The two-tailed test gets its name from testing the area under both tails of a normal distribution, although the test can be used in other non-normal distributions. A one-tailed test is a statistical test in which the critical area of a distribution is one-sided so that it is either greater than or less than a certain. The main advantage of using a one-tailed test is that it has more statistical power than a two-tailed test at the same significance (alpha) level. In general a test is called two-sided or two-tailed if the null hypothesis is rejected for values of the test statistic falling into either tail of its.
A hypothesis test that is designed to show whether the mean of a sample is significantly greater than and significantly less than the mean of a population is referred to as a two-tailed test. One-Sided or One-Tailed Hypothesis Tests In most applications, a two-sided or two-tailed hypothesis test is the most appropriate approach. By convention two-tailed tests are used to determine significance at the 5% level, meaning each side of the distribution is cut at 2.5%.Ī basic concept of inferential statistics is hypothesis testing, which determines whether a claim is true or not given a population parameter.If the sample being tested falls into either of the critical areas, the alternative hypothesis is accepted instead of the null hypothesis.
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