When statisticians want to understand how two different models fit the data, they might compare the general linear model to the generalized linear model.
The former is a more flexible framework that can accommodate a wider range of data types, while the latter is specifically designed for data that follows a normal distribution.
In practice, however, the two models often perform similarly, so it can be difficult to determine which is the better model.
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