Null Hypothesis Significance Testing: A Brief Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Null hypothesis significance testing (NHST) dominates the interpretation of quantitative data analysis in education, psychology, and other social science fields (Shaver, 1993). Meanwhile, the use of NHST has been under enduring and intense criticisms (Carver, 1978; Cohen, 1997; Cumming, 2013; Thompson, 1993, 1996, 1999). In 2015, the journal, Basic and Applied Social Psychology (BASP; Trafimow & Marks, 2015) banned the use of NHST, reigniting another round of intense discussions about whether continue using the NHST technique. In the present paper, I have elaborated the definition of NHST and six most commonmisinterpretations/false beliefs, and suggested reporting strategies, including reporting effect size along with its interval estimates. Finally, I briefly commented on the causes of misconceptions
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.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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