Final answer:
A one-tailed test with an alpha of .05 in one direction makes it easier to reject the null hypothesis than a two-tailed test which splits the alpha level, requiring more extreme data to be statistically significant.
Step-by-step explanation:
A one-tailed test, with the .05 all in one direction, makes it easier to reject the null hypothesis than a two-tailed test, which has to reach a .025 probability level at one of the ends for a difference to be statistically significant. When conducting hypothesis tests, the alternative hypothesis (Ha) is crucial in determining whether to use a left-tailed, right-tailed, or two-tailed test. A one-tailed test (either left or right) concentrates the entire alpha level (.05) in one tail of the distribution, thus requiring less extreme data to reject the null hypothesis (H0) compared to a two-tailed test where the alpha level is split between two tails (.025 in each tail).
A two-tailed test, as the name suggests, considers both directions of a distribution, and is appropriate when the alternative hypothesis includes the 'not equal to' (\(≠\)) sign. For example, if Ha: \(μ ≠ 88\), this would be a two-tailed test since we are looking for evidence the mean is either less than or greater than 88. This type of test requires more extreme evidence to reject H0 since the alpha level is divided across both ends of the distribution, making it less likely to occur by chance in either direction.