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