Base rate neglect is the tendency to ignore background statistical frequencies in favour of specific case information. When told someone is 'quiet and orderly,' most judge them more likely to be a librarian than a farmer — despite farmers vastly outnumbering librarians. The vivid specific description overwhelms the relevant base rate.
Bayesian Thinking is a framework for updating beliefs in proportion to evidence. Named for the Reverend Thomas Bayes, whose theorem formalizes the mathematics of belief revision, it provides a principled method for incorporating new information into prior beliefs — neither overreacting to single data points nor clinging to existing views against contradicting evidence. It is the foundation of modern statistics, machine learning, and rational decision-making under uncertainty.
Neglect of Probability is a cognitive bias where individuals completely disregard the statistical likelihood of an event when making decisions, especially when the outcome is emotionally charged. Coined by Cass Sunstein and explored by Rottenstreich and Hsee in 2001, this mental model explains why we fear rare shark attacks while ignoring the common risk of driving, and why we spend billions on lottery tickets despite the near-zero odds. Understanding Neglect of Probability allows decision-makers to replace "vividness" with "expected value," ensuring resources are allocated based on actual risk rather than emotional intensity.
Power Laws describe distributions where a small number of causes produce a disproportionately large fraction of effects. Unlike normal (bell-curve) distributions where most outcomes cluster around the average, power law distributions have no characteristic scale — a small number of observations can be many orders of magnitude larger than the median. They govern wealth distribution, city size, website traffic, startup outcomes, earthquake magnitude, and much more.
Reference Class Forecasting is a method of estimation and prediction developed by Nobel laureate Daniel Kahneman and Amos Tversky that deliberately anchors predictions to observed base rates from comparable past projects or situations, rather than relying on case-specific analysis. By forcing forecasters to consult the 'outside view' — the statistical distribution of outcomes for similar situations — it corrects for the systematic optimism bias and inside-view thinking that causes most projects to run over time and over budget.
The Representativeness Heuristic is a cognitive shortcut used to estimate the probability of an event by comparing it to an existing mental prototype or stereotype. Identified by Tversky and Kahneman in 1972, this mental model explains why we commit the "Conjunction Fallacy," ignore statistical base rates, and fall for the Gambler's Fallacy. By understanding how the brain prioritizes "story fit" over "statistical reality," decision-makers can avoid expensive hiring errors, improve investment accuracy, and neutralize systemic prejudice in organizational systems.
Scope Insensitivity is a cognitive bias where the valuation of a problem does not scale proportionally with its magnitude. First documented by Desvousges et al. in 1992, this "Scope Neglect" explains why we donate the same amount to save 2,000 birds as we do for 200,000, and why we struggle to comprehend existential risks like global pandemics or nuclear war. By understanding how the "Judgment by Prototype" mechanism fails in the face of large numbers, decision-makers can apply "Expected Value" logic to prioritize interventions that offer the greatest absolute impact.
Survivorship Bias is the logical error of focusing only on entities that passed a selection process while ignoring those that did not — typically because the failures are less visible. Named after Abraham Wald's World War II analysis of aircraft damage, it leads to false conclusions about what causes success, systematic overestimation of success rates, and catastrophically flawed decision-making when the non-survivors hold the critical information.