How many of you happen to know the difference right now without having to look it up? I'm just wondering if my case is unusual or if this is more prevalent than I thought.

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How many of you happen to know the difference right now without having to look it up? I'm just wondering if my case is unusual or if this is more prevalent than I thought.

How many of you happen to know the difference right now without having to look it up? I'm just wondering if my case is unusual or if this is more prevalent than I thought.

In a sentence, the general linear model is just the standard linear model form as we all know it Y=AX+E (Where X is the design matrix and A a matrix of parameters ) used in many procedures: ANOVA, ANCOVA ect and fit with OLS (with all normal assumptions). But in generalized linear modeling, the key difference is a relaxation of the OLS; i.e. the errors do not need to follow a normal distribution, but may follow any error model of your choice.

I totally agree with you that the names are not very useful. Lets think of some better names and start a trend, by stubbornly refusing to use the old names:yup:

I totally agree with you that the names are not very useful. Lets think of some better names and start a trend, by stubbornly refusing to use the old names:yup:

I did know the difference by the way but I wouldn't have been able to tell you the difference a year and a half ago...

Recently, I have been hearing more and more about how you ideally want to make the fewest assumptions you can about your data, which can help with avoiding model miss-specification. This seems to push one more towards non-parametric approaches (e.g., non-parametric bootstrapping, etc.).