<P> These effect sizes estimate the amount of the variance within an experiment that is "explained" or "accounted for" by the experiment's model . </P> <P> Pearson's correlation, often denoted r and introduced by Karl Pearson, is widely used as an effect size when paired quantitative data are available; for instance if one were studying the relationship between birth weight and longevity . The correlation coefficient can also be used when the data are binary . Pearson's r can vary in magnitude from − 1 to 1, with − 1 indicating a perfect negative linear relation, 1 indicating a perfect positive linear relation, and 0 indicating no linear relation between two variables . Cohen gives the following guidelines for the social sciences: </P> <Table> <Tr> <Th> Effect size </Th> <Th> r </Th> </Tr> <Tr> <Td> Small </Td> <Td> 0.10 </Td> </Tr> <Tr> <Td> Medium </Td> <Td> 0.30 </Td> </Tr> <Tr> <Td> Large </Td> <Td> 0.50 </Td> </Tr> </Table> <Tr> <Th> Effect size </Th> <Th> r </Th> </Tr>

A cohen's d of –0.19 is what type of effect size