The problems associated with sample statistics are complex and involved, more so for some distance indicators than for others, although all indicators are affected to some degree. For the purposes of this paper, we call the attention of the reader to a few common biases that afflict several techniques, and these should be kept in mind throughout this review. Thus, a sample of distant galaxies will have a higher average luminosity than a nearby sample. This effect, in itself, does not mandate biased distances provided that any parameter used to predict the galaxy luminosity is not also biased. The improper sample selection introduces biased distance estimates near the completeness limit which, in turn, biases the sample mean.
Biases can also result from differences in the environments of the samples.
For example, most methods are calibrated locally but applied in large galaxy clusters. If the galaxies in clusters are somehow different from those in the Local Group, a very simple bias arises. Another subtle form of bias can be introduced in the analysis phase of distance determination by binning data. A histogram of globular clusters or planetary nebulae, for example, will have a bias which scatters objects from heavily populated bins into bins with fewer objects. A variant of the problem is discussed by Lynden-Bell et al.