įor values of S < 10, this estimate is rough, and becomes extremely rough for values of S < 5. If E1 and E2 are the number of species (or words, or other phenomena) observed by two independent methods, and S is the number of observations in common, then the Lincoln Index is simply The Lincoln Index formalizes this phenomenon. A similar example involves estimating the number of typographical errors remaining in a text, from two proofreaders' counts. Given two independent samples, the overlap between their vocabularies enables a useful estimate of how many more vocabulary items exist but did not happen to show up in either sample. Īnother example arises in computational linguistics for estimating the total vocabulary of a language. If some animals in a given area are caught and marked, and later a second round of captures is done: the number of marked animals found in the second round can be used to generate an estimate of the total population. The same reasoning applies to mark and recapture. On the other hand, if 99 of the 100 species each observer found had been found by both, it is fair to expect that they have found a far higher percentage of the total species that are there to find. Thus, we know that both observers miss a lot. If they each come back having found 100 species but only 5 particular species are found by both observers, then each observer clearly missed at least 95 species (that is, the 95 that only the other observer found). Consider two observers who separately count the different species of plants or animals in a given area.
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