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The Forecaster Test- based on Tetlock's superforecasting research

Hacker News

The Forecaster Test- based on Tetlock's superforecasting research

In Tetlock's 20-year Expert Political Judgment study, domain experts performed no better in their own field than outside it — and "hedgehog" specialists actually did worse when predicting within their specialty. The Good Judgment Project (Tetlock & Mellers) later found that superforecasters weren't dramatically smarter than average participants — what separated them was thinking style. What predicts accuracy? Calibration, Bayesian reasoning, and willingness to update beliefs. These traits beat raw intelligence and domain knowledge. I built a 10-minute test that measures them: Bayesian updating: Commit to a probability, receive new evidence, update. Tests whether you move the right direction and magnitude. Calibration: When you say 80% confident, are you right 80% of the time? Replication prediction: Predict whether famous psychology studies replicated. Instant feedback since outcomes are known. Free, no signup. Results show where you're calibrated vs. overconfident, and how you handle disconfirming evidence. Looking for feedback on questions and scoring. Research basis is Tetlock, Mellers, Baron, and Stanovich — but I'm sure there are improvements to make.

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: new · Missing: mac, agents, macos
54%54% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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