Summary
The focus of this chapter has been hypothesis testing. The chapter contained only three sections, but those sections contained a wealth of new ideas and concepts. Most of the new concepts centered around p-values – what they are, what they are not, how we calculate them, and how we interpret them. These are concepts that you must get to grips with as a working data scientist. To get to grips with them, we specifically learned about the following,
- How a hypothesis test consists of two hypotheses, the null hypothesis
and the alternative hypothesis 
, and how we calculate the p-value as the probability of getting the observed test statistic, or larger, if the null hypothesis is true. - How we use the numerical value of the p-value in comparison to a small threshold value,
, as evidence to reject the null hypothesis and accept the alternative hypothesis - How the threshold
controls the false positive rate of the hypothesis test - What the p-value is and what...
