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CORRECTING FOR SAMPLING BIAS IN QUANTITATIVE MEASURES OF SELECTION WHEN FITNESS IS DISCRETE
Author(s) -
Blanckenhorn Wolf U.,
Reuter Max,
Ward Paul I.,
Barbour Andrew D.
Publication year - 1999
Publication title -
evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.84
H-Index - 199
eISSN - 1558-5646
pISSN - 0014-3820
DOI - 10.1111/j.1558-5646.1999.tb05354.x
Subject(s) - sampling (signal processing) , selection (genetic algorithm) , statistics , sample size determination , selection bias , sampling bias , biology , fraction (chemistry) , sampling error , a priori and a posteriori , sample (material) , class (philosophy) , representation (politics) , econometrics , mathematics , observational error , computer science , artificial intelligence , physics , philosophy , chemistry , organic chemistry , filter (signal processing) , epistemology , politics , political science , law , computer vision , thermodynamics
We show with a simulation that nonrepresentative sampling of two discrete fitness classes leads to biased estimates of selection. Systematic underestimation occurs if the selected class is overrepresented in the sample and overestimation if the unselected class is overrepresented. The bias is greater the stronger the selection intensity, the smaller the true fraction of individuals favored by selected, and the lower the sample size. We present a simple method that allows a posteriori statistical correction in cases of biased sampling given a separate estimate of the actual class representation, describe its practical implementation, and show that it works.