What will we do with all this statistics and probability theory? The science part of data science frequently involves forming and testing hypotheses about our data and the processes that generate it.

Statistical Hypothesis Testing

Often, as data scientists, we’ll want to test whether a certain hypothesis is likely to be true. For our purposes, hypotheses are assertions like “this coin is fair” or “data scientists prefer Python to R” or “people are more likely to navigate away from the page without ever reading the content if we pop up an irritating interstitial advertisement with a tiny, hard-to-find close button” that can be translated into statistics about data. Under various assumptions, those statistics can be thought of as observations of random variables from known distributions, which allows us to make statements about how likely those assumptions are to hold.

In the classical setup, we have a null hypothesis H0 that represents some default position, and some alternative hypothesis H1 that we’d like to compare it with. We use statistics to decide whether we can reject H0 as false or not. This will probably make more sense with an example.

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