Type I vs. Type II error: What’s more important?

A type I error, known as a false positive, occurs when a researcher incorrectly rejects a true null hypothesis. This means that you report that your findings are significant whereas in fact they have occurred only by chance. A type II error, known as a false negative, occurs when a researcher fails to reject a null hypothesis which is actually false. Therefore, the researcher concludes that there is not a significant effect whereas there really is.

 When a false positive is more important than a false negative?

A typical example is in the medical field. Let’s assume you have to give chemotherapy to patients. Based on the lab prediction, you think the patient is positive for cancer, but he actually doesn’t have cancer. This is a case of false positive. It is very dangerous to start chemotherapy on this patient when he doesn’t have cancer as chemotherapy will do certain damage to his normal healthy cells, leading to severe diseases or even cancer.

When a false negative more important than a false positive?

Let’s assume there is an airpot “A” having high-security threats. Based on the system, they identify whether a particular passenger can be a threat or not. Due to a shortage of staff, one day they decide to only scan passengers being predicted as risk positives by their prediction model. However, we all know that it is impossible for any model to return a 100% accuracy. Therefore, what will happen if a true threat passenger is flagged as a non-threat by the airport’s prediction model?

When a false negative and a false positive are equally important?

In the banking industry, one classic problem is to determine who to give loans to. Banks do not want to lose good customers and at the same time, they do not want to acquire bad customers. Let’s assume a good customer is ‘1’ and a bad customer is ‘0’. False positives in this case mean the bank decides to give loans to a bad customer. False negatives mean the bank decides not to give loans to a good customer. Both of these scenarios are equally damaging to the bank.