Statistical Power, <i>P</i> ‐Values, and the Positive Predictive Value
Bibliographic record
Abstract
This chapter focuses on a factor thought to have one of the greatest influences on the crisis of confidence: low statistical power. Statistical power is clearly a foundational principle of inferential statistics, yet it is commonly overlooked. If an entire discipline is built on underpowered research, its whole body of evidence becomes suspect. This is all to say that statistical power is the virus; the crisis of confidence is the symptom. The chapter describes the fundamental principles behind this. It explains hypothesis testing and its limitations. The chapter then discusses the characteristics of statistical power and the positive predictive value (PPV) needed to generate more trustworthy inferences from these tests. The PPV can be estimated, but more information besides the α-level and a P -value is needed. The chapter concludes with some suggestions that criminologists can incorporate into their research to help mitigate a crisis of confidence.
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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 | 0.091 | 0.459 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".