Truly Open Science: From Design and Data Collection to Analysis and Decision Making by Andrew Gelman. Visit https://rstats.ai/nyr/ to learn more. Abstract: "Open science" is more than data sharing, replication, preregistration, partial pooling, and version control. "Doing statistics right" is more than swapping Bayesian methods for p-values. To resolve the larger problems of the push-a-button, take-a-pill model of science, engineering, and policy., we need to move toward collaboration between researchers, data analysts, and people who design studies and analyze the people whose data are being collected. But this in turn requires the ability to simulate, graph, and analyze data using flexible platforms such as Stan and R. Bio: Andrew Gelman is a professor of statistics and political science and director of the Applied Statistics Center at Columbia University. He has received the Outstanding Statistical Application award from the American Statistical Association, the award for best article published in the American Political Science Review, and the Council of Presidents of Statistical Societies award for outstanding contributions by a person under the age of 40. His books include Bayesian Data Analysis (with John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Don Rubin), Teaching Statistics: A Bag of Tricks (with Deb Nolan), Data Analysis Using Regression and Multilevel/Hierarchical Models (with Jennifer Hill), Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do (with David Park, Boris Shor, and Jeronimo Cortina), and A Quantitative Tour of the Social Sciences (co-edited with Jeronimo Cortina). Andrew has done research on a wide range of topics, including: why it is rational to vote; why campaign polls are so variable when elections are so predictable; why redistricting is good for democracy; reversals of death sentences; police stops in New York City, the statistical challenges of estimating small effects; the probability that your vote will be decisive; seats and votes in Congress; social network structure; arsenic in Bangladesh; radon in your basement; toxicology; medical imaging; and methods in surveys, experimental design, statistical inference, computation, and graphics. Twitter: https://twitter.com/StatModeling Presented at the 2020 R Conference | New York (August 14th, 2020)