This is the description of logistic regression and probit regression as
generalized linear model from wiki:
Both the logistic and normal distributions are symmetric with a basic unimodal, "bell curve" shape. The only difference is that the logistic distribution has somewhat heavier tails, which means that it is less sensitive to outlying data (and hence somewhat more robust to model mis-specifications or erroneous data).
I don't understand the conclusion
somewhat heavier tails, which means that it is less sensitive to outlying data. Heavier tails means the relatively high probability of extreme values (outliers). Why do we say it is less sensitive to the extreme values?