Between Logit and Probit Models

Requirements Clarification & Assessment

When preparing to answer the interview question "How do logit and probit models differ from each other?", it's crucial to understand the context and requirements of the question:

1. Purpose of the Models: Both models are used for binary classification problems, where the outcome variable is dichotomous (e.g., success/failure, yes/no).

2. Mathematical Foundation: The question seeks to differentiate the mathematical underpinnings and assumptions of each model.

3. Interpretation and Practical Use: Understanding how the models are interpreted and applied in real-world scenarios is essential.

4. Error Distribution Assumptions: A key aspect of the question is the distinction in error distribution assumptions between the two models.

5. Complexity and Computational Considerations: The question may also touch on the computational complexity and ease of implementation for each model.

6. Contextual Relevance: It's important to consider when each model might be preferred based on the data or specific application.