9: Effect Sizes and Power Analysis
- Page ID
- 50679
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- Understand the value of effect sizes
- Examine how to do a Power Analysis
Key Terms
- Effect Size
- Power Analysis
- 9.1: Significant (P Value) Versus Meaningful (Effect Size) Results
- This page discusses the shortcomings of p-values in statistical analysis, asserting they indicate occurrence but not significance. It advocates for focusing on effect sizes, which measure the magnitude of differences and the strength of relationships, thus providing insight into treatment impact and variable importance. Examples are provided to demonstrate that statistically significant changes may lack clinical relevance.
- 9.2: What Are Effect Sizes?
- This page explains effect sizes measured in standard deviation units, focusing on mean differences and associations. Key measures include Cohen’s D and correlation coefficients, with suggested benchmarks for small, medium, and large effects. In psychology, low effect sizes are frequent and often signify desirable small changes in treatment contexts.
- 9.3: Statistical Power
- This page emphasizes the significance of statistical power and sample size in research, detailing how a power analysis helps determine minimum sample sizes to detect true effects and reduce Type II errors. While larger samples enhance power, they can incur higher costs and risk Type I errors.
- 9.4: How to Conduct a Power Analysis
- This page explains how to conduct a power analysis using programs like Gpower or SPSS, emphasizing three key components: the p value (usually p < .05), the effect size (typically around .4), and the desired power (commonly 80%). These elements guide the statistical test design, with the p value indicating significance, the effect size showing the magnitude of the effect to detect, and the desired power representing the probability of observing the effect.
- 9.5: Pause and Process Check, Pulling P Values and Effect Sizes Together
- This page outlines a systematic approach for evaluating statistical test results, emphasizing the importance of effect size alongside p-values. It details a decision-making process based on significance from statistical tests, followed by pattern analysis in data. The text stresses assessing result validity by considering sample size, sampling error, and variable distribution quality, prioritizing simplicity and consistency in interpretation.
- 9.6: Discussion Questions
- This page emphasizes the interconnectedness of sample size, p values, effect size, and power in research. It advocates for prioritizing effect sizes over p values to achieve a more meaningful interpretation of study results. Additionally, it suggests creating a visual tool, like a chart or decision tree, to illustrate these relationships, ultimately aiming to enhance the understanding and application of research findings.