<P> In statistical significance testing, a one - tailed test and a two - tailed test are alternative ways of computing the statistical significance of a parameter inferred from a data set, in terms of a test statistic . A two - tailed test is appropriate if the estimated value may be more than or less than the reference value, for example, whether a test taker may score above or below the historical average . A one - tailed test is appropriate if the estimated value may depart from the reference value in only one direction, for example, whether a machine produces more than one - percent defective products . Alternative names are one - sided and two - sided tests; the terminology "tail" is used because the extreme portions of distributions, where observations lead to rejection of the null hypothesis, are small and often "tail off" toward zero as in the normal distribution or "bell curve", pictured on the right . </P> <P> One - tailed tests are used for asymmetric distributions that have a single tail, such as the chi - squared distribution, which are common in measuring goodness - of - fit, or for one side of a distribution that has two tails, such as the normal distribution, which is common in estimating location; this corresponds to specifying a direction . Two - tailed tests are only applicable when there are two tails, such as in the normal distribution, and correspond to considering either direction significant . </P>

When do you use a 1-tailed or 2-tail test
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