Ascertainment bias seems to be defined to two different, but related ways.
Ascertainment bias generally refers to situations in which the way data is collected is more likely to include some members of a population than others. This can happen when there is more intense surveillance or screening for the outcome of interest in certain populations. For example, one might find a higher rate of breast cancer in a richer population with easy access to mammography when compare to a poorer population with limited healthcare access.
Ascertainment bias is also used to refer to the situation when the results of a clinical trial are distorted by knowledge about which intervention each participants is receiving, either because of lack of blinding or improper allocation concealment.
This post is part of a series of posts on bias in medical research. You can find the whole bias catalogue here.
Sedgwick P. Non-response bias versus response bias BMJ. 2014; 348:g2573-g2573.