The Probit model differs fundamentally from the Logit model in that:

Both Logit and Probit models are commonly used when the dependent variable is binary. For example: Y = 1 if an individual is employed Y = 0 if an individual is unemployed Both models are generally estimated using Maximum Likelihood Estimation (MLE). The fundamental difference between the two models concerns the assumed probability distribution. Probit Model: The Probit model assumes that the latent error term follows a standard normal distribution: ε ~ N(0,1) Its link function is based on the Standard Normal Cumulative Distribution Function, commonly represented by Φ. Logit Model: The Logit model assumes that the error follows a logistic distribution. The standard logistic distribution has variance: π² / 3 Its link function is based on the Logistic Cumulative Distribution Function. Therefore, the key difference between Logit and Probit lies in the assumed underlying probability distribution.