Statistical Methods of Data Analysis
WS00/01 No. 6793
Ian C. Brock

From the first lab. course that you take to the design and construction of an experiment; from the first simulations to the final analysis of the data from your experiment, the proper application of statistical methods is essential.

The aim of this course is to provide a foundation in statistical methods and to give some concrete examples of how the methods are applied to data analysis. Standard statistical distributions will be discussed and examples given of when they are expected to occur and how they are related.

Techniques for fitting data will be discussed. The treatment of systematic errors, as well as methods to combine results from different experiments which may have common error sources will also be covered.

The search for new physics, even when no signal is observed, allows limits to be placed on the size of possible effects. These can provide severe constraints on theoretical models. Methods for calculating upper limits taking into account several error sources will also be considered.

How and where to find me

Ian Brock, PI 155, Tel: 3616, Email:


Wednesdays 8-10, HS 1, PI
Lecture 10 will take place on Thur. 25th. Jan from 8-10, PI Conference Room II, instead of 24.1.01 as orginally scheduled.

By clicking on the chapter number you can access the outline of that lecture, and then also find my lecture notes for each lecture. I looked into providing an HTML version of the notes, but the conversion of lots of mathematical formulae does not produce a very nice result. The tool one can use is latex2html.

Lecture Date Topics

1 18.10 1.0: Introduction
2.0: Characterising Distributions
2 25.10 3.0: Standard Theoretical Distributions
3 8.11 4.0: Errors
4 15.11 4.2: Errors cont., especially systematic
5 22.11 5.0: Estimation
6 29.11 6.0: Maximum Likelihood
7 13.12 6.4: Maximum Likelihood cont.
8 10.01 7.0: Least Squares
9 17.01 7.6: Least Squares cont.
8.0: Probability
10 25.01 8.3: Probability and Confidence Levels
11 31.01 9.0: Hypothesis Testing
12 07.02 9.7: Hypothesis Testing cont.
13 14.02 10.0: Monte Carlo
11.0: Numerical Minimisation


Applying the correct techniques to solve a given statistical problem can only really be learnt by doing!

The exercises will offer the opportunity to use the methods learnt in the lectures Statistical Methods of Data Analysis on concrete problems. Most of the exercises will involve the use of your home computer or the CIP-Pool. This will also give you the chance to learn some of the standard programs used for data presentation and analysis.


Mondays 13:00 - 15:00, CIP pool, AVZ

Exercises will usually be held every 2 weeks for 2 hours. They will mostly need computers to solve them, and/or display the results.


Peter Irrgang,
Joachim Tandler,

Collected Information



I have also made a glossary of standard statistical vocabulary in German and English. Suggestions on other terms that should be included are very welcome.


Responsible: Prof. Dr. Ian C. Brock, Email:
Last modified: Fri Aug 17 12:06:00 CEST 2001