How is effect size different from the p-value?

How is effect size different from the p-value?

• A. Effect size measures the magnitude of a phenomenon; p-value measures evidence against H0.
• B. Its opposite: p-value measures magnitude; effect size measures evidence against H0.
• C. Effect size is a probability; p-value is a sample mean.
• D. Effect size determines sample size; p-value determines confidence level.

Answer: (A)

Effect size focuses on how big the observed effect is—the magnitude of the difference or the strength of a relationship. The p-value, on the other hand, measures how likely the observed data would be if the null hypothesis were true; it’s a statement about evidence against the null, not about how large the effect is. These two ideas are linked but different. The effect size gives you practical significance—how meaningful the finding is—and is largely about the size of the effect (with standardized forms like Cohen’s d or Pearson’s r helping comparisons across studies). The p-value tells you whether the data provide statistically significant evidence against no effect, factoring in sample size and variability. Because the p-value depends on sample size, you can have a tiny effect that’s statistically significant with a large sample, or a large effect that isn’t significant with a small sample.

Effect size focuses on how big the observed effect is—the magnitude of the difference or the strength of a relationship. The p-value, on the other hand, measures how likely the observed data would be if the null hypothesis were true; it’s a statement about evidence against the null, not about how large the effect is.

These two ideas are linked but different. The effect size gives you practical significance—how meaningful the finding is—and is largely about the size of the effect (with standardized forms like Cohen’s d or Pearson’s r helping comparisons across studies). The p-value tells you whether the data provide statistically significant evidence against no effect, factoring in sample size and variability. Because the p-value depends on sample size, you can have a tiny effect that’s statistically significant with a large sample, or a large effect that isn’t significant with a small sample.