Predictive Confidence Interval
A single probability can look precise, but it rarely tells the full story. A predictive confidence interval adds context by showing a reasonable upper and lower bound around that estimate. It answers the question of how confident the market really is, not just what it currently favors.
In prediction markets, confidence intervals are inferred from price behavior, volatility, liquidity, and disagreement across outcomes. When markets are calm and liquid, the interval is narrow. When uncertainty is high or information is incomplete, the interval widens. On platforms like Polymarket, Kalshi, Myriad, and Manifold, this behavior appears in prediction markets data as periods of tight clustering versus wide dispersion in probabilities.
Confidence intervals help explain why prices can change quickly even when no major news appears. They reveal how fragile or stable a forecast actually is.
Why It Matters
Predictive confidence intervals prevent overconfidence. They help analysts interpret prediction markets data by showing how much uncertainty surrounds a forecast.
How is a predictive confidence interval derived in prediction markets?
It is not usually posted directly, but inferred from market signals. Analysts look at volatility, spread behavior, trading depth, and how probabilities respond to small trades. These inputs from prediction markets data help estimate how wide the confidence range should be.
How does a confidence interval differ from forecast volatility?
Volatility shows how much prices move over time. A confidence interval describes uncertainty at a specific moment. A market can have low recent volatility but still a wide confidence interval if information is thin or unresolved. Prediction markets data helps distinguish between these cases.
When should analysts pay attention to widening confidence intervals?
Widening intervals often signal rising uncertainty, hidden risk, or unresolved questions. They frequently appear before major updates or during complex, multi-stage events. Tracking these shifts improves interpretation of prediction markets data and avoids false certainty.
Real-World Example
A Kalshi market forecasts whether a policy will pass by a deadline. The headline probability sits near 60%, but low liquidity and frequent small reversals suggest a wide confidence interval. Analysts treat the forecast as tentative until clearer signals arrive.
Predictive Confidence Interval and FinFeedAPI
Estimating confidence intervals requires rich, time-stamped market context. FinFeed's Prediction Markets API provides structured prediction markets data that developers and analysts can use to model confidence intervals and assess forecast reliability.