Confounding occurs when you can’t distinguish the effects of certain factor interactions because of other potential factor effects. This most commonly happens when:

- Uncontrolled or ‘nuisance’ factors are affecting the results.
- Partial factorial designs are used.

For example, let’s look at a simple partial factorial design with confounding:

Trial | Factor A | Factor B | Factor C |
---|---|---|---|

1 | – | – | + |

2 | + | – | – |

3 | – | + | – |

4 | + | + | + |

In this experiment design, F_{c} = F_{a} * F_{b} (see Partial/Fractional Factorial Design if you need to revisit the notation used). That means that the effects of factor C on the experiment can’t be distinguished from the interactions between factor A and factor B.

Blocking can help limit confounding by distributing extra factors evenly across all experimental factors.

## Comments (2)

Excellent and simple explanation about CONFOUNDING

Thank you! Glad it was helpful.