Statistics are tools to help end users (here,researchers) accomplish their task (advance scientific knowledge). Science is a collective and cumulative enterprise, so to be qualified as usable, statistical tools should support and promote clear thinking as well as clear and truthful communication. Yet areas such as human-computer interaction (HCI) have adopted tools – i.e., p-values and statistical significance testing – that have proven to be quite poor at supporting these tasks. The use and misuse of p-values and significance testing has been severely criticized in a range of disciplines for several decades, suggesting that tools should be blamed, not end users. This article explains why it would be beneficial for HCI to switch from statistical significance testing to estimation, i.e., reporting informative charts with effect sizes and confidence intervals, and offering nuanced interpretations of our results. Advice is offered on how to communicate our empirical results in a clear, accurate, and transparent way without using any p-value.