probabilistic-thinking

What problem does it solve?

This Skill helps you replace vague confidence and “probably” judgments with explicit probability estimates, so you can reason clearly under uncertainty, compare risks, and update beliefs when new evidence arrives.

Core Features & Use Cases

• Base-rate reasoning: checks reference-class likelihoods to avoid common base-rate errors when estimating how often an event type occurs.
• Bayesian updating: updates beliefs using priors and evidence-weighting so estimates move appropriately with stronger vs. weaker information.
• Decision support via expected value: compares options using EV (probability × payoff) while accounting for both upside and downside, not just the best-case outcome.
• Calibration & uncertainty hygiene: prompts users to use confidence ranges and flags issues like treating uncertain outcomes as certainties, anchoring, and overreacting to recent data.

Quick Start

Ask: "Given these facts, estimate the probability of X, state the base rate you used, perform a Bayesian update with the new evidence, and compare options A vs B by expected value."