Abstract
Randomization is used in experimental design to reduce the prevalence of unanticipated confounders. Complete randomization can however create imbalanced designs, for example, grouping all samples of the same condition in the same batch. Block randomization is an approach that can prevent severe imbalances in sample allocation with respect to both known and unknown confounders. This feature provides the reader with an introduction to blocking and randomization, and insights into how to effectively organize samples during experimental design, with special considerations with respect to proteomics.
Highlights
A vital part of experimental design consists of defining the order of sample processing and, if necessary, the creation of batches
One of the most famous examples of a confounded experiment is the observation of “water memory” by Davenas et al.,[1] i.e., the claimed ability of water to retain a memory of substances previously dissolved in it, which could not be replicated in a double-blinded experimental design,[2] suggesting that the initial data were the results of experimenter bias
Knowing how to deal with the challenges of experimental design is central to achieving reproducible experiments
Summary
A vital part of experimental design consists of defining the order of sample processing and, if necessary, the creation of batches. To illustrate the effects of randomized versus ordered allocation, let us assume that we have ten patients receiving a given Treatment and ten a Placebo, with the treatment resulting in a minor mean increase in the analytical readout e.g., the abundance of a given protein, see Figure 1.
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