Being one tailed refers to a statistical concept that is commonly used in hypothesis testing. In statistical analysis, a one-tailed test is conducted when the researcher has a specific directional hypothesis. This means that the researcher expects a change or difference in a specific direction, either an increase or a decrease, but not both.
In a one-tailed test, statistical significance is assessed based on a single tail of the distribution. This is in contrast to a two-tailed test, where significance is assessed in both tails of the distribution. The tail of a distribution refers to the extreme ends of the distribution curve.
The decision to conduct a one-tailed test is typically made before data collection, based on prior knowledge or theoretical grounds. By specifying a one-tailed test, the researcher is essentially focusing on one specific hypothesis, while ignoring the possibility of an opposite outcome.
When analyzing the data, statistical tests are applied to calculate the probability of obtaining results as extreme or more extreme than what was observed, given the null hypothesis. If the obtained p-value is smaller than the predetermined significance level, typically denoted as α, the researcher concludes that the result is statistically significant.
In summary, being one tailed in statistical analysis refers to conducting a hypothesis test with a specific directional expectation, either an increase or a decrease, and evaluating the statistical significance based on observations in one tail of the distribution.
