# One Proportion Z Test Calculator Run a one-proportion Z test from success count, trial count, and hypothesized population proportion > Canonical page: https://elysiatools.com/en/tools/one-proportion-z-test-calculator - **Category:** Math & Numbers - **Keywords:** one proportion z test, proportion test, conversion rate, z statistic, p value, calculator ## Overview The One Proportion Z Test Calculator allows you to quickly determine if an observed sample proportion significantly differs from a hypothesized population proportion. By entering your success count, total trials, and expected proportion, this tool calculates the sample proportion, Z-statistic, and P-value to help you evaluate your statistical hypothesis with precision. ## Inputs - **Success Count** (number) - **Trial Count** (number) - **Hypothesized Proportion** (number) - **Alternative Hypothesis** (select) - **Alpha** (number) - **Decimal Places** (number) ## When to use - When comparing an observed conversion rate or success rate against a known industry standard. - When evaluating if a manufacturing defect rate significantly exceeds an acceptable baseline threshold. - When analyzing survey data to see if a specific demographic's response proportion differs from historical averages. ## How it works - Enter the number of successful events (Success Count) and the total number of observations (Trial Count). - Input the expected baseline rate as the Hypothesized Proportion (e.g., 0.5 for 50%). - Select your alternative hypothesis (two-sided, greater, or less) and set your desired significance level (Alpha). - The calculator computes the sample proportion, Z-statistic, and P-value, indicating whether to reject the null hypothesis. ## Use cases - A marketing team testing if a new landing page's conversion rate is significantly higher than their historical 5% baseline. - A quality assurance manager checking if a batch of products has a defect rate greater than the acceptable 2% limit. - A political analyst determining if the proportion of voters supporting a specific policy differs from a previously established 50% benchmark. ## Frequently asked questions ### What is a one-proportion Z-test? It is a statistical test used to determine whether the proportion of successes in a single sample significantly differs from a known or hypothesized population proportion. ### What is the difference between a two-sided and one-sided test? A two-sided test checks for any difference (greater or less) from the hypothesized proportion. A one-sided test specifically checks if the sample proportion is strictly greater than or less than the hypothesized value. ### What does the P-value indicate? The P-value measures the probability of observing your sample results if the null hypothesis is true. A P-value lower than your Alpha (typically 0.05) suggests the difference is statistically significant. ### What is the Alpha value? Alpha is the significance level, representing the probability of rejecting the null hypothesis when it is actually true. The standard default is 0.05, which represents a 5% risk. ### Can I use this calculator for small sample sizes? 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