When our measurement instruments sample from only a subspace of the domain that we are seeking to understand, or when they sample with uneven sampling density from the target domain, the resulting data will be affected by a selection effect. If we ignore such selection effects, our conclusions may suffer from selection biases. A classic example of selection bias is the election poll taken by the Literary Digest in 1936. On the basis of a large survey, the Digest predicted that Alf Langdon, the Republican presidential candidate, would win by a large margin. But the actual election resulted in a landslide for the incumbent, Franklin D. Roosevelt. How could such a large sample yield such a wayward prediction? The Digest, it turned out, had harvested the addresses for its survey mainly from telephone books and motor vehicle registries. This introduced a strong selection effect. The poor of the depression era, a group that disproportionally supported Roosevelt, often did not have phones or cars. Observation selection effects are an especially subtle kind of selection effect that is introduced not by limitations in our measurement apparatuses but by the fact that all evidence is preconditioned on the existence of an observer to “have” the evidence and to build the instruments in the first place. Observation selection effects have only quite recently become the subject of systematic study. As well as being of philosophical interest, they are important in many scientific areas, including cosmology, parts of evolution theory, and the foundations of thermodynamics and quantum theory. There are also interesting applications to the search for extraterrestrial life and questions such as whether we might be living in a computer simulation created by an advanced civilization..
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