You have no doubt heard the legend about the Native American tribe being seduced by European explorers with glass beads, and other trinkets. The myth has it that the natives made a bad choice failing to capitalize on the technologically superior visitors that had landed amongst them. Rather than choosing what the explorers held in high esteem, their hosts seemed bedazzled by shiny mirrors. Contemporary research has now proven that the ‘ignorant savage’ story distorts a more complex reality in which the natives viewed their strange guests as the gullible party parting with decorative items. As a matter of fact, the historical record shows axes, iron kettles, and woollen clothing exchanged for what were, to the local inhabitants, almost worthless beaver skins, often worn and ready to be thrown out, or signatures on title deeds to property, which meant nothing to the locals who entertained no concept of land ownership. Both sides in this historic barter were astonished at the preferences of the other. Equally, criticisms seem to be bidirectional when it comes to statisticians and the other ‘tribes’ with whom they collaborate, so to speak. From the wealth of analytical tools that end users of statistics need only ask for, there seems to be, from the statisticians’ viewpoint, an ill-informed clamour for items of dubious merit in preference to the high value items that ought to be craved. The process of exchange, as in our historical example, seems continually challenged by communication issues and lost-in-translation episodes, making early stages of collaboration hard going, as all engaged in these transdisciplinary voyages of discovery will be well aware. Two particular communication break-downs have intrigued me throughout my years as a statistician, from student days into my professional life. On the one hand, I have become accustomed to hearing time and time again the misconception that statistics is “all about probabilities”. Any reputation statistics has, then, becomes tarnished in the face of an unexpected turn of events such as rain on your wedding party after a forecast of a sunny day, which is routinely taken to invalidate the judgement that the outcome was improbable. While true that there is an important side to statistics that focuses on predictions, its main role of interpreting what data is saying and telling a story that is both understandable and relevant to its audience is often overshadowed by this sort of wrong-headed thinking. On the other hand, when talking to fellow, young practitioners, a common complaint seems to arise regarding the search for significance on the client´s side. To illustrate this idea, I recently introduced myself as a statistician to a new acquaintance who’s first thought was to enquire what my favourite statistical test was. Although my immediate reaction was to smile, it got me thinking. Not only did it make me more conscious of the association of tests with statisticians being common-place, but it also made me wonder whether we all have some sort of favouritism that can lead us to apply one test rather than another.