The article describes the problem of causal inference, that is, how to distinguish true causal relationships from random or hidden factors in observed differences between similar cases, such as why two patients with similar presentations have different outcomes. The reader is presented with an example with a Christmas tree and an example with two patients that illustrate that attributing differences to "naughty elves" (chance) is formally possible, but less useful than looking for testable non-magical explanations. The author emphasizes that productive explanations are those that lead to testable hypotheses and experiments to verify causal mechanisms. The article discusses concepts such as random variation, latent variables, and the need for careful study design to remove or control for confounding. The main message is that it is preferable for the progress of medicine and science to prefer empirically verifiable explanations to accepting non-specific "chance" as the ultimate explanation. If the article presents statistics or specific results of studies, these are described in the text only as an illustration; the exact numbers from the original study are not available in the provided excerpt.