Failing Grade: 89% of Introduction-to-Psychology Textbooks That Define or Explain Statistical Significance Do So Incorrectly
Why this work is in the frame
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Bibliographic record
Abstract
Null-hypothesis significance testing (NHST) is commonly used in psychology; however, it is widely acknowledged that NHST is not well understood by either psychology professors or psychology students. In the current study, we investigated whether introduction-to-psychology textbooks accurately define and explain statistical significance. We examined 30 introductory-psychology textbooks, including the best-selling books from the United States and Canada, and found that 89% incorrectly defined or explained statistical significance. Incorrect definitions and explanations were most often consistent with the odds-against-chance fallacy. These results suggest that it is common for introduction-to-psychology students to be taught incorrect interpretations of statistical significance. We hope that our results will create awareness among authors of introductory-psychology books and provide the impetus for corrective action. To help with classroom instruction, we provide slides that correctly describe NHST and may be useful for introductory-psychology instructors.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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