State anxiety by itself does not change political attitudes: A threat of shock experiment.

Previous research suggests that state anxiety may sway political attitudes. However, previous experimental procedures induced anxiety using political contexts (e.g., social or economic threat). In a pre-registered laboratory experiment, we set out to examine if anxiety that is unrelated to political contexts can influence political attitudes. We induced anxiety with a threat of shock paradigm, void of any political connotation. All participants were instructed that they might receive an electric stimulus during specified threat periods and none during safety periods. Participants were randomly assigned to one of two conditions: Political attitudes (implicit and explicit) were assessed under safety in one condition and under threat in the other. Psychometric, as well as physiological data (skin conductance, heart rate), confirmed that anxiety was induced successfully. However, this emotional state did not alter political attitudes. In a Bayesian analytical approach, we confirmed the absence of an effect. Our results suggest that state anxiety by itself does not sway political attitudes. Previously observed effects that were attributed to anxiety may be conditional on a political context of threat.

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.