Statistics · Hypothesis testing

One-Proportion z-Test Calculator

Test a binomial proportion against a hypothesized value with a null-based normal approximation.

Statistics · Hypothesis testing

Enter your statistical inputs

Private in-browser calculation · explicit assumptions

Enter plain numbers without measurement units. Datasets accept commas, spaces, semicolons, or line breaks and are limited to 10,000 values. Results stay in this browser.

Statistics result

Enter valid values to see the result.

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Quick guide

How to use this calculator

  1. Enter the observations, probabilities, model parameters, or summary statistics requested by the visible labels.
  2. Keep every value on the same scale and confirm that the selected sampling relationship, distribution, and tail convention match the question you are investigating.
  3. Read the result together with its assumptions and interpretation. Statistical output summarizes uncertainty under a model; it does not repair biased data or establish causation.

Calculation method

How the one-proportion z-test calculator works

z=(p̂−p₀)/√[p₀(1−p₀)/n].

Show the observed proportion and effect difference with the p-value; statistical and practical significance are separate.

Worked example

One-Proportion z-Test example

60 successes in 100 trials tested against p₀=0.5 gives z=2.

z=(p̂−p₀)/√[p₀(1−p₀)/n].

Supported inputs

Precision and limits

Model and design

The normal approximation requires enough expected successes and failures under the null, plus independent stable-probability trials.

Numerical scope

Inputs use double-precision numerical methods with guarded domains. Datasets accept up to 10,000 finite plain-decimal values. Extremely large parameters or probabilities deep in a numerical tail may require specialist statistical software.

Interpretation

Show the observed proportion and effect difference with the p-value; statistical and practical significance are separate.

Decision boundary

The calculator does not validate how data were collected, diagnose dependence or bias, choose a scientifically meaningful effect, or replace review by a qualified statistician for consequential research, medical, regulatory, safety, or policy decisions.

Privacy

Entered values and calculated results stay in this browser and are not sent to an analytics service.

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