Applied Statistics - curriculum

Full curriculum:

The full curriculum is listed below, refering to chapters and sections in:
   Roger Barlow's "Statistics: A Guide to the Use of Statistical Methods in the Physical Sciences".

Essentially it is all chapters except chapter 9, and it is all sections except those giving proofs. Note that the examples are very much a part of the book, and should (hopefully) give insight into how to apply the methods discussed.
The exercises are a great opportunity to check if the material has been understood and how methods are applied. Solutions can be found at the back of the book. The full curriculum is about 130 pages in total.

Chapter 1 (All)
Chapter 2 (All)
Chapter 3 (Except 3.2.2, 3.3.2, 3.4.2, 3.5.2)
Chapter 4 (All)
Chapter 5 (Except 5.1.3, 5.3.2, 5.3.3 (formal part), 5.3.4, 5.5)
Chapter 6 (Except 6.4.1, 6.7)
Chapter 7 (Except 7.3.1)
Chapter 8 (Except 8.4.4, 8.4.5, 8.5.1, and 8.5.2)
Chapter 10 (All)

In addition, we will consider three other subjects:
(1) How to generate random numbers according to a specific distribution (following the "Monte Carlo" note linked to at the bottom of the page, essentialy chapter 3 in Glen Cowan's book or the PDG).
(2) How to produce an advanced fit starting from a simple fit and adding parameters to it (exercises).
(3) How to produce a Fisher discriminant, which is a linear combination of variables combined in the strongest possible (linear!) way.
The first requires calculus, the second experience, and the last linear algebra.

Core part of curriculum:

While the curriculum above is what is to be read (such that you can find it again), some parts are vital for the understanding and use of statistics. These parts, listed below, should be read several times until well understood! The core parts constitute about half of the curriculum.

Chapter 2: 2.1, 2.2, 2.3, 2.4.1, 2.4.2, 2.6
Chapter 3: 3.1, 3.2, 3.2.1, 3.3, 3.3.1, 3.4.1, 3.4.7, 3.5.1
Chapter 4: 4.1, 4.2, 4.3, 4.3.1, 4.3.2, 4.3.3
Chapter 5: 5.1, 5.1.1, 5.1.2, 5.2, 5.6
Chapter 6; 6.1, 6.2, 6.2.1, 6.2.2, 6.2.3, 6.2.4, 6.3, 6.4
Chapter 8: 8.1, 8.2, 8.3, 8.4, 8.4.1, 8.4.2, 8.4.3

Note that chapter 7 is not included in this! Not that the chapter is not worth reading, but it is more philosophical. However, Bayes theorem and the idea of confidence levels and limits is important. In a world of infinite time, we would (also) go through this chapter in detail.

Coverage of curriculum in slides and exercises:

The lectures and exercises will essentially cover the above curriculum, and thus the slides provided for the course are a solid basis for it, though without the rigor, that Barlow provides. The associated exercises provides further insights into the rigor, and perhaps more important into how to apply the statistical principles to real data in practice. This is the aim of the course, and for this the lectures and exercises play an essential role.
Barlow remains the reference, and is a great place to read about subjects as described by a master in the art of statistics.

Additional parts from other sources:

While the curriculum only refers to Barlow's "Statistics" (for simplicity and saving student money for buying books), I can generally recommend the following books and chapters for a second reading.
A. Bevington: Data Reduction and Error Analysis
The standard text for many years, and with a very good section on error propagation. The book also cover some of the basic rules on significant digits, precision vs. accuracy, and is in general a very good introductory text to statistics.
Glen Cowan: Introduction to Statistics
A good short book, with a better (shorter) introduction to PDFs. It also contains a very good chapter on unfolding.
Particle Data Group:
Very consise but extremely good writeups on three main and one related area:
- Probability.
- Statistics.
- Monte Carlo.
- Machine Learning.

---

Last updated 6th of November 2025.