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Nonparametric Tests

Nonparametric tests do not depend on probability distributions for population parameters, making them ideal for analyzing data that is heavily skewed, has small sample sizes, or is not measured on an interval or ratio scale. One such test is the Chi-Square test, useful for assessing frequency differences across two or more categories, commonly referred to as the goodness of fit test. This guide covers the formula for conducting a goodness of fit test and how to interpret the chi-square values by comparing the obtained value to a critical value from statistical tables.

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Nonparametric Tests

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Presentation Transcript

  1. Nonparametric Tests • Nonparametric tests are those that do not rely on probability distributions for population parameters • They are used when • Data are badly skewed • Sample sizes are small • Data are not on an interval or ratio scale

  2. Chi-Square and Goodness of Fit Test • Chi-square tests assess the differences in frequencies of two or more categories • This is commonly referred to as a goodness of fit test • Formula for a goodness of fit test

  3. Interpreting a χ2 • Chi-squares are interpreted by comparing the obtained value from the formula to a critical value obtained from a table

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