Improving Statistical Literacy for Physician Scientists: Sampling Distributions
Bibliographic record
Abstract
This is the first in a multi-part series on teaching statistical inference to physician-scientists training to work as members of interdisciplinary scientific teams. This unique student audience has greater scientific sophistication than a typical statistics student but less background in mathematics and computer programming, which presents challenges for traditional approaches to teaching statistics. Here, we illustrate an innovative approach to teaching sampling distributions to physician-scientists. Sampling distributions are a fundamental element of statistical inference; they are a building block of confidence intervals and hypothesis tests which are vital tools for performing clinical research. As such, it is essential that physician-scientists have a strong foundation in sampling distributions. Key elements of our innovative approach include the use of a running example, delivery of content in small pieces to reduce cognitive burden, preceding formulae with pictures, combining static and dynamic content using an R Shiny app, and use of self-graded quizzes to provide immediate feedback. The resulting course module can be reused in multiple contexts, including as part of self-directed, asynchronous learning or by incorporation into a traditional or flipped classroom setting.
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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 | 0.015 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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 source (direct Gemma or distilled Codex), 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".