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With the technology at our disposal today, we are trusting computers to do more and more everyday things for us. While some may be skeptical, there isn’t anything as such wrong with this as the more data we generate, the more precise the algorithms powering these computers will become. However,
Why the confusion of these concepts has profound implications, from healthcare to business management
In correlated data, a pair of variables are related in that one thing is likely to change when the other does. This relationship might lead us to assume that a change to one thing causes the change in the other. This article clarifies that kind of faulty thinking by explaining correlation, causation, and the bias that often lumps the two together.
The human brain simplifies incoming information, so we can make sense of it. Our brains often do that by making assumptions about things based on slight relationships, or bias. But that thinking process isn’t foolproof. An example is when we mistake correlation for causation. Bias can make us conclude that one thing must cause another if both change in the same way at the same time. This article clears up the misconception that correlation equals causation by exploring both of those subjects and the human brain’s tendency toward bias.