Chi-square goodness-of-fit test (C=3)
`x^2 = sum(("observed frequency" - "expected frequency")^2 /"expected frequency")`
`"How many categories are you comparing?"` | ||
`"Observed frequency for category 1"` | ||
`"Observed frequency for category 2"` | ||
`"Observed frequency for category 3"` | ||
`"Expected population proportion in category 1"` | ||
`"Expected population proportion in category 2"` | ||
`"Expected population proportion in category 3"` | ||
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The chi-square goodness-of-fit test, `x^2 = Σ(((fo – fe)^2) / (fe))`, is used to determine if the selected sample proportions of data are significantly different from the population proportions. The chi-square distribution is positively skewed, and the critical region lies in the extreme tail returning large chi-square values (Gravetter and Wallnau, 2013). Chi-square tests are necessary when your data are at a nominal scale of measurement.
References
Gravetter, F. J., & Wallnau, L. B. (2013). Statistics for the Behavioral Sciences. Wadsworth, CA: Cengage Learning.
