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Confidence intervals and credible intervals in forecast ranges

Ranges are everywhere in the data behind event contracts: poll margins of error, central bank fan charts, model forecast bands and official estimates with uncertainty. Reading them correctly helps readers translate a range into probabilities for bracket markets. This SoftCta guide explains the two main types of interval and the common mistakes.

Confidence intervals

A confidence interval is a classical statistics tool. A 95% confidence interval comes from a procedure that, if repeated across many samples, would capture the true value about 95% of the time. Strictly speaking, that is a statement about the method. In practice, many readers treat a 95% confidence interval as a 95% chance the truth lies inside, and for many well-behaved problems the numbers are similar. Poll margins of error are confidence intervals for sampling error only, and they exclude other sources of error, as explained in our guide on poll margins and house effects.

Credible intervals

A credible interval is a Bayesian concept: a range that contains the quantity with a stated probability, given the data, the model and the prior. A 90% credible interval means a 90% probability under the model's assumptions. This matches how event market readers think. Our guide on Bayesian updating explains how priors and evidence combine.

What an interval leaves out

An interval tells you where most of the probability lies, but not how it is spread. Two forecasts with the same 90% range can imply very different probabilities for a specific bracket if one is symmetric and the other is skewed or fat-tailed. The tails outside the interval still carry probability: a 90% interval leaves 10% outside, often split between both ends. When a contract's threshold sits near the edge of a published range, small assumptions about shape change the answer considerably. Our bracket strike guide shows how brackets carve up a distribution.

Model ranges are conditional

Forecast intervals are only as good as the model behind them. Central bank fan charts, for example, are often based on historical forecast errors, which can understate uncertainty during unusual periods. Ensemble weather ranges reflect model spread, not all possible outcomes. Read a published interval as one input, not a final answer.

Your own intervals

Studies of overprecision find that people's stated 90% ranges contain the true value much less than 90% of the time. Our guide on overconfidence and overprecision explains the evidence. A practical fix is to record your ranges and check later how often they were right, which ties into calibration and Brier scores.

A reading habit

• Note whether a published range is a confidence or credible interval, and at what level.
• Ask which sources of uncertainty it includes and excludes.
• Think about the shape of the distribution, not just the endpoints.
• Write your own range and check it against outcomes over time.

Convert market prices with the implied probability tool and compare them with your own range. Verify live prices on the venue rather than relying on old quotes.

Primary sources SoftCta readers open

• Statistical agency technical notes on sampling error
• Central bank fan chart methodology documents
• Introductory statistics references on interval estimation
• Forecast model documentation with stated intervals

Reading the quote step by step

• Read the full title and rules, including the named settlement source and the time zone.
• Convert the Yes and No prices to implied probabilities and note the gap between them.
• Subtract fees and spread to see the effective price you would actually face.
• Write your own probability range and the evidence behind it, then compare.
• Check related and near-duplicate markets on other venues for consistency.

Closing SoftCta checklist

• Re-read the venue definition in your own words before reacting to a price.
• Convert prices with fee and spread awareness; verify live cents on the operator.
• Prefer primary settlement sources over secondary headlines and social summaries.
• Treat ChanceWire hubs, movers, and tools as orientation — not order entry.
• Confirm age, location, and product eligibility; SoftCta pages will not suggest location spoofing.
• If clarity is incomplete, watching is a complete SoftCta outcome — no pick-selling urgency required.

SoftCta habits

• Write your own probability range before checking the crowd price.
• Prefer dated primary sources over social summaries.
• Compare related markets when a single book looks extreme.
• Size curiosity with fees and depth in mind; substantial risk of loss.