## Distributions

The concept of the probability distribution and the random variables which they describe underlies the mathematical discipline of probability theory, and the science of statistics. There is spread or variability in almost any value that can be measured in a population (e.g. height of people, durability of a metal, sales growth, traffic flow, etc.). For these and many other reasons, simple numbers are often inadequate for describing a quantity, while probability distributions are often more appropriate.

There are various probability distributions that show up in various different applications. Two of the most important ones are the normal distribution and the categorical distribution. The normal distribution, also known as the Gaussian distribution, has a familiar “bell curve” shape and approximates many different naturally occurring distributions over real numbers. The categorical distribution describes the result of an experiment with a fixed, finite number of outcomes. For example, the toss of a fair coin is a categorical distribution, where the possible outcomes are heads and tails, each with probability 1/2.

via Probability distribution – Wikipedia, the free encyclopedia.

For most people, knowing about distributions is only important insofar as it helps you to decide whether to use parametric or non-parametric tests on your data.

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