Better Questions, Better Results

How Are Causation and Correlation Related? Untangling the Knot: Why One Doesn’t Always Lead to the Other Frame the Question Causation and correlation are often confused in both casual conversations and professional analyses. Understanding how they’re related—and where they diverge—is foundational for clear thinking in business, science, and everyday life. While both describe relationships between variables, only causation implies a direct link of cause and effect. Confusing the two can lead to flawed conclusions, wasted resources, and missed opportunities. In this post, we’ll unpack their connection, highlight key differences, and show how to apply this insight across disciplines. Correlation: A Pattern Without a Cause Definition : Correlation is when two variables appear to move together—either in the same direction (positive) or opposite directions (negative). But that’s it. It tells you nothing about why that relationship exists. Examples : Shoe size and reading level (in children):...

  Can the Butterfly Effect Be Proven or Is It Beyond Science? How a Tiny Rounding Error Exposed One of Science’s Deepest Mysteries   Big Picture Thinking The butterfly effect forces us to confront an uncomfortable truth: in a deterministic universe governed by precise laws, prediction can still be impossible. This paradox, discovered when meteorologist Edward Lorenz found that rounding a number slightly could radically alter weather simulations, isn’t just about computation—it reveals the very limits of knowledge. This article dives into chaos theory, explores where the butterfly effect holds firm, and why trying to prove or disprove it touches the edges of what science, philosophy, and even ethics can handle. Main keyword: butterfly effect | Variants: chaos theory, sensitive dependence, unpredictability What Is the Butterfly Effect, Really? When Edward Lorenz rounded 0.506127 to 0.506 and observed radically different weather outcomes, he wasn’t just experiencing a bug in earl...

Does Correlation Always Mean Causation? The Dangerous Illusion of Connection in a Data-Driven World When two things appear to move together, it can be tempting to assume one causes the other. But in the world of data, jumping to conclusions can lead us astray. Understanding the difference between  correlation  and  causation  is vital for clear thinking, whether you’re analyzing business trends, health data, or societal shifts. In this post, we unravel these commonly confused concepts, explore hidden variables, and look at how scientists and statisticians determine true causal links. What Is Correlation? Correlation  refers to a statistical relationship between two variables. When one changes, the other tends to change in a predictable way. This can be positive (both increase or decrease together) or negative (one increases while the other decreases). Think of it like two people riding an escalator—they go up together, but not because one is pulling the other. T...