Compute posterior fake probability using Bayes' rule
MetaData Scientist · Technical Screen
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The problem evaluates understanding of Bayes' rule and conditional probability within the Statistics & Math domain for a Data Scientist role, at an introductory applied probability/interpretation level.
A platform runs an automated detector to flag fake accounts.
- Prior probability an account is fake: (P(F)=0.02).
- True positive rate (sensitivity): (P(\text{Flag}\mid F)=0.90).
- False positive rate: (P(\text{Flag}\mid\neg F)=0.05).
If an account is flagged, what is the probability it is actually fake, (P(F\mid \text{Flag}))? Show your calculation.
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