

\subsection{\bf Typical Confidence Level Distributions}
\begin{slide}
\begin{itemize}
\item If one can do the ``errors'' right, the resultant distribution
in $C(\chi^2)$ should be flat

% \vspace*{10pt}
\centerline{\psfig{figure=fig21.phys.ps,clip=true,width=3.25in}}
% \vspace*{10pt}

\item But if you have ``background,'' i.e., events that do not satisfy
claimed hypothesis, then one will have an excess of events at large
$\chi^2 >> (n-m)$ or small $C(\chi^2)$.  

\item $C(\chi^2)$ will look like

\vspace*{10pt}
\centerline{\psfig{figure=fig22.phys.ps,clip=true,width=3.25in}}
\vspace*{10pt}

\item The plot of $C(\chi^2)$ is a good way of estimating background.
\end{itemize}
\end{slide}

