There are a lots of things that I learned during this course, but the most notable thing may be that I finally managed to wrap my head around classical hypothesis testing with null hypotheses, p-values and the like. Unlike the vast majority of aspiring statisticians and researchers, my introductory statistics course foucussed soley on Bayesian statistics. The book we used during that course made a really big deal about the difference, but I didn’t understand the other camp at all: the ‘frequentist’ philosophy struck me as unscientific, its method of confirming and rejecting hypotheses confused me and the frequentist statistical tests were like magic to me.

I do get it now, I really do. *P*(data|*H*_{0}) i.e. the probability of the data given the null hypothesis (instead of *P*(*H*|data), i.e. the probability of some hypothesis given the data). When the data is unlikely given the null, we may choose to reject the latter.

I have to admit one thing though. The most famous frequentist tests: the t-tests and (more generally) analysis of variance tests are *still* mostly like magic to me. Sure, I know how to call t.test or aov from R, but what do these functions do exactly? How do these tests work? I tried to read more about it, to really understand these tests, but it seems like the more research I do, the deeper I dig, the more formulas, concepts and magical statistics I encounter that only serve to confuse me further.

Everything changed when we were introduced to randomization tests. You want to see if two populations differ? Well, just randomly shuffle all the numbers around a couple of hunderd times and calculate the means of each group. Brilliant! No t-statistic, normalization of the data, weird counter-intuitive prerequisites or formulas to remember – a simple algorithm is all it takes to compare two, or just any amount of groups!

So, you could say that I have gained ‘statistical enlightenment’ by being introduced to a statistical test that *just makes sense* to me. Once I start doing hard-core emperical research myself I may start to appriciate all the other statistical methods introduced in this course. Until then, I know what test I will use for quick and dirty (but no less valid) statistics.