Proportion Test Calculator
Perform a hypothesis test for a single proportion to determine if your sample data provides enough evidence to reject the null hypothesis.
The number of successful outcomes in your sample.
The total number of observations in your sample.
The proportion value stated in the null hypothesis (between 0 and 1).
Results
Test Statistic (Z):
P-Value:
Proportion Visualization
Understanding Proportion Tests
A proportion test is a type of hypothesis test used in statistics to determine if the proportion of a certain characteristic in a population is likely to be different from a hypothesized value. It's commonly used when dealing with categorical data, such as pass/fail rates, or survey responses.
For example, you might use a proportion test to check if the proportion of defective items from a manufacturing process has changed, or if the proportion of people who prefer a certain brand is significantly different from 50%. The calculator above helps you perform a one-proportion z-test, which is suitable when you have a simple random sample and want to test a hypothesis about a single population proportion.
- Null Hypothesis (H0): States that the population proportion is equal to the hypothesized proportion.
- Alternative Hypothesis (H1): States that the population proportion is different from the hypothesized proportion (two-tailed test), or greater/less than (one-tailed test). This calculator performs a two-tailed test.
- P-value: The probability of observing a test statistic as extreme as, or more extreme than, the statistic obtained from a sample if the null hypothesis is true. A small p-value (typically ≤ 0.05) suggests evidence against the null hypothesis.
This tool uses the normal approximation to the binomial distribution, which is valid when both np0 and n(1-p0) are greater than or equal to 10.