The average, or arithmetic mean, enjoys a prominent position in most analysis toolkits, and for good reason. It’s a simple and elegant way to compress a large data set into a single, descriptive number. It’s ever-present in the alphabet soup of “key performance indicators” that highlight most gaming dashboards: ARPU (average revenue per user), ARPPU (average revenue per paying user), and average session length, to name a few. But the average is not without its limitations. If your data set is not evenly or predictably distributed, an average might not give you a full picture, leading to misunderstandings of how players engage with your game. If you understand these limitations, however, you’ll be well-equipped to both avoid analysis pitfalls and address them with complementary data visualization techniques. Distribution of Game Data The chart below displays the number of daily active users (DAU) in a game over the past 30 days. Despite the minor fluctuations in game activity, this distribution is fairly uniform between the days. It’s close to an ideal candidate for an average: very little