Pooling: The construction of an error term from more than one source of variance in the response. A priori pooling occurs in designs without full replication, where untestable interactions with random factors are pooled into the residual variation. The analysis then proceeds on the assumption that the interactions are either present (Model 1) or absent (Model 2). Planned post hoc pooling is applied to mixed models by pooling a non-significant error term with its own error term. The design is thereby influenced by the outcome of the analysis (in terms of whether or not an error term is itself significant). More generally, pooling can describe the process of joining together samples, for example in calculating a main effect Mean Square by pooling across levels of a cross factor.

Doncaster, C. P. & Davey, A. J. H. (2007) Analysis of Variance and Covariance: How to Choose and Construct Models for the Life Sciences. Cambridge: Cambridge University Press.

http://www.southampton.ac.uk/~cpd/anovas/datasets/