Which test is used to determine goodness of fit between observed and expected frequencies?

Which test is used to determine goodness of fit between observed and expected frequencies?

• A. Chi Square Test
• B. Fisher Exact
• C. T-Test
• D. ANOVA

Answer: (A)

Determining whether observed frequencies match what you’d expect under a specified model is done with the chi-square goodness-of-fit test. It compares the actual counts in each category to the counts expected if the model is true, using the statistic that sums (O − E)² / E across all categories. A small value means observed and expected frequencies are close, suggesting the model fits; a large value points to a poor fit. The test relies on categorical data, independent observations, and sufficiently large expected counts (typically at least 5 per category). For small samples or certain table shapes, alternatives like Fisher’s exact test are used. T-tests and ANOVA, in contrast, assess differences in means for continuous outcomes, not how a distribution of frequencies fits a model.

Determining whether observed frequencies match what you’d expect under a specified model is done with the chi-square goodness-of-fit test. It compares the actual counts in each category to the counts expected if the model is true, using the statistic that sums (O − E)² / E across all categories. A small value means observed and expected frequencies are close, suggesting the model fits; a large value points to a poor fit. The test relies on categorical data, independent observations, and sufficiently large expected counts (typically at least 5 per category). For small samples or certain table shapes, alternatives like Fisher’s exact test are used. T-tests and ANOVA, in contrast, assess differences in means for continuous outcomes, not how a distribution of frequencies fits a model.