Statistics Courses in Psychology Undergraduate Programs: A Review of Canadian Institutions
Bibliographic record
Abstract
<p>Statistical literacy is an important learning outcome of an undergraduate psychology degree and a valuable skill as critical consumers of news, as researchers, and as employees in the workforce. The goals of this study were to examine the current curriculum and whether teaching methods used in psychology statistics courses in Canadian universities adhered to recommendations in the GAISE report. Critical evaluation of research, replication/reproducibility, and programming skills were three topics that saw an increase in frequency over time, while hand calculations decreased the most over time. While there is a shift towards including more modern statistics concepts such as confidence intervals, there is little change in the coverage of traditional topics, like calculating t-tests by hand since 2000. The mean GAISE rating across 2017-2021 remained around 7 out of 12 points, indicating considerable room for improvement in methods used to teach statistics. Results, limitations, and future directions are discussed.</p>
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".