What Is Discrete Distribution?
A discrete distribution is a statistical distribution that shows the probabilities of outcomes with finite values. Statistical distributions can be either discrete or continuous. A continuous distribution is built from outcomes that potentially have infinite measurable values.
Understanding Discrete Distribution
Distribution is a statistical concept used in data research. Statisticians seeking to identify the outcomes and probabilities of a particular study will chart measurable data points from a data set, 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-squared, gamma, and beta.
Distributions must be either discrete or continuous.
Statisticians can identify the development of either a discrete or continuous distribution by the nature of the outcomes to be measured. Discrete distributions have a finite number of outcomes. For example, when studying the probability distribution of a die with six numbered sides there can only be six possible outcomes, so the finite value is six. Another example can include flipping a coin. Flipping a coin can only result in two outcomes so the finite value is two.
Examples of Discrete Distribution
The most common discrete probability distributions include binomial, Poisson, Bernoulli, and multinomial. One example where discrete distribution can be valuable for businesses is in inventory management. Studying the frequency of inventory sold in conjunction with a finite amount of inventory available can provide a business with a probability distribution that leads to guidance on the proper allocation of inventory to best utilize square footage.
Discrete distributions can also arise in the Monte Carlo simulation. Monte Carlo simulation is a modeling technique that identifies the probabilities of different outcomes through programmed technology. It is primarily used to help forecast scenarios and identify risks. In Monte Carlo simulation, outcomes with discrete values will produce discrete distributions for analysis. These distributions are used in determining risk and trade-offs among different items being considered.