Ascertainment bias is a systematic distortion  in measuring the true frequency of a phenomenon due to the way in which the data are collected. In genetics, ascertainment bias is an important factor in the use of family pedigrees to establish modes of inheritance.

    Consider the inheritance of  the genetic condition Alkaptonuria, characterized by excretion of high amounts of "alkapton" (homogentisic acid) in the urine. We know that this is a rare autosomal recessive condition, so that almost all persons born with the condition (aa) will be the offspring of unaffected carrier (Aa) parents. Thus, among a large group of Aa x Aa crosses, 1/4 of offspring are expected to show the condition. Recognition of such a ratio would be an important clue to the pattern of inheritance. In the first inquiry into the genetics of this condition, Garrod (1902) reported the following data:

    Given the birth of offspring with alkaptonuria (aa) to "normal" parents, the parents may be assumed to all be carriers (Aa). Then, among a total of 48 offspring of such parents, the expected ratio should be 1:3 or 12 alkaptonuric : 36 normal members. The observed ratio is 19 : 29, that is, significantly more alkaptonurics are present in the sample than expected from the genetic model. Why is this? [Homework: Test the deviation between observed and expected results by the Chi-Square test]. The answer is the way in which Garrod collected his data.

    One source of ascertainment bias is that Garrod was a physician: his table includes data only from families in which at least one child has alkaptonuria. Note that for  3/4 of Aa parents with one child, that child will be unaffected, and the family has no reason to consult him. Thus a large number of families with Aa parents, and 1, 2, 3, or more unaffected children are excluded from his counts.

    A second source is the greater likelihood that families with a higher number of alkaptonuric children will consult Garrod: they are more likely to seek advice than families in which the condition is confined to a single child.  In the table, 6 of 9 families reported have more than one affected child, and in all of these (except family #7) the observed ratio of affected children exceeds 1:3. [Indeed, without family #7, the observed proportion would be 18:20, effectively 1:1]. This will again systematically bias the data towards a higher proportion of affected children.

    A classroom demonstration of ascertainment bias is a survey for the primary sex ratio in humans. Ask all women present to report the number of male and female siblings in their families. Ask the same question of men present. The women will report collectively a higher ratio of females: the survey method is biased towards families in which there is at least one woman (themselves), includes many families in which they are only-children, and excludes families with no female and multiple males. The reverse bias exists in the male survey, which will report an excess number of males.