I’ve recently developed an interest in "edge cases" - the extreme situations or data points that fall far outside the norm.
It was first piqued when I read a post by James Vornov about the impact of extreme cases on decision making. He notes: "Studies of decision making have shown that people are strongly influenced by single, uncommon events. Even when the pattern of frequent events indicates one type of behavior, the uncommon event prevails."
Edge cases do more than create the deepest impression; they also offer rich ground for learning. Consider two catalysts for learning: 1) frequent but ordinary events and 2) extreme events. Each offers a different type of lesson. When we learn from the ordinary, we gain the ability to predict likely outcomes and put clear dimensions around expected results. Learning from the edge cases is wholly different: it helps us define the bounds of reality. It tests our assumptions and creates sharp contrasts.
Storytelling is just one example where edge cases are a teaching tool:
- The legal profession uses the extreme cases to define precedents and test the limits of laws
- Engineers conduct stress testing on materials or products to understand the limits of capabilities. Similarly, programmers test code by defining edge cases.
- Individually, I think we learn most about ourselves in situations when we experience something new, unusual, and challenging.
Another way to view edge cases is that they test our common sense. Tato on Everything2 points out that "as science pushes our understanding of the universe and our selves, we are confronted with new complexities and edge cases where these instincts [common sense] are actually dysfunctional, or wrong."
Michael Feldstein offers a similar view in his blog when he says: "In any field of inquiry, the edge cases are where some of the most interesting work gets done."
In each case, edge cases help us understand the far reaches of the possible. They help us map out reality. In a future post, I want to talk about how businesses can use edge cases, in particular outlying customer data points, to better understand their products, customers, and marketplace.