Perhaps Psychology’s Replication Crisis is a Theoretical Crisis that is Only Masquerading as a Statistical One
Bibliographic record
Abstract
The “replication crisis” may well be the single most important challenge facing empirical psychological research today. It appears that highly trained scientists, often without understanding the potentially dire long-term implications, have been mishandling standard statistical procedures in the service of attaining statistical “significance.” Exacerbating the problem, most academic journals do not publish research that has not produced a “significant” result. This toxic combination has resulted in journals apparently publishing many Type I errors and declining to publish many true failures to reject H0. In response, there has been an urgent call from some psychologists that studies be registered in advance so that their rationales, hypotheses, variables, sample sizes, and statistical analyses are recorded in advance, leaving less room for post hoc manipulation. In this chapter, I argue that this “open science” approach, though laudable, will prove insufficient because the null hypothesis significance test (NHST) is a poor criterion for scientific truth, even when it is handled correctly. The root of the problem is that, whatever statistical problems psychology may have, the discipline never developed the theoretical maturity required. For decades it has been satisfied testing weak theories that predict, at best, only the direction of the effect, rather than the size of effect. Indeed, uncritical acceptance of NHST by the discipline may have served to stunt psychology’s theoretical growth by giving researchers a way of building a successful career without having to develop models that make precise predictions. Improving our statistical “hygiene” would be a good thing, to be sure, but it is unlikely to resolve psychology’s growing credibility problem until our theoretical practices mature considerably.
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How this classification was reachedexpand
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.139 | 0.002 |
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; both teacher heads 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".