Brief statistical background
The orthogonal main effects designs are of different types.
They all work well if there are indeed no interactions between factors.
Some of them have complete aliasing between main effects and two-factor interactions
at least for some factors. It is therefore advisable to check the design
before actually conducting the experiment with respect to its potential analysis
options and biases.
Note that it is usually preferable to create
an experiment with solely 2-level factors from the special menu for 2-level
situations (exceptions: resolution V nonregular arrays in 128, 256 or 2048 runs,
cf. Details section).
If there is just one factor at more than 2 levels, it may also be
useful to simply cross this factor with an otherwise 2-level design.
If only relatively few of the columns are used,
it is possible with some orthogonal arrays to also estimate interactions,
or at least to estimate main effects unbiasedly even in the presence of interactions.
This may e.g. be possible for some of the arrays in 2, 4 and 8 or 16 level factors (that have
arisen from regular fractional factorials). Automatic optimization can help finding such designs.
It is highly recommended to diagnose
the structure of the design before using it for experimentation, e.g.
using the Summarize design ... item in the Inspect design menu.