Building insights on true positives vs. False positives: Bayes' rule

COVID-19 pandemic policies requiring disease testing provide a rich context to build insights on true positives versus false positives. Our main contribution to the pedagogy of data analytics and statistics is to propose a method for teaching updating of probabilities using Bayes' rule reasoning to build understanding that true positives and false positives depend on the prior probability. Our instructional approach has three parts. First, we show how to construct and interpret raw frequency data tables, instead of using probabilities. Second, we use dynamic visual displays to develop insights and help overcome calculation avoidance or errors. Third, we look at graphs of positive predictive values and negative predictive values for different priors. The learning activities we use include lectures, in-class discussions and exercises, breakout group problem solving sessions, and homework. Our research offers teaching methods to help students understand that the veracity of test results depends on the prior probability as well as helps students develop fundamental skills in understanding probabilistic uncertainty alongside higher-level analytical and evaluative skills. Beyond learning to update the probability of having the disease given a positive test result, our material covers naive estimates of the positive predictive value, the common mistake of ignoring the disease's base rate, debating the relative harm from a false positive versus a false negative, and creating a new disease test. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Building insights on true positives vs. false positives: Bayes’ rule

COVID-19 pandemic policies requiring disease testing provide a rich context to build insights on true positives versus false positives. Our main contribution to the pedagogy of data analytics and statistics is to propose a method for teaching updating of probabilities using Bayes’ rule reasoning to build understanding that true positives and false positives depend on the prior probability. Our instructional approach has three parts. First, we show how to construct and interpret raw frequency data tables, instead of using probabilities. Second, we use dynamic visual displays to develop insights and help overcome calculation avoidance or errors. Third, we look at graphs of positive predictive values and negative predictive values for different priors. The learning activities we use include lectures, in-class discussions and exercises, breakout group problem solving sessions, and homework. Our research offers teaching methods to help students understand that the veracity of test results depends on the prior probability as well as helps students develop fundamental skills in understanding probabilistic uncertainty alongside higher-level analytical and evaluative skills. Beyond learning to update the probability of having the disease given a positive test result, our material covers naïve estimates of the positive predictive value, the common mistake of ignoring the disease's base rate, debating the relative harm from a false positive versus a false negative, and creating a new disease test. © 2022 Decision Sciences Institute.

Effect of Height in Telepresence Robots on the Users’ Spatial Awareness

Since reduction of personal contact is key in fighting the COVID-19 pandemic, remote communication solutions saw a rise in importance. Next to the more common forms like video and audio conference calls, telepresence solutions are also becoming more popular. Telepresence robots can be remotely driven and allow, with the help of cameras and displays on the robot and the users’ side, face-to-face communication with onsite personal, establishing a remote telepresence. Depending on the model, the height of the robot can be adjusted by the remote user. Even though the effect of the height in relation to onsite people is being researched, the effect on the users’ side has not been examined immensely. Therefore, this work examines the effect of the difference in height between a telepresence robot and its user on the users’ spatial awareness. Subjects have experienced the usage of a telepresence robot driving at a fixed height through a video. Afterwards, they filled out a questionnaire, which asks the user to answer questions about the experience. These questions were regarding the spatial awareness of the user in the remote location, asking them to estimate different parts of the tour. Their estimations were mapped to the users’ height, allowing to correlate the difference in height and the users’ spatial awareness. The work has shown, that only the perceived height of the telepresence robot was affected by the difference in size. However, more tests have to be conducted, to factor in multiple robot heights. © 2021, Springer Nature Switzerland AG.