How to choose probit or logit?

Model selection criteria are also valuable tools in discriminating between the logit and probit models. These criteria, such as the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC), provide a quantitative measure of the goodness of fit of each model. By comparing the criteria scores, researchers can select the model that best fits the data.

Discriminating between the logit and probit models involves evaluating their unique characteristics. One approach to this involves closely examining the properties of their respective distributions. This examination can reveal key differences that inform the choice of model for a given analysis.

Another strategy for distinguishing between the logit and probit models is to employ statistical inference techniques. These techniques allow researchers to compare the models and make informed decisions based on the results of either hypothesis testing or model selection criteria.

Hypothesis testing is a common method used in statistical inference. When applied to the logit and probit models, it involves testing specific assumptions about the models' parameters or distributions. The results of these tests can help determine which model better fits the data.

The choice between the logit and probit models ultimately depends on the specific research question and the characteristics of the data. Both models have their strengths and limitations, and the most appropriate model for a given analysis will vary depending on the context.