If you want to know whether one population mean is greater than or less than the other, perform a one-tailed t test. You have three scenarios, and the scenarios you are testing for go in different directions, so then you’d need a two-tailed hypothesis. One-tailed or two-tailed t test If you only care whether the two populations are different from one another, perform a two-tailed t test. If vanilla ice cream is supposed to count for exactly 30% of all sales, and 465 customers out of 2006 ordered vanilla, then you’d have 3 scenarios: one where 465/2006 is exactly 30% of all sales, greater than 30% of all sales, or less than 30% of all sales. Null Hypothesis H0 : Statement being tested Claim about or historical. Reject the null hypothesis if test statistics fall on either side. Therefore, you only need a one-tail to see if 465/2006 is or is not greater than 30%. A two-tailed test of hypothesis will reject the null hypothesis Ho, if the sample statistic is significantly higher than or lower than the hypothesized. In a two-tailed test, extreme values above or below are evidence against the null hypothesis. There’s only two options: 465/2006 is greater than 30%, or it isn’t. 05 when the sample mean (or difference between two sample means) falls in the outer parts of the distribution tails depicted below. The one-tailed version of the test is no different from the two-tailed version, but for one small tweak: when we come to find the p-value value to judge the. For example, if you were told that vanilla ice cream accounts for greater than 30% of all sales at the ice cream shop, and 465 out of 2006 people who visited the shop ordered vanilla, your null hypothesis would be that vanilla ice cream sales, or 465/2006, is greater than 30%. A one-tailed hypothesis is what you use when you’re testing for the relationship between your variables in a single direction.
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