  We discuss common errors and fallacies when using naive "evidence based"
empiricism and point forecasts for fat-tailed variables, as well as the
insufficiency of using naive first-order scientific methods for tail risk
management. We use the COVID-19 pandemic as the background for the discussion
and as an example of a phenomenon characterized by a multiplicative nature, and
what mitigating policies must result from the statistical properties and
associated risks. In doing so, we also respond to the points raised by
Ioannidis et al. (2020).
