Hi everyone…I’m gettin into data analysis and keep coming across terms like ‘false positive’ and ‘false negative.’ Could someone please explain the difference between the two? How do they impact data analysis and decision-making? Thanks…
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False positives and false negatives are clear in meaning. However, I often struggle with remembering which is Type I and which is Type II error.
Types 1 and 2 originate from theoretical statistics, whereas sensitivity and specificity are primarily drawn from psychometrics. In machine learning, these metrics are utilized more frequently than false positives and negatives, which appears to be the de facto terminology.
Thus, it just depends on your upbringing.