Poll Simulator
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Tap a term to see what it means, or .
Simulate
Example: in an electorate of 100,000 people, 38.6% vote for candidate A. Each poll interviews 1,000 voters drawn without replacement and publishes a 95% confidence interval. Draw polls and see how many intervals contain the true value. The electorate is split into neighbourhoods of about 500 people; switch the design to draw whole neighbourhoods (clusters) or to split the sample across regions (strata), and see what happens to the coverage when the poll publishes the margin of a simple random sample. The intraclass correlation says how alike neighbours vote.
Step by step
What each term means
- Exact Coverage (C)
- The share of all possible samples whose confidence interval contains the true value P. It is what "95% confidence" promises, and it is almost never exactly 95%: with 1,000 out of 100,000 voters and P = 38.6%, it is 94.98%. The coverage observed in the simulator fluctuates around it.
- Sum over the Hits
- Runs only over the counts k whose interval, computed with p̂ = k/n and the standard error estimated by the poll itself, contains P. Since k moves in steps of 1, the range of hits is a set of integers, and the ends lost to rounding make the coverage drift from the nominal level.
- Ways to Choose k Voters for A
- How many groups of k people can be formed from the K = P·N voters for A. In the simulator, K is the true proportion times the population, rounded.
- Ways to Complete the Sample
- How many groups of n − k people can be formed from the other N − K voters. Every choice of voters for A pairs with any of these, so the two counts multiply.
- All Possible Samples
- The number of samples of size n that can be drawn from N people without replacement, all equally likely. With n = 1,000 and N = 100,000, it exceeds 102,430. The whole fraction is the hypergeometric distribution, the exact probability that the poll finds k voters for A.