Blocked Laboratory Experiment — Randomized Block Design in Lab Settings
Randomized Block Design in Laboratory Experiments · Also known as: blocked lab experiment, laboratory randomized block design, RBD laboratory study, blocked within-lab experiment
A blocked laboratory experiment is a controlled laboratory study in which experimental units are grouped into homogeneous blocks before treatment assignment, and treatments are then randomly assigned within each block. Blocking removes the influence of a known nuisance variable — such as participant batch, equipment run, or testing day — from the error term, increasing the precision of treatment comparisons without expanding sample size.
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When to use it
Use a blocked laboratory experiment when you have a known nuisance variable likely to introduce systematic variation across treatment comparisons but that cannot be eliminated by experimental control alone. Typical triggers include experiments run across multiple days, operator shifts, equipment units, or participant cohorts. Blocking is most efficient when the blocking factor accounts for substantial outcome variance. Do NOT use blocking when there is no identifiable grouping factor worth blocking on — use a completely randomized design instead; when units within proposed blocks are not actually more similar to each other than to units in other blocks; or when blocking would require complex incomplete block designs beyond the analyst's capacity to handle correctly.
Strengths & limitations
- Removes a known source of systematic variance from the error term, increasing statistical power without increasing sample size.
- Maintains valid causal inference through within-block randomization.
- Applicable across many laboratory settings — biological assays, psychology experiments, materials testing, and chemical trials.
- Straightforward to analyze in standard statistical software using two-way ANOVA or mixed-effects models.
- Efficient: can detect treatment effects with smaller total N compared to a completely randomized design of equal power.
- Requires that the blocking factor be identified and measured before randomization; post-hoc blocking is not valid.
- If the blocking factor turns out to be unrelated to the outcome, blocking loses degrees of freedom without gaining precision.
- A complete block design requires each block to contain at least one unit per treatment, which constrains block size and may not always be feasible.
- Interactions between blocks and treatments cannot be estimated in a standard randomized complete block design — if such interactions exist, conclusions may be misleading.
Frequently asked
What is the difference between a blocked laboratory experiment and a within-subjects design?
In a within-subjects design every participant receives every treatment — the participant is the blocking unit. In a blocked laboratory experiment the blocking factor is typically external to individual participants, such as day, batch, or equipment unit. Technically, a within-subjects design is a special case of a complete block design where each block is a single participant.
How do I decide what to use as a blocking factor?
Choose a factor that is known before the experiment, logistically unavoidable as a source of heterogeneity, and likely to be substantially correlated with the outcome. Good candidates are sources you cannot experimentally equalize across all runs — testing day, equipment lot, operator shift. A blocking factor uncorrelated with the outcome costs degrees of freedom without benefit, so prior knowledge or pilot data about likely variance components guides the choice.
Can I combine blocking with factorial treatment structures?
Yes. A factorial treatment structure can be run inside a randomized complete block design. Each block contains a complete replicate of all factorial treatment combinations. The analysis uses a model with main effects, interactions, and the block term; block-by-treatment interactions are typically assumed negligible and pooled into error. Montgomery (2017) covers this combination extensively.
What if my blocks are too small to run all treatments?
Use an incomplete block design — the most common being a balanced incomplete block design (BIBD), in which each pair of treatments appears together in the same number of blocks. Incomplete block designs require specialized analysis and larger total sample sizes than complete block designs, but allow blocking when practical constraints limit block size.
What statistical test do I use to analyze a blocked laboratory experiment?
For a simple randomized complete block design with one treatment factor: two-way ANOVA with treatment and block as fixed effects, assuming no treatment-by-block interaction. When blocks represent a random sample of possible blocks, use a mixed-effects model with block as a random effect. Always include the block term in the model regardless of its significance level.
Sources
- Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd. link ↗
- Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443
How to cite this page
ScholarGate. (2026, June 3). Randomized Block Design in Laboratory Experiments. ScholarGate. https://scholargate.app/en/experimental-design/blocked-laboratory-experiment
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Blocked Randomized Controlled TrialExperimental design↔ compare
- Factorial ExperimentExperimental design↔ compare
- Laboratory ExperimentExperimental design↔ compare
- Latin Square DesignExperimental design↔ compare
- Randomized Controlled TrialExperimental design↔ compare