

\subsection{\bf 3.2\hspace{1em} Overview of $\chi^2$ Method}

\begin{slide}
\begin{itemize}
\item This is standard and (often) fully correct (best) method for
combining results from several experiments.

\item It is, in fact, used (because it is easy conceptually and for
computer) even when it is inapplicable,

\begin{description}
\item{$\bullet$} e.g., if there are correlations between supposed
``independent'' random variables
\item{$\bullet$} or if the basic experiments have too few events to be
Gaussian.
\end{description}

\item The problem to be solved is: given $N$ experimental measurements
$x_i$ and errors $\sigma_i$ plus estimates $\xi_i$ of the $x_i$, which
are functions of $n_T$ theoretical parameters.

\begin{description}
\item{$\bullet$} Find best values of theoretical parameters.
\end{description}
\end{itemize}
\end{slide}

