P-Values are not Sufficient but they are Necessary: Probing the Role and Application of Statistical Significance Testing in Public Administration Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it