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In statistics, a hypothesis test is a statistical test where you test an “alternative” hypothesis against a “null” hypothesis. The Role of Critical Values in Hypothesis Testsīefore we dive deeper, let’s do a quick refresher on hypothesis testing. Any test result greater than 1.96 falls into the rejection region in the distribution’s right tail, and any test result below -1.96 falls into the rejection region in the left tail of the distribution.Ī two-tailed Z-test with a 95% confidence level (or a significance level of ɑ = 0.05) has two critical values 1.96 and -1.96. The figure below shows how the critical values mark the boundaries of two rejection regions (shaded in pink). Gregory Matthews explains more about hypothesis testing and why to use it: In Outlier's Intro to Statistics course, Dr. We reject the null hypothesis in favor of the alternative hypothesis. In this test, if the statistician’s results are greater than 1.96 or less than -1.96.
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In a hypothesis test called a two-tailed Z-test with a 95% confidence level, the critical values are 1.96 and -1.96.
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Critical values vary depending on the type of hypothesis test you run and the type of data you are working with. While foul lines, poles, and the stadium fence mark off the foul territory in baseball, in statistics numbers called critical values mark off rejection regions.Ī critical value is a number that defines the rejection region of a hypothesis test. In statistics, we have something similar to a foul zone. In baseball, an ump cries “foul ball” any time a batter hits the ball into foul territory.