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

In the context of reducing Type I error in modeling, prioritizing a model with high precision is key. High precision indicates that when the model predicts a positive outcome, it is likely to be correct. Essentially, this means that the proportion of true positive results among all positive predictions is high, which directly minimizes the chances of incorrectly rejecting the null hypothesis—this is the essence of a Type I error.

By focusing on high precision, an analyst ensures that the model is more conservative with its positive predictions. This conservatism is particularly important when the cost of false positives is substantial, as it leads to greater confidence in the positive classifications that the model makes.

Other options, such as a model with high recall, are not as effective in reducing Type I errors because high recall focuses more on capturing as many true positives as possible, which can lead to a higher number of false positives in the process. Similarly, high complexity does not automatically equate to effective error reduction; it can lead to overfitting, which complicates the model’s ability to generalize and could inadvertently increase Type I errors.

Thus, prioritizing high precision is crucial in reducing Type I errors, ensuring that only the most certain positive predictions are made, thereby avoiding erroneous conclusions based on