Retrospective and prospective hindsight bias: Replications and extensions of Fischhoff (1975) and Slovic and Fischhoff (1977)
Bibliographic record
Abstract
Hindsight bias refers to the tendency to perceive an event outcome as more probable after being informed of that outcome. We conducted very close replications of two classic experiments of hindsight bias and a conceptual replication testing hindsight bias regarding the perceived replicability of hindsight bias. In Study 1 ( N = 890), we replicated Experiment 2 in Fischhoff (1975), and found support for hindsight bias in retrospective judgments ( d mean = 0.60). In Study 2 ( N = 608), we replicated Experiment 1 in Slovic and Fischhoff (1977), and found support for hindsight bias in prospective judgments ( d mean = 0.40). In Study 3 ( N = 520) we found strong support for hindsight bias regarding perceived likelihood of our replication of hindsight bias ( d = 0.43–1.03). We also included extensions examining surprise, confidence, and task difficulty, yet found mixed evidence with weak to no effects. We concluded support for hindsight bias in both retrospective and prospective judgments, and in evaluations of replication findings, and therefore call for establishing measures to address hindsight bias in valuations of replication work and interpreting research outcomes. All materials, data, and code, were shared on: https://osf.io/nrwpv/ .
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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 | 0.077 | 0.378 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".