Consider again an experiment that seeks to determine the causal relationships between factors and the response, where . Ideally, the sample size is large enough for a full factorial design to be used. However, if the sample size is small and the number of possible treatments is large, then a fractional factorial design can be used instead. Such a […]
In a recent “Machine Learning Lesson of the Day“, I discussed the difference between a supervised learning model in machine learning and a regression model in statistics. In that lesson, I mentioned that a statistical regression model usually consists of a systematic component and a random component. Today’s lesson strictly concerns the systematic component. An […]
An experimenter may seek to determine the causal relationships between factors and the response, where . On first instinct, you may be tempted to conduct separate experiments, each using the completely randomized design with 1 factor. Often, however, it is possible to conduct 1 experiment with factors at the same time. This is better than […]
The simplest experimental design is the completely randomized design with 1 factor. In this design, each experimental unit is randomly assigned to each factor level. This design is most useful for a homogeneous population (one that does not have major differences between any sub-populations). It is appealing because of its simplicity and flexibility – it can […]
In my recent lesson on controlling for confounders in experimental design, the control group was described as one that received a neutral or standard treatment, and the standard treatment may simply be nothing. This is a negative control group. Not all experiments require a negative control group; some experiments instead have positive control group. A […]
