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How should social scientists understand and communicate the uncertainty of statistically estimated causal effects? It is well-known that the conventional significance-vs.-insignificance approach is associated with misunderstandings and misuses. Behavioral research suggests people understand uncertainty more appropriately in a numerical, continuous scale than in a verbal, discrete scale. Motivated by these backgrounds, I propose presenting the probabilities of different effect sizes. Probability is an intuitive continuous measure of uncertainty. It allows researchers to better understand and communicate the uncertainty of statistically estimated effects. In addition, my approach needs no decision threshold for an uncertainty measure or an effect size, unlike the conventional approaches, allowing researchers to be agnostic about a decision threshold such as p<5% and a justification for that. I apply my approach to a previous social scientific study, showing it enables richer inference than the significance-vs.-insignificance approach taken by the original study. The accompanying R package makes my approach easy to implement.
Particle physics experiments such as those run in the Large Hadron Collider result in huge quantities of data, which are boiled down to a few numbers from which it is hoped that a signal will be detected. We discuss a simple probability model for thi
Spatial prediction of weather-elements like temperature, precipitation, and barometric pressure are generally based on satellite imagery or data collected at ground-stations. None of these data provide information at a more granular or hyper-local re
We generalize standard credal set models for imprecise probabilities to include higher order credal sets -- confidences about confidences. In doing so, we specify how an agents higher order confidences (credal sets) update upon observing an event. Ou
We propose a Bayesian nonparametric model to infer population admixture, extending the Hierarchical Dirichlet Process to allow for correlation between loci due to Linkage Disequilibrium. Given multilocus genotype data from a sample of individuals, th
Hot-filament process was recently employed to convert, totally or partially, few-layer graphene (FLG) with Bernal stacking into crystalline sp$^3$-C sheets at low pressure. Those materials constitute new synthetic carbon nanoforms. The result reporte