# Issue 16: A story of distributions

In this issue, I will talk about different distributions and their use.

TL;DR

• Binomial distribution presents the number of successes for different random experiments.
• Beta distribution presents the probability of an event. The result is bounded between 0 and 1.
• Poisson distribution is helpful when you want to model events that occur during a period of time.
• Gamma distribution is used to model continuous probability distributions.

## Binomial distribution

Discrete

### Example of use

Conversion on a website.

You convert or you don’t. It’s a success or a failure. This is what binomial distribution is about.

## Beta distribution

Continuous

### Example of use

Useful to model the prior probability distribution when doing ABTests with binomial distribution. There are two parameters: alpha is the successes, beta is the failures

## Poisson distribution

Discrete

### Example of use

Goals during a football match.

The period of time is the match. The number of goals are the events.

Better Know a Distribution: The Poisson Distribution

## Gamma distribution

Continuous

### Example of use

Useful to model the prior probability distribution when doing ABTests with binomial distribution. There are two parameters: alpha is the occurrences for a period of time, beta is the number of periods of time.

Thank you for reading. Feel free to contact me on Twitter if you want to discuss that.