The correct statement is:

An average estimate of the population variance is computed.

In an Analysis of Variance (ANOVA), one of the key assumptions is that the population variances across the groups being compared are equal (this is called homogeneity of variances). Because of this assumption, the ANOVA procedure pools the variances from different samples to compute a single estimate of the population variance, rather than treating each sample variance separately.

To summarize:

• Fewer degrees of freedom are required: Incorrect. Degrees of freedom in ANOVA depend on the number of groups and the total number of observations.
• Sample variances are not important: Incorrect. Sample variances are used to estimate the common population variance.
• An average estimate of the population variance is computed: Correct. The assumption of equal variances allows ANOVA to compute a pooled estimate of the population variance.
• Population parameters are used instead of sample statistics: Incorrect. ANOVA uses sample statistics to estimate population parameters.

Would you like further clarification or details on any specific aspect?

Here are five related questions to expand on this topic:

1. How does ANOVA handle unequal variances in a dataset?
2. What are the degrees of freedom in ANOVA and how are they calculated?
3. What is the F-ratio in ANOVA, and how is it interpreted?
4. Can ANOVA be used for comparing more than two groups? If so, how?
5. What are the assumptions required for conducting a valid ANOVA test?

Tip: Always check the assumptions of ANOVA, such as normality and equal variances, to ensure valid results. For unequal variances, consider using a Welch's ANOVA.