  # Geometric Distribution

## What is a Geometric Distribution?

A geometric distribution is a probability distribution that represents the number of trials needed to obtain a success in a Bernoulli experiment, also called a Bernoulli trial. A Bernoulli trial is a simple experiment conducted in probability and statistics. These trials are conducted where there are two possible outcomes and each trial results in either a success or a failure. The geometric distribution generates the probability of a specific numbers of trials needed to obtain a success, which is simply the result that you want to keep track of. The outcomes of each successive trial are independent of each other.

### Why is this Useful?

Geometric distributions are used to describe a discrete random variable. This distribution can be used to model probability. Being able to model probability is very important in statistics, and in everyday life. Businesses, governments, and families use their understanding of probability to make important decisions. Probability distributions, such as geometric distributions, provide statistical models that show the possible outcomes of a particular event and the statistical likelihood of each outcome for that event. These models give people the ability to make informed decisions, rather than making uneducated guesses.

### Practical Uses of a Geometric Distribution

• Scenario Analysis - Probability distributions, such as a geometric distribution, can be used to create scenario analyses. A scenario analysis is where several distinct possibilities for the outcome of a particular event are compared to make a decision.
• Sales Forecasting - Geometric distributions can be used with scenario analyses to predict future sales for businesses. While the precise value for future sales cannot be predicted, understanding the likely sales can allow businesses to prepare.
• Risk Evaluation - Another use of geometric distributions is to evaluate risks. The risk of making a particular decision can be evaluated with the use of probability distributions.