Fooled by Data

  Cognitive Bias - Making Decisions Sunk Cost Fallacy - You can’t stop once you open Imagine you bought a stock at $100. A few months later, it drops to $70. You feel uncomfortable, but instead of reassessing the business, you decide to buy more. You tell yourself you are being disciplined— “I’m doing dollar-cost averaging.”  After all, if you lower your average price, it will be easier to break even when the stock recovers. But let’s slow down and think. Rationally, each new investment decision should be treated independently. The question should be simple:  “If I had no position today, would I buy this stock at $70?”  In reality, that is not how most of us decide. The original purchase price becomes a psychological anchor. The money already invested feels like something that must be “rescued,” and buying more feels like action, progress, and commitment. Under the name of a rational strategy, we are often just digging deeper—letting sunk costs from the past quietly ...

At the heart of many analytical mistakes lies a simple truth about the human mind: it is a powerful pattern-finding and story-telling machine. Faced with data—especially incomplete, noisy, or extreme data—we instinctively search for order, meaning, and explanation. We look for winners to emulate, early signals to trust, trends to extrapolate, and causes to credit. These instincts are not flaws; they are the very tools that help us navigate a complex world. Yet when applied uncritically to data, they can quietly distort what we collect, how we interpret it, and the confidence we place in our conclusions. The result is analytics that feels rigorous and intuitive, but rests on fragile foundations—patterns that may not persist, stories that may not generalize, and insights that dissolve under closer scrutiny. Sampling Pitfalls Driven by Selection and Survivorship Bias Let’s imagine you are tasked with analyzing what characteristics make a school “great.” A common approach might be to ident...

As one data scientist aptly noted, “Data and data analytics can be used to support almost any assumption. However, the quality of analytics and the insights they generate depend heavily on the clarity of the problem definition and the assumptions made. Great business leaders distinguish themselves by asking the right questions to uncover new perspectives and solutions, while great analysts translate those business questions into rigorous analytical ones. Without a clear problem definition and well-founded assumptions, truly effective analytics is impossible.” Availability Biases  - You think as much as you remember A leadership team prides itself on being consumer-centric. To stay connected with customers, they regularly visit consumers’ homes and conduct in-depth interviews. During one such visit, they speak to two or three long-time, loyal customers who express dissatisfaction with a recently changed product. Their reasons for discontent vary, but they all share a common sentimen...