Statistics has evolved as one of the predominant applied sciences currently in the world. There is no place in the modern world where statistics doesn’t take a role in the decision-making process. Whenever you get a loan, a mortgage or whenever you are asking a bank for money, you’ll be object of a tight scrutiny, which is not based on how you look or how nice you are: your information gets inputted to complex statistical algorithms to assess how risky you are.
The list of instances where decisions are made based on statistics (and sometimes based solely in statistics) is huge. Not only financial institutions engross the list. Statistics is widely used in any big or small project where uncertainty is an element to consider in the analysis. When a construction company builds a road, there are certain situations that cannot be predicted (climate, labor strikes, etc) that need to be treated using some for of statistical analysis. The list goes on and on.
One thing that surprises me though is the fact, in spite of the fact that using statistical provides an immense potential, many decision makers trust blindly in the statistical predictions, without allowing space for a margin of error which is inherent to any statistical analysis. Sometimes I feel like decision makers feel that statistics is like some kind of oracle, a know-it-all entity that solves all the problems. Many times I find clients asking for models, and sometimes those models simply don’t exists. Do we live in a world of excessive adoration for statistics? Maybe so.