What is the difference between p-value and effect size?

What is the difference between p-value and effect size?

• A. p-value assesses the role of chance while effect size determines how great the change in results are
• B. p-value indicates the magnitude of the effect; effect size indicates probability of chance
• C. p-value measures study quality; effect size determines sample size
• D. p-value measures bias; effect size measures precision

Answer: (A)

The difference hinges on what each statistic is telling you about the results. The p-value tells you how much results like these would occur by chance if there were no real effect. It’s a probability under the assumption of no effect and is influenced by sample size; it doesn’t tell you how big the effect is. Effect size, in contrast, measures the magnitude of the observed effect—how large the difference or association actually is—so you can judge practical significance independent of how many participants were studied. So the best description is that the p-value assesses the role of chance while the effect size determines how great the change in results are. The other statements describe things that p-values or effect sizes do not measure, such as magnitude, study quality, sample size, bias, or precision.

The difference hinges on what each statistic is telling you about the results. The p-value tells you how much results like these would occur by chance if there were no real effect. It’s a probability under the assumption of no effect and is influenced by sample size; it doesn’t tell you how big the effect is.

Effect size, in contrast, measures the magnitude of the observed effect—how large the difference or association actually is—so you can judge practical significance independent of how many participants were studied.

So the best description is that the p-value assesses the role of chance while the effect size determines how great the change in results are. The other statements describe things that p-values or effect sizes do not measure, such as magnitude, study quality, sample size, bias, or precision.