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A one proportion z-test is a statistical test that is used to compare a given single proportion to a given population proportion. It is performed in R by first specifying the given population proportion, then calculating the z-score using the number of successes, number of trials, and population proportion. The z-score is then used to calculate the p-value which is used to determine the significance of the results. Examples of how to perform a one proportion z-test in R are provided in the article.

A one proportion z-test is used to compare an observed proportion to a theoretical one.

This test uses the following null hypotheses:

**H**p = p_{0}:_{0}(population proportion is equal to hypothesized proportion p_{0})

The alternative hypothesis can be either two-tailed, left-tailed, or right-tailed:

**H**p ≠ p_{1}(two-tailed):_{0}(population proportion is not equal to some hypothesized value p_{0})**H**p < p_{1}(left-tailed):_{0}(population proportion is less than some hypothesized value p_{0})**H**p > p_{1}(right-tailed):_{0}(population proportion is greater than some hypothesized value p_{0})

The test statistic is calculated as:

z = (p-p_{0}) / √p_{0}(1-p_{0})/n

where:

**p:**observed sample proportion**p**hypothesized population proportion_{0}:**n:**sample size

If the p-value that corresponds to the test statistic z is less than your chosen significance level (common choices are 0.10, 0.05, and 0.01) then you can reject the null hypothesis.

**One Proportion Z-Test in R**

To perform a one proportion z-test in R, we can use one of the following functions:

**If n ≤ 30:**binom.test(x, n, p = 0.5, alternative = “two.sided”)**If n> 30:**prop.test(x, n, p = 0.5, alternative = “two.sided”, correct=TRUE)

where:

**x:**The number of successes**n:**The number of trials**p:**The hypothesized population proportion**alternative:**The alternative hypothesis**correct:**Whether or not to apply Yates’ continuity correction

The following example shows how to carry out a one proportion z-test in R.

**Example: One Proportion Z-Test in R**

**p**hypothesized population proportion = 0.60_{0}:**x:**residents who support law: 64**n:**sample size = 100

Since our sample size is greater than 30, we can use the **prop.test() **function to perform a one sample z-test:

prop.test(x=64, n=100, p=0.60, alternative="two.sided") 1-sample proportions test with continuity correction data: 64 out of 100, null probability 0.6 X-squared = 0.51042, df = 1, p-value = 0.475 alternative hypothesis: true p is not equal to 0.6 95 percent confidence interval: 0.5372745 0.7318279 sample estimates: p 0.64

From the output we can see that the p-value is **0.475**. Since this value is not less than α = 0.05, we fail to reject the null hypothesis. We do not have sufficient evidence to say that the proportion of residents who support the law is different from 0.60.

The 95% confidence interval for the true proportion of residents in the county that support the law is also found to be:

**95% C.I. = [0.5373, 7318]**

Since this confidence interval contains the proportion **0.60**, we do not have evidence to say that the true proportion of residents who support the law is different from 0.60. This matches the conclusion we came to using just the p-value of the test.

An Introduction to the One Proportion Z-Test

One Proportion Z-Test Calculator

How to Perform a One Proportion Z-Test in Excel