What Is a Definition of Terms? Prediction-Market Examples

A definition of terms explains what important words or phrases mean in a particular document, study or analysis. As a section heading, “Definition of Terms” usually introduces several such explanations. Its purpose is to remove ambiguity: readers should know what you mean and, when measurement is involved, how you produced the numbers.

In prediction-market research, that distinction matters. “Price,” “probability” and “accuracy” are related, but they are not interchangeable. A useful definition tells the reader which price was recorded, what event it forecasts and how the forecast will be evaluated.

Conceptual versus operational definitions

A conceptual definition explains an idea. An operational definition specifies how you will observe, measure or classify it in your analysis. The University of Texas at Arlington’s research-methods text distinguishes a concept’s meaning from the procedures used to measure it.

For example:

• Conceptual: Liquidity is the ability to trade without substantially moving the price.
• Operational: In this study, liquidity is measured using the YES bid–ask spread and the number of shares available within two cents of the best bid and ask at each recorded timestamp.

The second statement is a proposed measurement rule, not a universal definition. Another study could choose different indicators. What matters is making the choice explicit rather than letting “liquid market” remain an unexplained label.

Prediction-market examples

The following table pairs plain-language meanings with illustrative study definitions, not mandatory platform standards.

Term	Conceptual meaning	Example operational definition
YES price	The price associated with the contract’s YES outcome	Midpoint of the best YES bid and ask, recorded at a specified UTC timestamp; observations missing either quote are excluded and counted separately
Implied probability	A price-based probability interpretation	For a binary contract paying zero on a loss, YES price divided by the winning payout; with a $1 winning payout, $0.63 is interpreted as 63%, before cost adjustments
Outcome	The result against which the forecast is evaluated	Final ordinary binary resolution, coded YES = 1 and NO = 0; voided or nonbinary results are excluded and reported
Calibration	Agreement between forecast probabilities and observed outcome frequencies	Group forecasts into stated probability bands and compare each band’s average forecast with its observed YES rate

The payout convention supports the probability interpretation: Polymarket’s resolution documentation says winning tokens redeem for $1 and losing tokens become worthless. That does not establish that every 63-cent price is a correctly calibrated 63% forecast. For the mechanics behind that interpretation, see how prediction-market prices become probabilities.

Calibration is a property assessed across forecasts. Roughly 80% of cases assigned probabilities near 80% should have positive outcomes in a well-calibrated system—not every individual case. The scikit-learn calibration guide explains this frequency-based definition and how reliability diagrams compare average predicted probabilities with observed frequencies.

Why “price” needs a precise definition

Suppose a hypothetical YES order book has a best bid of $0.60 and a best ask of $0.66. Its midpoint is $0.63, and its spread is $0.06.

Those are three different numbers with different meanings. A buyer can purchase the available shares at the best ask, not automatically at the midpoint; a larger order may encounter higher asks. As of October 19, 2026, Polymarket’s prices and order-book documentation makes this distinction and says its displayed price normally uses the midpoint, switching to the last traded price when the spread exceeds $0.10.

Thus, “we recorded the market price” is incomplete. Specify last trade, midpoint, bid or ask, along with the timestamp. Execution costs also matter if the question concerns returns rather than forecasting performance.

How to write your definition-of-terms section

Define terms whose interpretation could change the result. For each measured term, state:

1. Meaning: What concept does the term describe?
2. Measurement: Which field, formula or classification rule represents it?
3. Scope: Which markets, dates and forecast timestamps are included?
4. Exceptions: How are missing observations, cancellations and unusual resolutions handled?

For example:

YES price: The midpoint of the best YES bid and ask at 12:00 UTC on the day before the scheduled event. Both quotes must be present. Observations missing either quote are excluded, and the exclusion count is reported.

Finally, replace vague “accuracy” claims with a named measure. Calibration and Brier score answer different questions; a lower Brier score alone does not establish better calibration. See Brier score versus calibration before choosing how to define forecast performance.