### What is discrete distribution?

A discrete distribution is a statistical distribution which shows the probabilities of results with finite values. Statistical distributions can be discrete or continuous. A continuous distribution is constructed from results that potentially have infinite measurable values.

Overall, the concepts of discrete and continuous probability distributions and the random variables they describe are the foundations of probability theory and statistical analysis.

### Understanding the discrete distribution

Distribution is a statistical concept used in data research. Statisticians seeking to identify the results and probabilities of a particular study will plot measurable data points from a set of data, resulting in a probability distribution diagram. There are many types of probability distribution diagram shapes that can result from a distribution study. Some of the most common probability distributions include: normal, uniform, binomial, geometric, Poisson, exponential, chi-square, gamma, and beta.

Distributions must be discrete or continuous.

Statisticians can identify the development of a discrete or continuous distribution by the nature of the results to be measured. Discrete distributions have a finite number of results. For example, when studying the probability distribution of a six-sided numbered die, there can only be six possible outcomes, so the finite value is six. Another example may include flipping a part. Throwing a coin can only give two results, so the finite value is two.

### Examples of discrete distribution

The most common discrete probability distributions are binomial, Poisson, Bernoulli and multinomial. An example where discrete distribution can be useful for businesses is inventory management. Studying the frequency of stocks sold in conjunction with a limited amount of available stocks can provide a business with a probability distribution that leads to advice on the proper allocation of stocks to make the best use of square footage.

Discrete distributions can also appear in the Monte Carlo simulation. Monte Carlo simulation is a modeling technique that identifies the probabilities of different results using programmed technology. It is mainly used to help predict scenarios and identify risks. In Monte Carlo simulation, results with discrete values will produce discrete distributions for analysis. These distributions are used to determine the risk and the trade-offs between the different elements considered.