Let’s start with what an alias is. An alias when employing the use of a designed experiments methodology is the pattern of pluses (+) and minuses (-) into columns are identical. For example, a main effect is aliased with a two-factor interaction. During the analysis, it is impossible to know whether a change is due to a main effect or due to an interaction since the columns are identical. Confounding is similar, but it doesn’t mean 100% overlap with the pattern of pluses and minuses in the columns. Perhaps the column might be 80% confounded, or 90% confounded. It would be better if there was no confounding as far as resolution is concerned.
Use: There really isn’t a use for aliasing or confounding. Instead, it’s just good to know that when a full factorial design is fractionated, you lose some of the power of the design. One of the things you lose, is the ability to separate the effects of main factors from two-way, or three way (or more) interactions.
