Blocking and Randomization in DOE

Proper experimental design uses blocking to eliminate known nuisance factors and randomization to protect against unknown ones. Skipping either step risks confounding experimental effects with lurking variables.

Randomization

Run experiments in a random order rather than systematic order. This prevents time-related trends (tool wear, temperature drift, operator fatigue) from biasing one factor level versus another. In software: use a random number table or computer-generated run order.

Blocking

When a known nuisance factor (batch of material, day of week, machine shift) cannot be controlled, assign it as a block. Runs within a block are more alike than runs across blocks. Blocking accounts for block-to-block variation, leaving factor effects cleaner.

Example: 2² factorial on two shifts (A and B) Without blocking: shift effects mix with factor effects. With blocking: each shift runs a complete replicate. Shift A: (+,+),(+,−),(−,+),(−,−) Shift B: (+,+),(+,−),(−,+),(−,−) Block effect is estimated and removed from the analysis.

Randomized complete block design (RCBD)

Each treatment appears exactly once in each block. Blocks must be large enough to accommodate all treatments. If blocks are too small, use an incomplete block design.

CQE exam tips

Randomize within blocks — do not randomize the block assignment itself.
A replicated experiment and a blocked experiment look the same on paper; the difference is whether the grouping factor is a block (nuisance) or a true experimental factor.
Blocking reduces error variance; randomization prevents bias.

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