CandleFocus Glossary Editor
A one-tailed test is an important statistical hypothesis test used to assess whether or not a sample mean is different from a population mean. When using a one-tailed test, the analyst explicitly focuses on the likelihood of the relationship going in one particular direction and disregards the possibility of the relationship going in the other way. In other words, the analyst is testing to see if the sample mean is higher or lower than the population mean, but not both.
Since the test is focused on only one direction of the relationship, the null hypothesis is different from a two-tailed test. In a two-tailed test, the null hypothesis would state that the data has no relationship, whereas in a one-tailed test, the null hypothesis would state that the data has no relationship in the direction of interest. Moreover, the alternative hypothesis would state the opposite of the null hypothesis, which is that the data does have a relationship in the direction tested.
Before running a one-tailed test, an analyst has to pre-determine the probability value or the "p-value". A p-value is the probability of to observed result assuming that the null hypothesis is true. A p-value will be set at the beginning of the test, and if the test results are greater than or equal to that p-value, the analyst can reject the null hypothesis and accept the alternative hypothesis.
One-tailed tests are often used when investigators have an idea of the type of relationship they are looking for. For example, if a researcher wants to see if there is a positive relationship between exercise and health, she would use a one-tailed test because she is trying to see if the data shows a positive association.
In sum, a one-tailed test is a statistical hypothesis test used to assess whether or not a sample mean is different from a population mean. It is usually used when an analyst has a clear notion of the type of relationship they are testing for and is a good way to detect the direction of the relationship. To perform a one-tailed test, an analyst must create both a null and alternative hypothesis along with a predetermined p-value.
Since the test is focused on only one direction of the relationship, the null hypothesis is different from a two-tailed test. In a two-tailed test, the null hypothesis would state that the data has no relationship, whereas in a one-tailed test, the null hypothesis would state that the data has no relationship in the direction of interest. Moreover, the alternative hypothesis would state the opposite of the null hypothesis, which is that the data does have a relationship in the direction tested.
Before running a one-tailed test, an analyst has to pre-determine the probability value or the "p-value". A p-value is the probability of to observed result assuming that the null hypothesis is true. A p-value will be set at the beginning of the test, and if the test results are greater than or equal to that p-value, the analyst can reject the null hypothesis and accept the alternative hypothesis.
One-tailed tests are often used when investigators have an idea of the type of relationship they are looking for. For example, if a researcher wants to see if there is a positive relationship between exercise and health, she would use a one-tailed test because she is trying to see if the data shows a positive association.
In sum, a one-tailed test is a statistical hypothesis test used to assess whether or not a sample mean is different from a population mean. It is usually used when an analyst has a clear notion of the type of relationship they are testing for and is a good way to detect the direction of the relationship. To perform a one-tailed test, an analyst must create both a null and alternative hypothesis along with a predetermined p-value.