Data, decision makers and statistics

 As StatGroup-19, we always had a goal: to not lose our temper. Ok, we didn't make it. Last night we lost it!

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In the   17/11/2020  episode DiMartedì broadcast, yet another approximate and, in some ways, tragicomic debate on the "data" of COVID19 took place. This time, we went very far, bringing the discussion on the methodological reasons behind using some indicators (such as Rt) to define the Italian regions' levels of epidemiological criticality. To discuss the issue, journalists and experts (no data experts). The debate was characterized by a complete absence of statistical culture, thus becoming "comical" at times for those who know the subject. At times it was "tragic" because it brought to light, even more, the cultural chasm that is engulfing everyone from decision-makers to commentators in this very delicate moment for the country. The absence of quantitative culture and numerical illiteracy are genuinely a social emergency, now amplified by the need to make decisions based on numerical scientific evidence.

But let's get to the evening theater:

Prologue: the eternal contradiction. In the common imagination, it is recognized that statistics is a fundamental tool for explaining and predicting the behavior of complex phenomena, such as an epidemic. Being statistics a subject of study in many university courses, it is also true that everyone thinks they know it. Therefore it is often considered unnecessary to involve the community, the people, who are dedicated to studying it professionally. This is also true when dealing with issues at the highest levels of criticality. Knowing some basic notions of Statistics does not make statisticians or data analysis experts. Just as knowing how to drive a car does not make Formula 1 drivers.

Act (of faith). The debate runs off quickly. The journalist Damilano tackles the issue and asks Professor Richeldi (pulmonologist and member of the CTS, ed) to explain the reasons behind the choice of "parameters and if that index over which we all squirm (Rt, ed) is still realistic. " Professor Richeldi's reply will become an eternal citation in every Statistics course, in memory of how often decisions are made without having an appropriate statistical culture. Turning to the journalist, he reveals: "it is good that we understand each other; I don't know if you knew about the Rt index last year? I personally didn't […] now if we do not trust and if we do not entrust the experts with a direction, in this country, in my opinion, we will hardly jump out". Then pressed by Floris on the quality of the data and the role of Merler (defined by the conductor as "super professor"), he closes with an epic ending: "the super professor has in his hands the data evaluating their quality,  and reliability, and how they are inserted in a multiparametric parameter (! ed) ". Here is the mirror of the moment, meaningless words used to justify unclear choices, relying on faith, faith in a single person, the only one in possession of the data.

Epilogue: Rt. So it's no good at all. Relying on experts is fine but taking a leap of faith towards what a single expert, that has been chosen as the sole repository of the statistical truth on the COVID issue claims is not acceptable from a scientific point of view. The approximation with which such an important topic as that of the scientific reasons behind the use of indicators is approached and discussed cannot pass. In this situation, some commentators also fail to understand the relevant role of statistics experts, even in the public debate. On Rt, we must be able to discuss. First of all, Rt cannot be calculated. It can be estimated that it can be given a statistical approximation accompanied by a level of uncertainty. As an estimate, it is based on a procedure (model), and different methods will provide different estimates, with different uncertainty assessments. In this context, the act of faith cannot be done. It should be explained and clarified why this indicator should be used and why the assumptions made are the best possible, as well as guaranteeing their empirical verifiability.

Conclusion. Unfortunately, it is more and more frequent to attend debates on relevant issues from a decisional perspective in which statistics are present, except to realize that the only ones absent are the statisticians. Not knowing/wanting to recognize the scientific importance of their role in studying complex phenomena, in the management of critical issues, and in decision-making processes is extremely serious. Statistics is the methodological essence on which all scientific research rests, both in the experimental and social fields. It has the same role that Philosophy has for science. It is its theoretical and conceptual foundation: research without statistics would be blind. We must therefore understand and accept that in the (also) public debate on methodological issues, the only ones who can explain and clarify doubts to decision-makers and citizens are the statisticians.