I use mostly "Gaussian distribution" in my book, but someone just suggested I switch to "normal distribution". Any consensus on which term to use for beginners? 

Of course the two terms are synonyms, so this is not a question about substance, but purely a matter of which term is more commonly used. And of course I use both terms. But which should be used mostly?

Even though I tend to say 'normal' more often (since that's what I was taught when first learning), I think "Gaussian" is a better choice, as long as students/readers are quite familiar with both terms:


The normal isn't particularly typical, so the name is itself misleading. It certainly plays an important role (not least because of the CLT), but observed data is much less often particularly near Gaussian than is sometimes suggested.
The word (and associated words like "normalize") has several meanings that can be relevant in statistics (consider "orthonormal basis" for example). If someone says "I normalized my sample" I can't tell for sure if they transformed to normality, computed z-scores, scaled the vector to unit length, to length $\sqrt{n}$, or a number of other possibilities. If we tended to call the distribution "Gaussian" at least the first option is eliminated and something more descriptive replaces it.
Gauss at least has a reasonable degree of claim to the distribution. 

