When alternative hypothesis is in less than or greater than form test is used. (A). OOne-tailed test (B). OTwo-tailed test (C). OThree tailed test (D). ONone of the above
Answers
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Explanation:
A one-tailed test has the entire 5% of the alpha level in one tail (in either the left, or the right tail). A two-tailed test splits your alpha level in half (as in the image to the left). Let's say you're working with the standard alpha level of 0.5 (5%). A two tailed test will have half of this (2.5%) in each tail.
In all tests of hypothesis, there are two types of errors that can be committed. The first is called a Type I error and refers to the situation where we incorrectly reject H0 when in fact it is true. This is also called a false positive result (as we incorrectly conclude that the research hypothesis is true when in fact it is not). When we run a test of hypothesis and decide to reject H0 (e.g., because the test statistic exceeds the critical value in an upper tailed test) then either we make a correct decision because the research hypothesis is true or we commit a Type I error. The different conclusions are summarized in the table below. Note that we will never know whether the null hypothesis is really true or false (i.e., we will never know which row of the following table reflects reality).