Fake news

What this Bayes' theorem interview question tests

This is an easy probability question that tests your ability to apply Bayes' theorem to update beliefs in light of observed evidence. It's the canonical "reverse probability" setup: you know the prior likelihood of fake vs. real news, and the likelihood of seeing 100+ likes under each scenario. Your job is to compute the posterior probability that a story is real given that you've observed it crossed the 100-like threshold.

Firms ask questions like this to see whether you can recognize when conditioning matters and set up the calculation cleanly. The core skill is identifying what you know (priors and likelihoods) and what you want to find (a posterior), then applying the formula without algebraic mistakes.

• Prior vs. likelihood vs. posterior probability
• The law of total probability
• Bayes' theorem and belief updating