Explain core ML concepts and diagnostics

You are sitting in a machine learning breadth interview for a Senior Applied Scientist position. Give clear, practical answers to the conceptual questions below, covering definitions, when and why each idea matters, and frequent mistakes:

1. What does a p-value mean? What is the right way to interpret it, and what are common misinterpretations?
2. Define overfitting and underfitting. How do you recognize each one, and what steps can you take to reduce them?
3. What is causal inference? List and briefly explain the standard approaches.
4. In machine learning, what are encoding and decoding? Provide specific examples.
5. Give a high-level explanation of gradient descent and backpropagation.
6. What are vanishing and exploding gradients? What techniques help prevent or address them?
7. What is your approach for working with severely imbalanced data?
8. Give an example where a model shows 99% accuracy yet still performs badly. How would you evaluate it correctly and improve it?
9. What is an A/B test? If the results of an A/B test appear unusual or questionable, what could explain that, and how would you look into it?

Overview: This question tests command of fundamental machine learning concepts and diagnostic skills, including statistical inference with p-values, overfitting and underfitting along with bias-variance tradeoffs, causal inference methods, encoding and decoding, optimization via backpropagation, gradient stability, imbalanced data handling, evaluation metrics beyond accuracy, and A/B testing. Interviewers often use it to gauge both the range and depth of a candidate's machine learning knowledge, probing conceptual understanding and real-world application through theoretical questions, awareness of typical failure modes, and the diagnostic reasoning needed to validate models and experiments.