The choice between a one-tailed and a two-tailed test affects how we set up and interpret hypothesis testing. In a one-tailed test, we look for an effect in only one direction, either greater than or less than the hypothesized value. This makes it easier to reject the null hypothesis in that direction, but it ignores the possibility of an effect in the opposite direction. In a two-tailed test, we check for differences in both directions, whether the value is higher or lower than expected. Since the significance level is split between two ends of the distribution, it is harder to reject the null hypothesis compared to a one-tailed test. In short, one-tailed tests are more powerful for detecting a specific direction of change, while two-tailed tests are more general and cautious.