It Is Time To Adopt A ‘Red Team’ Approach In Science
The coronavirus crisis is causing the biggest economic and scientific crisis since the end of the Second World War. After the pandemic we need a radical reformation of the way science is organised and funded.
While tens of thousands are dying from the Covid-19 virus and hundreds of millions of people around the world are facing the loss of their jobs and livelihoods, the scientific community is deeply divided over the nature, spread and health risks of this new coronavirus.
The evident divisions and contradictory results published in thousands of new studies in recent weeks (and the conflicting scientific advice provided to governments) is causing growing confusion, anger and disarray both within the scientific community and the general public.
Scientific models and predictions based on widely differing assumptions are exposed as fatally flawed as never before. As a result, institutional science is hemorrhaging trust around the world while the way research is conducted and published is facing an existential crisis. In many ways, the coronavirus crisis has triggered the biggest crisis of science in modern history.
In light of this evident disarray, calls for a radical reform of quality control of scientific methods and claims and the introduction of institutional Red Teaming are gaining ground. In a compelling article in the journal Nature, Professor Daniël Lakens sets out the arguments for a radically new way to conduct quality-control of scientific research and its methods (see below).
The introduction of institutional red teams into the way science is organised and funded in open societies should be the top priority of a scientific reformation after the end of the Covid-19 crisis (see e.g. Red Teams Can Save Climate Science From Itself). This kind of scientific paradigm shift will be absolutely essential if we want to learn the biggest lesson of the coronavirus disaster. It would also help to ensure that free nations can avoid repeating similar catastrophic mistakes and disastrous policy decisions based on fallacious modelling and flawed predictions.