Why should autosamplers randomize samples in automated chemistry workflows?

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Randomizing sample order helps prevent systematic bias from time-dependent changes in the analytical system. In automated workflows, instrument sensitivity, carryover, reagent performance, and matrix effects can drift or vary over a run. If samples are processed in a fixed or sequential order, those drifts can become confounded with the sample identity, making it look like differences are due to the samples themselves when they’re actually due to the order of analysis. By randomizing, any such time-related factors are distributed across all samples, so the measured differences more accurately reflect true sample differences rather than the sequence.

That’s why the best choice is to minimize systematic bias and ensure representative sampling across batches. The other options don’t address the core goal: increasing random errors is undesirable, scheduling convenience isn’t the primary purpose, and randomization doesn’t eliminate the need for calibration.