# "Statistical Techniques for Particle Physics" (1/4)

- 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/3002297)
- 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

0:03:36 Slide 2

0:07:12 Slide 3

0:14:24 Slide 4

0:21:36 Slide 5

0:28:48 Slide 6

0:36:00 Slide 7

0:43:12 Slide 8

0:50:24 Slide 9

0:57:36 Slide 10

1:04:48 Slide 11

## Lectures

1. ["Statistical Techniques for Particle Physics" (1/4)](https://invisiblenetwork.ai/learn/cern/3002297/7fb3f054028c), 1 h 12 min (CERN's player)