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A one-sided hypothesis test, also known as a one-tailed test, is a statistical test used to determine whether there is a significant difference between a sample mean or proportion and a known or hypothesized value in one direction. It is used when there is prior knowledge or a strong belief that the difference will occur in a specific direction.
In a one-sided hypothesis test, the alternative hypothesis only considers one direction of the difference, while a two-sided test considers both directions. This means that a one-sided test is more specific and has a higher power to detect a significant difference in the desired direction, but it cannot detect differences in the opposite direction.
The steps involved in conducting a one-sided hypothesis test are:
A critical value in a one-sided hypothesis test is a value that is compared to the test statistic to determine whether the null hypothesis can be rejected. It is derived from the chosen significance level and the degrees of freedom in the data. If the test statistic is greater than the critical value, the null hypothesis can be rejected in favor of the alternative hypothesis.
The p-value in a one-sided hypothesis test is the probability of obtaining a test statistic at least as extreme as the observed value, assuming that the null hypothesis is true. It is compared to the chosen significance level, and if it is less than or equal to the significance level, the null hypothesis can be rejected in favor of the alternative hypothesis. A smaller p-value indicates stronger evidence against the null hypothesis.