Two-tailed tests have become increasingly common in the fields of science, economics and medicine in recent times and are used to ascertain the likelihood of a certain phenomenon happening given certain conditions.

A two-tail test is based on the idea that due to variance between different samples, the findings from each test may be different from that of the next. To hedge against this, researchers use a two-tailed test to create two critical regions in the data: one for if the sample is greater than the distribution and one for if the sample is smaller than the distribution.

In two-tailed testing, these two critical regions are inverse of one another (one is higher, one lower). If the sample is greater than the upper critical region, then the data rejects the null hypothesis and the alternative hypothesis is accepted. Conversely, if the sample is lower than the lower critical region, the data again rejects the null hypothesis and the alternative hypothesis is accepted. This allows researchers to appeal to both sides of the data, or what they are looking to possibly reject or accept. Two-tailed tests are significantly more versatile than one-tailed tests as they allow researchers to test for both “more” and “less” than a range of values.

In essence, two-tailed tests allow researchers to create two critical regions in the data to determine likelihood. This is very useful when a researcher is looking to test whether a certain phenomenon has happened due to certain conditions and circumstances, as they provide a more accurate representation of the data that would otherwise be obstructed. Two-tailed tests are a very helpful tool for researchers in any field and can provide a more accurate measurement of their data without having to rely on just one-tailed tests. They offer greater versatility and allow researchers to appeal to both sides of a data set, and are especially useful when researchers are looking to determine the probability of a certain phenomenon occurring.