A review of the 25 most popular distributions in statistics using Python

One subject that I never studied in my undergraduate and graduate days is statistics. I therefore have had to catch up on the education that I have lost by studying statistics now. One area in statistics is distributions.

A distribution is a summary of all the possible of values or ranges of values in a dataset and how frequently they appear. Distributions are important because they provide a person with a snapshot of the data he is using. Distributions reveal patterns, showing things like average value (central tendency), how spread out the values are (variability), and whether more values are on one side of the average than the other (skewness). Distributions form a basis for predictions and are the backbone of statistical models.

Distributions form the backbone of statistical models. They help the scientist to make educated guesses or predictions about larger groups based on smaller samples of data. It is important to know what type of distribution data forms, whether it is normal, binomial, or another type to give the scientist an idea of what tests he should be carrying out on the data.

Distributions and probabilities are linked because they help the scientist to understand how likely different outcomes will be.