Earlier today i came across something that i could not quite understand so i figured maybe someone here sits on enough knowlege to help me out. The basic question is that one should decide what $a_s$ should be in the expression below such that the entire expression converges in distribution towards $Y$ where practiacally nothing is known about $Y$.
$$\frac {\overline{X} - \mu}{a_s \cdot s}$$
I first figured that one could simply set $a_s = \frac 1{\sqrt n}$ and use the central limit theorem ad say that it converges into a $N(0,1)$ distribution.
However my professor chose to extend the expression isntead such that:
$$\frac {\overline{X} - \mu}{a_s \cdot s} =\frac {\overline{X} - \mu}{\frac s {\sqrt{n}}} = \frac {\overline{X} - \mu}{\frac \sigma {\sqrt{n}}} \cdot \frac 1 {\sqrt{\frac {s^2(n-1)}{\sigma^2 (n-1)}}}$$
and then went on to deduct the final distribution of $Y$.
What i dont understand is first of all why the last two expressions in the equation are equal, and why the sample variance cant be used in the central limit theorem.