How prior and p-value heuristics are used when interpreting data
Bibliographic record
Abstract
ABSTRACT Scientific conclusions are based on the ways that researchers interpret data, a process that is shaped by psychological and cultural factors. When researchers use shortcuts known as heuristics to interpret data, it can sometimes lead to errors. To test the use of heuristics, we surveyed 623 researchers in biology and asked them to interpret scatterplots that showed ambiguous relationships, altering only the labels on the graphs. Our manipulations tested the use of two heuristics based on major statistical frameworks: (1) the strong prior heuristic, where a relationship is viewed as stronger if it is expected a priori , following Bayesian statistics, and (2) the p-value heuristic, where a relationship is viewed as stronger if it is associated with a small p-value, following null hypothesis statistical testing. Our results show that both the strong prior and p-value heuristics are common. Surprisingly, the strong prior heuristic was more prevalent among inexperienced researchers, whereas its effect was diminished among the most experienced biologists in our survey. By contrast, we find that p-values cause researchers at all levels to report that an ambiguous graph shows a strong result. Together, these results suggest that experience in the sciences may diminish a researcher’s Bayesian intuitions, while reinforcing the use of p-values as a shortcut for effect size. Reform to data science training in STEM could help reduce researchers’ reliance on error-prone heuristics. Significance Statement Scientific researchers must interpret data and statistical tests to draw conclusions. When researchers use shortcuts known as heuristics, it can sometimes lead to errors. To test how this occurs, we asked biologists to interpret graphs that showed an ambiguous relationship between two variables, and report whether the relationship was strong, weak, or absent. We altered features of the graph to test whether prior expectations or a statistic called the p-value could influence their interpretations. Our results indicate that both prior expectations and p-values can increase the probability that researchers will report that ambiguous data shows a strong result. These findings suggest that current training and research practices promote the use of error-prone shortcuts in decision-making.
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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 | 0.263 | 0.775 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| 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".