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\section{Introduction}  While investigating the $\sim 7\%$ bias Nick was seeing in his velocity dispersion plot, it became obvious that this bias is a result of binning a sample with scatter in the observables and inherant functions in mass. Originally we thought these relationships would not manifest themselves in the median or mean values of the true observables (table values) that we compare with, but in  this walk through shows we will show  otherwise. There are several key probabilities we need to know in order to accurately predict the scatter/bias expectation value  of some observable with mass. In more statistical language, we must would like to  know $P(\hat{\theta}|M)$. That is, given a cluster of mass $M$, what is the probability the observable is detected as $\hat{\theta}$. For example, the observable $\hat{\theta}$ can be velocity dispersion, some richness, or total luminosity. Let's take velocity dispersion as an example here. There are two relevant velocity dispersions in our studies. The first is the true 3D/2D velocity dispersion $\sigma$ which we cannot measure in the real universe. \cite{Evrard08} measured $\sigma$ for halos in N-body simulations and showed that they relate to the critical mass $M_{200}$ of the host halo on a very tight relation  \begin{equation}  \sigma = 1093 \left(\frac{h(z) M}{1e15 M_{\odot}}\right)^{0.34}  \end{equation} This relationship has a very small scatter of $S_{\sigma | M}\sim 4\%$.