When You do Applied Statistics, You're Acting Like a Scientist. Why Does this matter? by Andrew Gelman Visit https://rstats.ai/nyr/ to learn more. Abstract: When you do applied statistics, you form hypotheses, gather data, run experiments, modify your theories, etc. Here, I'm not talking about hypotheses of the form "theta = 0" or whatever; I'm talking about hypotheses such as, "N=200 will be enough for this study" or "Instrumental variables should work on this problem" or "We can safely use the normal approximation here" or "We really need to include a measurement-error model here" or "The research question of interest is unanswerable from the data we have here; what we really need to do is . . .", etc. Existing treatments of statistical practice and workflow (including in my own textbooks) do not really capture the way that the steps of statistical design, data collection, analysis, and decision making feel like science. We discuss the implications of this perspective and how it can make us better statisticians and data scientists. 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 2022 New York R Conference (June 9, 2022)