Explaining Regularities or Individual Outcomes: Chance and the Limits of Social Science

Abstract

Can we explain individual outcomes by referring to patterns observed in populations? Social scientists generally assume that we can, at least to a certain degree, and they study populations partly with that goal in mind. However, while patterns can be observed on the population level, which suggest that, on average, certain segments of the population are more likely to experience some outcome, it is impossible, on the individual level, to predict who will actually experience the outcome, even if the individual’s relevant characteristics are known. Thus, an interesting tension emerges: on the one hand, individual action and experience produces population-level patterns, while on the other hand, individual experience appears to be ‘inherently underdetermined’ and partly or largely due to luck or chance. Accordingly, this article considers the relationship between regularities and individual outcomes and to what extent it is desirable to construct models which can explain all the variance in outcomes, and the roles of true chance and what one might call ‘as-if’ chance in this. An empirical demonstration based on ALLBUS data explores these issues further. It uses the example of the graduate premium to discuss that, while there is a pattern where, on average, graduates earn more than non-graduates, there is a certain degree of individual-level deviation from this pattern (even after taking account of other relevant factors) which is partly due to chance. Patterns identified in data can provide the upper and lower bounds within which chance plays its part. The article closes with a discussion of implications for research and policy, and for the understanding of research findings by the general public.