Bridging statistics and life sciences education for immunologists: Exploring significantly impactful teaching strategies and collaborations
Bibliographic record
Abstract
Abstract The widespread misuse of statistics is one of the contributing factors to reproducibility concerns in immunological research. However, not all immunologists have the statistical expertise to choose the best methods to evaluate and represent their experiments, and the formal statistics training required by undergraduate and graduate immunology curricula is often quite limited. How can we prepare immunologists for statistical practice in research? We previously developed a second-year undergraduate course to integrate statistics instruction with research design to improve the quantitative training of life sciences students. In a Scholarship of Teaching and Learning (SoTL) study conducted within the course, student attitudes and self-efficacies for statistics, as well as their abilities to recognize and handle problems related to statistical practice in life sciences research, were assessed through surveys administered at the beginning and end of the course (n=126). Here, we present our team-teaching model and strategies for promoting meaningful connections between statistics and immunological research through course design, activities and assessments. We also highlight the results from our SoTL study, which have guided the development of several specialized immunology courses focusing on statistical practice and experimental design at the University of Toronto. We hope that insights from this teaching partnership and our SoTL findings will help inform future quantitative course offerings and training initiatives, and ultimately, better prepare students to effectively engage with statistics in immunological research.
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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 | 0.037 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".