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This chapter reviews statistics and data-analysis tools. Starting from basic statistical concepts such as mean, variance, and the Gaussian distribution, we introduce the principal tools required for data analysis. We discuss both Bayesian and frequentist statistical approaches, with emphasis on the former. This leads us to describe how to calculate the goodness of fit of data to theory, and how to constrain the parameters of a model. Finally, we introduce and explain, both intuitively and mathematically, two important statistical tools: Markov chain Monte Carlo (MCMC) and the Fisher information matrix.
A link is made between epistemology – that is to say, the philosophy of knowledge – and statistics. Hume's criticism of induction is covered, as is Popper's. Various philosophies of statistics are described.