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Attribution Model Sample Size Estimator

This calculator estimates a recommended sample size for estimating a population proportion at a target margin of error for attribution model using a transparent statistical or operational model. It is designed for analytics teams that need a quick numerical check before interpreting adoption, segmentation, or model-performance data. The result converts a small set of measurable inputs into a consistent metric that can be compared across periods, cohorts, or implementation scenarios.

It helps scope validation work when a segmentation or attribution workflow needs enough observations to estimate a proportion with a chosen confidence level. Treat the output as a planning estimate rather than a substitute for a full experiment design or production capacity study. Data quality, sampling method, workload mix, and system architecture can materially change real-world results, so the calculator makes its assumptions visible and keeps the inputs editable.

Calculator inputs

%
%
Result
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Calculated result
initialOut—
adjustedOut—
confOut—
marginOut—

1. Set the margin of error
Enter the maximum half-width you want around the estimated proportion.

2. Estimate the expected proportion
Use prior data when available; 50% is conservative because it produces the largest variance.

3. Choose confidence
Select the confidence level used to determine the critical z value.

4. Add population size if known
Leave population size at zero to skip finite-population correction.

5. Use the rounded recommendation
The calculator rounds the adjusted requirement up to a whole observation.

Initial sample size: n₀ = z² × p × (1 − p) / e²

Finite-population adjustment: n = n₀ / (1 + (n₀ − 1) / N)

Here, z is the critical value, p is the expected proportion, e is the target margin of error as a decimal, and N is the optional finite population size. The result is rounded up because a fraction of an observation cannot be sampled.

What the result means

The main result is the approximate number of independent observations needed to estimate a proportion at the requested precision under this standard model.

Complex sampling, clustering, weighting, nonresponse, or model-based segmentation may require a larger effective sample.

Given: 5% margin of error, 50% expected proportion, and 95% confidence with no finite population limit.

Calculation: n₀ = 1.96² × 0.50 × 0.50 / 0.05² = 384.16.

Result: Round up to 385 observations.

Interpretation: Under simple random sampling assumptions, about 385 observations target a ±5 percentage-point margin at 95% confidence.

Why does 50% produce a larger sample?

For a binary proportion, p × (1 − p) is largest at 0.50, so it is the conservative choice when the expected rate is unknown.

When should I enter population size?

Use it when sampling without replacement from a known finite population and the sample is not negligible relative to that population.

Does this guarantee a representative sample?

No. Sample size controls statistical precision under the model; representativeness also depends on how observations are selected.

What if I need estimates for several segments?

Each segment needs enough observations for its own desired precision, so the total sample may need to be much larger.

Should I increase the result for missing data?

Yes. If some records may be unusable or unavailable, recruit or collect more than the minimum calculated sample.