Step 1: Understanding ANOVA.
Analysis of Variance (ANOVA) is a statistical tool used to compare the means of multiple groups.
It is based on the partitioning of total variance into variance between groups and variance within groups.
Step 2: Distribution assumption.
For the F-test used in ANOVA to be valid, the parent population (from which samples are drawn) must be normally distributed.
This ensures the sampling distribution of the test statistic follows the F-distribution.
Step 3: Analysis of options.
- (1) Normally distributed: Correct, this is the required assumption.
- (2) Binomial: Deals with success-failure trials, not suited for ANOVA assumption.
- (3) Poisson: For rare events, not applicable here.
- (4) Beta: Continuous distribution for bounded variables, not an assumption in ANOVA.
Step 4: Conclusion.
Thus, for ANOVA, the parent population must be normally distributed.