Practical statistics for particle physicists

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Learning to love the errror matrix lecture : Learning to love the errror matrix Introductory remarks. Conditional probability. Statistical and systematic errors. Combining results Binomial, Poisson and 1-D Gaussian 2-D Gaussian and the error matrix. Understanding the covariance. Using the error matrix. Estimating the error matrix. Combining correlated measurements Parameter determination by likelihood Do's and don'ts lecture : Parameter determination by likelihood : Do's and don'ts Introduction to likelihood. Error estimate. Simple examples: (1) Breit Wigner (2) Lifetime Binned and unbinned likelihood Several parameters Extended maximum likelihood. Common misapprehensions: Normalisation delta(lnL) = 1/2 rule and coverage Integrating the likelihood Unbinned L_max as goodness of fit Punzi effect Chi-squared and hypothesis testing lecture : Chi-squared and hypothesis testing Basic idea. Error estimates. Several parameters Correlated errors on y. Errors on x and y. Goodness of fit. Degrees of freedom. Why assymptotic? Errors of first kind and second kind. THE paradox Kinematic fits. Toy example. Bayes, Frequentism and limits lecture : Bayes, Frequentism and limits Bayes and frequentist probability. Everyday examples. Prob(data;theory) is not equal to Prob(theory;data) Bayes theorem. Bayesian prior. When priors are and are not important. Frequentist confidence intervals, and their properties. Limits calculations by Bayes, Neyman construction and Feldman-Cousins Summary of Bayes and Frequentist approaches Discovery and p-values lecture : Discovery and p-values Distinguishing a peak, a goof, and a statistical fluctuation Why 5 sigma for discovery? Blind analyses What p-values are and what they are not Combining p-values Simultaneous optimisation for discovery and exclusion Incorporating systematic effects.

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