Full and Fractional Factorial Test Design

An Essential Primer on Full and Fractional Factorial Test Design [the broken link was removed]

Since full factorial gathers additional data, it reveals all possible interactions, but as seen by the numbers above, there is a trade-off. More data equals more information but more data also equals a longer test duration. The minimum data requirements for full factorial are very high since you are showing every experiment.

Even if you are using full factorial to get the same amount of information as a fractional factorial test, it will take more time since you need more data to see statistically relevant differences between the many experiments. You might be wondering how fractional factorial can be accurate if interactions are possible?

Random interactions of high relevance are very rare, especially when looking for interactions of more than 2 factors. You really need to design tests where you look for meaningful interactions that are based on true business requirements rather than hoping for a random and low influence interaction between a red button, a hero shot and a headline.

I am a fan of design of experiments as long time readers know (see posts on design of experiments).

Some good resources for more on the topics discussed above: What Can You Find Out From 8 and 16 Experimental Runs? by George Box – Statistics for Experimenters – Design of Experiments in Advertising.

Related: Google Website Optimizer – factorial experiment articles – Using Design of Experiments – Marketers Are Embracing Statistical Design of Experiments