Which set of assumptions is correct for ANOVA?

Which set of assumptions is correct for ANOVA?

• A. Continuous DV; Categorical IV; Normality (approximately); Homogeneity of variance
• B. Continuous DV; Continuous IV; Normality (approximately); Homogeneity of variance
• C. Categorical DV; Categorical IV; Normality; Heterogeneity of variance
• D. Continuous DV; Categorical IV; Non-normal distribution; Heterogeneity of variance

Answer: (A)

ANOVA compares mean scores on a continuous outcome across different groups defined by a categorical factor. So the dependent variable must be continuous, and the independent variable must be categorical (a factor with distinct groups). The test also assumes that the residuals within each group are roughly normally distributed and that variances are about equal across groups (homogeneity of variance). These conditions help ensure the F statistic has its intended sampling distribution under the null hypothesis of equal means. If the independent variable were continuous, regression would be more appropriate; if the outcome were categorical, ANOVA isn’t suitable. When normality or equal variances are violated, you’d consider data transformation, nonparametric alternatives, or robust methods.

ANOVA compares mean scores on a continuous outcome across different groups defined by a categorical factor. So the dependent variable must be continuous, and the independent variable must be categorical (a factor with distinct groups). The test also assumes that the residuals within each group are roughly normally distributed and that variances are about equal across groups (homogeneity of variance). These conditions help ensure the F statistic has its intended sampling distribution under the null hypothesis of equal means. If the independent variable were continuous, regression would be more appropriate; if the outcome were categorical, ANOVA isn’t suitable. When normality or equal variances are violated, you’d consider data transformation, nonparametric alternatives, or robust methods.