ADD some blurbs about why DOEs might not be a good idea

tex/thesis.tex

@@ -1194,6 +1194,38 @@ influence the directions for future work. To this end, the types of \glspl{doe}
 we generally used were fractional factorial designs with three levels, which
 enable the estimation of both main effects and second order quadratic effects.
 
+While there are advantages of using \glspl{doe}, it is important to recognize
+that they are not necessary or recommended for all experimental aims. In
+particular, \glspl{doe} excel when multiple factors (possible with multiple
+levels) need to be investigated at once and with a known degree of power. This
+is especially important when interaction is expected or needs to be
+investigated. However, it could be the case that one already has data on many of
+the factors of concern. If one only cares about main effects, performing a
+\gls{doe} (particularly a lower-powered screening experiment such as a
+resolution III design) with these factors and a few others may not be
+productive, and one is better off performed a few extra pilot experiments before
+doing a more complex design such as a central composite if desired. Furthermore,
+it should be noted that while the goal of a \gls{doe} is to minimize resources,
+the size necessary to justify a \gls{doe} may not be worth the experimental
+return. For biological work (or any domain with little automation), performing a
+randomized experiment with 20 to 30 runs is not trivial from a logistical
+perspective, especially when considering the number of manual manipulations and
+the chance of human error.
+
+Despite these caveats, many of the principles used for a \gls{doe} are important
+in general for experimentation. The most obvious is randomization, which is
+often not employed (and also not explicitly mentioned in papers) even though it
+is empirically obvious that well plates have different evaporation rates
+depending on well position. Assuming the experiment is manual, the largest
+reason to avoid randomization is that the experimentalist has no pattern to
+follow when administering treatment (such as ``add X to row 1 in well plate''),
+thus cognitive burden and the likelihood of mistakes increases. While
+\glspl{doe} are usually bigger with more parameters, the one-factor-at-a-time
+experiment usually performed in biological disciplines is much smaller and only
+has a few parameters, thus these concerns are minimal. There is no reason to
+avoid randomization in these cases, as the added cognitive cost is far offset by
+the guarantee of eliminated bias due to run position.
+
 \subsection{Identification and Standardization of CPPs and
   CQAs}\label{sec:background_cqa}