Probability

Roles: distribution, prior, posterior, likelihood, sample, population, mean, variance, outlier, base-rate

The mathematics of uncertainty, chance, and inference under incomplete information. As a source domain, probability supplies models of distributions (normal, power-law), updating beliefs on evidence (Bayesian reasoning), regression effects, and the distinction between signal and noise. When we say someone “beat the odds,” that an outcome was “unlikely,” or that results “regressed to the mean,” we are borrowing from this frame. Its metaphorical power comes from making the invisible structure of uncertainty legible; its danger comes from false precision — assigning numbers to uncertainties that resist quantification, or mistaking the model’s assumptions for the world’s actual behavior.

As Source Frame (9)

• Bayesian Updating
• Birthday Paradox
• Fermi's Paradox → decision-making
• Gambler's Fallacy
• Infinite Monkey Theorem → reasoning-about-infinity
• Monty Hall Problem
• Power Laws
• Regression to the Mean
• The Drunkard's Walk