There has been much discussion and speculation about false positives and false negatives, and not all data scientists are in 100% agreement with what is interpreted as a false negative and false negative. Let's say for example you were diagnosed with not having a disease, but after numerous tests, it was verified and confirmed that the disease was in fact still present. That would be considered a false positive. If on the other hand you were diagnosed with a disease, but found out later that you didn't have a disease, which would be considered a false negative. In this type of interview situation, the hiring manager is looking for two things. Your opinion and explanation about false positives and false negatives when it comes to dealing with data management.
Data results can be misleading at times, and you need to be able to explain why this is the case, and in your opinion how the data is viewed negatively or positively based on whatever tests were run to arrive at the result from the data. It would help to offer your perspective to the interviewer on the way you determine whether you think it's better to have too many false positives or too many false negatives to sort out.
