This L^1 metric (to measure correlation) is more robust.īelow are a few examples of spurious correlations. Besides, the standard correlation (an L^2 metric) is sensitive to outliers, and indeed, not a great metric. Sometimes, it does not matter as long as it works, for instance a drug that works against a medical condition even if nobody knows why.įor more articles about cause versus correlations, or correlations in general, click here. But per capita, there is actually much more crime in rural areas. Cities have more people, so there’s more crime. Also, causation only matters in specific contexts such as root cause analysis, where you need to fix the cause. Urban areas and crime rates: People in rural areas often point to ‘crime-ridden’ cities to draw contrast to their wholesome rural hometowns. This confounding factor has a bigger influence than true causal factors, such as more administrators / government-funded student loans boosting college tuition.Įven 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. Height above sea level and temperature are examples. A negative correlation is a relationship between two variables in which an increase in one variable is associated with a decrease in the other. 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. When two variables have a negative correlation, it means that when one variable rises, the other falls. 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.
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