Spurious correlations: 15 examples

Sometimes a correlation means absolutely nothing, and is purely accidental (especially when you compute millions of correlations among thousands of variables) or it can be explained by confounding factors. For instance, the fact that the cost of electricity is correlated to how much people spend on education, is explained by a confounding factor: inflation, which makes both electricity and education costs grow over time. This confounding factor has a bigger influence than true causal factors, such as more administrators / government-funded student loans boosting college tuition. Even when there is a correlation that can be leveraged to solve a problem, for example a drug that was found to be better than placebo to help with a medical condition, it may work well for some people, and not well for others: the correlation is not universally strong. Also, causation only matters in specific contexts such as root cause analysis, where you need to fix the cause.