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

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Nathan Dwarshuis 2021-09-03 17:30:19 -04:00
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@ -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}