1 Scientific Reasoning

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1 1 Scientific Reasoning Scientific reasoning is the foundation supporting the entire structure of logic underpinning scientific research. It is impossible to explore the entire process, in any detail, because the exact nature varies etween the various scientific disciplines. Despite these differences, there are four basic foundations that underlie the idea, pulling together the cycle of scientific reasoning. Observation Most research has real world observation as its initial foundation. Looking at natural phenomena is what leads a researcher to question what is going on, and begin to formulate scientific questions and hypotheses. Any theory, and prediction, will need to be tested against observable data. Theories and Hypotheses This is where the scientist proposes the possible reasons behind the phenomenon, the laws of nature governing the behavior. Scientific research uses various scientific reasoning processes to arrive at a viable research problem and hypothesis. A theory is generally broken down into individual hypotheses, or problems, and tested gradually. Predictions A good researcher has to predict the results of their research, stating their idea about the outcome of the experiment, often in the form of an alternative hypothesis. Scientists usually test the predictions of a theory or hypothesis, rather than the theory itself. If the predictions are found to be incorrect, then the theory is incorrect, or in need of refinement. Data Data is the applied part of science, and the results of real world observations are tested against the predictions. If the observations match the predictions, the theory is strengthened. If not, the theory needs to be changed. A range of statistical tests is used to test predictions, although many observation based scientific disciplines cannot use statistics. The Virtuous Cycle This process is cyclical: as experimental results accept or refute hypotheses, these are applied to the real world observations, and future scientists can build upon these observations to generate further theories. Differences Whilst the scientific reasoning process is a solid foundation to the scientific method, there are variations between various disciplines. For example, social science, with its reliance on case studies, tends to emphasis the observation phase, using this to define research problems and questions. Physical sciences, on the other hand, tend to start at the theory stage, building on previous studies, and observation is probably the least important stage of the cycle. Many theoretical physicists spend their entire career building theories, without leaving their office. Observation is, however, always used as the final proof. Martyn Shuttleworth (May 7, 2008). Scientific Reasoning. Retrieved from Explorable.com: 1

2 2 Hypothetico-Deductive Method The hypothetico-deductive method is one of the mainstays of scientific research, often regarded as the only 'true' scientific research method. This area fuels intense debate and discussion between many fields of scientific specialization. Concisely, the method involves the traditional steps of observing the subject, in order to elaborate upon an area of study. This allows the researcher to generate a testable and realistic hypothesis. The hypothesis must be falsifiable by recognized scientific methods but can never be fully confirmed, because refined research methods may disprove it at a later date. From the hypothesis, the researcher must generate some initial predictions, which can be proved, or disproved, by the experimental process. These predictions must be inherently testable for the hypothetico-deductive method to be a valid process. For example, trying to test the hypothesis that God exists would be difficult, because there is no scientific way to evaluate it. Generating and Analyzing the Data The next stage is to perform the experiment, obtaining statistically testable results, which can be used to analyze the results and determine whether the hypothesis has validity or has little foundation. This experiment must involve some manipulation of variables to allow the generation of analyzable data. Finally, statistical tests will confirm whether the predictions are correct or not. This method is usually so rigorous that it is rare for a hypothesis to be completely proved, but some of the initial predictions may be correct and will lead to new areas of research and refinements of the hypothesis. Assessing the Validity of the Hypothesis Proving and confirming a hypothesis is never a clear-cut and definitive process. Statistics is a science based on probability, and however strong the results generated; there is always a chance of experimental error. In addition, there may be another unknown reason that explains the results. Most theories, however solid the proof, develop and evolve over time, changing and adapting as new research refines the known data. Proving a hypothesis is never completely accurate but, after a process of debate and retesting of the results, may become a scientific assumption. Science is built upon these 'paradigms' and even commonly accepted views may prove to be inaccurate upon further exploration. A false hypothesis does not necessarily mean that the area of research is now closed or incorrect. The experiment may not have been accurate enough, or there may have beensome other contributing error. This is why the hypothetico-deductive method relies on initial predictions; very few hypotheses, if the research is thorough, are completely wrong as they generate new directions for future research. Martyn Shuttleworth (Oct 10, 2008). Hypothetico-Deductive Method. Retrieved from Explorable.com: 2

3 2.1 Falsifiability Falsifiability, as defined by the philosopher, Karl Popper, defines the inherent testability of any scientific hypothesis. Science and philosophy have always worked together to try to uncover truths about the world and the universe around us. Both are a necessary element for the advancement of knowledge and the development of human society. Scientists design experiments and try to obtain results verifying or disproving a hypothesis, but philosophers are the driving force in determining what factors determine the validity of scientific results. Often, they even determine the nature of science itself and influence the direction of viable research. As one theory is falsified, another evolves to replace it and explain the new observations. One of the tenets behind science is that any scientific hypothesis and resultant experimental design must be inherently falsifiable. Although falsifiability is not universally accepted, it is still the foundation of the majority of scientific experiments. What is Falsifiability? In its basic form, falsifiability is the belief that for any hypothesis to have credence, it must be inherently disprovable before it can become accepted as a scientific hypothesis or theory. For example, if a scientist asks, "Does God exist?" then this can never be science because it is a theory that cannot be disproved. The idea is that no theory is completely correct, but if not falsified, it can be accepted as truth. For example, Newton's Theory of Gravity was accepted as truth for centuries, because objects do not randomly float away from the earth. It appeared to fit the figures obtained by experimentation and research, but was always subject to testing. However, Einstein's theory makes falsifiable predictions that are different from predictions made by Newton's theory, for example concerning the precession of the orbit of Mercury, and gravitational lensing of light. In nonextreme situations Einstein's and Newton's theories make the same predictions, so they are both correct. But Einstein's theory holds true in a superset of the conditions in which Newton's theory holds, so according to the principle of Occam's Razor, Einstein's theory is preferred. On the other hand, Newtonian calculations are simpler, so Newton's theory is useful for almost any engineering project, including some space projects. But for GPS we need Einstein's theory. Popper saw falsifiability as a black and white definition, that if a theory is falsifiable, it is scientific, and if not, then it is unscientific. Whilst most 'pure' sciences do adhere to this strict definition, pseudo-sciences may fall somewhere between the two extremes. Pseudo Science According to Popper, many branches of applied science, especially social science, are not scientific because they have no potential for falsification. Anthropology and sociology, for example, often use case studies to observe people in their natural environment without actually testing any specific hypotheses or theories. Whilst such studies and ideas are not falsifiable, most would agree that they are scientific because they significantly advance human knowledge. Even 'pure' or 'true' science must make compromises and assumptions on occasion. The testing of any theory must take into account the equipment and resources available. Falsifiability is not a simple black and white matter because a theory, which is difficult to falsify at the time, may be falsified in the future. The Raven Paradox shows the inherent danger of relying on falsifiability, because very few scientific experiments can measure all of the data, and rely upon generalization. Conclusion For many of the pure sciences, the idea of falsifiability is a useful tool for generating theories that are testable and realistic. If a falsifiable theory is tested and the results are significant, then it can become accepted as a scientific truth. 3

4 The advantage of Popper's idea is that such truths can be falsified when more knowledge and resources are available. Even long accepted theories such as Gravity, Relativity and Evolution are increasingly challenged and adapted. The major disadvantage of falsifiability is that it is very strict in its definitions and does not take into account that many sciences are observational and descriptive. Martyn Shuttleworth (Sep 21, 2008). Falsifiability. Retrieved from Explorable.com: Verification Error Gathering Data Only to Support a Theory Verification error is the process of trying to fit results to match preconceived ideas. Can You Solve This? In psychology and social science, results are naturally open to personal interpretation, especially for emotive issues, such as ethics or racism. A researcher must incorporate mechanisms to reduce the chance of confirmation bias, or risk losing validity and credibility. In the physical sciences, there is a growing trend towards verification error. This is not so much because of flaws in experimental design, but because the intense competition for funding drives scientists to find the results needed. Before studying how verification error occurs, it is necessary to understand a little about verificationism, an underlying philosophical viewpoint. What is Verificationism? Verificationism is the belief that any scientific statement must be verifiable to have any relevance. Concisely, this means that it should make sense, and also be worth researching. In its broadest term, verificationism refutes metaphysics, and theology as nonprovable, and scientifically nontestable. Verificationism is rooted in Aristotelian philosophy, where the basic tenet is that, Only what is known can be tested. The idea fell out of use, superseded by falsifiability and later theories. The growth in verification errors, which devalue all of science, has revived verificationism. Science is in serious danger of devaluation in the public perception, due to media saturation with sensationalist results. Verificationism acts as a barrier against junk science and useless research. If a theory is non-testable, or has no conceivable use, then it is not worth testing. A lack of verification leads to bias, where a scientist fits the evidence around a theory, rather than using the scientific method. For example, the writer, Erich Von Daniken, postulated a theory that the Nazca lines were landing guides for alien spacecraft. This idea was built around a flimsy premise; they can only be seen fully from the air; therefore, they must have been constructed for the benefit of aliens. Verification error occurred because he started with the assumption that his theory was right and looked for evidence, however tenuous, to fit. Verificationism points out the fallacies in this thesis statement but, undaunted, he went on to find evidence to fit his theory. Proudly, he proclaimed that he had proved beyond doubt that aliens landed in the Nazca desert. If he had followed the scientific method, he should have said, The Nazca lines are visible from the air. There must be a reason. What is the most likely reason? He could then use reasoning, and Occam s razor, to generate simpler hypothesis and avoid verification errors. 4

5 Verification Errors - The Danger of Funding In the physical sciences, whilst verification error should be a little rarer, it can still happen. For example, a scientist may suddenly decide upon a theory and then look for evidence to fit it. The correct way is to look at the evidence and propose a hypothesis to explain it. Performing this task the wrong way around destroys validity. From the literature review onwards, the scientist will filter the data to take the research in a certain direction, throwing out any conflicting evidence. This flaw breeds junk science and pseudo-science, where results are cherry picked to suit an agenda. For example, if a tobacco company gives a grant into research upon the effects of smoking, they want results proving that there is no increased health risk. An environmental group will tend to pick results proving climate change, whereas oil companies will pick results showing that man is having no effect. A few press releases later, and the results are heralded as a breakthrough. Confirmation Bias In the social sciences and psychology, confirmation bias is probably the biggest single source of experimental error. It is where a researcher looks at the results from their research, and tries to fit them around pre-existing expectations and hypotheses. On occasion, this may be intentional, and driven by the need for results and research grants. At other times, it is a wholly subconscious process, as the human mind often tries to make patterns from randomness. In any subjective experiment, pre-conceived ideas will always draw people, even subconsciously, to information shaped by their pre-existing beliefs. For example, debates about experiments on animals, or science versus religion, are colored by belief rather than scientific facts. Polarization occurs when people begin to select information supporting their own pre-existing beliefs, and drift further from the middle ground. Ideally, pure science would remain away from the media, but it is impossible to remain in an isolated bubble. For example, it takes a brave scientist to stick their head above the parapet and produce evidence to deny global warming, because of the intense pressure to conform to the majority view. Whether they are right or wrong, science revolves around debate and conflict, but media and political pressure often forces verification error. Deliberate bias often involves picking information to support an opinion already cast. The worst offenders are politicians, who routinely manipulate data to garner votes. The British Government s Sexing Up of the case for war against Iraq is a prime example. They carefully selected information that supported the existence of Weapons of Mass Destruction, and made the case for war. Perhaps the best summary for confirmation bias was given by Nickerson, in He stated his belief that confirmation bias accounts for a large number of the disputes between nations. This premonition has proved to be true, and hundreds of thousands of lives were lost to confirmation bias. References: (1) Evans, J. St. B. T. (1989). Bias in human reasoning: Causes and consequences. Hillsdale, NJ: Erlbaum. Martyn Shuttleworth (Feb 27, 2008). Verification Error. Retrieved from Explorable.com: 5

6 2.3 Testability The crucial part of obtaining proof for a hypothesis is ensuring that it has an inherent testability. With the growth of the Intelligent Design movement in certain parts of the world, the debate about the very nature of science has once again entered the public consciousness. Proponents of ID are trying to attack the established theories about evolution and natural selection, claiming that they are non-testable and non-falsifiable. They conveniently neglect the fact that their own theory fulfills none of the established scientific methods. Testability the Bedrock of Theory Whenever you create a hypothesis to prove a part of a theory, it must be testable and analyzable with current technology. You may develop a great hypothesis to try to verify part of a theory but, if it involves a lot of resources and money that you do not have, it is effectively invalid. This is speculation and cannot be regarded as a genuine hypothesis. Whenever you design an experiment, from the start, it must always revolve around this central tenet of testability. If you follow the Steps of the Scientific Method and use all of the scientific elements, including initial conception, hypothesis generation and obtaining analyzable results, then you will have fulfilled the fundamental basics of testability. Whilst a hypothesis is never completely confirmed, if repeated experiments show that a hypothesis is true, it becomes accepted as fact. This process has fulfilled all of the conditions of testability and falsifiability and it is therefore scientific. A theory will always remain falsifiable at some point in the future, however compelling the present evidence. Evolution and natural selection fall within this field and have been tested rigorously over the intervening years. To be truly testable, a hypothesis should be falsifiable, with counter-testing and proof of the null hypothesis possible. A hypothesis such as An Intelligent Designer created the Earth and all life according to biblical laws has no testability, so remains within the realms of theology and pseudo-science. The Evolution of Darwin s Theories Darwin could not have known that an understanding of genetics and DNA would lead to a more rigorous testing of his theory, nor could Newton have had any inkling of particle accelerators and quarks. The fact that their theories are not the complete answer does not make their research unscientific. Their ideas were genuinely testable and the experimentation followed all of the established scientific principles. In fact, the fact that these theories are under question shows that they were true science. Neither theory was completely jettisoned but they have evolved and adapted to new technologies and methods. This is how science keeps moving ahead and avoids entrapment in dogma and speculation. Testability, even more than falsifiability, is probably the most fundamental aspect of science, separating it from theology, maths and philosophy. As an aside, this places archaeology and history closer to science than maths! Martyn Shuttleworth (Oct 19, 2008). Testability. Retrieved from Explorable.com: Post Hoc Reasoning Post hoc reasoning is the fallacy where we believe that because one event follows another, the first must have been a cause of the second. In some cases this is true, but other factors may be responsible. Good science must always try to uncover other reasons, with causal reasoning a powerful method for eliminating unlikely causes. Imagine that you are ill after eating fish at a restaurant. You automatically assume that the fish was to blame for your illness. 6

7 In reality, it might not have been the seafood; you ate and drank other foods, too. The plate may have been dirty or the sickness may have been caused by something you ate in the morning. You have a bad cold and take a well known remedy. A few days later, you feel better and, through post hoc reasoning, you are convinced that the remedy worked. Cheerfully, you resolve to buy that medicine the next time you have a cold. The problem is that you may have recovered, just as quickly, without that medicine. The remedy may even have caused you to take longer to get better; there really is no way of knowing. Science always tries to look at all of the possible causes and, whilst the abductive reasoning process often leads to the simplest one, it does not always mean that it is the definite cause. Causal reasoning is often used to judge the quality of information and generate reasonable hypotheses. In the cold remedy example, imagine a friend had the same sickness but took no medicine, and took much longer to recover. He has acted as a control and causal reasoning allows you to make a tentative assumption that the remedy worked. A good researcher must still take into account other factors; maybe your friend has a weaker immune system or smokes too many cigarettes. The reasoning process has led you to a reasonable hypothesis and helped you avoid post hoc reasoning, but you still need to test the hypothesis comprehensively. This is why any true scientific design must use include controls to make sure that any other possible causes of the final effect are ruled out and eliminated so that only the variable being tested is able to influence the results. If an effect follows a cause, it may be a link worth investigating, but may not be the only contributing factor. It may not be linked at all. Martyn Shuttleworth (Jul 4, 2008). Post Hoc Reasoning. Retrieved from Explorable.com: 7

Philosophy of Science. Ross Arnold, Summer 2014 Lakeside institute of Theology

Philosophy of Science. Ross Arnold, Summer 2014 Lakeside institute of Theology Philosophy of Science Ross Arnold, Summer 2014 Lakeside institute of Theology Philosophical Theology 1 (TH5) Aug. 15 Intro to Philosophical Theology; Logic Aug. 22 Truth & Epistemology Aug. 29 Metaphysics