What does a large effect size indicate?

What does a large effect size indicate?

• A. A small difference between group means at the end of treatment.
• B. A moderate difference between groups.
• C. A large difference between group means at the end of treatment.
• D. The p-value is necessarily less than 0.05.

Answer: (C)

Effect size measures how big the difference between groups is, in a way that standardizes for variability and scale. A large effect size means the groups differ by a substantial amount on the outcome, relative to how spread out the data are. In practice, this reflects a meaningful, sizable difference between the treatment and comparison groups, often described by a large standardized difference between means (for example, a large Cohen’s d), at the time the outcome is measured. This concept is about magnitude, not whether the result is statistically significant. A large difference between means can exist with a large effect size, indicating a strong, practically important effect. The p-value, on the other hand, speaks to the probability that such a difference could occur by chance under no real effect; it depends on both the effect size and the sample size. So you can have a large effect size with a non-significant p-value if the sample is small, or a small p-value with a small effect size if the sample is very large. Therefore, describing a large difference in means at the end of treatment best captures what a large effect size indicates: a substantial, practically meaningful gap between groups on the outcome.

Effect size measures how big the difference between groups is, in a way that standardizes for variability and scale. A large effect size means the groups differ by a substantial amount on the outcome, relative to how spread out the data are. In practice, this reflects a meaningful, sizable difference between the treatment and comparison groups, often described by a large standardized difference between means (for example, a large Cohen’s d), at the time the outcome is measured.

This concept is about magnitude, not whether the result is statistically significant. A large difference between means can exist with a large effect size, indicating a strong, practically important effect. The p-value, on the other hand, speaks to the probability that such a difference could occur by chance under no real effect; it depends on both the effect size and the sample size. So you can have a large effect size with a non-significant p-value if the sample is small, or a small p-value with a small effect size if the sample is very large.

Therefore, describing a large difference in means at the end of treatment best captures what a large effect size indicates: a substantial, practically meaningful gap between groups on the outcome.