When teaching statistics, use "normal" or "Gaussian"? 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?
 A: According to the Wolfram encyclopedia:

While statisticians and mathematicians uniformly use the term "normal distribution" for this
      distribution, physicists sometimes call it a Gaussian distribution and, because of its
      curved flaring shape, social scientists refer to it as the "bell curve."

I agree that "normal" is easier to confuse - yet I suspect statistics books usually use "normal".
A: I'd like to point out that S. Stigler uses Normal / Gauss / Laplace-Gauss distribution to prove 'Stigler's law of eponymy' published in Statistics on the Table (some pages are available on books.google).
Particularly interesting and relevant to this questions is that on pg 287-288 there are tables of the historical usage of 'Normal' vs 'Gauss' vs 'Laplace' and it seems that over the years the usage shifted from 2:15 in favor of normal in 1816-1884 to 8:14 (1888-1917) to 5:17 (1919-1939) to 9:10 (1947-1976). 
So according to this the usage of 'normal' vs 'Gauss' is getting more equal. Or if you believe that the trend will continue then 'Gauss' will beat 'normal' in 50-100 years.
A: An answer I haven't seen yet among all the good answers:
I mostly use "normal" for reasons of previous familiarity, but I like to capitalize it to emphasize its technical meaning: "... if the data are Normally distributed ..." (I don't know whether I copied this practice from somewhere else or (re-)invented it myself)
A: Which to use depends on the level of statistics being taught. Unfortunately, my teaching experience indicates that the majority of undergraduate students never fully grasp the concept of a probability distribution.  However, they all must somehow come to grips with the CLT and ways to think about uncertainty.  For an undergraduate class, Normal is preferable because it doesn't add the anxiety of a new unfamiliar word.  For graduate students Gaussian is preferred because of all the above mentioned confusion over normalization and the historical context that it provides. I teach a an undergraduate research class requiring two prerequisite statistics classes and all the undergraduate books that I have seen used over the last 30 years have used Normal. 
A: 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. 
A: The name normal came from some of the observations that errors behave normally. You will find more details here. If that is the reason to call this distribution a normal distribution, it may create new confusion as normal distribution for counts of accidents is poisson. I believe we should move forward and start calling it a Gaussian instead.
A: I would use Gaussian.
One problem that faces people learning statistics is that we use everyday English words to mean different things (power, significant, distribution etc). To the extent that we can minimize this, we should. "Normal" already has a bunch of meanings. 
A: One argument in favor of normal is the entrenched $N(\mu, \sigma^2)$ notation for the distribution, in which $N$ stands for "normal".  I haven't seen anyone propose changing this to $G(\mu, \sigma^2)$.
A: In German it is often called Gaußsche Normalverteilung so it is nearly impossible to conflict easily.
Would it be appropriate for you to combine gaussian and normal?
