In hypothesis testing, what happens when the significance level is set too high?

When the significance level is set too high, it increases the likelihood of Type I errors. The significance level, often denoted as alpha (α), is the threshold used to determine whether to reject the null hypothesis. A common setting for alpha is 0.05, suggesting that there is a 5% risk of concluding that a difference exists when there is actually no difference. If this threshold is increased, for example to 0.10, it means that a larger portion of the data could lead to the rejection of the null hypothesis.

Consequently, this elevated threshold allows for a greater probability of falsely identifying a statistically significant effect when there isn't one, leading to more Type I errors, which are false positives. This situation is particularly problematic in research because it can lead to incorrect conclusions about the effectiveness of a treatment or intervention.

In contrast, lowering the significance level would reduce the chances of Type I errors but could increase the chances of Type II errors, which occur when a true hypothesis is incorrectly accepted. Thus, adjusting the significance level directly influences the balance between these types of errors in hypothesis testing.