Completely randomized design

By Jude Wallis · Updated

In a completely randomized design, all experimental units are assigned to treatments entirely at random, with no blocking or prior grouping.

A completely randomized design has one pool of experimental units and one randomization: treatments go to all of the units entirely at random, with nothing grouped, sorted or paired first. That last clause is what the word completely is pointing at.

Take 45 seedlings and three light levels. Number the seedlings 1 to 45, generate random integers from 1 to 45 while ignoring repeats, send the first 15 distinct numbers to the low level, the next 15 to the medium level, and the 15 whose numbers never came up to the high level. Each seedling had probability 1/31/3 of each treatment, and the design imposes nothing beyond that.

The error is a mislabel: "we randomly assigned the men to the two treatments and the women to the two treatments, so the design is completely randomized." Randomizing inside groups you formed beforehand is a randomized block design. If a variable was used to sort the units before the draw, the design is blocked, whatever the write-up calls it.

Equal group sizes are conventional, not required. A run that puts 22 subjects in one treatment and 18 in the other is still completely randomized. Flipping a fair coin for each of 20 subjects is genuine random assignment, and an exact 10 and 10 split then has probability (2010)/220=184756/10485760.176\binom{20}{10}/2^{20} = 184756/1048576 \approx 0.176, so it turns up on about 18 percent of runs. Use a random number generator instead whenever the group sizes have to come out as promised.

A known nuisance variable does not invalidate this design. It stays valid and simply leaves that variation in the noise, which is the variation blocking pulls out instead. Completely randomized, randomized block and matched pairs are the three designs named in topic 1.13 Experimental Design, and a question asking for one of them is not answered by another.

Where this comes up

More collecting data and study design terms, or browse the full statistics glossary.