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There are other nice properties. For example, because the logit link is canonical for the binomial GLM, inference about unknown parameters using it is based on sufficient statistics.

It's certainly not the only option though, and not always the best fit.

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Ah yep, I forgot it's the canonical link. That's more of a small computational convenience though, right, at least when fitting a straightforward GLM -- it should be very cheap to fit regardless.

I suppose the logistic having heavier tails than the normal is probably the main consideration in motivating one or the other as the better model for a given situation.

Logistic being is heavier-tailed, is potentially more robust to outliers. Which in terms of binary data, means that it might be a better choice in cases where an unexpected outcome is possible even in the most clear-cut cases. Probit regression with its heavier normal tails, might be a better fit in cases where the response is expected to be pretty much deterministic in clear-cut cases, and where quite severe inferences can be drawn from unexpected outcomes in those cases. Sound fair?

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