  Statisticians and data scientists find insights that help lead to better
understanding and better outcomes. When clients and managers come to us for
help (and even when they don't), we want to share our advice. While we should
be free to share our recommendations, we need to be clear about what the data
is telling us and what is based "only on our judgment".
  Gelman, et. al. wrote "As we have learned from the replication crisis
sweeping the biomedical and social sciences, it is frighteningly easy for
motivated researchers working in isolation to arrive at favored
conclusions-whether inadvertently or intentionally." One senior business leader
I know said, "if you have data, great; if we're just going on intuition we can
use mine". However, having data isn't enough. We need to be rigorous in our
analysis to avoid finding insights that aren't supported. This paper will go
through a number of examples to illustrate common mistakes.
