← Meta Interview Insights

Meta·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
Jul 2026

Summary

Meta data scientist interview with a probability question that sounds deceptively simple but requires you to actually think through Bayes properly. One question, pretty focused.

Questions Asked (1)

Q1

Lazy reviewers (20% of all reviewers) always leave positive feedback. Careful reviewers (the other 80%) leave positive feedback 60% of the time. Given that a review is negative, what is the probability it came from a lazy reviewer?

Product Analytics & MetricsAlgorithms & Data Structures
Author's notes

This one tripped me up for a second because the answer feels like it should be zero (lazy reviewers never go negative), and it basically is.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Recognize this as a Bayes' theorem problem where you need to find P(Lazy | Negative). First compute the overall probability of a negative review using the law of total probability, then apply Bayes' theorem to find the conditional probability.

Pro tip: State your assumptions clearly (e.g., independence of reviews) and walk through the calculation step-by-step to show your reasoning. Also, mention that the result is counterintuitive because despite lazy reviewers being only 20% of the population, they contribute a significant portion of negative reviews due to their zero false negative rate.

1. Define events and probabilities

Let L be the event that a reviewer is lazy, C for careful, and N for a negative review. Given: P(L)=0.2, P(C)=0.8, P(Positive|L)=1, so P(Negative|L)=0; P(Positive|C)=0.6, so P(Negative|C)=0.4.

2. Compute total probability of negative review

Use the law of total probability: P(N) = P(N|L)P(L) + P(N|C)P(C) = 0*0.2 + 0.4*0.8 = 0.32.

3. Apply Bayes' theorem

Compute P(L|N) = P(N|L)P(L) / P(N) = 0*0.2 / 0.32 = 0.

4. Interpret the result

The probability is 0, meaning that given a negative review, it is impossible that it came from a lazy reviewer. This is because lazy reviewers never leave negative feedback.

Key Points to Mention

  • Bayes' theorem and its application to conditional probability
  • Law of total probability to find the marginal probability of a negative review
  • The complement rule: P(Negative|Lazy) = 1 - P(Positive|Lazy) = 0
  • The base rate fallacy: the proportion of lazy reviewers (20%) might mislead, but the conditional probability is determined by the likelihoods
  • The importance of checking for zero probabilities and their implications
  • Clear communication of the final answer: 0% probability

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.