Post hoc power analysis

Definition

Post hoc power analysis is a statistical procedure conducted after a study has been completed, aimed at determining the power of the test that was used in the original analysis. This analysis helps researchers understand the likelihood that their study had enough sample size to detect an effect if one truly existed, providing insight into the effectiveness and reliability of the study’s findings. It often comes into play when results are non-significant, raising questions about whether the lack of findings was due to insufficient power rather than the absence of an effect.

5 Must Know Facts For Your Next Test

1. Post hoc power analysis is typically performed after results are analyzed to help assess whether the study had sufficient power to detect an effect.
2. This type of analysis is particularly useful when researchers find non-significant results and want to evaluate if their sample size was adequate.
3. The power of a test is influenced by factors such as sample size, effect size, and significance level (alpha), which are all considered during post hoc analysis.
4. Critics argue that post hoc power analysis can be misleading since it uses observed data to derive power estimates, which may not accurately reflect true power.
5. Instead of relying solely on post hoc power analysis, researchers are encouraged to conduct a priori power analyses during the planning phase to determine necessary sample sizes.

Review Questions

• How does post hoc power analysis help in interpreting non-significant results from a study?
  • Post hoc power analysis assists researchers in understanding whether their study's sample size was sufficient to detect an effect that might actually exist. If the analysis reveals low power, it suggests that the non-significant results could be due to insufficient sample size rather than a true absence of an effect. This understanding can guide future research design and decisions on whether to pursue further studies or modify methodologies.
• Discuss the limitations of using post hoc power analysis in research studies and why it may not be the most reliable method for evaluating statistical power.
  • One significant limitation of post hoc power analysis is that it relies on data from completed studies, which can lead to biased estimates of power. Since it uses observed effect sizes from non-significant results, it may overestimate or underestimate true power levels. Critics argue that this analysis can give a false sense of security regarding study findings. A more robust approach would involve conducting an a priori power analysis before data collection to ensure adequate sample sizes based on expected effect sizes and significance levels.
• Evaluate how post hoc power analysis relates to overall study validity and reliability, and its implications for future research directions.
  • Post hoc power analysis plays a crucial role in evaluating study validity by assessing whether non-significant findings were due to low statistical power. If post hoc analyses indicate inadequate power, it raises concerns about the reliability of the study's conclusions and suggests potential weaknesses in research design. This understanding can influence future research directions by highlighting the need for larger sample sizes or different methodologies, ultimately contributing to more robust and conclusive findings in subsequent studies.

Related terms

Statistical Power: The probability that a statistical test will correctly reject a false null hypothesis, often denoted as 1 - beta.

Type II Error: The error made when a false null hypothesis is not rejected, meaning a true effect is missed by the test.

Effect Size: A quantitative measure of the magnitude of a phenomenon or effect, used to understand the practical significance of research results.