How large a measured difference or relationship actually is, as opposed to whether it is statistically significant?

How large a measured difference or relationship actually is, as opposed to whether it is statistically significant?

• A. Reproducibility
• B. Effect Size
• C. Replicability
• D. Survey Design

Answer: (B)

The main idea here is quantifying how big the observed difference or relationship is, independent of whether it reaches statistical significance. Statistical significance tells you whether an observed effect is unlikely to be due to random chance, and it depends on sample size and variability. But magnitude—the practical size of the effect—tells you how meaningful or substantial that difference or association is in real terms. This is captured by the effect size, a standardized measure of how large the effect is. Examples include Cohen’s d for mean differences (measured in standard deviation units), Pearson’s r for correlation strength, and odds ratios for differences in probabilities. Interpreting the effect size focuses on practical impact: is the difference small, moderate, or large in a way that matters for theory, policy, or practice? The other terms relate to reliability or study design rather than how big the effect is. Reproducibility and replicability concern whether results come out the same when studies are repeated or independently conducted. Survey design concerns how data is collected and structured. They don’t directly quantify the size of the observed effect, which is what the question is asking about.

The main idea here is quantifying how big the observed difference or relationship is, independent of whether it reaches statistical significance. Statistical significance tells you whether an observed effect is unlikely to be due to random chance, and it depends on sample size and variability. But magnitude—the practical size of the effect—tells you how meaningful or substantial that difference or association is in real terms.

This is captured by the effect size, a standardized measure of how large the effect is. Examples include Cohen’s d for mean differences (measured in standard deviation units), Pearson’s r for correlation strength, and odds ratios for differences in probabilities. Interpreting the effect size focuses on practical impact: is the difference small, moderate, or large in a way that matters for theory, policy, or practice?

The other terms relate to reliability or study design rather than how big the effect is. Reproducibility and replicability concern whether results come out the same when studies are repeated or independently conducted. Survey design concerns how data is collected and structured. They don’t directly quantify the size of the observed effect, which is what the question is asking about.