Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty.

This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers' expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team's workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers' results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings.

Measuring Ethnic Diversity.

Researchers have investigated the effects of ethnic heterogeneity on a range of socioeconomic and political outcomes. However, approaches to measuring ethnic diversity vary not only across fields of study but even within subfields. In this review, we systematically dissect the computational approaches of prominent measures of diversity, including polarization, and discuss where and how differences emerge in their relationships with outcomes of interest to sociologists (social capital and trust, economic growth and redistribution, conflict, and crime). There are substantial similarities across computations, which are often generalizations or specializations of one another. Differences in how racial and ethnic groupings are constructed and in level of geographic analysis explain many divergences in empirical findings. We conclude by summarizing the type of measurement technique preferred by outcome, when relevant, and provide considerations for future researchers contemplating how best to operationalize diversity. Finally, we highlight two less widely used yet promising measures of diversity.