Hempel’s Account of Scientific Explanation





Hempel’s Account of Scientific Explanation

Abstract: Many things happen around us in the course of our everyday lives. While various objects are available for different purposes, some events occur that require explanations. Some of these occurrences yield explanations using generally accepted principles. However, for others, one can better understand them with the help of science. Whatever the case, any statement has to have the support of evidence. This helps one to make an informed decision. Similarly, there exists a big difference between explanation and prediction as determined by the presence or absence of facts. However, for every fact stated, there must be an underlying scientific law that forms the basis of that action which in turn becomes a theory. Scientific explanations are therefore the most reliable forms of providing a better understanding of procedures and phenomena as I will show in this paper.

            Carl Hempel came up with a certain model that sought to highlight how facts and laws relate. He termed it the Deductive Nomological model popularly referred to as DN. He further split it into the universal form and the probabilistic-statistical form. As a matter of principle, facts should have the backing of a series of statements that show reasons why the facts should be acceptable as the truth. The fact is thus an explanandum while the sentences or statements are explanans. Therefore, different laws that have a correlation can be of good use to explain a single fact.

            Laws are by their nature, certain conditions that are expected to be met for a specific expected result to happen. Those that are corresponding then end up forming a theory. Hence, in the DN model, when an event undergoes certain unrecoverable procedures, the event has to happen. This kind of thinking has quite a bit of bias because all the variables involved have to be constant. Any kind of alteration in the laws will result in a similar disruption in the outcome meaning the event might not occur as planned.

            These laws are the ones that maintain a balance in the human cycle. They therefore have a direct relationship with the activities that happen around us. There is significant human control on most of these activities yet in others; the laws of nature take their course. By coming up with this model, Hempel digs dip into the explanation debate and ensures that the importance of scientific explanation is easily detectable. Moreover, it is quite evident that he goes to great length to highlight the challenges that are lurking in the event of the use of any other form of explanation

            The regularity theory as per Sider is ‘’ a pattern in the world that holds at all times and places’’ (186). This shows that the laws of nature have existed for a very long time. They are also bound to be there way into the future. The ordinary way of life cannot satisfactorily answer all of life’s frequent happenings. Something more tangible has to be in use to offer credibility to the findings that constitute the explanations.

            In defining the DN model, it is of great importance to note this early that not all laws are applicable in some instances. This is largely due to the difference in their relevance compared to the explanandum at hand. For example, there are laws that are dependent on time while there are those that do not. In Newton’s laws of motion, any object anywhere will always be subject to gravitational force. This is what leads to the categorization of the theories as either fundamental or derived.

            Fundamental theories are because of a set of laws, which are irreversible and always converge in a given ratio. Derived theories are those that depend on laws with a high degree of possibilities. On the face value, without scientific research, it would not be easy to differentiate and explain why or how an explanandum occurred. This would have been chaotic, as many experiences would have had the same explanation. That difficulty diminishes by the use of science.

            In the universal form, the explanans that lead to an event occurring are finite. In other words, they are observable, well known and measurable. Usually, the statements that lead to a conclusion/explanandum are not assumptions. For example, if anyone who goes to church is a Christian and James goes to church, then it is correct to say that James is a Christian. The premises validly support the conclusion. On the contrary, in the case of the probabilistic-statistical form, there is a level of predictability without certainty involved. This implies that the probabilistic-statistical form deals with possibilities.

            When something is possible, it is often a subject of two mindsets. It could be because of one’s knowledge of the world or as a fact of nature. In order to get a clear and concise understanding of it, a scientific explanation offers a solution. It draws a line between divine intervention and human capabilities. Nevertheless, it gives room for speculation without being prejudicial to the truth. In this way, there is no confusion as to the accuracy of the arguments put across because of the logic in the scientific explanation.

            Hempel’s DN model has a few problems. In the case of the probability-statistical form, a certain law may be relevant to a specific condition now but it is not dependable to produce the same premise for another event in the future. This creates doubts and makes people uneasy in making unsubstantiated claims. In addition, the universal form suffers a lack of proper explanations on matters with statistical perspectives thereby making it selective in its usage.

            Furthermore, there is a common problem, which the DN model requires. It instructs that every explanation should have an accompaniment: the law. In addition, every law should also include the explanation. A majority of people have become victims to this problem. Several times people have used the cause of something as its explanation. For example, saying that the cause of an aircraft is the presence of a bird in one of the engines. What is expected is the actual narration of how the bird clogged the engine causing the propeller to malfunction. This in turn damaged the engine, which caught fire leading to the loss of stability and altitude.

            Another problem is that sometimes the explanations do not necessarily indicate that a given outcome was expected. Rather the explanans should show probable cause or raise the probability that an event will occur. Hempel calls this the requirement of ‘’maximal specifity( Strevens 8). This in my view is defective because it sets a high bar for anyone to use content that is knowledgable to the explainer and ignore useful content that is relevant but unknown to the explainer.

            Similarly, casual irrelevance is somehow misleading when one premise is read in tandem with the outcome. For example, if a horse is not given hay and then left unattended, should it manage to flee it cannot be said that it ran away because it was not fed hay. This would be a misconception. In the above example, one should go through all supportive statements to understand the context in which some statements come about. In fact, from such detailed scrutiny, a comprehensive overview of background information emerges thereby offering more insight into the subject matter.

            Symmetry, in explanations is when all the laws that support a claim are relevant yet these same claims fail to form genuine explanations that are convincing. Notably, it makes one to use effects in forecasting causes but he or she cannot use causes to predict their effects. Perhaps an additional claim is required in the section of causes to make the intended context more straightforward. It is common in instances where the different causes in use could produce different outcomes.

            Causation on its part is composed of cause and effect both of which interact on a regular basis. Normally, the effects are as a direct consequence of their causes. Therefore, a certain cause leads to a specific effect. However, every time there is an observation of an effect, there should be no linkage to just one cause. It has to be an association of different objects and processes along that chain that produce that explanandum. For example, when you turn on a tap, water flows. Who can say with finality that turning on a tap causes water to flow? Turning on a tap is a critical stage of that process but there also has to be water in the pipelines for that to happen. All the elements have to be present for the smooth flow of water upon turning on the tap.

            Effectively, a further examination of causal claims reveals that these claims tend to obey particular laws. This is the reason why it is an open secret that every cause has an effect. Furthermore, almost all effects have specific causes. This is similar to the discussion on explanation because both topics have interconnected variables that result in a given outcome. Explanation relies on statements or laws but causation goes hand in hand with effects. Effects should not just be statements. Rather they are supposed to have a detailed explanation and this would prompt the use of causes. Thus, causes lead to effects, which we become aware of through explanation. This would in turn mean that causes and explanations are connected.

            It is clear that our environment is full of different reactions and activities. The human brain is one that is curious and always yearns for information. The medium through which to get this information is only by explanation. However, there is a need for not only information but also for it to be accurate. This eliminates room for making assumptions hence the most suitable method is through scientific explanation. Though it has its limitations, it is a great way to use to relay information.

Works Cited

Conee, Earl B, and Theodore Sider. Riddles of Existence: A Guided Tour of Metaphysics. Oxford: Clarendon, 2005. 186. Print.

Scientific Eplanation. Macmillan Encyclopedia of Philosophy. second edition. 1967.8. Print.

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