The Learning and Teaching of Statistics and Probability
Bibliographic record
Abstract
Filled with practical learning activities to adopt within your classroom, The Learning and Teaching of Statistics and Probability places reasoning about quantities and quantification at the core of learning and teaching statistics. A companion website to this book is also available at https://neilhatfield.github.io/IMPACT_Statistics/, allowing readers to access a directory of resources – data collections and web-based applets – used in some of the instructional activities featured within this book. Through its presentation of conceptual analyses and resources for teaching with statistical data, the book’s five chapters establish key concepts and foundational ideas in statistics and probability, emphasizing the development of learner understanding and coherence, for example: Individual cases and their attributes Data collections, sub-collections, and relevant operations to quantify their attributes Samples, population, and quantifying variation Types of processes, meanings of randomness, and probability as a measure of stochastic tendency Sampling distributions and statistical inference. This highly informative yet practical book is an indispensable resource for teachers of secondary school mathematics, mathematics subject leads, and mathematics and statistics educators within the wider field of education.
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.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".