video1.0<iframe src="https://www.loom.com/embed/cad9a236725a4147b8aa7ff61767dec5" frameborder="0" width="1108" height="831" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>8311108Loomhttps://www.loom.com8311108https://cdn.loom.com/sessions/thumbnails/cad9a236725a4147b8aa7ff61767dec5-c3dfca16d318fe95.gif1264.145This Loom explains how to complete z-score and t-test style hypothesis tests using an experiential learning approach. It first walks through a fully built attendance scenario for the Reds, testing whether mean home attendance at Great American Ballpark is higher than the MLB average of 28,000 using a one tail alpha of 5 percent with population standard deviation 6,500 and a sample of 35 games. It defines the hypothesis, standard error, critical value logic, test statistic, p-value decision, margin of error, confidence bounds (28,491 to 32,798), and the final conclusion that attendance is higher. It then starts a second, partially built scenario about Reds game time being shorter than the MLB average of 1.56 minutes (population standard deviation 18) using 40 games and alpha 5 percent.