Design of Experiments: Factorial Design Overview

Factorial Design
Factorial Design

Factorial Design. Photo by Phred

One of the key issues in designing any experiment is identifying as many influences on the results as possible, and either minimizingor isolating their impact on the results. These influences are known as factors.

Designing an experiment while taking all factors into account is known as factorial design.

There are two basic levels of factorial design:

  • Full factorial: includes at least one trial for each possible combination of factors and levels.
  • Partial or fractional factorial: includes at least one trial for some, but not all, possible combinations of factors and levels.

Which one you choose depends on your particular experiment, the data being collected, and the budgetary/time constraints.

Each type of factorial experiment design has its pros and cons:

  Pro Con
Full factorial design Thorough

High resolution



Potential for too much data

Partial factorial design Cheaper


Low resolution

Could miss important interactions

Allows confounding

Factorial Design Terminology

We’ve collated a list of the most common terms used in factorial design, along with their meanings, for easy lookup. See the factorial design terminology list.

Types of Factors

There are a number of different factors that could affect your experiments. We’ve listed the various types that you need to be aware of. Read more about factors.

Full Factorial Design

Full factorial design includes at least one trial for every combination of factors and levels. Read more about full factorial design.

Fractional Factorial Design

Fractional or partial factorial design includes at least one trial for a selection of factor-level combinations. Read more about fractional factorial design.


Confounding can occur when factor interactions are not fully explored in an experiment’s design. Read more about confounding.


Blocking is a method of minimizing confounding, where extra factors are distributed as evenly as possible across an experiment. Read more about blocking.


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