12 Kellogg Insight are presented as having been achieved through complicated analysis, they tend to defer to the experts. “There is a real danger in managers assuming that the analysis was done in a reasonable way. I think this makes it incredibly important for managers to have a sixth sense for what they can actually learn from data.” To make informed decisions, he says, it helps to take a step back and establish some fundamentals. Because an analysis often boils down to making comparisons between groups, it is important to know how those groups are selected. For example, a market - ing department may want to judge the effectiveness of an ad by comparing consumers who were exposed to the ad with those who were not. If the consum - ers were selected randomly, the groups are what data scientists call “probabi - listically equivalent,” which is the basis for good analytics. But if, say, they were exposed to the ad because they had shown prior interest in the product, this will lead to bad analytics, since not even the most sophisticated analytical tech - niques could provide an answer to the basic question: Was the ad truly effective Based on insights from Florian Zettelmeyer