When we want to make observations about the data we are analyzing, we are often, if not always, turning to statistics in some fashion. The data we have is referred to as the sample, which was observed from (and is a subset of) the population. Two broad categories of statistics are descriptive and inferential statistics. With descriptive statistics, as the name implies, we are looking to describe the sample. Inferential statistics involves using the sample statistics to infer, or deduce, something about the population, such as the underlying distribution.
The sample statistics are used as estimators of the population parameters, meaning that we have to quantify their bias and variance. There are a multitude of methods for this; some will make assumptions on the shape of the distribution (parametric) and others won't (non-parametric). This is all well...
