How might a type I error arise?

There are several ways in which a Type I error might arise. For example:

• Setting a higher significance level increases the chance of rejecting the null hypothesis, making it more likely to commit a Type I error.
• When conducting multiple hypothesis tests simultaneously, the likelihood of making at least one Type I error across all the tests increases
• Outliers or extreme data points can disproportionately influence the results and lead to an erroneous rejection of the null hypothesis.

How might a type II error arise?

As a researcher, there are two main reasons that you are not able to come to the correct conclusion:

• It is too hard to gather enough evidence to reject the null hypothesis. This is typically caused by a small sample size.
• It could be a case where the researcher simply had an “unlucky” sample that resulted in a large P-value.
• Poorly designed experiments with insufficient control groups can lead to a higher chance of Type II errors.
• If the assumptions underlying the statistical test are not met, the test may not perform optimally, leading to an increased risk of Type II errors.