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What can I know?

—Immanuel Kant, philosopher

The need to know things is essential to our being in the world. Without learning we die. At the very least, we must learn how to find food and keep ourselves warm. Most people, of course, are interested in more than these basics, in developing lives which could be described as fulfilling. We endeavour to learn how to develop relationships, earn a livelihood, cope with illness, write poetry (most of it pretty terrible), and make sense of our existence. At the core of these endeavours is the belief that somewhere there is the ‘truth’ about how things ‘really’ are.

Much of the seeking after truth is based on feelings and intuition. We may ‘believe’ that all politicians are corrupt (based on one lot of evidence), and at the same time believe that people are inherently good (based on a different lot of evidence). Underlying these beliefs is a tacit conviction that there is truth in what we believe, even though all of our observations are not consistent. There are useful expressions like: ‘It is the exception that proves the rule’ to help us cope with observations that do not fit neatly into our belief systems. But fundamentally, we want to be able to ‘prove’ that what we believe is correct (i.e. true), and we busy ourselves collecting examples that support our point of view.

Some beliefs are easier to prove than others. Arguments rage about politics and religion, mainly because the evidence which is presented in favour of one position is often seen as biased and invalid by those who hold differing or opposing points of view. Some types of observations, however, are seen as more ‘objective’. In science, it is these so‐called objective measures, ostensibly free from bias, that are supposed to enable us to discover truths which will help us, in a systematic way, to make progress in improving our understanding of the world and how it works. This notion may be thought to apply not only to the physical and biological sciences but also the social sciences, and even disciplines such as economics. There are ‘laws’ which are meant to govern the ways in which things or people or economies behave or interact. These ‘laws’ are developed from careful observation of systems. They may even be derived from controlled experiments in which researchers try to hold constant the many factors which can vary from one setting to another, and allow only one or two factors to vary in ways which can be measured systematically.

It is clear, however, that most of the laws which are derived are soon superseded by other laws (or truths) which are meant to provide better understanding of the ways in which the world behaves. This process of old truths being supplanted by new truths is often a source of frustration to those who seek an absolute truth which is secure and immutable. It is also a source of frustration to those who believe that science provides us with objective facts, and who cannot therefore understand why one set of ‘facts’ is regularly replaced by another set of ‘facts’ which are somehow ‘more true’ than the last lot. It is possible, however, to view this process of continual replacement as a truth in itself: this law states that we are unlikely¹ ever to find absolute truths or wholly objective observations, but we can work to refine our understanding and observations so that they more nearly approximate the truth (the world ‘as it is’). This assumes that there is in fact an underlying truth which (for reasons which we will discuss shortly) we are unable to observe directly.²

Karl Popper puts it this way:

We can learn from our mistakes. The way in which knowledge progresses, and especially our scientific knowledge, is by unjustified (and unjustifiable) anticipations, by guesses, by tentative solutions to our problems, by conjectures. These conjectures are controlled by criticism; that is, by attempted refutations, which include severely critical tests. Criticism of our conjectures is of decisive importance: by bringing out our mistakes it makes us understand the difficulties of the problems which we are trying to solve. This is how we become better acquainted with our problem, and able to propose more mature solutions: the very refutation of a theory – that is, of any serious tentative solution to our problem – is always a step forward that takes us nearer to the truth. And this is how we can learn from our mistakes.

From ‘Conjectures and Refutations. The Growth of Scientific Knowledge’ [1].

This is a very compassionate view of human scientific endeavour. It recognizes that even the simplest of measurements is likely to be flawed, and that it is only as we refine and improve our ability to make measurements that we will be able to develop laws which more closely approximate the truth. It also emphasizes a notion which the atomic physicist Heisenberg formulated in his Uncertainty Principle. The Uncertainty Principle states in general terms that as we stop a process to measure it, we change its characteristics. This is allied to the other argument which states that the observer interacts with the measurement process. Heisenberg was talking in terms of subatomic particles, but the same problem applies when measuring diet, or blood pressure, or even more subjective things like pain or well‐being. Asking someone to reflect on how they feel, and the interaction between the person doing the measuring and the subject, has the potential to change the subject’s behaviour and responses. This is contrary to Newton’s idea that measurement, if carried out properly, could be entirely objective. It helps to explain why the discovery of the ‘truth’ is a process under continual refinement and not something which can be achieved ‘if only we could get the measurements right’.

Consider the question: ‘What do you understand if someone says that something has been proven “scientifically”?’ While we might like to apply to the demonstration of scientific proof words like ‘objective’, ‘valid’, ‘reliable’, ‘measured’, ‘true’, and so on, the common meanings of these words are very difficult to establish in relation to scientific investigation. For all practical purposes, it is impossible to ‘prove’ that something is ‘true’. This does not allow you to go out and rob a bank, for example. An inability on the part of the prosecution to establish the precise moment of the robbery, for instance, would not excuse your action in a court of law. We cope in the world by recognizing that there is a substantial amount of inexactitude, or ‘error’, in all that we do and consider. This does not mean we accept a shop‐keeper giving us the wrong change, or worry that a train timetable gives us information only to the nearest minute when the train might arrive before (or more commonly after) the advertised time. There are lots of ‘gross’ measurements that are good enough for us to plan our days without having to worry about the microdetail of all that we see and do.

For example, it is very difficult to describe an individual’s ‘usual’ intake of vitamin C, or to relate that person’s intake of vitamin C to risk of stroke. On the other hand, if we can accumulate sufficient evidence from many observations to show that increasing levels of usual vitamin C intake are associated with reduced risk of stroke (allowing for measurement error in assessing vitamin C intake and the diagnosis of particular types of stroke), it helps us to understand that we can work with imprecise observations and laws which are not immutable. Moreover, it is important (for the sake of the growth of scientific knowledge) that any belief which we hold is formulated in a statement in such a way as to make it possible to test whether or not that statement is true. Statements which convey great certainty about the world but which cannot be tested will do nothing to improve our scientific understanding of the world. The purpose of this book, therefore, is to learn how to design studies which allow beliefs to be tested, and how to cope with the imprecision and variation inherent in all measurements when both collecting and analyzing data.