Understanding the Mechanisms That Can Cause Sample Bias

Computational models are very useful for studying many of the physical mechanisms which can cause sample bias. Within such models we can predict the trajectories of all individual particles and thereby make observations which are not possible for physical systems. Physical mechanisms highlighted include congestion at the cutter aperture, particles bouncing over the cutter aperture, air drag on fine particles, waves of material being bulldozed off the belt by the upstream side of the body of square cutters, and particles being thrown by the leading edges of cross-belt cutter blades. Having identified relevant physical mechanisms for an existing cutter, we can estimate the extent to which they are likely to result in unequal representation of portions of a stream of material and thereby estimate the maximum likely sample bias.