A hypothetical experiment and Monte Carlo simulations were used to examine
the effectiveness of statistical design of experiments methods in identifyi
ng from the experimental data the correct terms in postulated regression mo
dels for a variety of experimental conditions. Two analysis of variance tec
hniques (components of variance and pooled mean square error) combined with
F-test statistics were investigated with first-order and second-order regr
ession models. It was concluded that there are experimental conditions for
which one or the other of the procedures results in model identification wi
th high confidence, but there are also other conditions in which neither pr
ocedure is successful. The ability of the statistical approaches to identif
y the correct models varies so drastically, depending on experimental condi
tions, that it seems unlikely that arbitrarily choosing a method and applyi
ng it will lend to identification of the effects that are significant with
a reasonable degree of confidence. It is concluded that before designing an
d conducting an experiment, one should use simulations of the proposed expe
riment with postulated truths in order to determine which statistical desig
n of experiments approach, if any will identify the correct model from the
experimental data with an acceptable degree of confidence. In addition, no
significant change in the effectiveness of the methods in identifying the c
orrect model was observed when systematic uncertainties of up to 10 percent
in the independent variables and in the response were introduced into the
simulations. An explanation is that the systematic errors in the simulation
data caused a shift of the whole response surface up or down from the true
value, without a significant change in shape. [S0098-2202(00)03102-3].