One-factor-at-a-time method: Difference between revisions

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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|balance puzzles]], which includes the Twelve Coins puzzle. See alsoAt the [[factorial design|factorial experimental design]] methods pioneered by [[Ronald Fisher|Sir Ronald A. Fisher]]. Reasonsundergraduate forlevel, disfavoringone OFATcould include:compare

Bevington's<ref>Bevington and Robinson, ''Data Reduction and Error Analysis for the Physical Sciences'', 2nd Ed. McGraw&ndash;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:

1. OFAT requires more runs for the same precision in effect estimation<br />