Explain and use p-values and regression regularization

Q: Explain and use p-values and regression regularization

Evaluates understanding of hypothesis testing (p-values), regression interpretation with confounders, and regularization techniques (L1 vs L2) within the Statistics & Math domain for a Data Scientist position, at an intermediate-to-advanced conceptual and applied level.

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Question

You are interviewing for a Senior Data Scientist role. Answer the following statistics questions clearly and precisely:

How would you explain a p-value to a Product Manager (PM) in plain language?
What is the formal definition of a p-value?
How should a p-value be interpreted? What are common misinterpretations?
If you run an A/B test and obtain p = 0.03 for the primary metric, how would you make a decision? What additional context would you request before shipping?
In a linear regression that includes potential confounders, how do you interpret each coefficient? What does “controlling for other variables” mean, and when can that interpretation fail?
When and why would you use L1 (Lasso) vs L2 (Ridge) regularization? Describe practical tradeoffs and how you would select the regularization strength.

Explain and use p-values and regression regularization

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