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5: Discrete Probability Distributions

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    • 5.0: Prelude to Discrete Random Variables
      Random Variable (RV) a characteristic of interest in a population being studied
    • 5.1: Probability Distribution Function (PDF) for a Discrete Random Variable
      A discrete probability distribution function has two characteristics: Each probability is between zero and one, inclusive. The sum of the probabilities is one.
    • 5.2: Mean or Expected Value and Standard Deviation
      The expected value is often referred to as the "long-term" average or mean. This means that over the long term of doing an experiment over and over, you would expect this average. This “long-term average” is known as the mean or expected value of the experiment and is denoted by the Greek letter μμ . In other words, after conducting many trials of an experiment, you would expect this average value.
    • 5.3: Binomial Distribution
      A statistical experiment can be classified as a binomial experiment if the following conditions are met: (1) There are a fixed number of trials. (2)There are only two possible outcomes: "success" or "failure" for each trial. (3) The trials are independent and are repeated using identical conditions. The outcomes of a binomial experiment fit a binomial probability distribution.
    • 5.E: Discrete Random Variables (Optional Exercises)
      These are homework exercises to accompany the Textmap created for "Introductory Statistics" by OpenStax.

    Contributors and Attributions

    • Barbara Illowsky and Susan Dean (De Anza College) with many other contributing authors. Content produced by OpenStax College is licensed under a Creative Commons Attribution License 4.0 license. Download for free at

    This page titled 5: Discrete Probability Distributions is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by OpenStax via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.