A significance test in which two critical regions are defined, one in each tail of the probability distribution. Significance is achieved when the observed value of the test statistic is more extreme than either critical value. A two-tailed test is used when the sign of the test statistic is not specified by the alternative hypothesis. Thus a two-tailed test would be used if the alternative hypothesis were that two quantities are unequal, without prior specification of which is larger. Synonymous with nondirectional test.
a test of the prediction that two values are equal, or a test that they are not equal.
If there are two ways to disprove a null hypothesis, the statistical test is two-tailed. EX: “There is no correlation between age at first dental visit and DMF at age 30†is a two-tailed test – there are two ways to disprove it (finding a positive correlation or finding a negative correlation). The operational significance of a two-tail test comes in determining the p-value from the test statistic. Two-tail tests have higher p-values for a given test statistic than do one-tail tests. [See one-tailed test, null hypothesis
a common choice when doing a Chi-square analysis
hypothesis test in which the rejection region consists of both large values and small values of the test statistic. Such a test is appropriate against two-sided alternatives. (Note, however, that or chi-squared tests against two-sided alternatives are one-tailed.)
a statistical test in which the critical region for rejecting the null hypothesis falls in both directions of the probability distribution.
A test in which the alternative hypothesis specifies a deviation from the null hypothesis in either direction. The critical region is located in both ends of the distribution of the test statistic. It is also called a directional test.
(statistics) A statistical test of significance based on the null value of no difference versus the set of all alternative values (i.e., those that lie to the right and left of the null value).
The two-tailed test is a statistical test used in inference, in which a given statistical hypothesis will be rejected when the value of the statistic is either sufficiently small or sufficiently large. The test is named after the "tail" of data under the far left and far right of a bell-shaped normal data distribution, or bell curve. However, the terminology is extended to tests relating to distributions other than normal.
