CFA Level 2 Exam 2025 – 400 Free Practice Questions to Pass the Test

In hypothesis testing, a Type 1 error occurs when the null hypothesis is rejected when it is actually true. This is often referred to as a "false positive," indicating that the test indicates a relationship or effect exists when it does not. It represents a situation where the test mistakenly signals a significant finding, leading to potentially incorrect conclusions about the data.

Understanding Type 1 errors is crucial since they can lead to erroneous decisions, especially in fields such as medicine or finance where a false positive can have significant consequences. The significance level (alpha) is directly related to the probability of making a Type 1 error; for example, if the significance level is set to 0.05, there is a 5% chance of making a Type 1 error.

This concept differentiates Type 1 errors from other types of errors in hypothesis testing. A false negative, for instance, relates to a Type 2 error, which occurs when the null hypothesis is not rejected when it is false. This illustrates the importance of balancing the risks of Type 1 and Type 2 errors when designing experiments or analyses, as both have their implications depending on the context of the test.