Both models are used for similar types of data and often yield comparable results. However, the choice between the two can depend on several factors:
Interpretability: The logit model's coefficients can be interpreted in terms of odds ratios, which are often more intuitive in clinical and public health contexts. Distribution Assumptions: The probit model assumes a normal distribution of the error terms, whereas the logit model assumes a logistic distribution. Software and Convergence: Some statistical software might favor one model over the other in terms of convergence and computational efficiency.
