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There exist cases where the mental effort required to conduct a complex multi-factor analysis exceeds the effort required to acquire extra data, in which case OFAT might make sense. Furthermore, some researchers have shown that OFAT can be more effective than [[Fractional factorial design|fractional factorials]] under certain conditions (number of runs is limited, primary goal is to attain improvements in the system, and experimental error is not large compared to factor effects, which must be additive and independent of each other).<ref name=" Friedman, M., and Savage, L. J. (1947), “Planning Experiments Seeking Maxima,” in Techniques of Statistical Analysis, eds. C. Eisenhart, M. W. Hastay, and W. A. Wallis, New York: McGraw-Hill, pp. 365-372.
">Friedman, M., and Savage, L. J. (1947), “Planning Experiments Seeking Maxima,” in Techniques of Statistical Analysis, eds. C. Eisenhart, M. W. Hastay, and W. A. Wallis, New York: McGraw-Hill, pp. 365-372.</ref><ref name= "Cuthbert Daniel">Daniel , C. (1973) ,“One-at-a-Time Plans,” Journal of the American Statistical Association 68, 353-360</ref>
==Disadvantages==
In contrast, in situations where data is precious and must be analyzed with care, it is almost always better to
change multiple factors at once. A middle-school-level example illustrating this point is the family of [[balance puzzle
Bevington's<ref>Bevington and Robinson, ''Data Reduction and Error Analysis for the Physical Sciences'', 2nd Ed. McGraw–Hill (1992)</ref> <code>GRIDLS</code> versus <code>GRADLS</code>. The latter is far from optimal, but the former, which changes only one variable at a time, is worse. See also the [[factorial design|factorial experimental design]] methods pioneered by [[Ronald Fisher|Sir Ronald A. Fisher]]. Reasons for disfavoring OFAT include:
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[[Category:Design of experiments]]
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