This post demonstrates how to apply Bayes' Theorem, a fundamental concept in probability theory, to estimate the true number of COVID-19 infections based on early, fragmentary testing data. It breaks down the mathematical model, utilizes official population and early testing statistics from the US, introduces crucial conditional probabilities, and discusses how asymptomatic cases or low testing rates (represented by a variable) significantly impact the accuracy of official infection counts. The approach showcases practical data interpretation under uncertainty.