It is not without irony that statistics currently seems to live through an identity crisis, since this discipline is often named the “science of uncertainty”. If there is an identity crisis at all, in what way can it be conceived of? And why did this crisis come now – is there a nexus between the rise of ‘big data’, algorithm and the development of the discipline? Three developments can be identified that make the hypothesis of an identity crisis of statistics more tangible.
- A crisis of legitimacy: Statistics’ findings result from value-free and transparent methods; but this is nothing (and maybe never has been) which can easily be communicated to the broad public. Hence, in the age of ‘big data’, a loss of legitimacy: the more complex the collection of data, statistical methods and presented results are, the more disorienting an average person may find them, especially if there is a steep increase in the sheer quantity of statistical findings. Even for citizens who are willing to occupy themselves with data, statistics, and probability algorithms, Nobel prize laureate Danel Kahnemann has underlined the counterintuitive and intellectually challenging character of statistics (see his book “Thinking, fast and slow” or the classical article “Judgment under Uncertainty”). These peculiarities of the discipline lower the general public’s trust in the discipline: “Whom should I believe?”
- Crisis of expertise: Statistics has become part of a broad range of scientific disciplines, far beyond mathematics. But the acquisition of competences in statistics quite obviously has its limits. As Gerd Gigerenzer has pointed out already 13 years ago, “mindless statistics” has become a custom and ritual in sciences like f.ex. psychology. In recent years, this crisis of expertise has been termed the crisis of reproducibility (for data from a previous publication) or replicability (for data from an experiment); the renowned journal “Nature” has devoted in 2014 an article series onto this problem, with focus on f.ex. the use of p-values in scientific arguments. The report of the 2013 London Workshop on the Future of the Statistical Sciences is outspoken on this problem, and there is even a Wikipedia article on the crisis of replicability. Statisticians themselves defend themselves by pointing to these scientists’ lack of training in statistics and computation [see Jeff Leek’s recent article here], but quite obviously this crisis of expertise undermines the credibility of scientists as experts.
- Crisis of the societal function of the discipline: Statistics as a scientific discipline established itself alongside with the rise of nation-states; hence its close connection to national economies and the data collected across large populations. As has been explained in a “Guardian” article posted earlier in this blog, statistics served as the basis of “evidence-based policy“, and statisticians were seen by politicians as the caste of scientific pundits and consultants. But this has changed completely: Nowadays big data are assets of globalised companies which act across the borders of nation-states. This points to a shift in the core societal function of statistics, not longer serving politics and hence the nation, but global companies and their interests: Statistics leaves representative democracy, and it has become unclear how the benefits of digital analytics might ever be offered to the public. Even if the case is still obscure, the possible role of “Cambridge Analytica” in the U.S. presidential election campaign shows that the privatisation of expertise can be turned against the interests of a nation’s citizens.