The big bad bias book. A field guide to over 200 cognitive biases that shape how we think, decide, and behave
Bibliographic record
Abstract
Big Bad Bias Book: A Field Guide to Over 200 Cognitive Biases That Shape How We Think, Decide, and Behave provides a comprehensive reference guide to cognitive biases and their role in human judgement, behaviour and decision-making. Designed for students, educators, professionals and general readers, the book catalogues more than 200 biases through concise definitions, historical and conceptual origins, research summaries, case studies and visual explanations. Its field-guide structure allows readers to examine individual biases while also recognising broader patterns in perception, memory, reasoning, social influence, risk assessment and choice. By combining psychological theory with examples from business, education, media, leadership, history and everyday life, the book translates behavioural science into an accessible format for teaching, training and independent study. The volume contributes to public and applied understanding of cognitive bias by presenting complex ideas in a structured, illustrated and practically oriented form, supporting the development of critical thinking, self-reflection and more informed decision-making.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.065 | 0.050 |
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".