Abstract
Explaining phenomena is a primary goal of science. Consequently, it is unsurprising that gaining a proper understanding of the nature of explanation is an important goal of science education. In order to properly understand explanation, however, it is not enough to simply consider theories of the nature of explanation. Properly understanding explanation requires grasping the relation between explanation and understanding, as well as how explanations can lead to scientific knowledge. This article examines the nature of explanation, its relation to understanding, and how explanations are used to generate scientific knowledge via inferences to the best explanation. Studying these features and applications of explanation not only provides insight into a concept that is important for science education in its own right, but also sheds light on an aspect of recent debates concerning the so-called consensus view of nature of science (NOS). Once the relation between explanation, understanding, and knowledge is clear, it becomes apparent that science is unified in important ways. Seeing this unification provides some support for thinking that there are general features of NOS of the sort proposed by the consensus view and that teaching about these general features of NOS should be a goal of science education.
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Notes
Although this is widely held, it is not universally so. Theorists sympathetic to constructive empiricism, such as van Fraassen (1980, 1989), are apt to maintain that the primary aim of science is to construct theories that simply fit the observable phenomena—explanations that go beyond what is observable are superfluous at best and epistemically unacceptable at worst. Two points are worth keeping in mind here though. First, the primacy of explanation in science and science education is widely endorsed in science education reform documents. Take the National Research Council’s (2012) framework for K-12 science education, for example. This framework consists of three dimensions: scientific and engineering practices, crosscutting concepts, and disciplinary core ideas. Explanation figures prominently in the first two of these three dimensions. It is similarly emphasized in other education reform documents such as AAAS (1993), National Research Council (1996, 2007), and NGSS (2013). Second, constructive empiricists themselves recognize that explanation is important—they simply claim that the goal of such explanations is only to adequately describe the observable world, not to give us knowledge of theoretical entities. So, even constructive empiricists, who deny that explanation is the primary aim of science, can agree that explanation is important.
Philosophers with very diverse views on the nature of explanation and understanding agree that the two are closely linked with explanation providing a means to achieving understanding. See, for example, Achinstein (1983), de Regt (2009, 2013), de Regt and Dieks (2005), Friedman (1974), Harman (1986), Khalifa (2012), Khalifa and Gadomski (2013), Kim (1994), Kitcher (1981, 2002), Kvanvig (2003), Lewis (1986), Lipton (2004), Moser (1989), Railton (1993), Salmon (1984, 1998), Sober (1983), Strevens (2006, 2013), Trout (2002), van Fraassen (1980), von Wright (1971), Wilkenfeld (2013, 2014), and Woodward (2003). Even Hempel (1965) and Hempel and Oppenheim (1948), who thought that understanding was too subject dependent to be a proper focus of philosophical study, agree that explanations provide understanding.
This is one of the major criticisms that some press against String Theory. For discussion of this criticism, see Dawid (2013).
In some ways, this view may be a bit simplistic. As we will see later, one might think that explanation and prediction are very similar (perhaps even the same—one is simply backward looking in time and the other forward looking). Also, it is reasonable to think that making predictions is a factor that can make one explanation better than another. So, the relationship between explanation and prediction is not completely clear-cut. However, for the present purpose we can treat them as separate in which case explanation is primary.
Strevens (2013) argues that when it comes to science understanding can only be achieved via explanations, i.e., scientific understanding without explanation is impossible. de Regt (2009), Gijsbers (2013), Hindriks (2013), and Lipton (2009) disagree. It is worth noting that even though they argue that it is possible to gain understanding without having an explanation, none of these philosophers contest the claim that we typically come to have understanding via explanations or the claim that providing understanding is the goal of producing explanations.
Sometimes “NOS” is used in such a way that it refers to only nature of scientific knowledge, and at other times “NOS” is used to refer to both nature of scientific knowledge and scientific inquiry. Throughout this article, “NOS” will be used to refer to only nature of scientific knowledge.
This view, which is sometimes referred to as the “consensus view,” is largely the result of work by Norman Lederman along with his various collaborators. See, for example, Abd-El-Khalick (2004), Bell (2004), Cobern and Loving (2001), Flick and Lederman (2004), Hanuscin et al. (2006), Khishfe and Lederman (2006), Lederman (1999, 2007), Lederman and Niess (1997), McComas et al. (1998), McComas and Olson (1998), Osborne et al. (2003), Schwartz and Lederman (2008), Smith and Scharmann (1999), and Ziedler et al. (2002).
Thanks to an anonymous reviewer for suggesting this way of expressing the distinction between explanation and explaining.
Strevens (2008) endorses this general conception of explanation as well.
Admittedly, one might think that in order to have a “real” explanation one needs to provide information about causal dependence relations or information about natural laws governing the dependence relations in question. Two points about this concern are worth keeping in mind. First, insofar as it is plausible to think that there are genuine explanations in pure mathematics it is unclear that such restrictions are necessary. For an overview of reasons for and against thinking there are genuine explanations in pure mathematics, see Mancosu (2011). Second, the addition of such restrictions would not affect the points made in this paper. So, the reader is welcome to understand the account of explanation here as having such restrictions, if she believes they are necessary for genuine explanations.
For a discussion of these issues, see Franklin (1981).
It is worth noting that most philosophers agree with Trout that this sort of “aha” experience is neither necessary nor sufficient for possessing genuine understanding. However, several philosophers have argued that Trout’s arguments concerning this phenomenal sense of understanding do not undermine the importance of understanding as an aim of science or a goal of everyday explanations (de Regt 2004, 2009; Grimm 2009; Lipton 2009). Some (Lipton 2009; Grimm 2009) go so far as to argue that the “aha” experience may not be as misleading as Trout suggests, and it may in fact be a reliable guide to the presence of genuine understanding in some cases.
Although we tend to have an intuitive grasp of the notion, clearly defining “positive epistemic status” is difficult. Perhaps the best way to understand this notion is by noting other familiar states that have positive epistemic status such as rational beliefs and knowledge on the one hand and states that lack positive epistemic status such as mere opinions, beliefs arising from wishful thinking, and guesses on the other hand. The idea that understanding is a cognitive achievement (a mental state with positive epistemic status) is widespread; see Elgin (1996, 2007), Grimm (2001, 2006, 2014a, b), Khalifa (2011, 2012, 2013), Khalifa and Gadomski (2013), Kvanvig (2003, 2009), Pritchard (2009, 2014), Wilkenfeld (2013), and Zagzebski (2001).
Strevens (2013) draws a similar distinction between what he calls “understanding why” and “understanding with.” The former corresponds to de Regt’s UP, while the latter corresponds to his UT.
Plausibly, one way of understanding the distinction between UP and UT is in terms of their objects. The object of UP is natural phenomena; the object of UT is abstract content (theories).
There is at least one other important sense of understanding, which is often characterized as “understanding-that.” For example, Ted understands that the theory of relativity says X. While this sort of understanding is important, we will not focus on it here. The primary reason for this is that ascriptions of this sort of understanding are widely held to simply be knowledge ascriptions (see Kvanvig 2009; Pritchard 2009). For instance, when we say, “Ted understands that the theory of relativity says X” all we are saying is that “Ted knows that the theory of relativity says X.” While this sort of understanding is important, it is fairly clear that it is simply a precondition of UT. One cannot have the ability to use a theory to construct explanations without knowing what the theory says. So, for present purposes we can assume that individuals have this sort of understanding of the theories in question.
These two very different understandings of the nature of theories, the ordinary and the scientific, may be partly to blame for some of the misguided objections to evolution such as the “it is just a theory” objection. For more on this and other misguided objections to evolution, see McCain and Weslake (2013). See Kampourakis (2014) for an in-depth discussion of some of the common misunderstandings that lead to resistance against accepting evolutionary theory.
See National Research Council (2012) for a similar account of scientific theories. Of course, there are important issues concerning how we should understand the fundamental nature of scientific theories. It is not clear whether theories are best understood as ultimately collections of axiomatized sentences or nonlinguistic models, or both. Fortunately, for present purposes, it is not necessary to settle the debate concerning the fundamental ontology of scientific theories. For more on this, see Winther (2015).
Plausibly, using a theory to make predictions about phenomena is simply an aspect of constructing explanations. After all, if explanations amount to information about dependence relations, then they allow one to make predictions about what will happen as well as to explain what has happened—explanation and prediction are simply backward looking (explanations) and forward looking (predictions) approaches to the same dependence relations. For this reason, we will focus primarily on explanation and the role that it plays in UT and UP. If one is convinced that explanation and prediction are not this closely connected, then she can construe the current discussion of UT in terms of the ability to construct explanations and the ability to make predictions.
Both UT and UP come in degrees. One might have a greater or lesser degree of UT and so understand a particular phenomenon that is explained by the relevant theory to a greater or lesser degree. In the text, we are considering the high end of the scale of UT—being able to construct explanations of phenomena requires a fairly significant degree of UT. This level of UT is something to strive for as an educational goal, but it is good to keep in mind that one can exhibit UT and UP without reaching this highest level. For example, one might have UT of a theory and UP of phenomena by being able to appreciate explanations of phenomena that are provided by a particular theory without being able to come up with such explanations on her own.
Similar considerations apply to our knowledge of the explanatory hypotheses generated from theories and our knowledge of laws of nature.
Here, we are concerned with the sort of knowledge that is gained in a particular scientific context—how a theorist can come to know that a particular theory is true.
See Beebe (2009), Kuhn (1977), Lacey (2005), Lipton (2004), Longino (1990), Lycan (1988), McAllister (1996), McMullin (1982), Quine and Ullian (1978), Thagard (1978), and Vogel (1990) for a sampling of the explanatory virtues that have been proposed in various scientific contexts and the literature on the nature of explanation. Some might question whether the virtues listed are distinct—for example, some claim that predictive power is what separates ad hoc theories from those that are not (Popper 1959; Psillos 1999). So, they might question whether predictive power and non-ad hocness are actually two virtues rather than the same thing. Fortunately, for our purposes it is sufficient to simply have a grasp of what some of the most commonly cited explanatory virtues are.
For more on this, see Lipton (2004).
This picture of scientific knowledge is developed in much greater detail in McCain (2016).
It is worth noting that several of these examples of IBE from the history of science could be beneficial to cover in a classroom setting. For example, the discovery of Neptune nicely illustrates the struggle to deal with anomalous data. It also provides an exemplar of how explanatory reasoning can be used to make predictions and new discoveries. Additionally, discussion of Darwin’s arguments in support of evolution could help illustrate why evolutionary theory is so well supported. It could also show students how a theory can be well supported by being the best explanation of a large and diverse set of data.
This claim is somewhat controversial because some think that appeal to likelihood ratios alone may be the key to medical diagnosis. Although it is plausible that likelihood ratios can be important tools in medical diagnosis (see Grimes and Schulz 2005), it is not clear that even their use cannot be accounted for under the umbrella of IBE. For present purposes, it is enough to note that it has been claimed that IBE is the primary method of medical diagnosis, and this claim has some plausibility.
Despite its widespread use in science and everyday life, IBE is not without its critics. See van Fraassen (1989), Ladyman et al. (1997), Roche and Sober (2013), and Wray (2008). One of the lines of criticism that many find particularly troubling is the claim that IBE leads to probabilistic incoherence. In other words, critics charge that IBE is inconsistent with accepted theories of probabilistic reasoning such as Bayesianism. For a survey of responses to objections to IBE, see Douven (2011). For responses to the claim that IBE runs afoul of probabilistic reasoning, see Lipton (2004), McCain and Poston (2014), McGrew (2003), Okasha (2000), Psillos (1999), and Weisberg (2009). Some (Huemer 2009; Poston 2014) even go so far as to argue that without IBE probabilistic reasoning, including Bayesian confirmation theory, straightforwardly falls prey to the skeptical problem of induction.
See Schwartz et al. (2012) for reasons to think that these criticisms, particularly those of Allchin, are not grounded in the available empirical data.
Matthews (2015) also presses this criticism of the consensus view.
van Dijk (2014) reaffirms her earlier critical stance toward the consensus view.
See Abd-El-Khalick (2012) for a different line of response to these sorts of criticisms. Essentially, Abd-El-Khalick offers good reasons for doubting that critics have really made a case for their claims against the consensus view. This differs from the response that we have developed here because here we have reasons arising from the connections between explanation, understanding, and knowledge for thinking the consensus view is correct, and so for thinking these criticisms are mistaken.
Of course, this is not to say that there are no differences in our reasoning in ordinary life and our reasoning in scientific contexts. As we have already noted, in scientific contexts our reasoning tends to be much more precise and rigorous. Plus, there are reasons to think that probabilistic reasoning is handled much more accurately in scientific contexts than in our ordinary life. See Gilovich (1991), Kahneman et al. (1982), Nisbett and Ross (1980), Plous (1993), Tversky and Kahneman (1983), and Tweney et al. (1981) for discussions of the sorts of errors in probabilistic reasoning we are prone to in ordinary life.
Even critics of the consensus view, such as Irzik and Nola (2011, 2014), seem to accept that there is a great deal of unity to the sciences. Plausibly, it is for this reason that they propose a family resemblance approach as a holistic and unified way of understanding NOS. Erduran and Dagher (2014) also support the family resemblance approach as a method of properly capturing the unified nature of science. Although we have seen that exploring IBE and the nature of explanation gives good reasons for thinking that there is more unity to the sciences than mere family resemblances, the family resemblance approach is worth taking seriously for at least two reasons. First, as already noted, the family resemblance approach helps to emphasize that the view that science is “disunified by its very nature” is misguided. Second, this approach may be useful in making progress in solving the demarcation problem, which is largely left untouched by the present proposal. For alternative approaches to solving this problem, see the essays collected in Pigliucci and Boudry (2013).
It is worth emphasizing that we do not always need to appeal to laws in order to provide an explanation. In everyday contexts, we seldom appeal to natural laws to explain phenomena. Even in scientific contexts, we do not always appeal to a natural law in order to generate an explanation, though we often do.
This is a useful contribution to the already significant support that has been garnered for the consensus view of NOS. See the following for a sampling of this support: Abd-El-Khalick (2004), Bell (2004), Cobern and Loving (2001), Flick and Lederman (2004), Hanuscin et al. (2006), Khishfe and Lederman (2006), McComas et al. (1998), McComas and Olson (1998), Osborne et al. (2003), Schwartz and Lederman (2008), Smith and Scharmann (1999), and Ziedler et al. (2002).
The parenthetical remarks were added to the quote in order to emphasize the connections with the present discussion.
Some even go so far as to claim that a general facility in generating and using explanations is more important than domain-specific knowledge (Schank 2011).
For a nice illustration of how this can be done, see McComas and Kampourakis (2015).
For a more extensive defense of the consensus view of NOS and an articulation of how an understanding of various epistemological issues strengthens its foundation, see McCain (2016).
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McCain, K. Explanation and the Nature of Scientific Knowledge. Sci & Educ 24, 827–854 (2015). https://doi.org/10.1007/s11191-015-9775-5
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DOI: https://doi.org/10.1007/s11191-015-9775-5