Define "false positive" and "false negative." What distinguishes them? Give examples of when false positives are more important than false negatives and vice versa, and when both types of mistakes are significant

Data Science, AI and ML
1

A false positive is when a condition is mistakenly identified as present when it is not.
A false negative occurs when a condition is mistakenly identified as absent when it is actually present.

When it comes to cancer screening, false negatives are more significant than false positives. It’s far worse to claim someone doesn’t have cancer when they do than to say they do and then find out later that they don’t.
Although this is a subjective argument, false positives can be more psychologically damaging than false negatives. A false positive for winning the lottery, for example, might be worse than a false negative because people don’t expect to win the lottery in the first place.