Post hoc power analysis

Definition

Post hoc power analysis is a statistical technique used to determine the power of a study after the data has been collected and analyzed. It assesses the likelihood that a study's results would have detected an effect, given the sample size and effect size observed. This analysis is often conducted to evaluate whether a non-significant result may have been due to insufficient power, helping researchers understand the adequacy of their study design.

5 Must Know Facts For Your Next Test

1. Post hoc power analysis is typically used after an experiment to reflect on its ability to detect effects that were either observed or not observed.
2. It can help identify if a study was underpowered, meaning it may have failed to find an effect due to having too small a sample size.
3. Power values range from 0 to 1, where higher values indicate greater power; however, post hoc analyses can sometimes lead to misleading conclusions if misinterpreted.
4. It is generally considered more useful to conduct power analysis during the planning phase of a study rather than relying on post hoc evaluations.
5. Post hoc power analyses often use estimated effect sizes derived from the data, which may not accurately represent true population effects.

Review Questions

• How does post hoc power analysis help researchers evaluate their study results after data collection?
  • Post hoc power analysis assists researchers in understanding whether their study had sufficient power to detect an effect, particularly if they obtained non-significant results. By analyzing the relationship between sample size and effect size, researchers can assess whether a lack of significant findings might be attributed to inadequate power. This reflective practice helps in recognizing potential limitations of their experimental design and informs future research planning.
• Discuss the advantages and disadvantages of conducting post hoc power analysis compared to pre-study power analysis.
  • The main advantage of post hoc power analysis is that it allows researchers to understand their study's performance based on actual data collected, which can provide insights into the adequacy of their methodology. However, one major disadvantage is that it may lead to misconceptions; since it relies on observed effect sizes, it can falsely suggest that a lack of significance was due to low power rather than true absence of effects. In contrast, pre-study power analysis proactively informs sample size decisions and experimental design, making it generally more beneficial for planning effective studies.
• Evaluate the implications of relying on post hoc power analysis when interpreting research findings and how it could impact scientific conclusions.
  • Relying on post hoc power analysis when interpreting research findings can significantly skew scientific conclusions. If researchers focus solely on post hoc power without considering context or underlying assumptions, they may incorrectly conclude that non-significant results were due to inadequate study design rather than genuine absence of effects. This reliance risks perpetuating erroneous beliefs about the effectiveness or significance of treatments or interventions, ultimately affecting policy decisions and future research directions based on flawed interpretations.

Related terms

Statistical Power: The probability that a statistical test will correctly reject a false null hypothesis, often influenced by sample size, effect size, and significance level.

Effect Size:

A quantitative measure of the magnitude of a phenomenon or difference, providing context to the practical significance of study findings beyond mere statistical significance.

Sample Size:

The number of observations or data points collected in a study, which directly impacts the power of statistical tests and the reliability of conclusions drawn.