Searching for Reliable Relationships With Statistics Packages: An Empirical Example of the Potential Problems
Bibliographic record
Abstract
Many social scientists appear to possess an overconfidence in the reliability of research results from a single, small-sample, inferential study. In this article, the authors speculate that "user-friendly" statistics packages have the potential to exacerbate statistical misinterpretation by providing researchers with a tool to explore data easily and identify what is interpreted as "reliable" relationships. This article contains an empirical demonstration of the potential problems that arise when a large number of statistical tests are interpreted. Results show that statistically significant results may be unreliable. Also, a zero relationship can erroneously appear as a medium to large effect size relationship when a small sample is used (e.g., n = 30). The authors suggest the need for multiple replications as the criterion of a reliable 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 | 0.483 | 0.864 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".