-Smoothing of the sampled curve $f_s(x)$ (by application of a moving average) will reduce the deviation of points the smooth curve which best fits the data. As shown in Figures \ref{siI.eps} and \ref{siI_tcs.eps}, smoothing of $f_s(x)$ has a far greater effect on the derivative of $f_s$ than on $f_s$ itself.
+Smoothing of the sampled points $f_s(x)$ (by application of a moving average) will reduce the deviation of points the smooth curve which best fits the data; We can think of the points $f_s(x)$ as sampling a \emph{different} function to $f(x)$, but with smaller uncertainties. Smoothing of the original sampled points removes fine structure.
+
+The alternative is to increase $h$.
+
+As shown in Figures \ref{siI.eps} and \ref{siI_tcs.eps}, smoothing of $f_s(x)$ has a far greater effect on the derivative of $f_s$ than on $f_s$ itself.
+
+\emph{TODO: Calculate MSE for both curves}
+\emph{TODO: Show curves created with large $h$}