Known differences between units
When some differences between the experimental units are understood before the experiment is conducted, it is possible to do better than a completely randomised design.
Simple block design
If possible, researchers usually try to define blocks that have equal size and use each treatment the same number of times within each block. If the experimental units within one block tend to have a higher mean response, all treatments are affected equally.
As a result, our assessment of differences between the treatments is not affected by differences between the blocks so the treatment effects are more accurately estimated.
Effect of irrigation on grass growth
We now look at an example in which data from a randomised block design have been collected.
Uptake of amino acids by fish
Comparison of completely randomised and randomised block designs
If we know anything about differences between the experimental units before the experiment is conducted, it is always worthwhile to group them into blocks and conduct a randomised block design — the effects of the treatments will be estimated more accurately.
The benefits are greatest when there are large differences between the mean response in different blocks — if all blocks are essentially the same, there is nothing to be gained from using a randomised block design.
Antibiotic and weight gain of calves