Conditional probability of IIDs $\{X_i\}$ are i.i.d. standard Normal variables, with mean 0 and standard deviation 1, $$S={1\over{n}}\sum_{i=1}^nX_i$$
What is the conditional probability $P(X_1|S\ge1)$?
Call $\varphi$ the standard normal PDF and $\Phi$ its CDF. For each $n\geqslant2$, the density $g_n$ of the desired conditional distribution is such that
  $$
g_n(x)=\frac{\varphi(x)\,\Phi\left(\frac{x-n}{\sqrt{n-1}}\right)}{\Phi(-\sqrt{n})}.
$$


To see this, fix a bounded measurable function $u$ and compute
$$
E(u(X_1); S\geqslant 1)=E(u(X_1);X_1+\sqrt{n-1}Y\geqslant n),
$$
where $Y=\frac1{\sqrt{n-1}}(X_2+\cdots+X_n)$ is standard normal and independent of $X_1$. Thus,
$$
E(u(X_1); S\geqslant 1)=\iint u(x)\mathbf 1_{x+\sqrt{n-1}y\geqslant n}\varphi(x)\varphi(y)\mathrm dx\mathrm dy.
$$
Note that, for every fixed $x$,
$$
\int\mathbf 1_{x+\sqrt{n-1}y\geqslant n}\varphi(y)\mathrm dy=1-\Phi\left(\frac{n-x}{\sqrt{n-1}}\right)=\Phi\left(\frac{x-n}{\sqrt{n-1}}\right),
$$
hence
$$
E(u(X_1)\mid S\geqslant 1)=\frac1{P(S\geqslant1)}\int u(x)\varphi(x)\Phi\left(\frac{x-n}{\sqrt{n-1}}\right)\mathrm dx.
$$
Finally, $S$ is centered normal with variance $n$ hence $P(S\geqslant1)=1-\Phi(\sqrt{n})=\Phi(-\sqrt{n})$, and the expression of $E(u(X_1)\mid S\geqslant 1)$ computed above, holds for every bounded measurable function $u$, hence the density $g_n$ follows.
As $N$ goes to infinity, this conditional probability does not make sense, because the probability of the event $\hat{S} \geq 1$ goes to 0 by the LLN.