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Despite these criticisms, some researchers have articulated a role for OFAT and showed that they are more effective than [[Fractional factorial design|fractional factorials]] under certain conditions (that the primary goal is to attain improvements in the system and that the experimental error is not too large compared to the factor effects). <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> Nevertheless, designed experiments remain nearly always preferred to OFATs with many types and methods available, in addition to fractional factorials which, though usually requiring more runs than OFATs, can address the three concerns above.<ref>[http://www.questia.com/googleScholar.qst?docId=5001888588 Czitrom (1999) "One-Factor-at-a-Time Versus Designed Experiments", American Statistician, 53, 2.]</ref> <!--formerly http://www.amstat.org/publications/tas/czitrom.pdf--> One modern design over which OFATs have no advantage in number of runs is the [[Plackett-Burman_design|Plackett-Burman]] which, by having all factors vary simultaneously (an important quality in designs)<ref>Ibid. Czitrom.</ref>, gives generally [[Efficiency (statistics)|greater precision in effect estimation]].
== References==
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