I went with the classic disease-test setup because it's easy to plug numbers into, but I fumbled the denominator for a bit.
Choose a concrete, relatable example—ideally from Snapchat's domain like ad click-through or spam detection—and clearly define the prior and likelihoods before plugging into Bayes' theorem. Walk through the arithmetic step-by-step, narrating each component, and end with a clear numeric posterior and its practical interpretation.
Pro tip: Use a Snapchat-relevant example (e.g., detecting spam accounts or predicting ad engagement) to show product sense, and sanity-check your result by comparing it to the base rate—this demonstrates statistical maturity.
Select a simple, realistic scenario relevant to Snapchat, such as whether a user will tap on an ad given their past behavior. State the event of interest and the available evidence.
Clearly assign numeric values to the prior probability P(H) and the conditional probabilities P(E|H) and P(E|¬H). Explain what each represents in your example.
State Bayes' theorem: P(H|E) = [P(E|H) * P(H)] / [P(E|H)*P(H) + P(E|¬H)*P(¬H)]. Show the denominator as the total probability of the evidence.
Plug in the numbers and perform the arithmetic step-by-step, simplifying fractions or decimals as needed. Arrive at a numeric posterior probability.
Explain what the posterior means in context (e.g., 'There is a 75% chance the user will tap the ad'). Compare it to the prior to show how the evidence updated the belief.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.