This talk will examine the selection bias that occurred in studying some most contentious problems in 2020. In the first case study, we will look at the estimation of the growth rate and incubation period of COVID-19 and demonstrate how early studies drastically misestimated them. In the second case study, we will review and hopefully clarify a recent debate on post-treatment selection in studying racial discrimination in policing. In the era of data, causal inference researchers are uniquely positioned to recognize selection bias, but the cost of no or slow action is high.