PHY 310: Statistical Data Analysis

This course covers theory and practice of experimental data analysis, and will touch on three basic components: 1) The basic theory of probability and it's use in physics 2) analysis techniques all experimentalists will used every day, 3) computational methods made possible by readily available computers and, if time allows, several advanced topics that experimentalists should be aware of. This course is level appropriate to the analysis requirements of PHY445 and PHY515 (the senior/graduate laboratory course).

This is not a course in numerical methods, but will concentrate heavily on the use of computers in data analysis. While prior programming experience will be helpful, the curriculum assumes that students are familiar with modern PCs, but have no prior programming experience. Programming will be taught as it is needed. To be successful in PHY310, students must have access to a PC and to be able to complete simple programing assignments.

Useful Links


PHY 310 is a three credit course. There will be regular homework, an in-class midterm, and a take-home final/project.

  1. Class Meetings: Tu, Th 5:30 pm to 6:50 pm in Physics D-122

  2. Grading: Homework, Midterm and the final project will be counted equally.

Contact Information

Prof. Clark McGrew

Prof. Clark McGrew




Physics D-134



  1. Measurement, analysis, and interpretation in physics

  2. The basic mathematics of probability

    1. Definitions: probability, distribution functions, density functions

    2. Common distributions: binomial, Poisson, Gaussian, chi-squared

    3. Expectation values &c: mean, mode, variance, covariance

    4. Confidence and statistics

  3. Data simulation techniques

  4. Introduction to parameter estimation: least squares minimization

    1. Linear and non-linear minimization

  5. Hypothesis testing: Student's “t” test, chi-squared test, trials factor

  6. Error analysis: error propagation, statistical vs systematic uncertainty

  7. Confidence intervals: definitions, estimation, the role of assumptions

  8. Advanced parameter estimation: maximum likelihood, robust estimators

  9. Discriminants: maximum likelihood, Fischer's discriminant, neural nets

Texts, Software and Computer Use

Required Text

This is an excellent text covering statistical data analysis from the point of view of a practicing particle physicist.

Additional Texts

This book is required for PHY445/PHY515. It's the classic statistics introductory text for physicists. If you purchase this book, be sure to get the third edition which corrects many errors.

Be sure to get the second edition. This presents a very technical view of data analysis, but is very opinionated. It covers some interesting topics not usually touched upon.

Computer Use

In addition to the material from the text, and lectures, a significant portion of this course will be computer based. Students are expected to have access to a computer, and may use any program, or programing language they wish, as long as print-outs, CD-ROM, DVD-ROM, or EMail attachments can be handed in. Examples will be provided using several freely available programs and libraries.


PHY310 is not a programing course, but many assignments will require you to use a computer. Students in this course will need access to a data presentation package, and a programing language. This is a short list of some of the software that you may find useful during this class. While you won't be required these particular program, you will need access to something similar. Examples and solutions will be provided using several programs that can be downloaded and installed on a students machine. Since not all students will have programming experience, I will teach just enough to complete the homework assignments and exams. In-class examples will mostly use ROOT (and MAXIMA when I need symbolic algebra).

Clark McGrew (