Experimentation is the foundation of scientific discovery, yet it is complicated by factors that are not the focus of the study but can still influence the results. Researchers aim to isolate the effect of a specific treatment or intervention, but the inherent variability among experimental units can mask the true outcome. This underlying variation, often referred to as “noise,” makes it difficult to determine if an observed change is genuinely due to the treatment or merely external influences. Techniques are integrated into the experimental plan to manage this background variability and ensure the experiment has the necessary precision to detect the effect of interest.
Defining Blocking in Research
Blocking is a strategic approach in experimental design that involves partitioning experimental units into subgroups based on known sources of variability. These subgroups, known as blocks, are created to be as internally homogeneous as possible regarding a characteristic that is not the main treatment but is known to affect the outcome. For example, in a study testing different fertilizers, a researcher might use shadier and sunnier sections of a field to form separate blocks. Once the blocks are formed, all treatment levels are applied within each block, ensuring that every treatment is tested against the same background conditions. This method ensures that any differences in outcome between the treatments are more likely due to the treatments themselves.
Why Blocking Reduces Experimental Error
The statistical benefit of blocking lies in its ability to separate and account for a portion of the overall variability within the experiment. In any study, the total observed variation can be split into variation caused by the treatment and variation caused by error, which represents unaccounted-for factors. By grouping similar experimental units, the variability attributable to the blocking factor—such as age or soil type—is moved out of the error term and into a separate, measurable component of the statistical model. This process significantly reduces the size of the experimental error, the baseline noise against which the treatment effect is compared. A smaller error term means the experiment becomes more precise and sensitive, increasing statistical power.
Identifying and Forming Blocks
The process of blocking begins with identifying potential “nuisance factors,” which are variables expected to influence the response but are not the focus of the investigation. A researcher must rely on prior knowledge to determine which factors contribute the most to the outcome’s variability. For instance, in a clinical trial, the patient’s gender or age bracket might be known to affect drug response, making them the blocking factor.
Blocks are then formed by grouping the experimental units that share a similar level of the nuisance factor. In the medical example, researchers would create a block of male patients and a separate block of female patients. Within each block, every treatment and the control would be applied. This practical step ensures that the effect of the treatment is measured fairly across the spectrum of the nuisance variable.
The Distinction Between Blocking and Randomization
Blocking and randomization are complementary techniques that address different types of variability in an experiment. Blocking is deliberately used to control for a source of variability that is known and measurable, such as different batches of material or geographical locations. It achieves this by isolating the effect of that factor so it does not obscure the treatment effect. The experimental units are first grouped by the blocking factor, and then the treatments are randomly assigned within those groups.
Randomization, conversely, handles variability that is unknown or uncontrollable. By randomly assigning treatments to units, the effects of unforeseen background factors are distributed evenly across all treatment groups, preventing systematic bias. The general principle guiding robust design is often summarized as: “Block what you can, randomize what you cannot.”