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# One tailed and two tailed Test

When comparing two groups of continuous data, the null hypothesis is that there is no real difference between the groups (A and B).

One-tailed and two-tailed Test:

When comparing two groups of continuous data, the null hypothesis is that there is no real difference between the groups (A and B). The alternative hypothesis is that there is a real difference between the groups. This difference could be in either direction e.g. A > B or A < B. When there is some sure way to know in advance that the difference could only be in one direction e.g. A > B and when a good ground considers only one possibility, the test is called one-tailed test. Whenever we consider both the possibilities, the test of significance is known as a two-tailed test. For example, when we know that English boys are taller than Indian boys, the result will lie at one end that is one tail distribution, hence one tail test is used. When we are not absolutely sure of the direction of difference, which is usual, it is always better to use two-tailed test. For example, a new drug ‘X’

is supposed to have an antihypertensive activity, and we want to compare it with atenolol. In this case, as we don’t know exact direction of effect of drug ‘X’, so one should prefer two-tailed test. When you want to know the action of particular drug is different from that of another, but the direction is not specific, always use two-tailed test. At present, most of the journals use two-sided P values as a standard norm in biomedical research.

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