One might think that the theory-ladenness of measurement raises methodological questions. Measurements are supposed to deliver data, which in turn can be used as evidence for or against some theory. Obviously, if the truth of a theory to be tested by data delivered by a certain measurement procedure is presupposed by that very measurement procedure, we face a circularity problem. Fortunately, this is hardly ever the case, and current consensus has it that the theory-ladenness of measurement poses no significant methodological problem. After all, since the assumption that certain background beliefs are true is unavoidable for any measurement procedure, we can’t escape it. Moreover, as we will see in the next chapter, observation is also influenced by background beliefs.
Not only are background beliefs involved in the measurement process, there is an influence in the other direction as well: The measurement process can influence the measured. As a simple example, imagine a tiny amount of water the temperature of which is being measured by immersing a thermometer into it. Suppose the thermometer’s housing is quite a bit colder than the water. If the quantities involved are just right, immersing the thermometer, and then waiting to see where the mercury column settles, will of course change the water’s temperature, even if only by a small amount. Usually, the amount is so small as to be negligible. The situation is, however, different in the science of the very small – quantum mechanics (QM). There, we find the so-called measurement problem, which we will briefly discuss, together with other quantum oddities, in Chapter 11 .
2.4 Conclusion
We saw in this chapter that neither naked observation nor measurement provide access to evidence that is fully independent from background beliefs. Seeing something as evidence typically involves bringing certain additional information to the table, as our example of the physician who can see lesions on the gum as Koplik spots, and thus as evidence for measles, shows. Such theory-ladenness of observation can perhaps explain certain forms of scientific disagreement, as we suggest in Chapter 4. Measurement exhibits a similar dependence on background theories, often of a highly sophisticated kind. In addition, the use of measurement raises questions about appropriate measurement scales and also also about the conditions under which measurement changes the values of the parameters we want to measure. We next turn to the question what to do with the evidence we have collected.
Note
1 1 According to the Standard Model of particle physics, the strong and weak force are responsible for the existence of atoms. See https://home.cern/science/physics/standard-model for a quick overview.
Annotated Bibliography
Peter Achinstein, 2001, The Book of Evidence. New York and Oxford: Oxford University Press. Arguing against traditional accounts of evidence, Achinstein introduces a distinction between four different notions of evidence and tries to show how this differentiated approach can solve various puzzles, such as the Raven Paradox and the Problem of Old Evidence.
Hasok Chang, 2004, Inventing Temperature: Measurement and Scientific Progress. New York and Oxford: Oxford University Press. The book contains several studies of the historical development of the concept of temperature between the seveneenth and the nineteenth centuries. Among other things, the author argues that carrying out measurements not only leads to measurement results, but also to measurement conventions and determinations and refinements of scales.
Thomas Kelly, 2014, “Evidence,” Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/entries/evidence Discusses in greater detail the distinction between the phenomenal and the causal conceptions of evidence, which the author treats in a somewhat different way under the heading of evidence as a sign or mark of truth.
3 USES OF EVIDENCE
3.1 From Observation to Hypothesis
Empirical science often (but not always) starts with observations. You might notice that a squirrel searches through the trash bin in the park every day during your afternoon stroll. This makes you curious: Does the squirrel for some reason only look through the trash at that time, or does it forage at other times too? So you start observing the squirrel’s behavior in a more systematic way: You spend long hours in the park, carefully cataloguing the squirrel’s trash pursuits. Pretty soon, you see that your initial suspicion was correct – it only goes through the trash in the afternoons (you can rule out that the squirrel searches at night, because the trash is emptied every evening). You have found a regularity, and now you are curious about how to account for it. It quickly occurs to you that the squirrel probably only spends the effort of searching when there is a good chance to find food. Since the park is a favorite lunch spot for quite a few people, who often bring their dogs, some leftovers are bound to end up in the trash bin in the afternoon. By 1:00 p.m. (the time of your stroll), most people have left, the dogs are gone, and the area is fairly safe for the squirrel to commence its foraging. The regularity in the squirrel’s behavior seems best explained in terms of the regularity exhibited in people’s lunch behavior and that of their canine companions.
Admittedly, this little story is a far cry from what goes on in the empirical sciences, but it is close enough to extract some lessons from it. First, a scientist might notice something that deserves greater scrutiny. Often, in the case of scientific research, what we notice is influenced by background theories that we already accept (e.g., many animals exhibit activity patterns and squirrels seem quite adept at learning). Second, to confirm the suspicion that something unexpected is really going on, we resort to systematic observations (i.e., sitting in the park for hours on end). These will often, or at least sometimes, lead to the discovery of an interesting regularity, viz. squirrel foraging is concentrated in the afternoons. Third, the regularity prompts us to search for an explanation – unless we are dealing with extremely fundamental issues (more on that later), we assume that observed regularities aren’t just brute facts. So we try to find an explanatory hypothesis (e.g., squirrels rummage in trash cans at times when food is abundant and predators are scarce).
But how should we go about finding appropriate candidates for explanations? This is a tricky question and perhaps not something for which there exists a general answer. Some people reason by analogy, others in terms of plausibility, etc. How a person comes up with a candidate explanation is a psychological question. There doesn’t seem to be any foolproof methodology that guides the researcher from observed regularity to explanatory hypothesis. Rather, discoveries of explanations are often influenced by individual background beliefs, the person’s creativity, and many other factors that defy a clear systematization. Thus, philosophers of science have routinely distinguished between the context of discovery and the context of justification. The context of discovery is characterized by the circumstances in which a scientist finds, or discovers, her explanatory theories. Because different scientists go about finding explanations in