Select the alternative hypothesis or specify the significance level for the test.
Less than: Use this one-sided test to determine whether the population proportion is less than the hypothesized proportion. This one-sided test has greater power than a two-sided test, but it cannot detect whether the population proportion is greater than the hypothesized proportion.
For example, an engineer uses this one-sided test to determine whether the proportion of defective parts is less than 0.001 (0.1%). The one-sided test has greater power to determine whether the proportion is less than 0.001, but it cannot detect whether the proportion is greater than 0.001.
Not equal: Use this two-sided test to determine whether the population proportion differs from the hypothesized proportion. This two-sided test can detect differences that are less than or greater than the hypothesized value, but it has less power than a one-sided test.
For example, a bank manager tests whether the proportion of customers who have savings accounts this year differs from last year's proportion, 0.57 (57%). Because any difference from last year's proportion is important, the manager uses this two-sided test to determine whether this year's proportion is greater than or less than last year's proportion.
Greater than: Use this one-sided test to determine whether the population proportion is greater than the hypothesized proportion. This one-sided test has greater power than a two-sided test, but it cannot detect whether the population proportion is less than the hypothesized proportion.
For example, a quality analyst uses this one-sided test to determine whether the proportion of acceptable electrical switches is greater than 0.98. This one-sided test has greater power to determine whether the proportion is greater than 0.98, but it cannot determine whether the proportion is less than 0.98.
For more information on selecting a one-sided or two-sided alternative hypothesis, go to About the null and alternative hypotheses.
Use the significance level to minimize the power value of the test when the null hypothesis (H_{0}) is true. Higher values for the significance level give the test more power, but also increase the chance of making a type I error, which is rejecting the null hypothesis when it is true.