Post hoc power analysis

Definition

Post hoc power analysis is a method used to determine the statistical power of a test after a study has been conducted, allowing researchers to evaluate whether their sample size was sufficient to detect an effect. This type of analysis helps in understanding the likelihood of correctly rejecting a false null hypothesis given the data obtained. It serves as a tool for interpreting study results, especially when non-significant findings occur.

5 Must Know Facts For Your Next Test

1. Post hoc power analysis can indicate whether the study had enough participants to detect an effect if one truly exists.
2. This analysis is often critiqued because it can provide misleading conclusions if used improperly or without considering the context of the study.
3. Researchers may use post hoc power analysis to justify non-significant results by demonstrating that the study was underpowered.
4. The findings of a post hoc power analysis are dependent on the effect size observed in the data, which can vary across studies.
5. It is generally recommended to conduct power analysis before collecting data to ensure adequate sample sizes and avoid issues related to low power.

Review Questions

• How does post hoc power analysis help researchers interpret non-significant findings in their studies?
  • Post hoc power analysis helps researchers interpret non-significant findings by providing insights into whether their sample size was adequate to detect an effect. If the power is found to be low, it may suggest that even if an effect exists, it was not identified due to insufficient sample size rather than no effect being present. This allows researchers to better understand the limitations of their study and guides future research efforts.
• Discuss the limitations and potential pitfalls associated with post hoc power analysis.
  • Post hoc power analysis has limitations, including its reliance on effect size estimates that can vary widely across studies. This can lead to misleading conclusions if researchers use post hoc power to justify non-significant results without recognizing the specific context of their study. Additionally, conducting this type of analysis after data collection may lead to confirmation bias, as researchers might focus on justifying their findings instead of objectively evaluating them. As a result, it is essential for researchers to approach post hoc power analysis critically and transparently.
• Evaluate how conducting a pre-study power analysis differs from performing a post hoc power analysis in terms of research design and outcomes.
  • Conducting a pre-study power analysis involves estimating necessary sample sizes and determining statistical power before data collection, which ensures that a study is designed adequately to detect effects. In contrast, post hoc power analysis is conducted after the study has been completed and focuses on assessing whether sufficient power was achieved based on observed data. This difference is crucial because pre-study power analysis contributes directly to effective research design, while post hoc analyses often attempt to rationalize or explain findings after the fact. As such, relying on post hoc analyses may obscure potential weaknesses in study design or highlight biases in interpreting results.

Related terms

Statistical Power: The probability that a statistical test will correctly reject a false null hypothesis, typically represented as 1 minus the probability of making a Type II error.

Effect Size:

A quantitative measure of the magnitude of a phenomenon, reflecting the strength of the relationship between variables in a study.

Type II Error:

The error made when a test fails to reject a false null hypothesis, leading to the conclusion that there is no effect when there actually is one.