Blocking in statistics allows you to control the sources of variation (” non-essential variables“) by creating blocks (homogeneous groups). The treatment is then assigned to the different blocks. blocking is a term that comes from agriculture. Different pesticides and growing techniques were used on different blocks (or plots) of land.
What is a blocking factor?
A blocking element in statistics is a factor that can be used to create blocks. It is a variable that has an effect but not itself.
The experiment will determine the blockers. In human studies, gender and age are common blocking factors. To remove the effects of population size or institution type on medical studies, the type of institution might be used. Microarray batches or experimenters can be used to block microarray experiments (Wit & Mclure, 2004). There are other options for blocking factors
- Certain foods are not allowed to be consumed.
- Over-the-counter food supplements.
- Respect the dosage regimen.
- There are genetic differences in metabolism.
- Other diseases and disorders can coexist.
- Other medications may also be used.
Different types of blocking design in statistics
There are many types of blocking designs available in Six Sigma, including:
- Randomized Block Design: This design type allows the researcher to divide experimental subjects into homogeneous blocks. The blocks are randomly assigned treatments.
- Matched pair design: a special case for randomized block design. This design assigns two treatments to each block of subjects. Each pair should be homogeneous. This means that you want the pairs as similar as possible.
- Latin Square Designs: These are built on the Latin square. This ancient puzzle asks you to find out how many Latin letters can be placed in a certain number of columns and rows (a matrix). Each symbol is only one-to-one. To protect against order effects, the Latin square design makes sure that every letter is equally followed by another letter.
