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What Americans think about prediction markets

Overview

Prediction markets appear familiar to many adults, but that familiarity has not translated into broad participation.


About 68.9% are at least slightly familiar with prediction markets, while 80.3% say they never bet on them. On the broader social question, 44.8% say prediction markets cause more harm than benefit and 38.0% are not sure.

Stacked breakdown

68.9% are at least slightly familiar with prediction markets.

How familiar are you with prediction markets, platforms where people buy and sell contracts based on the outcome of future events, such as elections, economic data, or world events?

Very familiar — I know a lot about how they work
8.6%
Somewhat familiar — I have a general sense of what they are
27.0%
Slightly familiar — I have heard of them but don't know much
33.3%
Not at all familiar — this is the first I am hearing of them
31.1%

2026 · base n 2,000 · +/- 2.3%

April 2026 Verasight Variety Survey

View source data

Additional supporting data from this section.

Topline

80.3% say they never bet on prediction markets.

How often, if at all, do you do each of the following activities? - Bet on prediction markets

  • Never 80.3%
  • A few times a year 7.0%
  • A few times a month 4.1%
  • About once a month 3.9%
  • A few times a week 3.1%
  • Daily 1.6%

2026 · base n 2,000 · +/- 2.3%

April 2026 Verasight Variety Survey

View source data

Awareness is broader than use

Roughly 68.9% of adults are at least slightly familiar with prediction markets. That includes 27.0% who are somewhat familiar and 8.6% who are very familiar.

Actual use is much lower. About 80.3% say they never bet on prediction markets, and 50.3% say they have not participated and are not interested.

Regulation views cluster around casinos or stricter treatment

The largest regulation bucket is parity with casinos: 35.4% say prediction markets should be regulated about the same as casinos.

Another 29.4% say prediction markets should be regulated more strictly than casinos, while 9.1% say they should be regulated less strictly and 24.2% are not sure.

Topline

44.8% say prediction markets cause more harm than benefit.

Overall, do you think prediction markets cause more benefit or more harm to society?

  • Not sure 38.0%
  • Somewhat more harm than benefit 24.5%
  • Much more harm than benefit 20.3%
  • Somewhat more benefit than harm 13.2%
  • Much more benefit than harm 4.1%

2026 · base n 2,000 · +/- 2.3%

April 2026 Verasight Variety Survey

View source data

Social benefit remains unsettled

Views of social impact lean more negative than positive. About 44.8% say prediction markets cause more harm than benefit, compared with 17.3% who say they cause more benefit than harm.

The large 38.0% not-sure share matters because it sits alongside broader familiarity. The pattern is not simply that adults have never heard of prediction markets. It is that many remain unconvinced or undecided.

Methodology

Full methodology
Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting
Population
US adults age 18+
Field dates
2026-04-21 → 2026-04-23
Base (unweighted)
2,000
Margin of error
+/- 2.3%
Module
April 2026 Verasight Variety Survey
Sponsor
Verasight
Weight variable
weight
Weighting targets
age, race/ethnicity, sex, income, education, region, metropolitan status

Sources

[5]

Citation

April 2026 Verasight Variety Survey, fielded April 21-23, 2026, N=2,000 US adults age 18+, +/- 2.3%.

https://reports.verasight.io/reports/variety10126#q-3

Verasight survey methodology

How Verasight conducts surveys.

This page describes the Verasight general survey contract, separate from how the Data Library packages it. Each wave's specific field dates, sample sizes, and module breakdown are listed in that wave's report.

Mode
Verasight panel recruited via random address-based sampling, random person-to-person text messaging, and dynamic online targeting.
Population
US adults age 18+.
Sample design
Surveys are run as omnibus or single-topic waves. Omnibus waves are split into modules with their own respondent set, typically around one thousand respondents per module.
Field window
Each wave specifies its own field dates. Most omnibus waves field across roughly two weeks.
Weighting
Per-module weighting to CPS targets including age, race and ethnicity, sex, income, education, region, and metropolitan status.
Partisanship benchmark
Pew Research Center's NPORS benchmarking surveys, three-year running average.
Vote benchmark
2024 presidential vote population benchmarks.
Margin of error
Typically about plus or minus 3.4 to 3.6 percent per module at standard module sizes. Question-level MoE is recomputed when a base shrinks materially below the module baseline.
Reporting
Every wave is published as a standalone report at verasight.io/reports with full instrument and methodology.
Transparency
Verasight is a member of the American Association for Public Opinion Research Transparency Initiative.

Wave-specific methodology, full weighting variable lists, and verbatim instrument text live in each report at verasight.io/reports.