The traditional way of equating the scores on a new test form X to those on an old form Y is equipercentile equating for a population of examinees. Because the population is likely to change between the two administrations, a popular approach is to equate for a "synthetic population." The authors of the articles in this issue of the journal try to avoid the arbitrariness in the definition of a synthetic population by equating X to Y for the population G1 that takes the new form. The author has been happy to notice the authors' attention to the topic of bias in linear equating. The equating literature has been dominated by an interest in the standard error of equating, but bias is the primary criterion for evaluating the success of an equating. After all, equating is an attempt to remove the bias in the score on the new test form as an estimate of the score on the old form due to scale differences between them. A focus only on the standard error of equating prevents one from noticing any remaining bias in the equated scores, or even possible new bias added to them in the equating process. In this article, the author discusses a little further the issue of bias in linear equating. (Contains 1 figure.)