The old adage that there are "lies, damn lies, and statistics" warns of the perils of relying on statistics but yet our safety and reliability arguments are often underpinned by statistics such as component failure frequencies, service history, accident rates and numerous distribution models. How do we really know we can trust what we draw from these figures? Statistics and importantly, their perceived conclusions and implications drawn from them, are readily published in the media and readily proliferated mostly unquestioned through social media and other outlets, but as we shall see, publications, even from reputable and learned authors can turn out to be flawed. If even experts can make mistakes, then we need to look carefully at the reliance we place on 3rd party interpretations of data, and of course, be very careful with our own data and the wisdom we draw from it. Through the use of real-life examples, this paper highlights the typical pitfalls that arise from the interpretation of statistical data and will conclude that it is often the data that we don't see, the 'dark data', that can fundamentally undermine the conclusions we draw from seemingly compelling data.