An explanation should attempt to clarify the unfamiliar and make it familiar. However, how certain can we be that an explanation fulfils this notion? We do not have any direct access to absolute truth, and the only way to evaluate explanations is to compare them with our pre-existing knowledge. Naturally, different criteria are used within different areas of knowledge when it comes to both knowledge acquisition and production. In relation to knowledge acquisition, subjective and objective quality of explanations will be explored within the realm of religious knowledge systems and natural sciences. With regard to knowledge production, it will be examined how the level of certainty achievable within an area of knowledge influences the way it evaluates the quality of an explanation. In this regard, there seems to be a conflict between human sciences, which perceive explanations as simplified models, and mathematics, where any simplification would compromise the quality of the explanation. The relationship between the quality of knowledge and truth will be scrutinised through the lens of different areas of knowledge to evaluate whether good explanations have to be true. So is knowledge justified true belief or not?
Firstly, should the quality of explanation be measured on subjective or objective grounds? Let us consider faith as a way of knowing. An explanation can be considered good from a subjective perspective, when it is coherent with the set of beliefs of a knower. In religion, knowledge claims are accepted pragmatically through faith, being justified by a religious authority or subjective experience. In return, religious beliefs provide emotional support, along with a sense of certainty. This has important implications for the role of truth in religious explanations. As long as they are consistent with the teachings of the religion, and provide comfort and certainty to the believer, they do not need to correspond with objective reality. This allows religious explanations to be virtually permanent, immune to any new knowledge acquired by a believer that could contradict it. In other words, the quality of an explanation is evaluated subjectively by believers. For instance, my grandmother believes in God, and so she holds that the origin of our existence is in divine creation. This belief is coherent with the Christian doctrine. From her subjective perspective, the explanation that an omnipotent entity created universe and overlooks it provides a sense of comfort and protection. As comfort is associated with positive emotions, the creationist explanation is, from her subjective perspective, a good one – that is, despite being justified solely by Christian teachings. In religious knowledge systems, it may be irrelevant whether an explanation is true or objectively justified, as its quality is determined by the level of coherence with one’s belief and perception of reality.
However, it can be argued that a good explanation needs to correspond with objective reality. This is because explanations that are subjectively justified only cannot be the basis for reliable predictions. They portray an individual’s perception of reality, rather than reality itself. Thus, in order to derive reliable explanations from evidence, natural sciences aim to eliminate human subjectivity. To ensure that explanations are objective, there is extensive use of peer review to refute biased hypotheses. Unlike permanent religious explanations, the scientific ones are subject to change – they continually strive to approximate the explanations to correspond with reality. Only this way can the production of trustworthy knowledge be achieved. The scientific endeavours of Linus Pauling demonstrate both an objective and a subjective approach to deriving explanations. First, he developed the orbital hybridisation theory. This was based on evidence such as thermodynamic data, and stated that p orbitals in carbon compounds combine to form hybridised sp orbitals. This theory was approved by peer review, supported by experimental data and successful at predicting the behaviour of molecules. Thus, the explanation was objectively justified using reason, and reliably corresponds with reality. However, in another of his scientific ventures, Pauling believed high-dosages of vitamin C to increase survival rates of cancer patients. Unfortunately, peer reviews revealed sampling bias in his study, and several experiments failed to replicate the results. Nonetheless, he continued to advocate the effects of vitamin C. His explanation was subjectively justified by emotion and faith – however, his conviction seemed to have obscured his reasoning. Based on the evidence, Pauling’s explanation did not correspond with reality, therefore it did not allow to produce reliable knowledge. Thus, in natural sciences, explanations need to have strong objective justifications in order to be perceived as good.
Moreover, there are various degrees of certainty achievable in different areas of knowledge. How does this affect their perception of explanation quality in relation to certainty? It can be argued that good explanations do not need to be certainly true, as their role is to organise human knowledge instead of literal portrayal of reality. Explanations create a consistent model of reality with the purpose of communicating knowledge. They are communicated and understood as mere mental representations, so explanations do not have to provide a literal account of reality. This is consistent with the perspective of human sciences, which observe trends and patterns to generalise them to broader population. Explanations are formed using inductive reasoning – thus, even justified, valid explanations are reductions and models. For instance, psychology studies human behaviour, which is extremely complex, being affected by previous personal experience and innumerable environmental effects. As uncertainty is inherent in the production of psychological knowledge, explanations of the observed patterns of behaviour are simplifications. They do not take into account all factors that influence behaviour; and yet, psychological knowledge is valuable and applicable. This is supported by innumerable studies, such as the one by Tversky and Kahneman. They explained that irrational choice-making is systematic by their Prospect theory. Specifically, the cognitive bias “framing effect”, according to which a potential loss is perceived as more significant than a potential gain of the same value, was experimentally demonstrated. Although this does not take into consideration all potential environmental factors, or the personal history of choice makers, it is applicable to real life. This simplified model was understood and widely implemented in advertisement industry. Framing offers in terms of what the customer might ‘lose’ when they do not buy a product, instead of what they might gain when they buy it, is a well-established strategy. Therefore, explanations are empirical models that produce knowledge that can be communicated and implemented in real life. To achieve this, they do not need to be true in the sense of literally depicting reality – in fact, some level of simplifications is necessary.
On the other hand, how can simplified explanations organise human knowledge reliably, if they are, strictly put, not true? An untrue explanation produces knowledge that is misleading, hindering the development in an area of knowledge. For instance, in mathematics, explanations of why mathematical statements are true are accepted only in the form of rigorous proofs. These are valued because they are formed by deductive reasoning from true premises. Naturally, if the reasoning is valid, the conclusion reached from these premises must be true as well. However, using reason does not automatically mean that the explanation is justified – if the reasoning is not valid, the proof does not have any explanatory value. Thus, it cannot be considered a good explanation. For this reason, there is no spectrum of certainty in mathematics. Explanations are either true or untrue. However, a grain of doubt is introduced by Gödel’s incompleteness theorem, according to which a consistent axiomatic system cannot demonstrate that it does not contradict itself. This suggests that mathematical explanations are not true with complete certainty; they are only true within a closed axiomatic system, assuming it does not contradict itself. Overall, the reasoning that supports a good explanation in mathematics must be valid, without any room for simplification. The only point of uncertainty is whether axioms are true.
In conclusion, it might be difficult to reconcile the various parameters that determine knowledge quality across areas of knowledge. In religion, appraising explanations through subjective ways of knowing, proves useful in enhancing the personal experience of living. However, a potential concern arises here, as it may obscure other well-justified knowledge for the knower outside the religious system. Such knowledge could come from natural sciences, where objective justifications are key in obtaining a good explanation. They try to approximate to objective truth as much as possible in order to form reliable predictions. However, this creates a paradox. As there can be no proof in natural sciences – only experimental evidence that justifies an explanation beyond reasonable doubt – no complete certainty is achievable. Thus, natural sciences strive for good explanations that are true, but can never attain full certainty in their explanations. Similarly, human sciences have a high variability of studied subjects, and need to select the vital factors of the subject matter to form a reliable model. Thus, simplification of truth in explanations is necessary for the production and acquisition of understandable knowledge. Interestingly, mathematics reject this need for simplifications. And yet, explanations in mathematics cannot be evaluated based on whether they are true, as axiomatic systems cannot be proven to be consistent. Due to this inherent uncertainty, the explanations need to be valid, rather than true.
We can venture to say that in no area of knowledge can we be unequivocally certain that an explanation is true. Nonetheless, given that explanations are strongly justified, they are applicable and useful in everyday life – from improving one’s subjective experience to objectively curing an illness. Therefore, when evaluating explanations, the focus should not be on whether they are true, but how well-justified they are.