I knew the formula but fumbled the intuition part.
State Bayes' theorem clearly and concisely, then walk through a concrete example that is relevant to the role, such as A/B testing or product analytics. Use the example to demonstrate how prior beliefs are updated with new evidence to produce a posterior probability.
Pro tip: Choose an example that ties directly to Lyft's business, like estimating the probability a rider is a repeat user given they took a ride during surge pricing, to show practical application. Avoid overly complex math; focus on intuition and business impact.
Write Bayes' theorem in its standard form: P(A|B) = P(B|A) * P(A) / P(B). Define each term clearly (prior, likelihood, marginal probability, posterior).
Choose a realistic example, such as determining whether a user will churn given they had a poor ride experience. Define events A and B and assign plausible probabilities.
Plug the numbers into Bayes' theorem, showing each step of the calculation. Explain what each component represents in the context of the example.
Explain what the posterior probability means in business terms. Discuss how the prior was updated by the evidence and what actions might follow.
Briefly mention how Bayesian reasoning applies to A/B testing, product analytics, or decision-making under uncertainty at Lyft.
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The 'does not tell you' part is where I stumbled.
Start by defining a p-value in intuitive terms, then clearly state what it does not mean, and finally connect it to the context of A/B testing at a company like Lyft. Use a concrete example to illustrate the concept and avoid technical jargon.
Pro tip: Emphasize that a p-value is not the probability that the null hypothesis is true, and that it must be interpreted alongside effect size and confidence intervals to make sound business decisions.
Explain that a p-value is the probability of observing data as extreme as (or more extreme than) what we saw, assuming the null hypothesis is true. Avoid saying it's the probability the null is true.
State that it does not measure the size or importance of an effect, nor the probability that the null hypothesis is true or that results are due to chance alone.
Relate it to experimentation: a low p-value suggests the observed difference is unlikely under the null, but it doesn't guarantee practical significance. Mention the need to consider effect size and business impact.
Provide a concrete example, such as testing a new feature at Lyft, to illustrate how a p-value is computed and interpreted in decision-making.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.