P-Values are not Sufficient but they are Necessary: Probing the Role and Application of Statistical Significance Testing in Public Administration Research
Bibliographic record
Abstract
P-values are important to assert the relevance of findings. They should be reported in statistical studies. Although presented differently, they rely on the same sufficient statistics as confidence intervals, and they provide more information. They are a necessary, but not sufficient, condition to communicating practical significance. Effect size, or magnitude analysis, is missing in current Public Administration studies and should be at the core of ensuing discussions. Practitioners should indeed be able to know if the effects on the dependent variable are important in magnitude, but also if they can be acted upon through a cause-to-effect relationship. As we have yet to fight the old battle of p-values, Public Administrationists might be repeating the same errors that led psychologists and economists to delay their use of sound experimental designs instead of reported results from underpowered studies. In other words, the methodological turn that we should discuss and (we hope) embrace is the combined use of proper identification strategies and larger sample sizes.
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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 | 0.593 | 0.860 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.007 | 0.096 |
| Scholarly communication | 0.019 | 0.036 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.013 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".