Here is a bunch of papers and books that you may find useful for further information. Books are marked with (b), papers with (p).
Daniela:
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Simon Vaughan: Scientific Inference (b) An excellent and intuitive introduction to classical statistics at the senior undergraduate level. I pulled most of my talk together using this book. Incidentally, it uses R rather than python, but for R it has lots of very nice examples and exercises that allow you to learn basic R along with your statistics
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Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas & Alexander Gray: Statistics, Data Mining and Machine Learning in Astronomy (b) This book is more geared towards data mining and machine learning, but has two introductory chapters into probability and classical statistical inference. It uses AstroML, which is in python.
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David Hogg: Data analysis recipes: Probability calculus for inference (2012) (p) short and straightforward introduction to probabilities and scientific inference
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Protassov+: Statistics, Handle with Care: Detecting Multiple Model Components with the Likelihood Ratio Test (2002) (p) Useful reference paper for when (and why) likelihood ratio tests and F-tests fail at model selection
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Roberto Trotta: Bayes in the sky: Bayesian inference and model selection in cosmology (2008) (p) Mostly Bayesian paper, but has a bit about model selection and information criteria that I found quite useful to read.
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Andrew Liddle: Information criteria for astrophysical model selection (2007) (p) More stuff about information criteria