(IS12) Monte-Carlo Simulations (with R)

In this video, we continue our exploration of hypothesis testing (in particular non-parametric hypothesis testing), and discuss a numerical statistics method that can be used to generate empirical distributions associated to test statistics that do not follow distributions that we are familiar with. We discuss the framework of Monte-Carlo simulations and discuss what the method does and does not give to us, and also walk through a demonstration of how to do a simple Monte-Carlo simulation (to test normality with the Lilliefors' test) in the R language.
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[@jonathanran7154]

14:48 did you mean to write H_o or is it the alternative hypothesis?

[@LetsLearnNemo]

It should be H_a, the alternative. This will calculate an approximation for the probability of getting our test statistic, or something more in favor of the alternative, which is the definition of a p-value :)