The key assumptions of ANOVA include independence, normality, homogeneity of variances, and random sampling. Violating these assumptions can affect the accuracy of the test results.
Step 1: Independence of observations.
The observations should be independent of each other. This means that the value of one observation should not influence the value of another observation.
Step 2: Normality of the population.
The data in each group should be normally distributed. While ANOVA is fairly robust to deviations from normality, this assumption is important for valid results.
Step 3: Homogeneity of variances.
The variances in each of the groups being compared should be equal. This is also known as the assumption of homoscedasticity.
Step 4: Random sampling.
The samples should be randomly selected from the population to ensure that the results can be generalized to the larger population.
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