How to spot the truth

Abstract

How to spot the truth 1 INTRODUCTION'Truth' is under attack, more so now than ever before, and for many reasons one of which is social media. We hear and read remarkable, often preposterous claims from many sources. This may be in political debate, the presentation of new products, or new health-enhancing exercises ranging from hot water pools to cold water swimming.These frequently claim to be 'scientific findings' often reporting 'new studies have shown' stories, underpinned by 'expert'opinion. They are amplified in the media until the next fad comes along.This pervasive form of persuasion is a war of beliefs, which in many cases may contradict accepted knowledge. It is always possible, in fact likely, that some of the more absurd claims may not involve, or even be properly aware of, current scientific understanding, in which case these claims may be logical, but based on incorrect assumptions or understanding. Flat earthers have a consistent world view, which is probably logical to them; it just is not compatible with other known facts. But truth is the first casualty of war, and now more than ever, we must equip ourselves and others with the skills needed to judge how valid the information we are presented with is. This is not as simple as it might appear. The context is all-important.Interestingly, there are far fewer exact rules, firm guidelines and exact cut-off levels than people might imagine for establishing the truth.Most scientific knowledge is rarely expressed in terms of utter validity, but rather expressed as 'fits' or 'is not inconsistent with' what we know already, or 'suitable for predicting performance' . For example, we now know that gravity can be bent; but Newton's simple straightline approximation has taken astronauts to the moon and back (sorry, flat earthers). In addition, although statisticians use words consistently and exactly, they do not use words such as 'population' and 'sample' in the way they are used in general parlance. Nor is the logic of statistics straightforward. For example, the most commonly used tests of likelihood assume 'if, and only if, these random samples were drawn from a single population, then. . . ' Logical and consistent, yes, but not well understood, even by some scientists. For example, in one study, trainee doctors, who should be reading this sort of stuff all the time, were given a simple statement using this test. When asked to choose the correct conclusion out of four possibilities, almost half made a wrong choice (Windish et al., 2007).