Statistical Inference¶

Although data scientists often work with individual samples of data, we are almost always interested in making generalizations about the population that the data were collected from. This chapter discusses methods for statistical inference, the process of drawing conclusions about a entire population using a dataset.

Statistical inference primarily leans on two methods: hypothesis tests and confidence intervals. In the recent past these methods relied heavily on normal theory, a branch of statistics that requires substantial assumptions about the population. Today, the rapid rise of powerful computing resources has enabled a new class of methods based on resampling that generalize to many types of populations.

We first review inference using permutation tests and the bootstrap method. We then introduce bootstrap methods for regression inference and skewed distributions.