What is an Empirical Discrete Distribution?

An Empirical Discrete Distribution is defined as one that uses observed data to assign probabilities based on the relative frequencies of outcomes within that data set. This method entails analyzing historical or collected data to figure out how often various outcomes occur, which then allows for the creation of a probability distribution that reflects those actual observed frequencies.

When you gather empirical data, you can calculate the probability of each discrete outcome by dividing the number of times that outcome occurs by the total number of observations. This makes the empirical discrete distribution a practical tool in fields where data is collected from real-world observations rather than relying solely on theoretical assumptions.

By considering the other options, it’s clear that theoretical probabilities are used in distributions based on models rather than observed data, while a distribution based on survey data might involve empirical elements but isn't exclusively focused on relative frequency. A distribution without assigned probabilities doesn't fit the definition either, because an empirical discrete distribution is characterized specifically by having its probabilities derived from actual data.