What sorts of graphical formats best convey effect size and degree of certainty of a finding? Confidence intervals are commonly used to show uncertainty, yet lay people and experts fail to correctly interpret their meaning. There has been a recent push to present individual data points rather than only presenting aggregated summary statistics (e.g., means, confidence intervals, lines of best fit). But it is unclear how well people can aggregate raw data presented in a graphical format. Across two studies, we presented participants with hypothetical study outcomes of two independent groups in three graph styles: dot plots, mean with 95% confidence interval (CI) plots, combined plots, and bee plots. We asked participants to make judgments about the effect size using the Common Language Effect Size or Bayes Factors. Participants were more likely to underestimate effect sizes and Bayes Factors for dot plots and bee plots compared to mean + 95% CI plots and combined plots. These findings suggest that people have trouble making statistical inferences when presented with raw data points in graphs.

In prior research, arguments using both anecdotal andstatistical evidence are more persuasive than arguments usingeither alone (Allen, Bruflat, Fucilla, Kramer, McKellips,Ryan, & Spiegelhoff, 2000; Hornikx, 2005). However, it isless clear how people integrate information when the statisticsand the anecdotes present conflicting information. In threepreregistered experiments, we tested how people integrateconflicting information to judge the efficacy of a medicine ina clinical trial. Participants read either an anecdote fromsomeone in the trial, summary statistics about the trial, orboth types of information. We found that reading an anecdotefrom a member of the trial for whom treatment wasineffective reduced people’s beliefs in a medical treatmenteven when participants received strong evidence that thetreatment was effective. In Experiment 3, we found thatintroducing icon arrays increased the perceived efficacy of thetreatment but did not eliminate the effect of the anecdote.

Do young children have a teleological conception of the essence of natural kinds? We tested this by examining how the preservation or alteration of an animal’s purpose affected children’s persistence judgments (N = 40, ages 4 - 12, Mean Age = 7.04, 61% female). We found that even when surface-level features of an animal (e.g., a bee) were preserved, if the entity’s purpose changed (e.g., the bee now spins webs), children were more likely to categorize the entity as a member of a different natural kind (e.g., a spider) and these effects were similar in magnitude to altering the surface-features of a natural kind. Our results suggest that we might view teleological properties as partially constitutive of the essence of natural kinds.

A key element of conceptual understanding in physics education is the ability to make qualitative inferences. In this study, we investigated whether people use causal notions when solving simple, qualitative physics problems. We utilized the principles of directionality and holding variables constant to assess participant's accuracy in answering questions about inferences from cause to effect (CE), effect to cause (EC), and from one cause to the other cause (CC). Participants responded with greater accuracy for CE and EC questions when alternative causal information was held constant compared to when it is made explicitly unknown. For CC questions accuracy was lower across all alternative information types. Further, participants generally treated ambiguous alternative causes as if they were held explicitly constant. These results indicate that using notions of causality can potentially help identify difficult qualitative physics questions and be used as a tool in instructional design.

Jurors operate as legal fact-finders, incorporating multiple pieces of evidence into their decisions. Prior work suggestsjurors may not be able to distinguish flaws in scientific evidence (Schweitzer & Saks, 2012) or properly assess the relia-bility of evidence (Thompson, 1989; Kaasa et al., 2007). However, it remains unclear how much probative value is givento individual evidence types. What prior weights do jurors place on different types of evidence and how do these predicttheir decisions? We built a Bayesian model of how people weigh individual pieces of evidence and used this to predictguilt ratings. Consistent with previous work, we found people have trouble distinguishing differences in quality amongevidence, and assign similar probative value to even flimsy types of evidence. The model also revealed individual weight-ing of evidence can exert large, and sometimes problematic effects on decisions to convict. We discuss the implications ofour findings for legal decision-making.