Post Hoc Power Calculations: An Inappropriate Method for Interpreting the Findings of a Research Study
Bibliographic record
Abstract
Power calculations are a key study design step in research studies. However, such power analysis is often inappropriately performed in the medical literature by attempting to help interpret the findings of a completed study, instead of attempting to aid in choosing an optimal sample size for a future study. The aim of this article is to provide a brief discussion of the drawbacks of performing these post hoc power calculations, and to correspondingly suggest best practices regarding the use of statistical power and the interpretation of study results. Specifically, power analysis should always be considered before any research study in order to choose an ideal sample size and/or to examine the feasibility of properly evaluating study aims, but it should never be used in order to help interpret the results of an already completed study. Alternatively, 95% confidence intervals for effect sizes (eg, odds ratio, hazard ratio, mean difference) or other relevant parameter estimates should be used when attempting to draw conclusions from results, such as the likelihood of a type II error (ie, a false negative finding).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.579 | 0.841 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".