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5.1: Introduction

  • Page ID
    143139
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    Continuous random variables have numerous applications across the health sciences. A baseball player’s swing velocity, VO2 max scores in endurance athletes, the duration of a patient’s physical therapy session, the percentage of body fat in a clinical cohort, the lifespan of a prosthetic limb or joint replacement, and the rate of recovery for a post-surgical heart rate are just a few examples. The field of biomechanical reliability depends on a variety of continuous random variables, as do all areas of clinical risk analysis and epidemiological modeling.

    NOTE

    The values of discrete and continuous random variables can be ambiguous depending on how they are recorded. For example, if X is equal to the number of minutes (to the nearest whole minute) a patient spends on a treadmill, then X is a discrete random variable because you are counting the individual minute markers. However, if X is the exact duration of the exercise session, then you measure the values of X (e.g., 12.42 minutes), and X is a continuous random variable.

    For a second example, if X is equal to the number of physical therapy tools in a treatment kit, then X is a discrete random variable. If X is the weight of a single dumbbell or resistance ball, then X is a continuous random variable because weight is measured on a scale and can take on any value within a range. How the random variable is defined is very important to ensure you choose the correct statistical test for your data.


    This page titled 5.1: Introduction is shared under a CC BY-NC 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.