Type I errors, also known as false positives, occur when a predictive model or statistical test incorrectly indicates that an effect has occurred when it actually hasn’t. For example:
- In a survey comparing the performance of Business Development Representatives (BDRs) with and without a sales engagement platform (SEP), a Type I error arises if the test suggests that BDRs with SEPs outperform those without when, in reality, there’s no reliable difference in their performance.
It’s important to note that false positive errors are managed by adjusting the threshold for statistical significance. Relaxing the threshold for statistical significance (say, from .05 to .10) increases the risk of false positive (Type I) errors.
For a description of Type II Errors, click here.
