You claim that factorial experiments make highly economical use of experimental participants, but I was taught factorial experiments require massive sample sizes to power. Can you explain this discrepancy?


This is explained on our Informal Introduction to Factorial Experiments web page and in Chapter 3 of Collins (2018). Very briefly, you may be thinking of a factorial experiment as a many-armed RCT. It isn’t. The logical underpinnings of the factorial experiment are different from those of the RCT, and therefore the approach to powering the two designs is different. In an RCT, all else being equal, power is driven by the per-arm sample size. By contrast, a factorial experiment can have very small per-condition sample sizes as long as the overall sample size per level of each factor is sufficiently large.


Collins, L.M. (2018). Optimization of behavioral, biobehavioral, and biomedical interventions: The multiphase optimization strategy (MOST). New York: Springer.

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Further Learning

Whether you are looking for additional support as you prepare a grant proposal involving MOST or practical information helpful in managing your optimization trial, this section provides resources for a deeper dive into intervention optimization.

REDCap with Most

The goal of this manual is to show how one might setup a REDCap project to support a research study with multiple conditions, such as factorial experiments common in the Multiphase Optimization Strategy (MOST) framework.

Informal introduction to factorial experimental designs

The purpose of this page is to clarify some concepts, notation, and terminology related to factorial experimental designs, and to compare and contrast factorial experiments to randomized controlled trials (RCTs). A more in-depth introduction can...


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