Tutorial: How to detect spurious correlations, and how to find the real ones

Specifically designed in the context of big data in our research lab, the new and simple strong correlation synthetic metric proposed in this article should be used, whenever you want to check if there is a real association between two variables, especially in large-scale automated data science or machine learning projects. Use this new metric now, to avoid being accused of reckless data science and even being sued for wrongful analytic practice. In this paper, the traditional correlation is referred to as the weak correlation, as it captures only a small part of the association between two variables: weak correlation results in capturing spurious correlations and predictive modeling deficiencies, even with as few as 100 variables. In short, our strong correlation (with a value between 0 and 1) is high (say above 0.80) if not only the weak correlation is also high (in absolute value), but when the internal structures (auto-dependencies) of both variables X and Y that you want to compare, exhibit a similar pattern or correlogram. Yet this new metric is simple and involves just one parameter a (with a 0 corresponding to weak correlation, and a 1 being the recommended value for strong correlation).