ANOVA Assumptions

What are the assumptions of ANOVA?

• Dependence, Skewness, Heteroscedasticity

• Independence, Normality, Homogeneity of variances

Explain the assumption of homogeneity of variances in ANOVA.

• Homogeneity of variances implies that the means of the groups are equal

• In ANOVA, homogeneity of variances refers to the similarity in sample sizes across groups

• The assumption of homogeneity of variances means that the variances are completely different in each group

• The assumption of homogeneity of variances in ANOVA is that the variances of the different groups being compared are approximately equal.

Why is the assumption of normality important in ANOVA?

• Normality guarantees accurate p-values in ANOVA

• The assumption of normality is important in ANOVA to ensure that the residuals are normally distributed.

• Normality is not important in ANOVA

• Normality ensures equal group sizes in ANOVA

Discuss the assumption of independence in ANOVA.

• The assumption of independence in ANOVA is that the observations within each group are independent of each other.

• Independence in ANOVA refers to the assumption that the data points are not normally distributed.

• The assumption of independence in ANOVA is that the observations within each group are dependent on each other.

• In ANOVA, independence means that the groups themselves are not related to each other.

How can violations of ANOVA assumptions affect the validity of results?

• Violations of ANOVA assumptions have no impact on the validity of results

• Violations of ANOVA assumptions only affect the interpretation of results

• Violations of ANOVA assumptions can lead to inaccurate results by affecting the underlying statistical assumptions of the test.

• Violations of ANOVA assumptions improve the accuracy of results