# "Statistical Techniques for Particle Physics" (4/4)

- Institution: CERN
- Department: Academic Training Lecture Regular Programme,2008-2009
- Speaker: Kyle Cranmer
- Year: 2009
- Level: Graduate
- Language: English
- Subjects: Physics
- Source: [CERN](https://videos.cern.ch/record/3007029)
- Licence: [CERN](http://copyright.web.cern.ch)
- Who can watch: Tweens profiles and older (sources give no ratings; placed by Invisible Network from the source's own words)

This series will consist of four 1-hour lectures on statistics for particle physics. The goal will be to build up to techniques meant for dealing with problems of realistic complexity while maintaining a formal approach. I will also try to incorporate usage of common tools like ROOT, RooFit, and the newly developed RooStats framework into the lectures. The first lecture will begin with a review the basic principles of probability, some terminology, and the three main approaches towards statistical inference (Frequentist, Bayesian, and Likelihood-based). I will then outline the statistical basis for multivariate analysis techniques (the Neyman-Pearson lemma) and the motivation for machine learning algorithms. Later, I will extend simple hypothesis testing to the case in which the statistical model has one or many parameters (the Neyman Construction and the Feldman-Cousins technique). From there I will outline techniques to incorporate background uncertainties. If time allows, I will touch on the statistical challenges of searches for physics beyond the standard model and the look-elsewhere effect..

0:00:00 Slide 1

0:03:26 Slide 2

0:06:52 Slide 3

0:13:44 Slide 4

0:20:36 Slide 5

0:27:27 Slide 6

0:34:19 Slide 7

0:41:11 Slide 8

0:48:02 Slide 9

0:54:54 Slide 10

1:01:46 Slide 11

## Lectures

1. ["Statistical Techniques for Particle Physics" (4/4)](https://invisiblenetwork.ai/learn/cern/3007029/dc12a45b54d6), 1 h 9 min (CERN's player)