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     Z-test for One Population Proportion

Instructions: This calculator conducts a Z-test for one population proportion (p). Please select the null and alternative hypotheses, type the hypothesized population proportion \(p_0\), the significance level \(\alpha\), the sample mean, the population standard deviation, and the sample size, and the results of the z-test for one proportion will be displayed for you:

Ho: \(p\)     \(p_0\)

Ha: \(p\)      \(p_0\)

Hypothesized Population Proportion (\(p_0\)) =   

Sample Size (N) =

Number of favorable cases (X) = (If you know the sample proportion, type that instead)

Significance Level (\(\alpha\)) = (Ex: 0.01, 0.05, or 5, 10 without "%", etc)

More about the z-test for one population proportion so you can better interpret the results obtained by this solver: A z-test for one proportion is a hypothesis test that attempts to make a claim about the population proportion (p) for a certain population attribute (proportion of males, proportion of people underage). The test has two non-overlaping hypotheses, the null and the alternative hypothesis. The null hypothesis is a statement about the population proportion, which corresponds to the assumption of no effect, and the alternative hypothesis is the complementary hypothesis to the null hypothesis. The main properties of a one sample z-test for one population proportion are:

  • Depending on our knowledge about the "no effect" situation, the z-test can be two-tailed, left-tailed or right-tailed

  • The main principle of hypothesis testing is that the null hypothesis is rejected if the test statistic obtained is sufficiently unlikely under the assumption that the null hypothesis is true

  • The sampling distribution used to construct the test statistics is approximately normal

  • The p-value is the probability of obtaining sample results as extreme or more extreme than the sample results obtained, under the assumption that the null hypothesis is true

  • In a hypothesis tests there are two types of errors. Type I error occurs when we reject a true null hypothesis, and the Type II error occurs when we fail to reject a false null hypothesis

The formula for a z-statistic is

\[z = \frac{\bar p - p_0 }{\sqrt{p_0(1-p_0)/n}}\]

The null hypothesis is rejected when the z-statistic lies on the rejection region, which is determined by the significance level (\(\alpha\)) and the type of tail (two-tailed, left-tailed or right-tailed).

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