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Margin of Error Calculator — Proportion or Mean with FPC

Calculate a z-based margin of error for a proportion or mean from sample size and confidence, with optional finite-population correction and a transparent conservative p = 50% option.

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Calculated result

±3 percentage points

Standard error: 0.015307

Finite-population correction not applied

Confidence z-score: 1.96

Show the working
  1. 1. SE = √(0.5 × 0.5 ÷ 1067) = 0.015307.
  2. 2. MOE = 1.96 × 0.015307 × 1 = 3.0002 percentage points.

Sampling margin does not include nonresponse bias, coverage error, wording effects or other non-sampling error.

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The calculation, without hidden assumptions

Margin of error is half the width of a confidence interval under a stated sampling model. For a proportion it depends on p(1−p); for a mean it depends on a standard deviation. This calculator keeps those two cases separate, supports an optional finite-population correction, and reports the proportion result in percentage points so “±3” is not confused with “3% of the estimate.”

How to use this calculator

1

Choose proportion or mean, enter sample size and select the confidence level.

2

For a proportion, enter an expected proportion; for a mean, enter the standard deviation used by the model.

3

Optionally enter a finite population size and read both the margin and the assumptions shown in the working.

Where people use it

  • Checking a survey margin
  • Reproducing a reported confidence calculation
  • Exploring how sample size changes precision

Example: n = 1,067, p = 50%, 95% confidence

With z = 1.96 and p = 0.5, the standard error is √(0.25/1067). Multiplying by 1.96 gives a margin a little above three percentage points before any finite-population correction.

What the result does not assume

  • This implementation is z-based. For a mean with an unknown population standard deviation—especially with a small sample—a t-based interval can be more appropriate.
  • A margin of error describes sampling uncertainty under the model; it does not include nonresponse bias, coverage error, question wording effects or other non-sampling errors.

Frequently asked questions

Why is 50% called conservative for a proportion?+

Because p(1−p) reaches its maximum at p = 0.5, producing the widest basic z-based margin for a fixed sample size.

When does population size matter?+

Finite-population correction matters when sampling without replacement from a known population and the sample is a meaningful share of that population.

Does ±3 points mean the estimate is guaranteed to be within three points?+

No. It is a confidence-interval quantity tied to a sampling procedure and stated confidence level, not a guarantee about one estimate.

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