Which statement is true about the Huynh-Feldt estimate?

Which statement is true about the Huynh-Feldt estimate?

• A. It is more conservative than Greenhouse-Geisser
• B. It is never used
• C. It corrects p-values directly
• D. It is used to correct degrees of freedom by multiplying them by the estimate

Answer: (D)

Huynh-Feldt provides an epsilon estimate used when sphericity is violated in repeated-measures ANOVA. The key idea is that the F-test’s validity depends on certain degrees of freedom that assume sphericity; when that assumption doesn’t hold, we adjust those degrees of freedom by multiplying them by an estimate of epsilon. The corrected test uses the same F statistic but with these adjusted degrees of freedom, which changes the p-value accordingly. Huynh-Feldt is often less conservative than Greenhouse-Geisser, especially when the epsilon estimate is near 1, and is used as an alternative when the Greenhouse-Geisser correction would be overly strict. It does not directly modify p-values; it changes the df that determine the p-value from the F distribution. So the statement that is true is that it is used to correct degrees of freedom by multiplying them by the estimate.

Huynh-Feldt provides an epsilon estimate used when sphericity is violated in repeated-measures ANOVA. The key idea is that the F-test’s validity depends on certain degrees of freedom that assume sphericity; when that assumption doesn’t hold, we adjust those degrees of freedom by multiplying them by an estimate of epsilon. The corrected test uses the same F statistic but with these adjusted degrees of freedom, which changes the p-value accordingly. Huynh-Feldt is often less conservative than Greenhouse-Geisser, especially when the epsilon estimate is near 1, and is used as an alternative when the Greenhouse-Geisser correction would be overly strict. It does not directly modify p-values; it changes the df that determine the p-value from the F distribution. So the statement that is true is that it is used to correct degrees of freedom by multiplying them by the estimate.