Ascertainment bias describes the incorrect results of a study due to the way in which the data were collected.

For example, to find the male/female ratio in a country, you needn't count everyone in the country, but rather would select a statistical sample of the population. The way you select the sample can influence the result. (If you counted the residents of a housing project for elderly persons, the result would be biased in favor of females, who statistically live longer than males.) A sample is that part of a population which is actually observed. ...

Ascertainment bias is important in studying the genetics of medical conditions, since data are typically collected by physicians in a clinical setting. The results may be skewed because the sample is of patients who have seen a doctor, rather than a random sample of the population as a whole. Berkson's paradox illustrates this effect. It has been suggested that this article or section be merged with Simple random sample. ... Berksons paradox is a result in conditional probability and statistics which is counter-intuitive for some people, and so has been described as a paradox. ...

Proper design of experiments can minimize this effect. The first statistician to consider a methodology for the design of experiments was Sir Ronald A. Fisher. ...