From 4f0576cb6f633aa5c3f5b1fcad17a2c7c5fbf410 Mon Sep 17 00:00:00 2001 From: ndwarshuis Date: Fri, 3 Sep 2021 17:30:19 -0400 Subject: [PATCH] ADD some blurbs about why DOEs might not be a good idea --- tex/thesis.tex | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/tex/thesis.tex b/tex/thesis.tex index 6457422..0e0523a 100644 --- a/tex/thesis.tex +++ b/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}