short as a year.

A masters or a PhD?

If you want a career in academia or as a researcher in industry or government you need a PhD. In general, a PhD will give you more career options. If you want to become a data analyst or research assistant, a masters may be enough. A masters is also a good way to test out if this career is a good match for you. Many people do a masters before applying to PhD Programs. The rest of this guide focuses on those interested in a PhD.

What discipline?

There are many disciplines that can lead you to a career in data science: Statistics, Biostatistics, Astronomy, Economics, Machine Learning, Computational Biology, and Ecology are examples that come to mind. I did my PhD in Statistics and got a job in a Department of Biostatistics. So this guide focuses on Statistics/Biostatistics.

Note that once you finish your PhD you have a chance to become a postdoctoral fellow and further focus your training. By then you will have a much better idea of what you want to do and will have the opportunity to chose a lab that closely matches your interests.

What is the difference between Statistics and Biostatistics?

Short answer: very little. I treat them as the same in this guide. Long answer: read this .

How should I prepare during my senior year?

Good grades in math and statistics classes are almost a requirement. Good GRE scores help and you need to get a near perfect score in the Quantitative Reasoning part of the GRE. Get yourself a practice book and start preparing. Note that to survive the first two years of a statistics PhD program you need to prove theorems and derive relatively complicated mathematical results. If you can’t easily handle the math part of the GRE, this will be quite challenging.

When choosing classes note that the area of math most related to your stat PhD courses is Real Analysis. The area of math most used in applied work is Linear Algebra, specifically matrix theory including understanding eigenvalues and eigenvectors. You might not make the connection between what you learn in class and what you use in practice until much later. This is totally normal.

If you don’t feel ready, consider doing a masters first. But also, get a second opinion. You might be being too hard on yourself.

Programming

You will be using a computer to analyze data so knowing some programming is a must these days. At a minimum, take a basic programming class. Other computer science classes will help especially if you go into an area dealing with large datasets. In hindsight, I wish I had taken classes on optimization and algorithm design.

Know that learning to program and learning a computer language are different things. You need to learn to program. The choice of language is up for debate. If you only learn one, learn R. If you learn three, learn R, Python and C++.

Knowing Linux/Unix is an advantage. If you have a Mac try to use the terminal as much as possible. On Windows get an emulator.

Writing and Communicating

My biggest educational regret is that, as a college student, I underestimated the importance of writing. To this day I am correcting that mistake.

Your success as a researcher greatly depends on how well you write and communicate. Your thesis, papers, grant proposals and even emails have to be well written. So practice as much as possible. Take classes, read works by good writers, and practice . Consider starting a blog even if you don’t make it public. Also note that in academia, job interviews will involve a 50 minute talk as well as several conversations about your work and future plans. So communication skills are also a big plus.

But wait, why so much math?

The PhD curriculum is indeed math heavy. Faculty often debate the possibility of changing the curriculum. But regardless of differing opinions on what is the right amount, math is the foundation of our discipline. Although it is true that you will not directly use much of what you learn, I don’t regret learning so much abstract math because I believe it positively shaped the way I think and attack problems.

Note that after the first two years you are pretty much done with courses and you start on your research. If you work with an applied statistician you will learn data analysis via the apprenticeship model. You will learn the most, by far, during this stage. So be patient. Watch these two Karate Kid scenes for some inspiration.

What department should I apply to?

The top 20-30 departments are practically interchangeable in my opinion. If you are interested in applied statistics make sure you pick a department with faculty doing applied research. Note that some professors focus their research on the mathematical aspects of statistics. By reading some of their recent papers you will be able to tell. An applied paper usually shows data (not simulated) and motivates a subject area challenge in the abstract or introduction. A theory paper shows no data at all or uses it only as an example.

Can I take a year off?

Absolutely. Especially if it’s to work in a data related job. In general, maturity and life experiences are an advantage in grad school.

What should I expect when I finish?

You will have many many options. The demand of your expertise is great and growing. As a result there are many high-paying options. If you want to become an academic I recommend doing a postdoc. Here is why. But there are many other options as we describe here and here .

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Department of Statistics

Columbian College of Arts & Sciences

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PhD in Statistics

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The STEM-designated PhD in Statistics program provides advanced training in topics including probability, linear models, time series analysis, Bayesian statistics, inference, reliability, statistics in law and regulatory policy and much more.

Nearly all GW statistics PhD graduates have secured job placements in the statistics or data science industry, with employers  including Amazon, Facebook and Capital One. During the program, PhD students work closely with faculty on original research in their area of interest. 

The degree provides training in theory and applications and is suitable for both full-time and part-time students. Most graduate courses are offered in the early evening to accommodate student schedules. 

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Graduate Program Advisors

Application Requirements

Prospective PhD students typically have earned a master’s degree in statistics or a related discipline. Students need a strong background in mathematics, including courses in advanced calculus, linear algebra and mathematical statistics.

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"GW encouraged me to tap into expertise from within as well as outside the university while researching my dissertation topic. I learned about the value of collaboration throughout my doctoral studies. Collaboration is so important in science, and it’s been instrumental in our success at Emmes."

Anne Lindblad PhD ’90 President, The Emmes Company

Students in their first semester of the PhD in Statistics program must meet with the program director  prior to signing up for classes. Students should continue to seek advice from the advisor throughout the program, particularly when determining whether any previous coursework can be applied toward their degree.

General Examinations

The general examination consists of two parts: a qualifying examination and an examination to determine the student's readiness to carry out the proposed dissertation research.

Each PhD candidate is required to take and pass the PhD qualifying exam. The written exam is given at the beginning of the fall semester each year. It consists of two papers:

• Inference: STAT 6202 and 8263
• Probability: STAT 6201 and 8257

The written exam is required for the first attempt. If a student cannot pass it, then there are two options for the second attempt.

• Option #1 for the second attempt : after approximately a year, the student will retake the written exam (see above for exam description).
• Option #2 for the second attempt : within approximately half a year, based on the scope of the written exam (see above for exam description), the student must demonstrate satisfactory improvements through (open-book, take-home) problem solving and an oral exam (with questions and answers).

No more than two attempts are permitted.

After passing the qualifying examination, the candidate should select a dissertation advisor. In consultation with the advisor, the candidate should pass a readiness examination, usually consisting of a research proposal and an oral examination. A committee of at least two professors should administer the readiness examination.

Dissertation

Students are required to complete a written dissertation that should be defended before an examination committee of at least four examiners. The dissertation should contain original scholarly research and must comply with all other GW rules and regulations. For more guidance on dissertation process, review the CCAS PhD Student Handbook . For formatting and submission guidelines, visit the Electronic Theses and Dissertations Submission website .

Past Theses

Course Requirements 

The program requires 72 credit hours, of which at least 48 must be from coursework and at least 12 must be from dissertation research. Up to 24 credit hours may be transferred from a prior master’s degree (contrary to general GW doctoral program requirements , which allow up to 30 transfer credit hours).

PhD in Statistics

Program description.

The Ph.D. program in statistics prepares students for a career pursuing research in either academia or industry.  The program provides rigorous classroom training in the theory, methodology, and application of statistics, and provides the opportunity to work with faculty on advanced research topics over a wide range of theory and application areas. To enter, students need a bachelor’s degree in mathematics, statistics, or a closely related discipline. Students graduating with a PhD in Statistics are expected to:

• Demonstrate an understanding the core principles of Probability Theory, Estimation Theory, and Statistical Methods.
• Demonstrate the ability to conduct original research in statistics.
• Demonstrate the ability to present research-level statistics in a formal lecture

Requirements for the Ph.D. (Statistics Track)

Course Work A Ph.D. student in our department must complete sixteen courses for the Ph.D. At most, four of these courses may be transferred from another institution. If the Ph.D. student is admitted to the program at the post-Master’s level, then eight courses are usually required.

Qualifying Examinations First, all Ph.D. students in the statistics track must take the following two-semester sequences: MA779 and MA780 (Probability Theory I and II), MA781 (Estimation Theory) and MA782 (Hypothesis Testing), and MA750 and MA751 (Advanced Statistical Methods I and II). Then, to qualify a student to begin work on a PhD dissertation, they must pass two of the following three exams at the PhD level: probability, mathematical statistics, and applied statistics. The probability and mathematical statistics exams are offered every September and the applied statistics exam is offered every April.

• PhD Exam in Probability: This exam covers the material covered in MA779 and MA780 (Probability Theory I and II).
• PhD Exam in Mathematical Statistics: This exam covers material covered in MA781 (Estimation Theory) and MA782 (Hypothesis Testing).
• PhD Exam in Applied Statistics: This exam covers the same material as the M.A. Applied exam and is offered at the same time, except that in order to pass it at the PhD level a student must correctly solve all four problems.

Note: Students concentrating in probability may choose to do so either through the statistics track or through the mathematics track. If a student wishes to do so through the mathematics track, the course and exam requirements are different. Details are available here .

Dissertation The dissertation is the major requirement for a Ph.D. student. After the student has completed all course work, the Director of Graduate Studies, in consultation with the student, selects a three-member dissertation committee. One member of this committee is designated by the Director of Graduate Studies as the Major Advisor for the student. Once completed, the dissertation must be defended in an oral examination conducted by at least five members of the Department.

The Dissertation and Final Oral Examination follows the   GRS General Requirements for the Doctor of Philosophy Degree .

Satisfactory Progress Toward the Degree Upon entering the graduate program, each student should consult the Director of Graduate Studies (Prof. David Rohrlich) and the Associate Director of the Program in Statistics (Prof. Konstantinos Spiliopoulos). Initially, the Associate Director of the Program in Statistics will serve as the default advisor to the student. Eventually the student’s advisor will be determined in conjunction with their dissertation research. The Associate Director of the Program in Statistics, who will be able to guide the student through the course selection and possible directed study, should be consulted often, as should the Director of Graduate Studies. Indeed, the Department considers it important that each student progress in a timely manner toward the degree. Each M.A. student must have completed the examination by the end of their second year in the program, while a Ph.D. student must have completed the qualifying examination by the third year. Students entering the Ph.D. program with an M.A. degree must have completed the qualifying examination by October of the second year. Failure to meet these deadlines may jeopardize financial aid. Some flexibility in the deadlines is possible upon petition to the graduate committee in cases of inadequate preparation.

Students enrolled in the Graduate School of Arts & Sciences (GRS) are expected to adhere to a number of policies at the university, college, and departmental levels. View the policies on the Academic Bulletin and GRS website .

Residency Post-BA students must complete all of the requirements for a Ph.D. within seven years of enrolling in the program and post-MA students must complete all requirements within five years. This total time limit is set by the Graduate School. Students needing extra time must petition the Graduate School. Also, financial aid is not guaranteed after the student’s fifth year in the program.

Financial Aid

As with all Ph.D. students in the Department of Mathematics and Statistics, the main source of financial aid for graduate students studying statistics is a Teaching Fellowship. These awards carry a stipend as well as tuition remission for six courses per year. Teaching Fellows are required to assist a faculty member who is teaching a course, usually a large lecture section of an introductory statistics course. Generally, the Teaching Fellow is responsible for conducting a number of discussion sections consisting of approximately twenty-five students each, as well as for holding office hours and assisting with grading. The Teaching Fellowship usually entails about twenty hours of work per week. For that reason, Teaching Fellows enroll in at most three courses per semester. A Teaching Fellow Seminar is conducted to help new Teaching Fellows develop as instructors and to promote the continuing development of experienced Teaching Fellows.

Other sources of financial aid include University Fellowships and Research Assistantships. The University Fellowships are one-year awards for outstanding students and are service-free. They carry stipends plus full tuition remission. Students do not need to apply for these fellowships. Research Assistantships are linked to research done with individual faculty, and are paid for through those faculty members’ grants. As a result, except on rare occasions, Research Assistantships typically are awarded to students in their second year and beyond, after student and faculty have had sufficient time to determine mutuality of their research interests.

Regular reviews of the performance of Teaching Fellows and Research Assistants in their duties as well as their course work are conducted by members of the Department’s Graduate Committee.

Ph.D. Program Milestones

The department considers it essential that each student progress in a timely manner toward completion of the degree. The following are the deadlines for achieving the milestones described in the Degree Requirements and constitute the basis for evaluating satisfactory progress towards the Ph.D. These deadlines are not to be construed as expected times to complete the various milestones, but rather as upper bounds. In other words,   a student in good standing expecting to complete   the degree within the five years of guaranteed funding will meet these milestones by the much e arlier target dates indicated below.   Failure to achieve these milestones in a timely manner may affectfinancial aid.

• Target: April of Year 1
• Deadline: April of Year 2
• Target: Spring of Year 2 post-BA/Spring of Year 1 post-MA
• Deadline: End of Year 3 post-BA/Fall of Year 2 post-MA
• Target: Spring of Year 2
• Deadline: End of Year 3
• Target: Spring of Year 2 or Fall of Year 3 post-BA/October of Year 2 post-MA
• Deadline: End of Year 3 post-BA/October of Year 2 post-MA
• Target: end of Year 3
• Deadline: End of Year 4
• Target: End of Year 5
• Deadline: End of Year 6

If you have any questions regarding our PhD program in Statistics, please reach out to us at [email protected]

Cornell University does not offer a separate Masters of Science (MS) degree program in the field of Statistics. Applicants interested in obtaining a masters-level degree in statistics should consider applying to Cornell's MPS Program in Applied Statistics.

Choosing a Field of Study

There are many graduate fields of study at Cornell University. The best choice of graduate field in which to pursue a degree depends on your major interests. Statistics is a subject that lies at the interface of theory, applications, and computing. Statisticians must therefore possess a broad spectrum of skills, including expertise in statistical theory, study design, data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and methods to their collaborators, and assist them in properly communicating their results. If the study of statistics is your major interest then you should seriously consider applying to the Field of Statistics.

There are also several related fields that may fit even better with your interests and career goals. For example, if you are mainly interested in mathematics and computation as they relate to modeling genetics and other biological processes (e.g, protein structure and function, computational neuroscience, biomechanics, population genetics, high throughput genetic scanning), you might consider the Field of Computational Biology . You may wish to consider applying to the Field of Electrical and Computer Engineering if you are interested in the applications of probability and statistics to signal processing, data compression, information theory, and image processing. Those with a background in the social sciences might wish to consider the Field of Industrial and Labor Relations with a major or minor in the subject of Economic and Social Statistics. Strong interest and training in mathematics or probability might lead you to choose the Field of Mathematics . Lastly, if you have a strong mathematics background and an interest in general problem-solving techniques (e.g., optimization and simulation) or applied stochastic processes (e.g., mathematical finance, queuing theory, traffic theory, and inventory theory) you should consider the Field of Operations Research .

Residency Requirements

Students admitted to PhD program must be "in residence" for at least four semesters, although it is generally expected that a PhD will require between 8 and 10 semesters to complete. The chair of your Special Committee awards one residence unit after the satisfactory completion of each semester of full-time study. Fractional units may be awarded for unsatisfactory progress.

Your Advisor and Special Committee

The Director of Graduate Studies is in charge of general issues pertaining to graduate students in the field of Statistics. Upon arrival, a temporary Special Committee is also declared for you, consisting of the Director of Graduate Studies (chair) and two other faculty members in the field of Statistics. This temporary committee shall remain in place until you form your own Special Committee for the purposes of writing your doctoral dissertation. The chair of your Special Committee serves as your primary academic advisor; however, you should always feel free to contact and/or chat with any of the graduate faculty in the field of Statistics.

The formation of a Special Committee for your dissertation research should serve your objective of writing the best possible dissertation. The Graduate School requires that this committee contain at least three members that simultaneously represent a certain combination of subjects and concentrations. The chair of the committee is your principal dissertation advisor and always represents a specified concentration within the subject & field of Statistics. The Graduate School additionally requires PhD students to have at least two minor subjects represented on your special committee. For students in the field of Statistics, these remaining two members must either represent (i) a second concentration within the subject of Statistics, and one external minor subject; or, (ii) two external minor subjects. Each minor advisor must agree to serve on your special committee; as a result, the identification of these minor members should occur at least 6 months prior to your A examination.

Some examples of external minors include Computational Biology, Demography, Computer Science, Economics, Epidemiology, Mathematics, Applied Mathematics and Operations Research. The declaration of an external minor entails selecting (i) a field other than Statistics in which to minor; (ii) a subject & concentration within the specified field; and, (iii) a minor advisor representing this field/subject/concentration that will work with you in setting the minor requirements. Typically, external minors involve gaining knowledge in 3-5 graduate courses in the specified field/subject, though expectations can vary by field and even by the choice of advisor. While any choice of external minor subject is technically acceptable, the requirement that the minor representative serve on your Special Committee strongly suggests that the ideal choice(s) should share some natural connection with your choice of dissertation topic.

The fields, subjects and concentrations represented on your committee must be officially recognized by the Graduate School ; the Degrees, Subjects & Concentrations tab listed under each field of study provides this information. Information on the concentrations available for committee members chosen to represent the subject of Statistics can be found on the Graduate School webpage . 

Statistics PhD Travel Support

The Department of Statistics and Data Science has established a fund for professional travel for graduate students. The intent of the Department is to encourage travel that enhances the Statistics community at Cornell by providing funding for graduate students in statistics that will be presenting at conferences. Please review the Graduate Student Travel Award Policy website for more information. 

Completion of the PhD Degree

In addition to the specified residency requirements, students must meet all program requirements as outlined in Program Course Requirements and Timetables and Evaluations and Examinations, as well as complete a doctoral dissertation approved by your Special Committee. The target time to PhD completion is between 4 and 5 years; the actual time to completion varies by student.

Students should consult both the Guide to Graduate Study and Code of Legislation of the Graduate Faculty (available at www.gradschool.cornell.edu ) for further information on all academic and procedural matters pertinent to pursuing a graduate degree at Cornell University.

PhD Admissions

Our PhD program welcomes students from a broad range of theoretical, applied, and interdisciplinary backgrounds, and provides rigorous preparation for a future career in statistics, probability, or data science. Our top-ranked program usually takes 5 years to complete. PhD theses are diverse and varied, reflecting the scope of faculty research interests, with many students involved in interdisciplinary research. There are also Designated Emphases in Computational and Genomic Biology; Computational Precision Health; and Computational Science and Engineering if one chooses to take a more concentrated approach.

Our department has been a leader in embracing machine learning and data science. We helped found the Division of Computing, Data Science, and Society (CDSS) , which was launched in 2019 under Associate Provost Jennifer Chayes and continues to strengthen both our interdisciplinary ties and foundational research. Our graduates go on to solve impactful problems in academia, industry, and non-profits, informing consequential decisions such as election auditing, medical treatment, police reform, and scientific reproducibility, and developing elegant mathematical tools for understanding networks, genetics, and language, among other areas.

Financial Support

Program information, the application for fall 2024 is closed., the fall 2025 phd application will open in september 2024., we do not offer spring admissions. , for fall 2024 gre is not required and will not be accepted. subject tests are optional..

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PhD Program

Wharton’s PhD program in Statistics provides the foundational education that allows students to engage both cutting-edge theory and applied problems. These include problems from a wide variety of fields within Wharton, such as finance, marketing, and public policy, as well as fields across the rest of the University such as biostatistics within the Medical School and computer science within the Engineering School.

Major areas of departmental research include: analysis of observational studies; Bayesian inference, bioinformatics; decision theory; game theory; high dimensional inference; information theory; machine learning; model selection; nonparametric function estimation; and time series analysis.

Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important. Graduates of the department typically take positions in academia, government, financial services, and bio-pharmaceutical industries.

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Department of Statistics and Data Science

The Wharton School, University of Pennsylvania Academic Research Building 265 South 37th Street, 3rd & 4th Floors Philadelphia, PA 19104-1686

Phone: (215) 898-8222

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Ph.D. in Statistics

Our doctoral program in statistics gives future researchers preparation to teach and lead in academic and industry careers.

Program Description

Degree type.

approximately 5 years

The relatively new Ph.D. in Statistics strives to be an exemplar of graduate training in statistics. Students are exposed to cutting edge statistical methodology through the modern curriculum and have the opportunity to work with multiple faculty members to take a deeper dive into special topics, gain experience in working in interdisciplinary teams and learn research skills through flexible research electives. Graduates of our program are prepared to be leaders in statistics and machine learning in both academia and industry.

The Ph.D. in Statistics is expected to take approximately five years to complete, and students participate as full-time graduate students.  Some students are able to finish the program in four years, but all admitted students are guaranteed five years of financial support.  

Within our program, students learn from global leaders in statistics and data sciences and have:

20 credits of required courses in statistical theory and methods, computation, and applications

18 credits of research electives working with two or more faculty members, elective coursework (optional), and a guided reading course

Dissertation research

Coursework Timeline

Year 1: focus on core learning.

The first year consists of the core courses:

• SDS 384.2 Mathematical Statistics I
• SDS 383C Statistical Modeling I
• SDS 387 Linear Models
• SDS 384.11 Theoretical Statistics
• SDS 383D Statistical Modeling II
• SDS 386D Monte Carlo Methods

In addition to the core courses, students of the first year are expected to participate in SDS 190 Readings in Statistics. This class focuses on learning how to read scientific papers and how to grasp the main ideas, as well as on practicing presentations and getting familiar with important statistics literature.

At the end of the first year, students are expected to take a written preliminary exam. The examination has two purposes: to assess the student’s strengths and weaknesses and to determine whether the student should continue in the Ph.D. program. The exam covers the core material covered in the core courses and it consists of two parts: a 3-hour closed book in-class portion and a take-home applied statistics component. The in-class portion is scheduled at the end of the Spring Semester after final exams (usually late May). The take-home problem is distributed at the end of the in-class exam, with a due-time 24 hours later. 

Year 2: Transitioning from Student to Researcher

In the second year of the program, students take the following courses totaling 9 credit hours each semester:

• Required: SDS 190 Readings in Statistics (1 credit hour)
• Required: SDS 389/489 Research Elective* (3 or 4 credit hours) in which the student engages in independent research under the guidance of a member of the Statistics Graduate Studies Committee
• One or more elective courses selected from approved electives ; and/or
• One or more sections of SDS 289/389/489 Research Elective* (2 to 4 credit hours) in which the student engages in independent research with a member(s) of the Statistics Graduate Studies Committee OR guided readings/self-study in an area of statistics or machine learning. 
• Internship course (0 or 1 credit hour; for international students to obtain Curricular Practical Training; contact Graduate Coordinator for appropriate course options)
• GRS 097 Teaching Assistant Fundamentals or NSC 088L Introduction to Evidence-Based Teaching (0 credit hours; for TA and AI preparation)

* Research electives allow students to explore different advising possibilities by working for a semester with a particular professor. These projects can also serve as the beginning of a dissertation research path. No more than six credit hours of research electives can be taken with a single faculty member in a semester.

Year 3: Advance to Candidacy

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. At the end of the second year or during their third year, students are expected to present their plan of study for the dissertation in an Oral candidacy exam. During this exam, students should demonstrate their research proficiency to their Ph.D. committee members. Students who successfully complete the candidacy exam can apply for admission to candidacy for the Ph.D. once they have completed their required coursework and satisfied departmental requirements. The steps to advance to candidacy are:

• Discuss potential candidacy exam topics with advisor
• Propose Ph.D. committee: the proposed committee must follow the Graduate School and departmental regulations on committee membership for what will become the Ph.D. Dissertation Committee
•   Application for candidacy

Year 4+: Dissertation Completion and Defense

Students are encouraged to attend conferences, give presentations, as well as to develop their dissertation research. Moreover, they are expected to present part of their work in the framework of the department's Ph.D. poster session.

Students who are admitted to candidacy will be expected to complete and defend their Ph.D. thesis before their Ph.D. committee to be awarded the degree. The final examination, which is oral, is administered only after all coursework, research and dissertation requirements have been fulfilled. It is expected that students will be prepared to defend by the end of their fifth year in the doctoral program.

General Information and Expectations for All Ph.D. students

• 2023-24 Student Handbook
• Annual Review At the end of every year (due May 1), students are expected to fill out the Annual Progress Review . 
• Seminar Series All students are expected to attend the SDS Seminar Series
• SDS 189R Course Description (when taken for internship)
• Internship Course Registration form
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Attending Conferences 

Students are encouraged to attend conferences to share their work. All research-related travel while in student status require prior authorization.

• Request for Travel Authorization (both domestic and international travel)
• Request for Authorization for International Travel  

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College of Liberal Arts and Sciences

Department of Statistics

Ph.d. in statistics.

The Doctor of Philosophy (Ph.D.) in Statistics provides students with rigorous training in the theory, methodology, computation, and application of statistics.

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Program Details

UConn statistics Ph.D. students work closely with faculty on advanced research topics over a wide range of theory and application areas. They also engage with an active community of scholars and students who engage with peers on campus and with professional networks beyond UConn.

Through their coursework, mentorship, and community engagement experiences, our students develop diverse skills that allow them to collaborate and innovate with researchers in applied fields. Graduates of our program go on to high profile positions in academia, industry, and government as both statisticians and data scientists.

Academic Requirements

UConn’s Ph.D. in Statistics offers students rigorous training in statistical theories and methodologies, which they can apply to a wide range of academic and professional fields. Starting in their second year, Ph.D. students establish an advisory committee, consisting of a major advisor and two associate advisors. Together they develop an individualized plan of study based on the students career goals and interests.

All Ph.D. students are required to complete:

• A sequence of required core courses and elective courses from another field of study.
• A qualifying examination and general examination.
• A dissertation.

View full degree requirements

Students entering the program with a bachelor’s degree are typically required to take 16 to 18 courses to earn a Ph.D. in Statistics.

Core Courses

The following core courses are required for all Ph.D. students:

• STAT 5545 and 5555. Mathematical Statistics.
• STAT 5505 and 5605. Applied Statistics.
• STAT 5725 and 5735. Linear Models.
• STAT 6315 and 6515. Theory of Statistics.
• STAT 6325 and 6894. Measure Theory and Probability Theory.
• STAT 5515. Design of Experiments.
• STAT 5091. Statistics Internship or
• STAT 5094. Seminars in Statistics.

Each core course carries three credits, except for the one-credit STAT 5091 or 5094, for a total of 34 credits. Additional credits can be earned from the list of elective courses.

Elective Courses

In general, Ph.D. students are required to elect one or two courses from other departments. However, it is sufficient to take one graduate-level course from the Department of Mathematics. Ph.D. students are also encouraged to take courses in computer science and in application areas such as biology or economics. The elective course(s) must be approved by the student’s major advisor.

Under certain circumstances, a major advisor can exempt their student from the above requirement, if the student has had internships or a research assistantship in interdisciplinary areas.

Browse the UConn graduate course catalog.

Financial Aid

The Department expects Ph.D. students to finish their studies within four years. For students arriving without an MS degree in mathematics or statistics, the Department may provide up to five years of financial support. For those arriving with such a degree, the Department may provide up to four years of financial support.

In order to receive continuous support, Ph.D. students should take at at least nine credits per semester until taking the Ph.D. qualifying exam.

Learn more about financial aid

February 1 (early deadline) April 1 (final deadline)

Please apply by February 1 if you wish to be considered for financial aid.

Individuals with a bachelor’s degree in any major, with a background in mathematics and statistics, are encouraged to apply.

International students must consult with UConn International Student and Scholar Services for visa rules and University requirements.

Full Admissions Requirements

  Please note: The Department does not offer a joint MS/Ph.D. program. Current UConn students enrolled in a statistics master’s program who wish to pursue the Ph.D. in Statistics must reapply to the Graduate School.

For questions about the Ph.D. in Statistics, please contact:

Vladimir Pozdnyakov

Professor and Director of Graduate Admission [email protected]

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• Previous Program Requirements

The Ph.D. in Statistics is flexible and allows students to pursue a variety of directions, ranging from statistical methodology and interdisciplinary research to theoretical statistics and probability theory. Students typically start the Ph.D. program by taking courses and gradually transition to research that will ultimately lead to their dissertation, the most important component of the Ph.D. program.

These requirements apply to students admitted for Fall 2020 and after. Students admitted in Fall 2019 and earlier should consult the Previous Program Requirements page .

PhD Coursework:

The core PhD curriculum is divided into five areas: 

Methods — STATS 600 and 601

Practice — STATS 604

Statistical Theory — STATS 511, 610, 611

Probability — STATS 510, 620, 621

Computing — STATS 507, 606, 608 

All doctoral students must complete the following in their first three semesters in the program and before advancing to candidacy: 

Take all methods and practice courses (600, 601, 604)

Take at least two courses in the combined areas of statistical theory and probability,  including at least one course in statistical theory and at least one 600-level course 

Take at least one computing course

Achieve a 3.5 average grade (on the 4.0 scale used by Rackham) in 600, 601, 604, and one 600-level statistical theory or probability course

Not completing requirements 1-4 by the end of the third semester will trigger probation which, if not resolved by the end of the fourth semester, may lead to dismissal from the program.  For more details, see the link below. 

By the end of the PhD program, all students must take at least 30 credits of graduate statistics courses.    All courses from the core areas count towards this total, as well as all 600-level, 700-level, and selected additional  500-level courses with approval of the PhD Program Director. Seminars and independent study courses do not count. At least 21 credits must be at the 600 level or higher. The Rackham Graduate School requires PhD students to maintain an overall GPA of at least 3.0 to remain in good standing.   

In addition, all doctoral  students must take 3 credits of cognate courses as required by the Rackham graduate school, and two professional development seminar courses. Cognate courses are 400- and higher-level courses from outside Statistics and not cross-listed with Statistics. All cognate course selections must be approved by the PhD Program Director. The professional development courses are 

STATS 810, research ethics and introduction to research tools, in the first semester in the program.

STATS 811, technical writing in statistics. Students are strongly advised to complete this course in their second or third year.

Typical Course Schedules:

Our Ph.D. program admits students with diverse academic backgrounds. All PhD students take STATS 600/601  in their first year. Students are strongly encouraged to take STATS 604 in their second year (Stats 600 is a prerequisite).  

Students with less mathematical preparation typically take STATS 510/511 (the Master’s level probability and statistical theory) in their first year and 600-level probability and/or statistical theory courses in their second year.    

Advanced students, for example those with a Master’s degree, typically do not need to take 510/511, and in some cases may skip 610 and 621. Students who wish to take 600-level probability and statistical theory courses in their first year must take a placement test just before the fall semester of their first year to get approved. The PhD Program Director will help each student choose their individual path towards completing the requirements.  

Some typical sample schedules are listed below. In most cases, we do not recommend taking more than three full-load courses per semester (not counting seminars).

Sample schedule 1:

Sample schedule 2:

Advancing to Candidacy:

Students are expected to find a faculty advisor and start research leading to their dissertation proposal no later than the summer after their first year. The PhD Program Director and the faculty mentor assigned to each first year student can assist with finding a faculty advisor. Students are expected to submit a dissertation proposal and advance to candidacy some time during their second or third year in the program.   

Requirements for advancing to candidacy are:

Satisfying Requirements 1-4

Completing at least 3 credit hours of cognate courses

Writing a dissertation proposal and passing the oral preliminary exam, which consists of presenting the proposal to the student's preliminary thesis committee

A dissertation proposal should identify an interesting research problem, provide motivation for studying it, review the relevant literature, propose an approach for solving the problem​, and present at least some preliminary results​. The written proposal must be submitted to the preliminary thesis committee and the graduate coordinator a​head of time (one week minimum, two weeks recommended)​ and then presented in the oral preliminary exam. The preliminary thesis committee is chaired by the faculty advisor and must include at least two more faculty members, at least one of them from Statistics. ​​The faculty on the preliminary thesis committee typically continue t​o serve ​on ​the doctoral thesis committee​​, but changes are allowed.  Please see Rackham rules on thesis committees for more information.  

At the oral preliminary exam, the committee will ask questions about the proposal and the relevant background and either elect to accept the proposal as both substantial and feasible, ask for specific revisions, or decline the proposal. The unanimous approval of the proposal by the committee is necessary for the student to advance to candidacy.

Additional Information:

Students are encouraged to complete the bulk of their coursework beyond Requirements 1-4 in the first two years of study.  Candidates are allowed to take only one course per semester without an increase in tuition.

All PhD students are expected to register for Stats 808/809  (Department Seminar) every semester unless restricted by candidacy, and attend the seminar regularly regardless of whether they are registered.  

Exceptions to the PhD program requirements may be granted by the PhD Program Director.

Annual Report:

Each candidate is required to meet with the members of their thesis committee annually. This could be in the form of either giving a short presentation on their research progress to the thesis committee as a group, or meeting with committee members individually.

Each committee member should complete a Thesis Committee Member Report and return it to the student. The student should share the completed Thesis Committee Member Reports with both the PhD Program Coordinator and their advisor.

All meetings with the committee members should take place by April 15.

Following the meetings, the student and the advisor should complete the Annual PhD Candidate Self-Evaluation and Feedback Form . The advisor should review the committee members’ Thesis Committee Member Reports and take them into account when completing the advisor’s portion. The completed Annual PhD Candidate Self-Evaluation and Advisor Feedback Form must be submitted to the PhD Program Coordinator by May 31. The completed form will be saved with the department, and a copy will be shared with the student.

Dissertation and Defense:

Each doctoral student is expected to write a dissertation that makes a substantial and original contribution to statistics or a closely related field. This is the most important element of the doctoral program. After advancing to candidacy, students are expected to focus on their thesis research under the supervision of the thesis advisor and the doctoral committee. The composition of the doctoral committee must follow the Rackham's  guidelines for dissertation committee service . The written dissertation is submitted to the committee for evaluation and presented in an oral defense open to the public.

Rackham Requirements:

The Rackham Graduate School imposes some additional requirements concerning residency, fees, and time limits. Students are expected to know and comply with these requirements.

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Graduate Student Handbook (Coming Soon: New Graduate Student Handbook)

Phd program overview.

The PhD program prepares students for research careers in probability and statistics in academia and industry. Students admitted to the PhD program earn the MA and MPhil along the way. The first year of the program is spent on foundational courses in theoretical statistics, applied statistics, and probability. In the following years, students take advanced topics courses. Research toward the dissertation typically begins in the second year. Students also have opportunities to take part in a wide variety of projects involving applied probability or applications of statistics.

Students are expected to register continuously until they distribute and successfully defend their dissertation. Our core required and elective curricula in Statistics, Probability, and Machine Learning aim to provide our doctoral students with advanced learning that is both broad and focused. We expect our students to make Satisfactory Academic Progress in their advanced learning and research training by meeting the following program milestones through courseworks, independent research, and dissertation research:

By the end of year 1: passing the qualifying exams;

By the end of year 2: fulfilling all course requirements for the MA degree and finding a dissertation advisor;

By the end of year 3: passing the oral exam (dissertation prospectus) and fulfilling all requirements for the MPhil degree

By the end of year 5: distributing and defending the dissertation.

We believe in the Professional Development value of active participation in intellectual exchange and pedagogical practices for future statistical faculty and researchers. Students are required to serve as teaching assistants and present research during their training. In addition, each student is expected to attend seminars regularly and participate in Statistical Practicum activities before graduation.

We provide in the following sections a comprehensive collection of the PhD program requirements and milestones. Also included are policies that outline how these requirements will be enforced with ample flexibility. Questions on these requirements should be directed to ADAA Cindy Meekins at [email protected] and the DGS, Professor John Cunningham at [email protected] .

Applications for Admission

• Our students receive very solid training in all aspects of modern statistics. See Graduate Student Handbook for more information.
• Our students receive Fellowship and full financial support for the entire duration of their PhD. See more details here .
• Our students receive job offers from top academic and non-academic institutions .
• Our students can work with world-class faculty members from Statistics Department or the Data Science Institute .
• Our students have access to high-speed computer clusters for their ambitious, computationally demanding research.
• Our students benefit from a wide range of seminars, workshops, and Boot Camps organized by our department and the data science institute .
• Suggested Prerequisites: A student admitted to the PhD program normally has a background in linear algebra and real analysis, and has taken a few courses in statistics, probability, and programming. Students who are quantitatively trained or have substantial background/experience in other scientific disciplines are also encouraged to apply for admission.
• GRE requirement: Waived for Fall 2024.
• Language requirement: The English Proficiency Test requirement (TOEFL) is a Provost's requirement that cannot be waived.
• The Columbia GSAS minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS. To see if this requirement can be waived for you, please check the frequently asked questions below.
• Deadline: Jan 8, 2024 .
• Application process: Please apply by completing the Application for Admission to the Columbia University Graduate School of Arts & Sciences .
• Timeline: P.hD students begin the program in September only.  Admissions decisions are made in mid-March of each year for the Fall semester.

Frequently Asked Questions

• What is the application deadline? What is the deadline for financial aid? Our application deadline is January 5, 2024 .
• Can I meet with you in person or talk to you on the phone? Unfortunately given the high number of applications we receive, we are unable to meet or speak with our applicants.
• What are the required application materials? Specific admission requirements for our programs can be found here .
• Due to financial hardship, I cannot pay the application fee, can I still apply to your program? Yes. Many of our prospective students are eligible for fee waivers. The Graduate School of Arts and Sciences offers a variety of application fee waivers . If you have further questions regarding the waiver please contact  gsas-admissions@ columbia.edu .
• How many students do you admit each year? It varies year to year. We finalize our numbers between December - early February.
• What is the distribution of students currently enrolled in your program? (their background, GPA, standard tests, etc)? Unfortunately, we are unable to share this information.
• How many accepted students receive financial aid? All students in the PhD program receive, for up to five years, a funding package consisting of tuition, fees, and a stipend. These fellowships are awarded in recognition of academic achievement and in expectation of scholarly success; they are contingent upon the student remaining in good academic standing. Summer support, while not guaranteed, is generally provided. Teaching and research experience are considered important aspects of the training of graduate students. Thus, graduate fellowships include some teaching and research apprenticeship. PhD students are given funds to purchase a laptop PC, and additional computing resources are supplied for research projects as necessary. The Department also subsidizes travel expenses for up to two scientific meetings and/or conferences per year for those students selected to present. Additional matching funds from the Graduate School Arts and Sciences are available to students who have passed the oral qualifying exam.
• Can I contact the department with specific scores and get feedback on my competitiveness for the program? We receive more than 450 applications a year and there are many students in our applicant pool who are qualified for our program. However, we can only admit a few top students. Before seeing the entire applicant pool, we cannot comment on admission probabilities.
• What is the minimum GPA for admissions? While we don’t have a GPA threshold, we will carefully review applicants’ transcripts and grades obtained in individual courses.
• Is there a minimum GRE requirement? No. The general GRE exam is waived for the Fall 2024 admissions cycle. 
• Can I upload a copy of my GRE score to the application? Yes, but make sure you arrange for ETS to send the official score to the Graduate School of Arts and Sciences.
• Is the GRE math subject exam required? No, we do not require the GRE math subject exam.
• What is the minimum TOEFL or IELTS  requirement? The Columbia Graduate School of Arts and Sciences minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS
•  I took the TOEFL and IELTS more than two years ago; is my score valid? Scores more than two years old are not accepted. Applicants are strongly urged to make arrangements to take these examinations early in the fall and before completing their application.
• I am an international student and earned a master’s degree from a US university. Can I obtain a TOEFL or IELTS waiver? You may only request a waiver of the English proficiency requirement from the Graduate School of Arts and Sciences by submitting the English Proficiency Waiver Request form and if you meet any of the criteria described here . If you have further questions regarding the waiver please contact  gsas-admissions@ columbia.edu .
• My transcript is not in English. What should I do? You have to submit a notarized translated copy along with the original transcript.

Can I apply to more than one PhD program? You may not submit more than one PhD application to the Graduate School of Arts and Sciences. However, you may elect to have your application reviewed by a second program or department within the Graduate School of Arts and Sciences if you are not offered admission by your first-choice program. Please see the application instructions for a more detailed explanation of this policy and the various restrictions that apply to a second choice. You may apply concurrently to a program housed at the Graduate School of Arts and Sciences and to programs housed at other divisions of the University. However, since the Graduate School of Arts and Sciences does not share application materials with other divisions, you must complete the application requirements for each school.

How do I apply to a dual- or joint-degree program? The Graduate School of Arts and Sciences refers to these programs as dual-degree programs. Applicants must complete the application requirements for both schools. Application materials are not shared between schools. Students can only apply to an established dual-degree program and may not create their own.

With the sole exception of approved dual-degree programs , students may not pursue a degree in more than one Columbia program concurrently, and may not be registered in more than one degree program at any institution in the same semester. Enrollment in another degree program at Columbia or elsewhere while enrolled in a Graduate School of Arts and Sciences master's or doctoral program is strictly prohibited by the Graduate School. Violation of this policy will lead to the rescission of an offer of admission, or termination for a current student.

When will I receive a decision on my application? Notification of decisions for all PhD applicants generally takes place by the end of March.

Notification of MA decisions varies by department and application deadlines. Some MA decisions are sent out in early spring; others may be released as late as mid-August.

Can I apply to both MA Statistics and PhD statistics simultaneously?  For any given entry term, applicants may elect to apply to up to two programs—either one PhD program and one MA program, or two MA programs—by submitting a single (combined) application to the Graduate School of Arts and Sciences.  Applicants who attempt to submit more than one Graduate School of Arts and Sciences application for the same entry term will be required to withdraw one of the applications.

The Graduate School of Arts and Sciences permits applicants to be reviewed by a second program if they do not receive an offer of admission from their first-choice program, with the following restrictions:

• This option is only available for fall-term applicants.
• Applicants will be able to view and opt for a second choice (if applicable) after selecting their first choice. Applicants should not submit a second application. (Note: Selecting a second choice will not affect the consideration of your application by your first choice.)
• Applicants must upload a separate Statement of Purpose and submit any additional supporting materials required by the second program. Transcripts, letters, and test scores should only be submitted once.
• An application will be forwarded to the second-choice program only after the first-choice program has completed its review and rendered its decision. An application file will not be reviewed concurrently by both programs.
• Programs may stop considering second-choice applications at any time during the season; Graduate School of Arts and Sciences cannot guarantee that your application will receive a second review.
• What is the mailing address for your PhD admission office? Students are encouraged to apply online . Please note: Materials should not be mailed to the Graduate School of Arts and Sciences unless specifically requested by the Office of Admissions. Unofficial transcripts and other supplemental application materials should be uploaded through the online application system. Graduate School of Arts and Sciences Office of Admissions Columbia University  107 Low Library, MC 4303 535 West 116th Street  New York, NY 10027
• How many years does it take to pursue a PhD degree in your program? Our students usually graduate in 4‐6 years.
• Can the PhD be pursued part-time? No, all of our students are full-time students. We do not offer a part-time option.
• One of the requirements is to have knowledge of linear algebra (through the level of MATH V2020 at Columbia) and advanced calculus (through the level of MATH V1201). I studied these topics; how do I know if I meet the knowledge content requirement? We interview our top candidates and based on the information on your transcripts and your grades, if we are not sure about what you covered in your courses we will ask you during the interview.
• Can I contact faculty members to learn more about their research and hopefully gain their support? Yes, you are more than welcome to contact faculty members and discuss your research interests with them. However, please note that all the applications are processed by a central admission committee, and individual faculty members cannot and will not guarantee admission to our program.
• How do I find out which professors are taking on new students to mentor this year?  Applications are evaluated through a central admissions committee. Openings in individual faculty groups are not considered during the admissions process. Therefore, we suggest contacting the faculty members you would like to work with and asking if they are planning to take on new students.

For more information please contact us at [email protected] .

For more information please contact us at  [email protected]

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PhD Program

Advanced undergraduate or masters level work in mathematics and statistics will provide a good background for the doctoral program. Quantitatively oriented students with degrees in other scientific fields are also encouraged to apply for admission. In particular, the department has expanded its research and educational activities towards computational biology, mathematical finance and information science. The doctoral program normally takes four to five years to complete.

Doctoral Program in Statistics

Statistics phd minor.

• Doing a PhD in Statistics

We live in a data-rich world. The study of statistics allows us to better understand data, measure uncertainty, and calculate risk. The applications of such knowledge are widespread – from economics to medicine. A PhD in Statistics will give you a deep understanding of the mathematical framework which underpins data analysis as we know it. Read on to find out the key information about a PhD in statistics, and whether it is worth it for you.

What Does a PhD in Statistics Focus On?

A Statistics PhD programme can focus on:

• Statistical theory and statistical methods
• Bayesian statistics
• Covariance modelling
• High dimensional data
• Probability theory
• Causal inference
• Extreme value theory
• Non-parametric regression
• Symbolic computation
• Applied statistics

The list above is only a small sample of the many different areas within probability and statistics. Many PhD research projects place a particular emphasis on statistics within environmental, biomedical, and social science. Aside from this there is also overlap with other field such as computer science, applied mathematics, and linear algebra.

Browse PhDs in Statistics

Application of artificial intelligence to multiphysics problems in materials design, study of the human-vehicle interactions by a high-end dynamic driving simulator, physical layer algorithm design in 6g non-terrestrial communications, machine learning for autonomous robot exploration, detecting subtle but clinically significant cognitive change in an ageing population, entry requirements for a phd in statistics.

Most Statistics PhD programmes require applicants to have, or expect to obtain, a bachelor’s degree (or international equivalent) in Mathematics or Statistics. However, many Statistics PhD research projects also accept applications from graduate students with a bachelor’s degree in other subjects if they involve a significant mathematical component (such as Data Science , Physics, or Computer Science). Many universities expect first class honours due to the high competition for places, though for some institutions second class honours (2:1) is adequate.

It is also common for universities to accept second class honours (2:1), if the graduate has a master’s degree or relevant work experience.

Universities typically expect international students to provide evidence of their English Language ability. This is usually benchmarked by a IELTS score of 6.5 (with a minimum score of 6 in each component), a TOEFL (iBT) score 92, a CAE and CPE score of 176 or another equivalent. The exact score requirements may differ across different universities.

Duration and Programme Types

The typical doctoral programme in Statistics takes 3-4 years full-time, or 6 years part-time.

A PhD research project in Statistics can focus on a particular application of statistics. For example, you may undertake a PhD in statistical genomics or biostatistics, which would involve interdisciplinary work and additional training modules to understand how statistics can improve biological and genetic study.

In addition to the statistics course modules, you will likely undertake ‘ transferable skills ‘ training in communication, management, and commerce – all of which are skills a good postgraduate research student needs.

As with most PhDs, you will have to complete a dissertation at the end of your postgraduate research project, and undertake an oral examination known as the viva , where you are required to defend your dissertation to a supervisory committee/dissertation committee usually made up of two examiners.

Costs and Funding

Annual tuition fees for PhDs in Statistics are typically around £4,000 to £5,000 for UK/EU students. Tuition fees for international students are usually much higher, typically around £20,000 – £25,000 per academic year. Tuition fees for part time programmes are typically scaled down according to the programme length.

Some Statistics PhD programmes also have additional costs to cover laboratory resources, administration and computational costs.

Together with EPSRC and other national funding sources, many Universities offer postgraduate studentships which cover the tuition fees for Statistical PhD programmes. EPSRC DTA research studentships are available in all areas for UK and EU students. Students who are normally resident in the EU but not in the UK are eligible for EPSRC PhD studentships, but the awards in such cases currently cover only the course fees, not maintenance stipends .

Available Career Paths in Statistics

One of the key advantages of Statistics is that it is a fundamental concept which underpins most industries. Consequently, there are an abundance of career paths available for Statistics PhD doctorates such as agriculture, forensics, machine learning, informatics, geosciences, law and biomathematics.

Examples of common destinations for a Statistics PhD student include:

• Actuarial Science – Actuaries are responsible for analysing data to help non-specialists make informed decisions about risks. A good understanding of probability and investment is crucial in this field. Salaries for Statistics PhD students in this field vary, but with around 10 years’ experience typically are around £60,000.
• Environmental statistician – In this role, Statistics doctorates use their knowledge to contribute to environmental study. This can include monitoring climate patterns, carrying out flood risk assessments, or transforming large amounts of temperature data into information for the public.
• Data Analyst – Some people use their PhD in stats to become data analysts, responsible for data management, developing automated processes, tracking KPIs, and more. Data analysts can be found in various industries form logistics & transport to marketing. Again, with experience Statistics doctorates in this path can expect a lucrative salary.
• Medical statistician – PhD graduates in the medical field aid health research in a number of ways, for example analysing data from clinical studies to identify patterns. The NHS, private health companies and the pharmaceutical industry are common employers for those with a PhD degree in statistics or applied statistics.
• University lecturer – Often PhD students opt to stay in academia. This can be as a university lecturer where you will teach students about statistical theory.

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Department of Statistics

Handbook for phd students in statistics.

TABLE OF CONTENTS

• Introduction
• Incoming Students
• Course Registration
• First Year Course Requirements and Preliminary Examinations
• Second Year Requirements
• Third Year Requirements
• Fourth Year Requirements
• Fifth Year and Beyond
• Dissertation Defense and Submission
• Consulting Program
• Academic Year Student Support and Teaching Duties
• Summer Support
• Off Campus Work
• Student Representatives
• Student Offices
• Reimbursements

INTRODUCTION

Dear Students,

We have compiled this manual summarizing all the rules, requirements and deadlines governing the PhD program in the Statistics Department. We intend this manual to be the primary repository of these rules and we encourage you to refer to it periodically as you progress through our program. If you have any questions regarding the content you are welcome to contact the Department Graduate Advisor (Yali Amit) or the Student Affairs Specialist (Keisha Prowoznik).

Good luck with your studies!

INCOMING STUDENTS

• New Graduate Student Information–UChicago Grad: Information regarding the University of Chicago campus, living in the neighborhood, security, health, and other resources for incoming graduate students can be found here: https://grad.uchicago.edu/life-at-uchicago/admitted-students-welcome/
• Diagnostic Exam: A diagnostic exam will be emailed to all students the week before orientation to be returned to their advisor by the end of that week in order to help determine which courses to take for the upcoming year.
• Orientation: This event will take place on the week before classes start where new students will attend meetings throughout campus and the Department to become acclimated with procedures and guidelines for the PhD program. This is also course registration week, where all students will have to meet with their advisor to determine which courses to take during Autumn quarter. Incoming students will meet with the Department Graduate Advisor (DGA) for course registration.

COURSE REGISTRATION

Course registration starts the Monday of the 8th week of every quarter when the Student Affairs Specialist sends out an email to all students with the attached registration form for each student to fill out, and gain consent from their advisor and return back to the Student Affairs Specialist. Students must register themselves and if they cannot, they can seek help from the Student Affairs Specialist. All course registration must be completed the Friday of 10th week by 12:00PM Central Standard Time in order to avoid a late fee.

Drop/add week will always take place the first week of every quarter and run for three weeks and end on the Friday of third week for all PhD students. This is a time where students are able to drop and/or add courses to their schedule if they do not wish to take the courses they registered for during course registration week. Courses can be changed upon the advisor and instructor’s approval.

FIRST YEAR COURSE REQUIREMENTS AND PRELIMINARY EXAMINATIONS

The program offers four core sequences:

• Probability (STAT 30400, 38100, 38300)
• Mathematical statistics (STAT 30400, 30100, 30210)
• Applied statistics (STAT 34300, 34700, 34800 and 34900)
• Computational sequence (STAT 30900, 31015/31020, 37710).

All students must take the Applied Statistics and Mathematical Statistics sequence. In addition, it is highly recommended that students take a third core sequence based on their interests and in consultation with the Department Graduate Advisor (DGA).

Preliminary exams: At the start of their second year, several weeks before the start of the Fall quarter , the students take two preliminary examinations. The students will be informed by June 1 of the precise dates. All students must take the Applied Statistics Prelim. The prelim is a take-home exam provided online to the students during  prelim week. Student written reports are handed in two days later. A few days later, after the faculty review the reports the students have a 30 minute oral interview about their report.

For the second prelim, students can choose to take either the Theoretical Statistics or the Probability prelim. Students planning to take the Probability prelim should take the Probability sequence as their third first year course sequence and must receive approval from the DGA to take 38300 in the Spring instead of 348.  

During six weeks leading up to the prelims, two advanced PhD students will assist the first year students in preparing for the exams, holding weekly meetings one for the Applied Statistics exam and one for the Theoretical Statistics exam.

Incoming first-year students may request the DGA to take one or both of their preliminary exams. This will only be considered if the students have had extensive training in statistics in their prior studies. If approved, and if the student passes one or more of these, then he/she may be excused from the requirement of taking the first-year courses in that subject.

First year summer reading courses: It is highly recommended for first year students to take a reading course with a faculty member during the summer. This does not require formal registration, only coordination with a faculty member. Such a reading course typically involves reading a number of papers recommended by the faculty member and presenting them during the meetings.

Incoming students are advised by the DGA until they find a faculty advisor for their PhD thesis work.

First year students also share responsibility for organizing lunches with faculty to hear about their research, lunches with visiting seminar speakers and weekly departmental tea time.

SECOND YEAR REQUIREMENTS

In their second year, PhD students typically take several advanced topics courses in statistics, probability, computation, and applications. These should be selected with the dual objective of (i) acquiring a broad overview of current research areas, and (ii) settling on a particular research topic and dissertation supervisor. It is recommended that the students take at least one regular class based course each quarter. In addition, students can ask to take reading courses with faculty to learn more in depth about their fields of research. Students have considerable latitude in selecting their second-year courses, but their programs must be approved by the Department Graduate Advisor.  Students are expected to find a dissertation/thesis advisor by the end of the second year. The thesis advisor does not need to be a faculty member of the Statistics Department, however the dissertation/thesis committee must include at least two members of the Statistics Department (see below.)

Mini-seminars: During the second half of Spring Quarter second year students are required to give a short 10-12 minute presentation on a paper/papers they have read, followed by a short Q & A period. This provides the students with their first experience giving a presentation and both faculty and other students can provide feedback. The students typically present papers they have read in one of the reading courses they have taken with a faculty member during the second year.

THIRD YEAR REQUIREMENTS

Thesis Advisor and Dissertation Committee

By the end of the third year, each PhD student, after consultation with his or her dissertation advisor, shall establish a committee of at least three members, at least two of whom should be from Statistics. The departmental form listing the committee members, with their signatures, must be filed in the Department office by the end of Spring Quarter of the third year. The composition of the committee may be changed at any time if the student or faculty so choose; however, it must always include the student's dissertation advisor and at least two of the committee members must be regular faculty members from the Department of Statistics. Any such change must be filed as a resubmitted and newly completed and signed form with the Department office. As long as a student has not found a thesis advisor the DGA will remain the student’s advisor.

Interdisciplinary Theses

Many of our students choose to pursue research combining statistics and computation with another area of scientific research, such as genetics, neuroscience, health studies, environmental science, or social science. Students who choose to write an interdisciplinary thesis can work with a thesis advisor from another department as long as the two other committee members are from the Statistics Department.

FOURTH YEAR REQUIREMENTS

Proposal Presentation and Admission to Candidacy

By the end of Autumn Quarter of the fourth year, students should have completed a proposal presentation to their committee. This consists of a written (typically 5-10 page) report on completed and planned research with relevant references and a meeting with the committee discussing the proposed research (format is flexible, but typically a 1.5 hour meeting, with 45 minutes for student presentation and 45 minutes for questions and discussion). The proposal meeting will be scheduled by the student and his or her committee and reported to the Department office. Acceptance of the proposal by the Dissertation Committee is a formal requirement of the Department's PhD program. After a successful proposal presentation, the student will be formally admitted to candidacy for the PhD degree. By University rules, the dissertation defense cannot occur earlier than 8 months after admission to candidacy, and the student should keep this in mind when scheduling both the proposal presentation and the defense.

Following the fourth year, during each year that the student remains, the student is required to have a meeting with the committee no later than November 30 th of Autumn Quarter or defend by that time.

FIFTH YEAR AND BEYOND

The Department goal is for the majority of students to complete and defend their thesis by the end of their 5th year. Foreign students will have their visas extended beyond the fifth year on a yearly basis depending on the decision of the committee.

In the first 4 weeks of the Fall quarter of the 5th year students should convene their Dissertation Committee for an update on their progress. Committee members will confirm satisfactory progress on a form provided by the Department office.

Students who have not completed their thesis by the end of Fifth year must petition their committee and the Department Chair in order to continue in the program into their Sixth year and maintain their stipend. If their petition is  approved and they are not supported as RA’s they  will be required to teach every quarter.

Students who continue to their 6th year should again  convene their Dissertation Committee in the first 4 weeks of the Fall quarter  of the 6th year and Committee members will confirm satisfactory progress on a form provided by the Department office.

Students who have not completed their dissertation and defense by the end of the sixth year will no longer receive stipends or be employed by the Department. These students are required to petition their committee and the Department Chair both in order to continue in the doctoral program and for any financial support (tuition, fees). The petition is to be made before the end of Spring Quarter of the sixth year.

DISSERTATION DEFENSE AND SUBMISSION

The PhD degree will be awarded following a successful defense and the electronic submission of the final version of the dissertation to the University's Dissertation Office. In this process, a number of University and Department deadlines have to be obeyed. Listed in reverse order, the steps are:

a) Submission of Final Version of Dissertation: The deadline is set by the University and is generally on a Friday in the 6th or 7th week of the quarter when the degree will be awarded. See:

• Information for PhD Students: https://www.lib.uchicago.edu/research/scholar/phd/students/
• Dissertation Deadlines: https://www.lib.uchicago.edu/research/scholar/phd/students/dissertation-deadlines/
• Information about dissertations: https://www.lib.uchicago.edu/research/scholar/phd/students/
• Latex template for dissertation: https://www.overleaf.com/latex/templates/university-of-chicago-phd-dissertation-template/syvxgkqhvqqt

for this deadline as well as guidelines for the formatting of dissertations.

b) Dissertation Defense: The thesis defense will be an open seminar announced to the Department. Following the regular question-and-answer session, the committee will remain, together with any interested faculty, and continue questioning the candidate. The decision on the thesis will then be reached in a closed meeting of the dissertation committee. The defense is to be scheduled at least two weeks before the University deadline indicated in point (a). A final draft of the dissertation must be made available to the entire faculty 8 days before the dissertation presentation.

c) Committee Approval of Scheduled Defense: A draft of the dissertation should be distributed to the members of the dissertation committee no later than five weeks before the dissertation defense. The committee then has two weeks to approve that the student can reasonably expect to defend the thesis, and three more weeks to fully assess.

These rules delineate the minimum level of involvement of the dissertation committee. We strongly recommend that students set up their committees early and that they interact regularly with the members of their committees once they are established. We strongly recommend that those students wishing to complete all degree requirements, including their defense, by the end of Summer quarter contact their committee to schedule their Summer defense date before Summer Quarter begins. Else unanticipated committee requirements may lead to the degree being delayed to the Winter Quarter.

CONSULTING PROGRAM

The Department runs a consulting for training purposes, at the same time providing a service for researchers in other departments in the University. Students serve as the consultants, working as the quantitative expert in statistics alongside the researchers. Two faculty members lead the consulting program. The consulting seminar meets once a week for an hour during academic quarters. In these meetings researchers may present a problem, the students may present their projects, or some interesting applied case study may be analyzed. The students rotate weekly through consulting `office hours', which are the times when researchers can approach with their requests. Typically, four to six graduate students work together as a team under the supervision of faculty members to address these requests.  The teams share their experience by presenting their analysis to the seminar. Students are required to register for the consulting program for two quarters each of years 1 through 3. Third year students can delay one of their consulting quarters to their fourth year.

ACADEMIC YEAR STUDENT SUPPORT AND TEACHING DUTIES

PhD students are guaranteed support for five years and in return are required to work as teaching assistants (TAs) for two quarters of each year and on one quarter they are off. Incoming first year students  are all off during the first quarter. TA assignments are determined 3-4 weeks prior to the start of the quarter, at which point the students are required to contact the faculty member teaching the course for instructions on their upcoming duties. Students may request the DGA to assign them to particular courses, or ask to have a particular quarter off. There is no guarantee that these requests will be satisfied, but the DGA does take them into account. Students are not allowed to work as TA's for any other University unit during their off quarter.

Research Assistants (RAs): Faculty members may decide to support their student  from a grant in one or more of their teaching quarters. In those quarters the students are not required to perform TA duties. Students can receive RA support from faculty advisors outside the Department.

Instructorship: Some students may be asked to be instructors in introductory Statistics courses, especially during the Spring quarter. These students receive a bonus in their summer support (at time of writing, July 2022, this is 2000 USD). The DGA determines which students are suitable for such positions.

Sixth year students and beyond: Students who have not completed their dissertation by the end of the fifth year must, by the end of Spring Quarter, obtain permission from their committee and the Department Chair to continue beyond the fifth year. If they are allowed to continue but are not hired as RA’s they will be funded by the Department, but  required to teach every quarter. Students who have not completed their dissertation and defense by the end of the sixth year will no longer receive stipends or be employed by the Department. These students are required to petition the Department both in order to continue in the doctoral program and for any financial support (tuition, fees). This petition is to be made to both their committee and the Department Chair before the end of Spring Quarter of the sixth year.

Quarterly Funding Letters: A few weeks before every quarter the Student Affairs Specialist will send out the quarterly funding letter which will list each students’ position (TA, Instructor, RA, OFF) for the upcoming quarter. This letter will also list stipend or paycheck dates depending on the students’ position and an itemized amount of costs for the quarter and who is responsible for the payment. This letter is very important in that it will tell the student if they will hold a position that quarter and what date/dates they will be paid.  

SUMMER SUPPORT

Students are provided with full 3 month summer support during their first 4 years. Support during the fifth summer is contingent on approval of the advisor and the Chair.

Internships: Students can choose to take on internships during the summer, in which case they forfeit the departmental summer support. The decision on whether to take an internship and which ones are appropriate are taken in consultation with the student's thesis advisor. It is not recommended to take internships before finding a thesis advisor.

OFF CAMPUS WORK

Students in a full-time registration status are expected to focus their attention and efforts principally on their academic work and additional employment is secondary to their student status. A domestic student wanting to take off-campus employment will typically need to take a leave of absence from the program. For international PhD students, OIA recently introduced a version of CPT (CPT RCOT) that may allow them to work off-campus outside of summer. [see https://internationalaffairs.uchicago.edu/page/curricular-practical-training-cpt ]. However, this requires approval by the PSD Dean of Students, and will involve careful consideration of a number of factors. Moreover, the Dean of Students views this mechanism as intended for only very short-term off-campus work (eg 1 quarter) and not for long term. Repeated enrollment in CPT RCOT will generally not be approved by the Dean of Students. Students who have questions about CPT RCOT should direct them to the PSD Dean of Students.

Note: work at Argonne national labs is excepted from usual "off-campus" regulations due to an agreement between Argonne and UChicago.

STUDENT REPRESENTATIVES

During the first week of Fall quarter the PhD students gather to elect a student representative(s), who are responsible for communicating with the DGA and the Chair regarding any issues arising among the student body. They are also asked on occasion to coordinate student social activities such as the annual picnic. The Departmental Student Affairs Specialist assists the student representatives with any administrative tasks associated with their duties.

STUDENT OFFICES

All keys for student offices will be given during orientation week in the Student Affairs office. 1st year PhD students will always have desks in Jones 208, 2nd-4th year students will have desks in Jones 203/204, and 5th and 6th year students will have desks in Jones 209. Students will sign a key check-out form which states they will be responsible for their desk key of $20 and the office key of $30 and if they lose the key they must pay the Department either of the amounts in order to obtain a new key.

REIMBURSEMENTS

Students who wish to travel throughout the year for conferences that are sponsored on a grant or research funds from their advisor can be reimbursed by the Student Affairs Specialist with detailed receipts and confirmation that this trip has been approved and sponsored by the advisor/faculty member.

In case the advisor is unable to support the student travel but still approves it, the student may petition the Department Chair for up to $1000 of Departmental support.

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Open Access

Ten simple rules for getting started with statistics in graduate school

Affiliation Department of Forest Ecosystems and Society, Oregon State University, Corvallis, Oregon, United States of America

Affiliation Department of Integrative Biology, Oregon State University, Corvallis, Oregon, United States of America

Affiliation Department of Forest Engineering, Resources, and Management, Oregon State University, Corvallis, Oregon, United States of America

Affiliation Department of Animal and Rangeland Sciences, Oregon State University, Corvallis, Oregon, United States of America

Current address: American Bird Conservancy, Veneta, Oregon, United States of America

Affiliation Department of Fisheries, Wildlife, and Conservation Sciences, Oregon State University, Corvallis, Oregon, United States of America

Affiliations Department of Forest Engineering, Resources, and Management, Oregon State University, Corvallis, Oregon, United States of America, Smithsonian Conservation Biology Institute, Migratory Bird Center, National Zoological Park, Washington, DC, United States of America

•  [ ... ],

* E-mail: [email protected]

• [ view all ]
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• Rachel A. Zitomer, 
• Jessica Karr, 
• Mark Kerstens, 
• Lindsey Perry, 
• Kayla Ruth, 
• Lindsay Adrean, 
• Suzanne Austin, 
• Jamie Cornelius, 
• Jonathan Dachenhaus, 

Published: April 21, 2022

• https://doi.org/10.1371/journal.pcbi.1010033
• Reader Comments

Citation: Zitomer RA, Karr J, Kerstens M, Perry L, Ruth K, Adrean L, et al. (2022) Ten simple rules for getting started with statistics in graduate school. PLoS Comput Biol 18(4): e1010033. https://doi.org/10.1371/journal.pcbi.1010033

Copyright: © 2022 Zitomer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Graduate school is often a time of enormous professional growth, and for many students, it is the first time they receive advanced training in statistics. Despite the foundational importance of statistics in many fields, learning proper statistical approaches can be challenging for beginning graduate students, especially for those that lack prior experience in this subject area. This can occur for several reasons but is often related to competing interests for students’ limited time at the start of a graduate program. New graduate students are typically working with a new advisor, taking on new research, and/or helping to instruct a class for the first time, in addition to taking advanced courses, applying for grants and fellowships, and settling into their new professional setting. Graduate school also marks a transition to self-directed learning that may diverge strongly from undergraduate degree programs, which can also bring new challenges. In short, the start of graduate school can be a demanding time for students with much to learn in a relatively short time period.

New graduate students often begin their program by taking an overview course in statistics such as basic statistical methods, experimental design, or linear modeling. However, in the collective experience of our group—which includes graduate students, research assistants, postdoctoral researchers, and university professors in the field of natural resources—we have found that learning to apply statistical analyses correctly requires course-based learning about specific analysis methods, as well as a broader understanding of the philosophy of applying statistical approaches in research. We have found that the latter topic, on which we focus here, often gets less attention in graduate courses because it does not fit neatly into any single course. Nevertheless, topics related to this theme regularly arise throughout graduate school and beyond and thus highlight its importance for learning how to design, undertake, and complete high-quality research. Although there have been several excellent reviews in the 10 simple rules series on topics that are linked to the broader points we raise here, such as guidelines for using statistics [ 1 ], managing data [ 2 ], and learning to program [ 3 ], none have been focused on a discussion of broad conceptual topics that relate to applying statistical approaches in research with new graduate students as a primary audience. Therefore, we offer what we view as 10 important rules for new students to reflect on as they begin to build their statistical skillsets in graduate school. It is important to note that some of the rules we offer cannot be met in all circumstances; thus, we view them as rules to which graduate students should aspire, recognizing that, at times, they may be bent in the name of pragmatism. Finally, although the primary audience for the rules we provide are new graduate students, we note that the principles we raise are broadly applicable to researchers in a wide range of disciplines. Indeed, we posit that some advisors of new graduate students might themselves benefit from a periodic review of these rules!

Rule 1: Start with a research question before data collection

New graduate students often feel pressure to get started with their research as soon as possible and that includes collecting data toward a thesis or dissertation. Despite this, it is critical to start the process by formulating a research question well before data collection begins [ 4 ]. Extensive reading of the primary literature, as well as recent review papers and edited volumes, is key to learning about the scope of work that has—and has not—been conducted in a particular field, which helps to hone and focus research questions [ 4 ]. Although it may seem more efficient to collect data while developing research questions, doing so is suboptimal and should be avoided for several reasons. First, it reduces the amount of time available to undertake a literature review expansive enough to identify the key knowledge gap(s) that could be the focus of a graduate project. Second, collecting data prior to finalizing a research question makes it more likely that key variables that need to be measured remain unidentified, at least at the start. Finally, collecting and analyzing data while developing hypotheses can lead to hypothesizing after the results are known, also known as HARKing [ 5 ]. Although it may be acceptable to revise the original hypotheses prior to data analysis, it is problematic if hypotheses are changed after data analysis is conducted and then presented as if they were developed prior to data collection [ 5 ]. Importantly, HARKing goes against the hypotheticodeductive premise of conducting science [ 6 ] in which a researcher generates a falsifiable hypothesis and then collects data to test it because, by definition, a hypothesis that has been created based on the results of an analysis cannot be falsified by the same analysis. Additional pitfalls of HARKing include a reduction in the search for alternative explanations for phenomena and strong potential for hindsight confirmation bias [ 5 ]. These issues can limit replicability and should be avoided by ensuring that a priori hypotheses are tested and that any speculation that is undertaken after results are known is clearly stated as such. Preregistration of research, wherein a research plan is deposited in a public repository before a project is undertaken [ 7 ], is a useful way of ensuring that hypotheses and analytical approaches are determined prior to data analysis, rather than in response to it. This practice increases transparency in the research process and is becoming increasingly common in many scientific fields [ 8 ].

It is worth noting that some graduate students may work on a research topic that has already been selected by their research advisor, join a project that is already underway, and/or receive a dataset that has already been collected. In such cases, they may not be starting from “square one” in the research process. Nevertheless, it is still important to have a thorough understanding of the background literature and the research question(s) that motivated data collection. Thus, rather than assuming that such an “inherited” study lacks room for improvement, we recommend that students critically examine the choices made in data collection, understand their strengths and weaknesses, and independently assess whether the resulting data are sufficient to address the research question(s) being examined.

Rule 2: Understand how manipulative experiments differ from descriptive studies

Studies come in many forms, with descriptive studies and manipulative experiments being among the most common. Of these, the randomized manipulative experiment is the gold standard in terms of inference [ 9 ], and in its simplest form, it is a representative group of experimental units (e.g., individuals, study sites) from a population that is randomly assigned to either a treatment or a control group. Units in the treatment group are subjected to an experimental manipulation, whereas those in the control group are not, and the average response across experimental units in the 2 groups is then compared. Because other factors are held constant, differences detected in the treatment group relative to the control group can be attributed to the experimental manipulation. In contrast, observational studies are those that describe relationships as they occur, without manipulation by a scientist, who in this case serves as a passive observer and not as a manipulator of the system under study. The differences between these 2 types of studies may seem subtle, but they have enormous importance for inference, and the strength of conclusions that can be drawn. Most importantly, randomized experiments can assign causation to the factor that is manipulated by a researcher, whereas descriptive studies are limited to providing correlative evidence of relationships [ 10 ]. It is critical to note that despite these differences, descriptive studies play an important role in generating knowledge; they are, in fact, the foundation for much of science as many systems do not allow for manipulation, especially across large scales [ 11 ]. However, their limitations must be understood relative to manipulative experiments so that proper inference is made (see Rule 3).

Rule 3: Understand the limits of inference

Inference in research involves drawing conclusions about a population based on observations of a sample of that population using inductive reasoning. The validity of inductive reasoning is dependent on the premise that the sample is representative of the population of interest [ 12 ]. The set of situations to which the conclusions of a study can be generalized is known as its scope of inference [ 13 ]. The ideal for most researchers is to apply the results of research as broadly as possible. However, the reasonable scope of inference for a research project is typically constrained by the realities of data collection. Thus, defining the scope of inference for a study involves thinking carefully about the chosen sampling approach, potential sources of bias, and acknowledging how they limit the generalizability of a study. Likewise, adopting an approach that is too narrow when defining the scope of inference for a study can also be limiting. Some graduate students may focus so intently on the specifics of their study that they fail to consider the larger range of conditions that it might represent. In some cases, the scope of inference for a study may be broadened considerably if there is evidence that the conditions in which sampling occur are typical of a larger geographic area or timeframe. In other cases, studies with a relatively narrow scope of inference might fill a gap in the existing body of research on their subject that allows more generalizable conclusions to be drawn. Thus, defining the scope of inference for a study requires a striking a balance where the scope is narrow enough that findings are appropriately applicable, yet broad enough to provide knowledge that extends beyond a single study system. Getting feedback from colleagues and peers about what is a reasonable scope of inference for a particular study is often a useful exercise (see Rule 10).

Rule 4: Start learning a statistical programming language early

Although some statistical programs are “canned” and do not require extensive knowledge of programming to perform statistical analysis, our experience has found that graduate students who learn a programming language early in their program become more proficient with data analysis throughout graduate school and beyond. Although it does take time, learning a programming language helps to solidify statistical concepts because it requires creating all of the steps needed to undertake an analysis, in marked contrast to statistical programs operated through a graphical user interface. In addition, learning how to program in a commonly used language increases efficiency and accessibility of analyses [ 14 ], especially when well-annotated scripts are created that can save time when code is reused in subsequent analyses. Using a statistical programming language can also increase reproducibility as scripts not only enable data manipulation and statistical testing but also record the nature and sequence of these steps [ 15 ]. Archiving all versions of scripts and datasets—including raw data—is a straightforward and efficient way to track changes made throughout the analysis process [ 16 ]. It is important to note that programming scripts should not be the only mechanism for ensuring reproducibility [ 16 ], but they are particularly useful for keeping a record of each step in the analysis so that important details are not lost.

We suggest that new students consult literature and talk with others in their field to find out which programming languages are commonly used and to understand their strengths and weaknesses with different types of data or analytical approaches before choosing the language(s) on which to focus [ 3 ]. For example, one programming language that is free and used widely in the natural resource sciences [ 17 ] is the R statistical environment ( https://www.r-project.org ). Because it is an open-source language, there are many packages that are created for specific analyses and freely available to researchers. There are also extensive tutorials and a rich online public support community that can provide relevant code and troubleshoot errors that may arise. Regardless of whether one uses R or another programming language (e.g., SAS, Python), the work that new graduate students put into learning programming early in graduate school will pay dividends later as they move beyond graduate school and into the next phase of their career.

Rule 5: Set up databases in a tidy format before data collection

Graduate students vary in how they obtain the data they use for their graduate research. Although some students are fortunate to receive a well-curated database in its final form, most end up spending a large amount of time wrangling their data. Indeed, it is estimated that up to 80% of data analysis time is spent cleaning and preparing data prior to conducting any statistical analyses [ 18 ]. Although different types of data analyses often require different data formats, there are some general rules to keep in mind when developing a database, and they should be implemented prior to data collection. First, standardize data entry through the establishment of metadata that, at a minimum, describes the actual names of the variables found within a database (e.g., “body_mass”), provides a definition for each variable (e.g., “body mass measurement taken with an electronic scale to ± 0.01 g”), and reports possible entries for each variable (e.g., “positive continuous values”). Including this information is critical so that anyone in possession of the data can clearly understand individual variables and how they were defined. Additionally, structure databases so that each variable forms a column, each observation forms a row, and each type of observational unit forms a table [ 19 ]. This structure—referred to as “tidy data”—is an ideal format for storing data that facilitates statistical analyses. Wickham [ 19 ] extolls the many virtues of this approach, and his paper on tidy data is a must-read for any new graduate student. Storing data in a tidy format also make it easier to reproduce results, when returning to previously used datasets and when sharing data with other researchers, both of which are critical for building scientific knowledge [ 1 ].

Rule 6: Understand the form of data

Once data are in hand (note that data is a plural term and datum is singular), whether they are collected directly or obtained indirectly, it is essential to have a good understanding of those data and how they should be analyzed. Introductory statistics courses typically focus on general linear models (e.g., t tests, linear regression, and analysis of variance) that assume that measured responses are normally and independently distributed around the modeled mean. The normality assumption in particular can be a source of great anxiety for many new graduate students when they discover their residuals do not follow such a form, often leading to arcane data transformations or even abandonment of certain data that have been collected. Thus, it is critical to recognize that techniques are now widely available for analyzing data with a variety of forms, including those that are binary (e.g., logistic regression and Fisher exact test) or are based on counts (e.g., Poisson and negative binomial regression) or proportions (e.g., Dirichlet regression). Of course, each of these approaches comes with its own assumptions, and, therefore, it is critical to ensure that they are met regardless of the method chosen. In some cases, nonparametric approaches, which tend to have fewer assumptions but also have lower statistical power, may be reasonable alternatives. Regardless, being able to understand the form of the data one has and identify an appropriate underlying distribution when evaluating statistical hypotheses with those data are critically important skills for new graduate students given the many analytical options available today.

In many sampling situations, the assumption of independence that underlies general linear models, as well as many other analytical techniques, may also be violated. Sometimes, nonindependence is purposeful; for example, a researcher may be interested in how herbicide exposure affects salamander body weight over time. In this case, multiple body weight measurements on the same salamander are not independent of one another, yet they are required to answer the research question. In other cases, nonindependence can be a side effect of sampling constraints, such as when samples collected closer together in space or time are more likely to have similar values due to environmental homogeneity at local scales. Several techniques have been developed to account for these sources of nonindependence, including mixed model analysis [ 20 ] and diverse methods for handling spatial and temporal autocorrelation [ 21 , 22 ]. New students should be aware that nonindependence in data must be recognized and accounted for, and failing to do so can lead to pseudoreplication and other issues related to data interpretation [ 23 ].

Rule 7: Understand what a p -value is and what it is not

P -values have been frequently overemphasized and misinterpreted in the scientific literature [ 24 ]. Technically defined, a p -value is the probability of obtaining an observed dataset if the null statistical hypothesis were true. Typically, a null hypothesis postulates the absence of an effect; that is, that there is no difference between groups being compared or no association between variables being evaluated [ 25 ]. A very small p -value therefore suggests that it is unlikely one would have obtained the observed results if there truly was no effect. p -Values are traditionally compared to a threshold value, alpha (α), which is defined a priori and serves to delineate the acceptable probability of mistakenly rejecting the null hypothesis when it is true [ 26 ]. This approach frequently results in interpretation of an observed effect as “significant” or “nonsignificant” if the p -value is smaller or larger than α, respectively [ 27 ].

It is critically important for new graduate students to recognize that a p -value is a function of the magnitude of an effect, the variability in a response, and the sample size of a dataset. Because of this, even trivial effects may be deemed statistically “significant” when sample sizes are sufficiently large [ 25 ]. Conversely, strong effects may not be reflected in the outcome of statistical tests when sample sizes are small or when variability in a response is large. For example, 2 studies could estimate similar effect sizes yet differ in the precision of their estimates—for example, if one study has a small error estimate and the error estimate of the other study is large—leading one to conclude the first study detected a statistically “significant” effect, whereas the second study was found to have no effect at all [ 26 ]. In reality, there is no conflict between these 2 hypothetical studies regarding the observed effect, yet adopting the use of statistical “significance” when describing their findings suggests that such a conflict exists, which can lead to erroneous conclusions [ 26 ]. Therefore, new graduate students should take care to appreciate that p -values should not be taken as an indicator of whether the effect of a particular variable is strong or has importance to the system under study. Instead, p -values are merely a means of quantifying how certain one can be that there is no effect, given the type and quantity of data that have been collected. The American Statistical Association’s statement on p -values provides excellent guidance on the use and interpretation of p -values and clarification of common mistakes and is as recommended reading for all new graduate students [ 27 ].

Rule 8: Learn how statistical power can influence results

In any study, there are constraints on the amount of data that can be collected. Therefore, it is important to understand the role of statistical power and sample size in influencing results, as well as the scope of inference [ 28 ] (see Rule 3). Statistical power is the probability of correctly rejecting a statistical null hypothesis that is false, and this probability increases as both sample size and effect size increase. Thus, it may be possible to detect differences between 2 populations even with small sample sizes if effect sizes are large, whereas large sample sizes might lead to detecting very small statistical differences [ 29 ]. Given these considerations, it is essential to turn a critical eye when examining results to the extent to which methodological effects (e.g., sample size) may be driving the results relative to the strength of the response in the system (i.e., effect size). Conversely, when sample sizes are small and effect sizes are either small or data are highly variable, statistical power to detect differences is low [ 25 ]. In such instances, it is important to recognize the old adage that “the absence of evidence is not evidence of absence” and that an effect may be present even though low statistical power does not allow researchers to detect it within a study. Furthermore, reporting such negative results should also reflect this possibility; for example, it is preferable to state that no differences were detected in a study rather than stating that no differences were present. Although retrospective power analysis is generally viewed unfavorably, power analysis may, in some instances, be useful when applied a priori to determine what sample size is necessary to detect the minimum effect size of interest given a specified expected variance [ 28 ].

Rule 9: Appreciate the importance of effect size

As noted above in Rule 7, statistical null hypotheses posit that there is no difference between groups being compared or no association between variables being studied [ 25 ]. In practice, however, there is almost always some expected difference in measures taken between any 2 populations, however small, simply due to sampling effects and random variability [ 30 ]. Thus, the purpose of performing statistical analysis is not usually to determine whether 2 groups are absolutely the same, but rather whether the difference between them (i.e., the effect size) is meaningful within the context of the study system.

The effect size is the magnitude of a relationship between variables or a difference among groups [ 31 ], and it is typically presented with confidence intervals that represent the degree of uncertainty around the estimate [ 24 ]. In most cases, estimates of effect size are more informative than p -values because they provide context for the magnitude and direction of an effect and therefore should be emphasized when evaluating and reporting results [ 30 ]. For example, stating that a species of interest was “3.2 × (95% CI: [1.3, 4.6]) more likely to use a treatment site than a control site” is more informative than stating that “there was a significant effect ( p < 0.01) of treatment on site use.” The former approach highlights the magnitude of the effect, which, in turn, may help resource managers evaluate whether the biological response is worth the cost of implementing similar treatments in the future. In other words, interpreting the results in the context of the system under study is what really matters, without undue focus on statistical outcomes.

Rule 10: Don’t fly solo

If you are a new graduate student who feels a bit overwhelmed by the previous rules outlined here, this last rule is especially for you. Although it may seem like you are embarking on a singular odyssey by yourself, graduate school is really a time to develop connections with new peers, colleagues, and mentors. As such, it is important to develop and make use of a new professional support network throughout graduate school, including in the exploration of statistical analysis techniques and theory. We have found that statistics can be especially challenging for new graduate students and that learning new techniques and programming languages is best facilitated by collaboration and conversation with others. In addition to the benefits that derive from working with other students, there are often other outlets that can provide help when learning statistics. For example, some universities have statistical consultants that provide tailored one-on-one help that can provide input and assistance on myriad topics. With regard to working with consultants, one oft-repeated piece of advice is to seek help from statisticians before data collection begins, as that is the phase of the research process in which they can best help to remedy problems that may be present in a study; when the time has come for data analysis, it is often too late. More broadly, there is a wealth of online tutorials on statistical methods and programming languages, as well as free online courses, which combine statistics and programming and extensive online help communities for many programming languages. In short, make sure you tap into the resources that are available so that when—not if—you get stuck you will have a support system that can help get you back on track.

Concluding thoughts

Graduate school is an exciting time, but it can also be stressful due to its finite nature. In many fields, developing an understanding of statistics is a key part of the professional growth of graduate students, and it takes many forms: classes, workshops, self-study, and more. Given its central importance, and the expanding body of statistical science, our collective experience has found that researchers must continue to learn about statistical methods throughout their career, and, thus, developing a solid base early in one’s graduate program is essential for continued growth and development. It is therefore our expectation that these rules, when taken together, will help make that process a little less daunting for new students as they develop a deeper understanding of the statistical methods they will use during graduate school and throughout the rest of their careers.

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What are the benefits of getting a PhD in statistics?

I've been working as a statistician in the marketing world for two years, and I have an M.S. in applied math. I want to change industries a bit (maybe epidemiology or engineering) and I also want to live overseas (ideally, a job where I'd live in one country for a few years and then move on to another).

So, my question is: to accomplish these life goals, does it make sense to get a PhD? Or, is there some other route that would be more beneficial?

• Obvious question but...have you considered a PhD in Epidemiology? –  Fomite Commented May 23, 2016 at 20:04

2 Answers 2

A doctoral degree is a credential signifying largely that you are capable of doing independent research at the highest possible level. It is not, in an of itself, a ticket to working in a particular industry or in a particular location any more than a bachelor's or a master's degree. In fact, PhD holders may have substantially more challenges in those aspects, because the additional qualifications make them unattractive for many positions in conventional businesses and industries. (You are unlikely to find a PhD working in a sales division of a multinational conglomerate, for instance.)

The reason to get a PhD is because you are interested in problem solving and doing original work. If this doesn't describe your motivation, I would recommend against pursuing a graduate degree, because it will be a very long few years of your life which are not guaranteed to achieve the objectives you've laid out.

• 1 So, if I understand correctly, the PhD is only valuable as a stepping stone to a career in research/academia? It doesn't really have much value for industry purposes? –  random_forest_fanatic Commented Jul 15, 2013 at 1:34
• 8 In most industries, a PhD has negative value. –  JeffE Commented Jul 15, 2013 at 2:27
• 3 @JeffE: Outside of research divisions. –  aeismail Commented Jul 15, 2013 at 4:19
• 1 @JoshBrowning: There are other career paths also: scientific project management, but also things like patent law, scientific journalism, and start-ups. –  aeismail Commented Jul 15, 2013 at 4:22
• 3 @JoshBrowning: I'd say a PhD is a stepping stone to a career in research or research-related work. That may also be outside academia. –  silvado Commented Jul 15, 2013 at 7:07

A PhD in statistics is more flexible and useful that PhDs in some other areas. The usual issue with PhDs one hears about is that one becomes over-qualified for non-academic work once one has a PhD. Additionally, there is a lot of time spent getting it.

However, statistics is intrinsically an applied science, and one that is in big demand across lots of areas, because it can be applied to lots of areas, unlike most academic disciplines. Specific anecdote: I was once told by a Statistics Professor that the head of a clinical trial is required to have a PhD in statistics (by the NHS, possibly). I don't know if this is true, but it sounds like something that is probably true. As he put it, this creates jobs for PhDs.

With computers being used more and more, and lots of data being created that needs to be analysed, new methods need to be invented to handle all this data. This is the kind of quasi-research work which is quite well suited for someone with a PhD.

Areas like data visualization and graphics are quite hot right now. Having a PhD in an area like that will probably not hurt you. See Hadley Wickham's thesis for example.

Of course, it is possible to get a PhD from a Statistics Department without learning any statistics, for example if you write a Probability (Mathematics) thesis. You probably don't want to do that.

My personal experience (I have a Statistics PhD) is that to get an interesting work, even in industry, a PhD is helpful. Much of the work so-called statisticians do is to mindlessly apply standard algorithms from some software package to data using things like SAS, and then package up the (machine produced) results. If you have a functioning brain, you don't want to do that.

BTW, it seems such questions are not on topic at stats.sx , but you could ask people on chat there - perhaps point to this question.

• Thanks Faheem, I'm glad to hear from another statistician! I'm considering focusing my PhD on spatial statistics, as I'm interested in epidemiology and geographical applications. Do you have any opinions on that particular topic? –  random_forest_fanatic Commented Jul 20, 2013 at 11:43
• Hi Josh. No, I don't know anything about spatial statistics. Have you thought about which dept you want to go to? Choosing a dept carefully is important. As is the adviser, of course. You should also consider, of course, what your aim is in getting the PhD. As I said, getting a statistics PhD does not necessarily constrain you to academia, but what you work on in your thesis should at least somewhat depend on what you want to do afterwards. –  Faheem Mitha Commented Jul 20, 2013 at 15:24

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Statistics PhD

Awards: PhD

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Research profile

Our society revolves around variation, uncertainty and risk. By gaining a greater understanding of these variables through the study of statistics, we’re able to create systems and techniques that benefit areas as diverse as science, law and finance.

Our Statistics research group explores a wide range of statistical theory and practice, often applying its findings in collaboration with researchers in related fields, such as informatics, geosciences, medicine and biomathematics. The group leads the interdisciplinary Centre for Statistics that spans across the whole breadth of the university, providing opportunities for collaborations with researchers in many different applied fields.

The School of Mathematics is a vibrant community with researchers in many different, but related, fields - including Data Science.

Our research is balanced between classical and Bayesian statistics. Particular areas of interest include, but not limited to, high-dimensional data, computationally intensive techniques, wavelets, nonparametric regression, extreme value theory, sampling and hidden process models.

While the group has a strong theoretical base, a key component in the research relates to the interdisciplinary aspects of statistics with specific application areas including for example, ecology, geosciences, medicine, forensic science, law, and functional genomics data, such as gene expression microarrays.

Training and support

As a research student, you’ll find a wealth of expertise available to you via our links with theorists and practitioners in related fields.

The School interacts with numerous other groups across the university, including for example, Informatics, Geosciences, Business, Clinical Trials Unit. The interdisciplinary Centre for Statistics connects individuals across the breadth of the university interested in cross-fertilisation and collaborative research. The recently opened Bayes Centre, which also hosts the International Centre for Mathematical Sciences is the College of Science and Engineering Data Science initiative providing an exciting interdisciplinary environment for interacting within and across Schools.

In addition, the Scottish Government-backed research provider Biomathematics and Statistics Scotland is an associated research institute of the University. With its main base in our building, it provides access to other researchers with an interest in statistical genomics and bioinformatics, process and systems modelling and statistical methodology.

If your research is in the expanding area of forensic statistics, you'll benefit from our link with the Joseph Bell Centre for Forensic Statistics and Legal Reasoning. The Centre applies and teaches statistical techniques for interpreting evidence, such as binomial probabilities, conditional probability and Bayes’ Theorem.

Mathematics is a discipline of high intellect with connections stretching across all the scientific disciplines and beyond, and in Edinburgh you can be certain of thriving in a rich academic setting. Our School is one of the country’s largest mathematics research communities in its own right, but you will also benefit from Edinburgh’s high-level collaborations, both regional and international.

Research students will have a primary and secondary supervisor and the opportunity to network with a large and varied peer group. You will be carrying out your research in the company of eminent figures and be exposed to a steady stream of distinguished researchers from all over the world.

Our status as one of the most prestigious schools in the UK for mathematical sciences attracts highly respected staff. Many of our 70 current academics are leaders in their fields and have been recognised with international awards.

Researchers are encouraged to travel and participate in conferences and seminars. You will also be in the right place in Edinburgh to meet distinguished researchers from all over the world who are attracted to conferences held at the School and the various collaborative centres based here. You will find opportunities for networking that could have far-reaching effects on your career in statistics.

You will enjoy excellent facilities, ranging from one of the world’s major supercomputing hubs to generous library provision for research at the leading level, including the new Noreen and Kenneth Murray Library at King’s Buildings.

Students have access to more than 1,400 computers in suites distributed across the University’s sites, many of which are open 24 hours a day. In addition, if you are a research student, you will have your own desk with desktop computer.

We provide all our mathematics postgraduates with access to software packages such as Maple, Matlab and Mathematica. Research students are allocated parallel computing time on ‘Eddie’ – the Edinburgh Compute and Data Facility. It is also possible to arrange use of the BlueGene/Q supercomputer facility if your research requires it.

Career opportunities

You will gain a qualification that is highly regarded in both academia and industry. Future career options are diverse, with past students finding positions in academic institutions, forensics, finance, law and biological and agricultural organisations.

Statistics MSc Graduates 2017

Entry requirements.

These entry requirements are for the 2024/25 academic year and requirements for future academic years may differ. Entry requirements for the 2025/26 academic year will be published on 1 Oct 2024.

A UK first class honours degree, or its international equivalent, in an appropriate subject; or a UK 2:1 honours degree plus a UK masters degree, or their international equivalents; or relevant qualifications and experience.

International qualifications

Check whether your international qualifications meet our general entry requirements:

• Entry requirements by country
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Regardless of your nationality or country of residence, you must demonstrate a level of English language competency at a level that will enable you to succeed in your studies.

English language tests

We accept the following English language qualifications at the grades specified:

• IELTS Academic: total 6.5 with at least 6.0 in each component. We do not accept IELTS One Skill Retake to meet our English language requirements.
• TOEFL-iBT (including Home Edition): total 92 with at least 20 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.
• C1 Advanced ( CAE ) / C2 Proficiency ( CPE ): total 176 with at least 169 in each component.
• Trinity ISE : ISE II with distinctions in all four components.
• PTE Academic: total 62 with at least 59 in each component.

Your English language qualification must be no more than three and a half years old from the start date of the programme you are applying to study, unless you are using IELTS , TOEFL, Trinity ISE or PTE , in which case it must be no more than two years old.

Degrees taught and assessed in English

We also accept an undergraduate or postgraduate degree that has been taught and assessed in English in a majority English speaking country, as defined by UK Visas and Immigration:

• UKVI list of majority English speaking countries

We also accept a degree that has been taught and assessed in English from a university on our list of approved universities in non-majority English speaking countries (non-MESC).

• Approved universities in non-MESC

If you are not a national of a majority English speaking country, then your degree must be no more than five years old* at the beginning of your programme of study. (*Revised 05 March 2024 to extend degree validity to five years.)

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If you are not an EU , EEA or Swiss national, you may need an Academic Technology Approval Scheme clearance certificate in order to study this programme.

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Tuition fees.

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Featured funding.

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If you live in the UK, you may be able to apply for a postgraduate loan from one of the UK's governments.

The type and amount of financial support you are eligible for will depend on:

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Programmes studied on a part-time intermittent basis are not eligible.

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PhD Statistics - 3 Years (Full-time)

Phd statistics - 6 years (part-time), application deadlines.

We strongly recommend you submit your completed application as early as possible, particularly if you are also applying for funding or will require a visa. We may consider late applications if we have places available. All applications received by 22 January 2024 will receive full consideration for funding. Later applications will be considered until all positions are filled.

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• Interdisciplinary PhD in Economics and Statistics

Students must complete their primary program’s degree requirements along with the IDPS requirements. Statistics requirements  must not unreasonably impact performance or progress in a student’s primary degree program.

The Economics program allows students to replace required courses in Probability and Statistics with more advanced courses by petition.

Special note about integrating IDPS requirements and Economics requirements:

The Doctoral Program in Economics requires students to complete two majors and two minors. IDPS requires one of these major fields to be Econometrics. The IDPS requirement for Computation & Statistics may be used to satisfy one of the minor field requirements in the Doctoral Program in Economics as long as the student’s other minor field is in Economics, and is not a research or ad-hoc minor.

PhD Earned on Completion: Economics and Statistics

IDPS/Economics Chair :  Victor Chernozhukov

*Advanced Research and Communication – 14.192 – no longer requires a focus on Data Analysis. Students pursuing the IDPS will need to keep this focus on Data Analysis to successfully meet IDPS requirements.  The IDPS/Economics Chair will verify that admitted students submit a paper that satisfies the IDPS requirements.

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PhD Statistics Eligibility, Colleges, Admission Process, Syllabus, Scope, Salary 2024

Waqar Niyazi

Content Curator

PhD Statistics is a 2-year Doctoral course in Statistics which can be pursued on completion of Master’s degree. 

This doctoral course is a branch of Mathematics that deals with the collection, analysis, interpretation, and presentation of numerical data. This program can be used for the measurement systems’ variability, control processes for abridging data, and for making data-driven choices.

Admission to PhD Statistics course will be done either on the basis of the candidate's performance in the Master’s degree exam or on the basis of their performance in the entrance exams conducted by admission authorities. 

PhD Statistics degree holders are hired in a wide range of fields as a Research Analyst, Assistant Professor, Data Analyst, Biostatistician, Data Interpreter, Lecturer, Research Scholar etc. They are recruited in Statistical Services, Social Research, Economics, Finance, Indian Economic Services, Businesses, Commerce, Govt. Jobs Consulting Firms etc. 

The average annual tuition fee charged for this course in India ranges between INR 10,000 and INR 1,50,000. In India, the average annual salary that a PhD Statistics degree holder can get ranges between INR 3,00,000 and INR 8,00,000. 

If students wish to do further research, they can become independent researchers and publish their research papers. Students can also opt for MPhil Mathematics and MPhil Statistics . They can also earn a DSc (Doctor of Science) degree in the related domain in future. 

PhD Statistics Course Highlights

Highlights for PhD Statistics program are provided in the table given below:

PhD Statistics: What is it about?

Information and details about the PhD Statistics program are as follows.

• The PhD program in Statistics is a great research-based analytic program for those preparing for advancing their knowledge and expertise in aspects of the collection, analysis, interpretation, and presentation of numerical data.
• Candidates can bring changes in documentation, policy, education, or technology, thus advancing the field of Statistics through their research.
• It is a branch of study that takes within it a broad spectrum of academic disciplines ranging from humanities to physical and social sciences.
• The study of Statistics involves both Mathematical as well as Applied Statistics. Applied Statistics is studied under two divisions’ namely descriptive Statistics and inferential Statistics.
• This course propagates to eligible candidates a wide base of advanced learning in Theoretical Statistics, Applied Statistics, Probability and such areas of study.

Why study PhD Statistics?

The goal to attain a PhD in Statistics degree will depend on each individual’s aspirations and goals. Some of the reasons to pursue this course are as follows.

• The Statistics course trains students for careers in the academic, public, private, or non-profit sectors and provides students with the ability to think critically, creatively and holistically in the field of Mathematics and Statistics.
• After completing the PhD Statistics course, candidates will be qualified for lecturer post in Statistics or Mathematics in colleges and universities. 
• They can join educational institutions as Teachers and Lecturers in schools and universities. 
• Candidates after earning their PhD degree can become the most skilled in their area of specialization. 
• Working professionals can also opt for a part-time program.
• On completion of this program, students will easily manage to get handsome salary packages ranging upto INR 8,00,000. 

What is the PhD Statistics Admission Process? 

Most colleges and institutes offering PhD Statistics courses admit students based on their performance in the Master’s degree examination. However, there are some colleges which do conduct entrance examinations to judge a candidate’s abilities and skill sets.

The following are the two major pathways through which a PhD Statistics admissions take place:

Merit-Based Admission

Most private colleges that offer PhD Statistics courses usually admit students based on the marks secured at the master’s/ graduate degree. Besides that, these colleges may conduct a Personal Interview or a Written Test to further know the candidate’s skills.

Entrance-Based Admission

Most of the colleges and universities offer admission to PhD Statistics on the basis of entrance exams like GATE, UGC NET, BHU UET etc.

The step-by-step procedure for entrance-based admission is as follows.

• Step 1: Students have to register on the official website. 
• Step 2 : Fill the application form with correct details.
• Step 3 : After examination, a cutoff list will be released on the website. Seats will then be allotted to candidates based on their scores in the entrance exam. 
• Step 4 : Some colleges also conduct personal interviews and group discussion for admission to this course. 
• Step 5 : On meeting all the eligibility criteria, a student will then be offered admission to the course.  

What is the PhD Statistics Eligibility Criteria?

The common PhD Statistics eligibility criteria that candidates need to fulfil to be successfully admitted into a college offering this course are as follows:

• M.Sc./ MA in Mathematics or Statistics from any recognized University or Institution in a relevant subject with minimum 55% marks or an equivalent grade.
• For candidates belonging to SC/ST/OBC / Differently-Abled and such other categories, 5% relaxation in marks will be given in accordance with the decision of the UGC & Govt. of India will be accorded.
• However, an applicant with CSIR-UGC/JRF/NET/SLET cleared in the relevant subject is exempted from the Entrance Test.

Which are the Top PhD Statistics Entrance Exams?

Some colleges that offer PhD Statistics programs require their candidates to sit for an entrance examination.

How to Prepare for a PhD Statistics Entrance Exams? 

Most of the institutes which conduct entrance exams for PhD Statistics program do not have a well-defined syllabus. Tips for preparation for entrance tests are as follows.

• The question paper for the entrance examination for admission to the PhD in Statistics course generally contains 100 Multiple Choice Questions based on topics studied at M.Sc. main level. 
• Brush up all basic Statistics study material learnt at masters and undergraduate level.
• Get up to date with the latest news, general knowledge etc., based on which questions might be asked in the personal interview round.
• The research proposal for the program is to be prepared and studied thoroughly.
• Past papers of entrance exams can be downloaded from university websites and used for practice. The practice must be done while timing oneself and recreating exam situations.

Also, read more about the GATE Exam Question Pattern, Syllabus, and Important Tips in detail.                          

How to get admission in a good PhD Statistics college?

To get admission in the top PhD Statistics colleges, the following points must be kept in mind:

• Candidates are shortlisted by the college through respective entrance exams followed by an interview.
• To get a good college for PhD appearing for CSIR-NET and UGC-NET exams is the best way and for some of the colleges’ GATE is also considerably good to approach. Check the list of Top CSIR NET Colleges in India.  
• A few private institutes are there offering the course to admit students based on their performance in a relevant entrance test followed by a round of Personal Interview.
• Getting into a good college for the admission in PhD Statistics program, candidates need to score well in the respective entrance exams.
• They should be well versed with their research topic so that they can impress the interview panel with their research idea. 

What is the PhD Statistics Syllabus?

Although the PhD in Statistics program syllabus varies from institute to institute, it mostly consists of some common foundation courses that students can select based on their interests.

The Semester-wise syllabus of PhD Statistics program is provided in the table given below: 

Which books to refer for PhD Statistics?

Tabulated below are some of the PhD Statistics subject books that can help students to have a broader and better understanding of the course. Mentioned below are the books that also help the students in cracking various PhD Statistics exams.

PhD Statistics Top Colleges

The table below shows the best PhD Statistics colleges and universities that offer the course in a full-time mode.

Source: College Websites

PhD Statistics College Comparison

To help choose between which university/ college to take admission in for PhD Statistics, a side by side comparison of the top three universities is given below.

PhD Statistics vs. PhD Mathematics 

Just like PhD Statistics, PhD in Mathematics is a research course. Both of these courses are similar. However there is a minute difference between these two programs. The difference between PhD Statistics and PhD Mathematics is as follows.

PhD Statistics deals with the collection, analysis, interpretation, and presentation of numerical data whereas PhD Mathematics is the study of structure, space, quantity, and change that seeks out patterns and formulates new conjectures. 

Check the table below for more comparative details:

What are the Job Prospects and Career Options after PhD Statistics?

After getting a degree in this field a candidate can get a variety of jobs to choose from. It will be easy for them to work for the government and private organizations and many other sectors. They can also choose to teach as a Professor or a Lecturer in colleges or universities.

• There are immense job opportunities in Statistics. Students can work in a wide range of industries. One can become a Psychometrist, Investment Analyst, Cryptologist, Commodities Traders, Financial Aid Director, Information Scientist etc.
• One can even get into the field of Education and become a Professor. The scope of research is also very wide.

The tabulation below shows some of the most common PhD Statistics job profiles and career prospects after completing the course is as follows:

What are the Future Scope after PhD Statistics?

PhD degree is a doctorate level degree and is the highest educational degree one can earn in the country. Generally one does not pursue further studies post completion of the PhD Statistics degree.

Employability is high and graduates are hired quickly upon completion of their degree in high pay job profiles. With this knowledge, there is no limit to learning and knowledge.

If one wishes, the following programs can be pursued after a PhD:

• MPhil Statistics: On completion of PhD Statistics program, students can opt for MPhil Statistics program which involves further research in Statistics. 
• MPhil Mathematics: Students can also opt for MPhil in Mathematics on completion of PhD in Statistics course. Check MPhil Mathematics job opportunities . 
• After completing the Doctorate in Statistics you can continue your further research work as a Postdoctoral Fellow with CSIR and UGC fellowship.

PhD Statistics FAQs

Some of the most frequently asked questions related to PhD Statistics course which will help students in clearing their doubts are provided below: 

Ques. What are the top colleges for the PhD Statistics program?

Ans . The top institutes and colleges for the PhD Statistics program are Banaras Hindu University, Amity University Noida, University of Hyderabad, Mumbai University, Banasthali Vidyapeeth, among others.

Ques. Which job profiles are available for PhD Statistics?

Ans . Students of PhD Statistics can work as an Assistant Professor, Biostatistician, Data Analyst, Research Analyst, Data Interpreter, Econometrician, Article Writer, Enumerator, Lecturer, Research Scholar, Statistician, Test Content Developer – Humanities, Content Developer, Labor Counsellor, Curriculum Trainer, Trainee Associate, Sociologist, Trainee - Teaching Associates, Asst. Professor & Lecturer, etc.

Ques. What is the average annual tuition fee charged for the PhD Statistics program?

Ans . The average annual tuition fee in the top PhD Statistics colleges ranges between INR 2,000 and INR 1,50,000.

Ques. What is the scope after pursuing a PhD Statistics degree?

Ans. After completing the Doctorate in Statistics, you can continue your further research work as a Postdoctoral Fellowship with CSIR and UGC fellowship. Once you finish your PhD you can also be qualified for a Lecturer post in Statistics and also for Mathematics in colleges and universities. 

Ques. What is the average annual salary offered to PhD Statistics degree holders?

Ans. The average annual salary ranges from INR 3,00,000 to INR 8,00,000 as per the job profile.

Ques. What are the areas of employment in the PhD Statistics field?

Ans. Several areas of employment are there in this field such as students can work at Ecological, Medical, Census, Election, Crime, Education, Govt. Jobs, Indian Statistical Services, Social Research, Film, Cricket, Tourism, Consulting Firms, Data Survey Agencies, Insurance, Statistical Research, Economics, Finance, Indian Economic Services, Businesses, Commerce,  Public Sector Undertakings (PSUs), Statistical and Economic Bureaus, Banks etc.

Ques. Is PhD Statistics a tough course? 

Ans . No, PhD Statistics is not tough at all. If you have an interest in Mathematics and Statistics, this course seems very easy to you. So, it totally depends on your interest. 

Ques. What is the admission process to apply to this course? 

Ans. Admission to this course will be done either on the basis of candidate’s performance in the qualifying examination i.e. Master’s degree examination or on the basis of candidate’s performance in the entrance exams conducted by the admission authorities. 

Ques. What is the eligibility criteria to seek admission to a PhD Statistics course? 

Ans . Candidates must hold a Master’s degree in Mathematics or Statistics with an aggregate of at least 55% marks to seek admission to this program. 

Ph.D. (Commerce)

M.phil. (commerce), ph.d. (commerce and management), ph.d. (business economics), ph.d. (accountancy), ph.d. (statistics) colleges in india.

Amity University

Banaras Hindu University - [BHU]

Panjab University - [PU]

Acharya Nagarjuna University - [ANU]

Loyola College

ISM Dhanbad - Indian Institute of Technology - [IITISM]

Presidency College

Aligarh Muslim University - [AMU]

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The Ohio State University

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Meet the 2025 Instructors & Speakers

Meet the speakers .

John Yorston  has been doing scanning electron microscopy and X-ray microanalysis (SEM-EDX) for almost thirty years, both in industry and in support of instrument sales and applications support. John is a veteran of countless SEM demonstrations and has conducted over 100 industrial new user short courses in SEM and microanalysis. John holds a BS in Chemistry from Fairleigh Dickinson University and has a background in Czockralski crystal growth of oxide and fluoride laser electro-optics and in ultra-high-pressure industrial diamond synthesis. John Yorston (Aka Fox Delta) is an accomplished and avid soaring pilot, and an active member of Aero Club Albatross based at Blairstown Airport, in New Jersey. 

Shawn Wallace joined EDAX in 2015 as an Applications Engineer specializing in EBSD. He has a strong background in all sorts of instrumentations including EBSD, electron microprobe, all sorts of mass spectrometry, and computed tomography. In his career, he has taught researchers of all levels, from post docs to undergrads, how to use instrumentation and he continues that here at EDAX.

Shawn earned his MS in Geology from the University of South Carolina. His research focused on method development for ICP-Mass spectrometry and geothermobarometry to help better understand planetary differentiation. After earning his degree, Shawn worked as a Scientific Assistant at the American Museum of Natural History in New York City, where he helped study the origin of the Solar System using EBSD and other instruments, including 3D work with computed tomography. Shawn hopes to bring those 3D skills to the rapidly expanding 3D EBSD world.

Meet the Instructors 

David McComb, PhD , is an Ohio Research Scholar and Professor of Materials Science and Engineering at The Ohio State University. David is an expert in the development and application of electron energy-loss spectroscopy (EELS) as a sub-nanometer scale probe for chemistry, structure, and bonding. He has developed and implemented approaches to studying inorganic, organic, and molecular systems using electron microscopy techniques.

Tyler Grassman, PhD ,   is an Associate Professor in the Departments of Materials Science & Engineering and Electrical & Computer Engineering at The Ohio State University. Tyler is an expert in semiconductor epitaxy, defect engineering, and device development, as well as structural and optoelectronic characterization. His group has pioneered and refined the use of various advanced SEM and TEM-based microscopy and spectroscopy methods for quantitative and correlative analysis of defects in a range of semiconductor systems.

Daniel Veghte, PhD , is a Senior Research Associate at the Center for Electron Microscopy and Analysis (CEMAS). In his role, he manages the scanning electron microscopes and works closely with users to characterize a broad range of materials. During his career, he has enjoyed pushing the limits of electron microscopy with an emphasis on performing in-situ experiments.

Elvin Beach has a B.S. in Metallurgical Engineering, a M.S. in Materials Science & Engineering from Michigan Technological University, and a Ph.D. in Materials Science & Engineering from The Ohio State University.  Dr. Beach has worked extensively in applied failure analysis while working at The Dow Chemical Company, Battelle Memorial Institute, and Worthington Industries.  Dr. Beach has extensive experience in metallographic sample preparation, optical microscopy, and electron microscopy techniques.  Dr. Beach currently teaches undergraduate characterization lab courses, materials processing, and materials testing laboratories along with fracture, fatigue and failure analysis.  Dr. Beach is the editor in chief of the Journal of Failure Analysis and Prevention and an associate editor for Metallography, Microstructure, and Analysis.

Undergraduate Program Overview

The field of mathematics.

Mathematics begins with simple questions in arithmetic. This has led to harder and harder questions involving a huge array of techniques. Perhaps the best way of understanding the scope of mathematics is to look at some examples of questions that mathematicians have worked on and are working on.

A prime number is an integer that cannot be factored into the product of two smaller integers. Every number can be written as the product of primes. Thus, primes are the building blocks of the integers. It is easy to tell if you have a prime number, but can you give a method for deciding whether a number, say one with 200 digits, is prime that works quickly? Can you give a method that works quickly for factoring a number into prime factors? These are simple questions but efforts to answer them have led to much elegant and deep mathematics. And the answers to these questions are useful. Many of the encryption devices we use every day are based on the fact that we  can  quickly tell if a number is prime and we  cannot  quickly factor numbers.

Is the planetary system stable? In other words, taking only gravity into account, will the planets keep revolving around the sun, or will they fall into the sun, or will they move farther and farther away from the sun? We assume that the sun does not change and that there are no visitors to the planetary system. Efforts to answer this question have led to the study of chaos and fractals.

Diseases sometimes appear in geographical clusters. When do these clusters indicate that the disease is caused by something in the environment near the cluster? More generally, one can ask how does exposure to a certain substance affect the probability of an individual developing a certain kind of cancer? Often one can find two explanations that describe a data set equally well. Which is the better explanation? These are examples of a broad array of questions having to do with using imperfect and incomplete data to understand the behavior of complicated systems.

The rapid advance in genetics has led to a plethora of problems that have a large mathematical component. We describe one technique and a pair of problems arising from this technique. A micro-array tells us which genes in an organism are being expressed at a single instant. This tells us roughly what proteins are being manufactured at that time. For example, from a single yeast cell, we obtain information about the productions of 5,000 kinds of protein. If we repeat this experiment 12 times, we seem to have the information needed to get a picture of the biochemical pathways of the organism. This knowledge will help us understand, for example, how undifferentiated stem cells become blood cells or muscle cells, or how diseases harm an organism.

But there are several challenges to overcome. First micro-array readings are prone to noise, which could come from tiny differences in the measurement methods or initial conditions (for example, ambient temperature). One needs to mathematically model this noise in order to compensate for it. The second challenge is to develop methods of handling the huge amount of data produced by micro-arrays. How does one find patterns in this set of data. This is called "data mining." Clearly techniques for analyzing a huge data base can be used in many endeavors—for example, ecology.

Mathematics Majors

The goal of the mathematics program at the University of Massachusetts Amherst has three aspects. First, students learn basic material, such as linear algebra, differential equations, and statistics, that are needed to successfully attack a wide range of problems. Second, they learn to think with rigor. Lastly, they learn to approach apparently unsolvable problems by studying simpler problems, doing experiments, and bringing together different concepts.

All majors must complete a calculus sequence and courses in linear algebra, modern algebra, and analysis. Each major has wide freedom of choice in upper-division courses and can, with the assistance of a faculty advisor, tailor a program to their interests and career goals. For example, one can prepare for a career in actuarial work, statistical analysis, computer programming, data processing, industry, government, or secondary school teaching. One can also prepare for graduate study in mathematics, statistics, computer science, and other fields or professional programs in business, law, medicine, and education.

• Undergraduate Overview
• Major Requirements
• Advising Information
• Activities, Research and Scholarships
• Actuarial Science
• Association for Women in Mathematics (AWM)
• Career Opportunities
• Minor, Secondary Major, Dual Degree
• Transfer and AP Credit and Study Abroad
• R1 Exemption
• Learning Resources
• Math Competition
• Directed Reading Program

Award-winning teaching, research opportunities, and interdisciplinary programs in a diverse, inclusive community of excellence.

Lederle Graduate Research Tower, 1654 University of Massachusetts Amherst 710 N. Pleasant Street Amherst, MA 01003-9305, USA

Department Phone: (413) 545-2762 Department Fax: (413) 545-1801 Department Office: LGRT 1622