Breznau, et al. (2022) asked a group of 161 researchers in 73 teams to analyse the same dataset and test the same hypothesis: greater immigration reduces public support for the welfare state. As we now expect in this genre of the literature, results varied. See the study’s figure below:
So roughly 60% of analyses found a non-statistically significant result. Of the 40% that were statistically significant, 60% found a negative association and 40% found a positive association.
Social scientists are well-versed in the replication crisis and, e.g., the importance of preregistering analyses and not relying too heavily on the findings from any one study.
Mathur et al. (2022) offer a glimmer of hope, though. The variation looks fairly wild when focussing on whether a hypothesis test was statistically significant or not. However, 90% of analyses found that a one-unit increase in immigration was associated with an increase or decrease in public support of less than 4% of a standard deviation – tiny effects!
I also find hope in all the meta-analyses transparently showing biases. It seems that quantitative social science is the most unreliable and difficult to replicate form of social science, except for all the others.
Breznau, N., et al. (2022). Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty. PNAS 119(44), e2203150119 (2022).
Mathur, M. B., Covington, C., & VanderWeele, T. (2022, November 22). Variation across analysts in statistical significance, yet consistently small effect sizes. Preprint.