Statistics is a set of theories and methods that can be successfully executed on any list of numbers and provably provides mathematically valid predictions unde

Statistics is a set of theories and methods that can be successfully executed on any list of numbers and provably provides mathematically valid predictions under a set of conditions that never occurs in the real world. This means, more precisely, statistics only works on data series that obey the law of large numbers, and combining/using 2 or more statistical predictions nearly always requires total independence of the predictions and their inputs, which is never the case (for one thing, they always occur on the same planet). Furthermore, the reason people make statistical predictions is to change the outcome, but doing anything to change the outcome always invalidates the statistical method used to collect the data. There are a couple of things statistics never does. Used correctly, it can never predict extreme values. It can never correctly predict values in systems that are too complex, where too many independent variables determine the outome. And "too many" is something like 50 to 500. It can never correctly be used to verify if a deliberate change worked. Machine learning is much the same, except it never provides mathematically valid predictions. Despite this, it should probably be mentioned that both do provide useful results, occasionally getting things very, very wrong. https://xkcd.com/605/ https://xkcd.com/605/