Statistics show that eating ice cream causes death by drowning. If this sounds baffling, this talk will help you to understand correlation, bias, statistical significance and other statistical techniques that are commonly (mis)used to support an argument that leads, by accident or on purpose, to drawing the wrong conclusions. The casual observer is exposed to the use of statistics and probability in everyday life, but it is extremely easy to fall victim of a statistical fallacy, even for professional users. The purpose of this talk is to help the audience understand how to recognise and avoid these fallacies, by combining an introduction to statistics with examples of lies and damned lies, in a way that is approachable for beginners. Agenda: Correlation and causation Simpson's Paradox Sampling bias Data visualisation gone wild Statistical significance (and Data dredging a.k.a. p-hacking)