Randomisation and blocking
Section of Analysis of variance examples, part of Data analysis
Randomisation and blocking
| Type | Section |
| Taught in | Analysis of variance examples |
| Part | Data analysis |
In a designed experiment, treatments are assigned to experimental units at random, so that the treatment groups are alike in every other respect. When the units fall into groups that are known to differ, such as plots in different fields, patients in different hospitals, or measurements on different days, randomising within each group, a block, removes that variation from the comparison. That is the randomised block design, analysed with block as a second factor; the tips-and-coupons example in Analysis of variance shows how much a block can matter.
Put the response in one column and each factor in another. Fit the model with lm, read the F tests in anova, check the residuals, and test the interaction before reading the main effects. A significant F says some means differ; a multiple-comparison method says which.
Used in
See also
Bonferroni correction · Interaction · Checking an analysis-of-variance model · Which groups differ? · Two-factor analysis of variance · Main effect