Explain p-values and interpret regressions

Stats Rapid-Fire: p-values, regression interpretation, L1/L2

Answer the following as if speaking to a PM and then to a technical audience.

Part A — p-values

1. Explain what a p-value is in plain language.
2. Give the formal definition of a p-value.
3. How should a p-value be interpreted (common misinterpretations to avoid)?
4. If you run an experiment and obtain p = 0.03 , what decision would you make? What else would you check before deciding?

Part B — Linear regression with confounding

You fit a linear regression with multiple features where you suspect confounding factors exist.

1. How do you interpret each parameter (coefficient) in the presence of confounding?
2. What’s the difference between associational and causal interpretation here?
3. What checks or approaches would you use to reduce confounding bias?

Part C — L1 vs L2 regularization

1. What are L1 (Lasso) and L2 (Ridge) regularization?
2. When would you prefer one over the other (feature selection, multicollinearity, prediction vs interpretability)?