This chapter details the practical, theoretical, and philosophical aspects of experimental science. It discusses how one chooses a project, performs experiments, interprets the resulting data, makes inferences, and develops and tests theories. It then asks the question, "are our theories accurate representations of the natural world, that is, do they reflect reality?" Surprisingly, this is not an easy question to answer. Scientists assume so, but are they warranted in this assumption? Realists say "yes," but anti-realists argue that realism is simply a mental representation of the world as we perceive it, that is, metaphysical in nature. Regardless of one's sense of reality, the fact remains that science has been and continues to be of tremendous practical value. It would have to be a miracle if our knowledge and manipulation of the nature were not real. Even if they were, how do we know they are true in an absolute sense, not just relative to our own experience? This is a thorny philosophical question, the answer to which depends on the context in which it is asked. The take-home message for the practicing scientist is "never assume your results are true."

The terms “inference to the best explanation” and “abduction” are often used interchangeably. Yet inference to the best explanation is concerned mostly with the choice of explanations by researchers based on their findings generated from data and is supposed to be in the last stage of an empirical study. In contrast, abduction is about formulating hypotheses and selecting the more promising ones to test before or after data collection, and thus corresponds to an earlier stage of inquiry. Given a set of data collected on a phenomenon, there are no systematic steps for reaching the best explanation for the phenomenon. That said, this concluding chapter suggests seven heuristics derived from Chapters 5 to 7, aimed at assisting management researchers to explain their findings. Constructing an explanation involves a great deal of judgement and decision making as well as requires imagination and intuition. The process arouses a variety of feelings and emotions. In short, explaining management phenomena is not just a scientific endeavor but also an art.

Although it is helpful to appreciate the general nature of explanations, we might reasonably want more than this. As this book is part of the Understanding Life series, we may expect to delve into details about kinds of explanations that are specific to the life sciences.

It is widely held that science is a (if not the) primary source of our knowledge of the world around us. Further, most accept that scientific knowledge is the best confirmed and well-supported kind of knowledge that we have of the world. But, how do scientific explanations lead to scientific knowledge? The short answer is that they do so via a method known as “inference to the best explanation” (IBE), sometimes called “abduction.” Before we get into the details of IBE, let’s take a quick look at an obvious way that scientific explanations give us scientific knowledge.

A general way of appreciating some of the main ideas of the previous chapter is to recognize that explanations aim at providing understanding. Scientists and philosophers agree that understanding is a (if not the) primary epistemic goal of scientific inquiry. Both explanation and prediction tend to be closely related to understanding. We want explanations in science because we want to understand why the world is as it is and how things happen. And, once we understand various phenomena, we can make accurate predictions about them. One simple, and widely accepted, way of assessing the quality of a given explanation is to look at the understanding it provides. Roughly, the better an explanation, the more understanding that explanation (if true) would provide. As philosopher Peter Lipton explained, the explanation that is the best is simply the explanation that, if true, would provide the deepest understanding of the phenomena being explained. That being said, some worry because it seems that we might misjudge how well we understand something.

In this chapter we’re looking at the relation between scientific explanations and predictions. It is tempting to think that the only difference between explanations and predictions is that one looks back and tells us how or why things happened as they did, and the other looks forward and tells us how or why certain things will (or are likely to) happen. This thought can seem particularly plausible when we consider that in many cases a good scientific hypothesis will both explain phenomena and allow us to make accurate predictions. Despite its initial plausibility, the idea that explanation and prediction are symmetrical is mistaken. The way to see this is to take a look at a particular theory of scientific explanation that entails this relationship between explanation and prediction. The particular theory of scientific explanation in question, the covering law model, which we discussed in Chapter 2, is false. One of the reasons that this theory of explanation fails helps illustrate the fact that explanation and prediction are not symmetrical.

Explanation is central to our lives, in general. We seem to have an innate (or nearly so) drive to explain and seek explanations. When our favorite app is not working, we want to know why, and we want to know how to fix it. When trying to understand why people engage in an odd behavior – refusing to wear masks during the COVID-19 pandemic, say – we want an explanation. What reasons do they have for doing something that seems so clearly misguided? Why are they resistant to expert advice on the issue? Ultimately, we seek explanations to help us understand and navigate the world around us.

While it isn’t necessary to do so, it’s often good to start a book by saying something that is clearly true. So, let’s do that. Science has had (and continues to have) a significant impact upon our lives. This fact is undeniable. Science has revealed to us how different species arise, the causes of our world’s changing climate, many of the microphysical particles that constitute all matter, among many other things. Science has made possible technology that has put computing power that was almost unimaginable a few decades ago literally in the palms of our hands. A common smartphone today has more computing power than the computers that NASA used to put astronauts on the Moon in 1969! There are, of course, many additional ways in which science has solved various problems and penetrated previously mysterious phenomena. A natural question to ask at this point is: why discuss this? While we all (or at least the vast majority of us!) appreciate science and what it has accomplished for modern society, there remain – especially among portions of the general public – confusions about science, how it works and what it aims to achieve. The primary goal of this book is to help address some specific confusions about one key aspect of science: how it explains the world.

In the previous chapter we discussed the importance of accurate explanations. Without explanations that are in fact accurate we cannot have genuine understanding. In this chapter we will explore whether false scientific theories can be used to generate accurate scientific explanations. Before jumping into this, let’s first briefly recall the relationship between scientific theories and scientific explanations. Scientific theories consist of laws, models, and principles. Together these components of scientific theories offer broad generalizations about the nature of the world.

Even though explanation plays a central role in science, it is not enough to simply come up with explanations. Scientists (and everyone else) must also evaluate explanations. After all, it’s clear that not every explanation is a good one, as well as that some explanations are better than others. For example, evolutionary theory provides a much better explanation of the diversity of life than, say, the hypothesis that all organisms appeared at the same time in their present form. But what makes one explanation better than another? Relatedly, how can we tell which of a set of competing hypotheses provides the best explanation?

In Chapter 8 I will reflect on the methods and the character of the metaphysical arguments employed in Chapters 1 to 7. I will argue that most of the arguments can be reconstructed as inferences to the best explanation. Objections against the use of inference to the best explanation in metaphysics can be countered with respect to the specific contexts discussed in this book. What counts as the best explanation in these contexts can be understood in terms of what is minimally required for giving an account of the success of certain features of scientific practice. I will situate this approach by contrasting it with naturalistic and aprioristic conceptions of doing metaphysics of science.

We examine some main arguments for skepticism. We consider in detail the argument from ignorance and various replies, including the relevant alternatives reply, the contextualist reply, the Moorean reply, and the inference to the best explanation reply.

The chapter introduces the iterative conception, according to which every set appears at one level or another of the mathematical structure known as the cumulative hierarchy, as well as theories based on the conception. The chapter presents various accounts of the iterative conception: the constructivist account, the dependency account and my own minimalist account. It is argued that the minimalist account is to be preferred to the others. A method – which I call inference to the best conception – is then described to defend the correctness of the iterative conception so understood. This method requires one to show that the iterative conception fares better than other conceptions with respect to a number of desiderata on conceptions of set. This provides additional motivation for exploring alternative conceptions of set in the remainder of the book.