Beyond conventional <i>p</i> -values: addressing statistical challenges in big data
Bibliographic record
Abstract
Do larger sample sizes lead to higher false positive rates in statistical analysis? The answer provided by ChatGPT 4o is ‘no’, which is a common opinion shared by many statisticians. However, empirical evidence from large datasets analyses, such as those from biobanks and single-cell genomics, challenges this conclusion. Common practice assesses both p-values and effect sizes to mitigate the risk of identifying spurious effects in large samples. Nonetheless, the need to adjust p-values in these contexts is unaddressed, which motivated this investigation. We found that common beliefs and practices are incorrect in real-world data analysis, since theoretical assumptions are always violated. Growing sample sizes can amplify violation impacts, inflating false positive rates. Using a simulation study, we provide examples to support our statement and illustrate a permutation-based remedy. This work’s intended contribution is to heighten awareness within our community about the pressing need to reevaluate standard statistical methods in analyzing datasets with huge sample sizes, thereby inspiring further substantial efforts to tackle this emerging challenge of the big data era.
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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 | 0.339 | 0.763 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.032 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.011 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 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".