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Statistics Definition

Blocking

Builds on Design of experimentsRandomization

Definition

Let TT be a finite set of treatments and UU a finite set of experimental units. Blocking is a partition U=B1∪⋯∪BbU = B_1 \cup \cdots \cup B_b of the units into disjoint blocks of units that are expected to respond alike. The partition is fixed before the treatments are assigned, and treatments are then assigned and compared within blocks. A design d ⁣:U→Td \colon U \to T is a randomized complete block design if:

  1. each block has exactly ∣T∣\abs{T} units;
  2. each treatment occurs exactly once in each block, that is, dd restricted to BjB_j is a bijection onto TT for every jj;
  3. the bijections on the different blocks are chosen independently and uniformly at random, by randomization within blocks.

Remarks

The usual model for a randomized complete block design is yij=μ+τi+βj+εijy_{ij} = \mu + \tau_i + \beta_j + \varepsilon_{ij}, with treatment effects τi\tau_i, block effects βj\beta_j and independent errors. Differences yij−yi′jy_{ij} - y_{i'j} within block jj do not contain βj\beta_j, so block effects drop out of treatment comparisons.

Example: three varieties T={A,B,C}T = \set{A, B, C} on four fields, each cut into three plots. The fields are the blocks, each of size 33; in each field the three varieties are put on the three plots in an order chosen at random.

Non-example: if each field receives a single variety on all its plots, clause 2 fails. The variety effect is then confounded with the field effect, and the two cannot be separated.

When blocks are smaller than the number of treatments, clause 1 fails and one uses incomplete block designs, such as balanced incomplete block designs.

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  1. Design of experiments concept
  2. Randomization concept
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Further reading

  1. Web https://en.wikipedia.org/wiki/Blocking_(statistics)
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