The assumption that the sum/difference of 2 dependant variables
will be normal requires the assumption that there are no other
variables that affect the endogeneous variable and are also
correlated with the exogeneous variable.
That means if changes in X causes changes in Y, and there is a
variable Z that affects Y but is also correlated to X, then the
sampling distribution of the sum/difference of X and Y will not be
normal.
An easier way would be that if the causality runs both
directions, ie X affects Y and Y affects X. Then the sum/difference
will also not be normally distributed.
Now that the tools have been given, it would be your job to
relate an example because there's plenty of them around us.
Thanks.
I know how to prove it mathematically at U level and I can
easily construct a few theoretic models to show it, but here
I need a specific example to illustrate this fact to JC
students. Your first paragraph to them will be some kind of alien
language already, they will not understand why there must be no
other variables that affect the 'endogeneous' (alien to them again)
variable in order to make the sum/different follow normal.
What I need is something like this:
In the context of a real-life example, X denotes ... Y
denotes...., when X and Y both follow normal distribution, it is
obvious that X+Y(or X-Y) does not follow normal, and the reason is
that X and Y here are dependent. 'Obvious' means JC students
should be able to see it without doing any mathematical
proof/reasoning.