Binary Outcome Definition: What YES and NO Mean

A binary outcome is an outcome variable with exactly two possible values, such as YES/NO, pass/fail or event/no event. It is also called dichotomous. In statistical analysis, those two values are often coded as 1 and 0. The University of Massachusetts Amherst’s statistics notes define binary variables this way and describe the Bernoulli model for a single 0/1 outcome.

In a prediction market, the question is usually whether a specified event satisfies the contract’s rules. YES and NO are the two answers—not two equally likely answers.

Two outcomes does not mean a 50–50 chance

A properly defined binary question has answers that are:

• Mutually exclusive: both cannot be true for the same observation.
• Collectively exhaustive: together, they cover every possible observation within the question’s scope.

For a YES/NO forecast, let p be the probability of YES. Then the probability of NO is 1 − p. If YES has a 70% probability, NO has a 30% probability. The outcome is still binary even though the chances are unequal.

The distinction is between the forecast, which can take any probability from 0% to 100%, and the observed outcome, which is coded as 0 or 1.

A continuous measurement can produce a binary outcome

Consider this hypothetical question:

Will Station A’s maximum temperature on November 1, 2026, be at least 30.0°C, according to the designated weather agency’s daily report?

Temperature has many possible values. Applying the threshold turns that measurement into a binary outcome:

Hypothetical reported maximum	Answer	Outcome code, y
29.9°C	NO	0
30.0°C	YES	1
32.4°C	YES	1

“At least” includes exactly 30.0°C. “Above 30.0°C” would not. That small wording change defines a different event.

Likewise, a contest with several candidates is not inherently binary. But “Will Candidate A win?” is: YES means A wins; NO covers all outcomes in which A does not win, as specified by the rules.

In markets, the rules define the outcome

A short market title is not enough to establish what counts as YES. Check the measured event, threshold, deadline, time zone, designated evidence source and treatment of missing or revised data.

Polymarket’s resolution documentation states that markets resolve according to predefined rules. It describes winning shares paying $1 each and losing shares becoming worthless. Those rules connect the real-world evidence to the contract’s answer.

Unresolved is not the same as NO. For a forecasting dataset, do not assign zero merely because a result is pending or evidence is missing. If a contract has cancellation or special-settlement provisions, record how those cases are handled rather than silently treating them as ordinary NO outcomes. See closed versus settled markets for the timing distinction.

Why binary outcomes matter for measuring forecasts

A 70-cent YES price can be read as an implied 70% forecast in the standard $1-or-$0 setup. That interpretation does not establish that the true probability is 70%. Polymarket describes this price-to-probability convention; our guide to how prices become probabilities explains the practical frictions.

Once the market resolves, compare the saved forecast with the 0/1 outcome. One NO result does not, by itself, disprove a 70% forecast: that forecast allowed a 30% chance of NO.

Calibration asks a broader question: across many forecasts near 70%, does YES occur about 70% of the time? This is the frequency-based test described in scikit-learn’s calibration documentation. For market evaluation, save the forecast timestamp and resolution rule alongside the outcome code; otherwise, the number and the result may refer to different questions.