Effect Size

Definition

Effect size is a quantitative measure that indicates the magnitude or strength of the relationship between two variables or the difference between two groups. It provides information about the practical significance of a statistical finding, beyond just the statistical significance.

5 Must Know Facts For Your Next Test

1. Effect size is an important complement to statistical significance, as it provides information about the magnitude or strength of an effect.
2. Effect size can be used to compare the relative importance of different findings or to determine the appropriate sample size for a study.
3. Common effect size measures include Cohen's d, Hedges' g, and the correlation coefficient (r).
4. Effect size is particularly useful in interpreting the results of hypothesis tests, as it helps determine the practical relevance of the findings.
5. Effect size can be interpreted in terms of small, medium, or large effects, which provides a standardized way to evaluate the importance of a finding.

Review Questions

• Explain how effect size relates to the concept of statistical significance in hypothesis testing.
  • Effect size and statistical significance are related but distinct concepts in hypothesis testing. Statistical significance indicates the likelihood that an observed difference or relationship occurred by chance, as represented by a p-value. Effect size, on the other hand, provides information about the magnitude or strength of the effect, which is important for determining the practical significance of the findings. While a statistically significant result may indicate that an effect is unlikely to have occurred by chance, the effect size helps determine the real-world importance or relevance of that effect. Considering both statistical significance and effect size is crucial for interpreting the results of a hypothesis test and making informed decisions.
• Describe how effect size can be used to determine the appropriate sample size for a study.
  • Effect size can be used to calculate the appropriate sample size for a study, which is important for ensuring that the study has sufficient statistical power to detect an effect of a given magnitude. By estimating the expected effect size based on previous research or theoretical considerations, researchers can use power analysis to determine the minimum sample size required to have a high probability of detecting the effect, if it truly exists. Considering effect size in sample size calculations helps ensure that the study is designed to provide meaningful and practically relevant results, rather than just statistically significant findings that may have little real-world importance.
• Analyze how effect size can be used to compare the relative importance of different findings in a study or across multiple studies.
  • Effect size provides a standardized way to compare the relative importance or practical significance of different findings, both within a single study and across multiple studies. Unlike p-values, which can be influenced by factors like sample size, effect size allows for a more direct comparison of the magnitude of effects. By calculating and reporting effect sizes, researchers can determine which findings are more important or impactful, even if they all meet the threshold for statistical significance. This is particularly useful when interpreting the results of a study with multiple outcomes or when trying to synthesize the findings from a body of research on a given topic. Considering effect size can help researchers and decision-makers prioritize the most meaningful and impactful results.

Related terms

Statistical Significance: The likelihood that an observed difference or relationship occurred by chance, typically represented by a p-value.

Practical Significance: The real-world importance or relevance of a finding, which may be independent of statistical significance.

Standardized Mean Difference: A type of effect size that measures the difference between two group means in terms of standard deviations.