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Scientific Process



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    Scientists make progress by using the scientific method, a process of checking conclusions against nature. After observing something, a scientist tries to explain what has been seen. The explanation is called an hypothesis. There is always at least one alternative hypothesis. A part of nature is tested in a “controlled experiment” to see if the explanation matches reality. A controlled experiment is one in which all treatments are identical except that some are exposed to the hypothetical cause and some are not. Any differences in the way the treatments behave is attributed to the presence and lack of the cause.

    If the results of the experiment are consistent with the hypothesis, there is evidence to support the hypothesis. If the two do not match, the scientist seeks an alternative explanation and redesigns the experiment. When enough evidence accumulates, the understanding of this natural phenomenon is considered a scientific theory. A scientific theory persists until additional evidence causes it to be revised. Nature’s reality is always the final judge of a scientific theory. The following is a list of the thirteen science processes advocated by the American Association for the Advancement of Science (AAAS).

    These are best thought of as a set of intellectual skills that are associated with acquiring reliable information about nature. Each process is defined. In addition, comment about the inherent nature of each of the skills is provided. The first eight processes are called “basic processes” and are appropriate for children in the primary grades. The last five are called “integrated processes” and are more appropriate for children at grades four and above. 1. Observation This is the most fundamental of all of the processes.

    Observation may be defined as the gathering of information through the use of any one, or combination of the five basic senses; sight, hearing, touch, taste, and smell. The term observation may also be used to express the result of observing. In other words one might observe and, as a result, gather observations. These observations can also be called data or facts. Observation should suggest objectivity as opposed to the expression of opinion. For example, “John is a bad boy” is not an observation. On the other hand, “John exhibits behavior that we characterize as bad” is an observation. John is throwing Mary out of the window” is also an observation. Skilled observers seem to proceed from general perceptions of a system to more specific ones so the nature of skilled observing can be thought of as analytical. Systems are first observed as a whole then analyzed for subsystem information. Subsequently, subsystems can be treated as a whole and subjected to further analysis in an ever tightening spiral. Technology can be used to amplify the senses, which provides for even more analysis. A microscope, for example, is a technology that allows us to see things that are too small to be seen with the unaided eye.

    In summary, observation is an objective process of gathering data through the use of one’s senses applied in an analytical way. 2. Measurement Measurement is an observation made more specific by comparing some attribute of a system to a standard of reference. An example is when the length of an object is expressed in terms of the length of a meter or when the mass of an object is expressed by referring to a standard such as a gram. Measurement and observation are the only process skills that are actually two forms of the same thing.

    There are many standards that can be employed to make observations more precise. For instance, academic scholarship can be expressed as a grade. When one receives an “A” or a “C” in a course one’s performance has been measured relative to a standard. In a similar fashion, a four star restaurant is a measure of quality. As one can see from these examples, a measurement can range from highly concrete and universal to rather conditional. Observing that a stick is 27 centimeters long requires little interpretation. The meaning is rigid and understood by anyone, anywhere who is familiar with the metric system.

    On the other hand, being an “A” student may require considerable interpretation with meaning highly dependent upon circumstance. And, of course, with respect to restaurants, “Charlie’s Four Star Chili Dog Heaven” may be just that to some. The nature of this process entails the description of some system attribute by comparison to a standard of reference. 3. Classification Classification is the process of grouping objects on the basis of observable traits. Objects that share a given characteristic can be said to belong to the same set. The process is somewhat arbitrary depending upon the identifying trait selected.

    This is an important process to science because of an underlying assumption that kinship in one regard may entail kinship in others. Science assumes that to a large degree the universe is consistent with it’s laws holding true everywhere. Therefore, if a set of objects share one thing in common they may well share other attributes. Also there is the notion of realness or depth. This means that the more characteristic a trait is of a particular system the closer the kinship of those sharing the trait. For example, consider the idea of a marble. What makes a marble a marble? Is color a fundamental component of being a marble?

    We could, of course, classify objects on the basis of color but is that a deep characteristic? Because some marbles are red does it follow that all red objects are marbles? The issue here is that some traits are more expressive of the essence of the system than are other shared traits. In most instances we should seek to classify on the basis of traits that are essential to the idea of the set. The nature of the skill of classification is two fold. First, one must be able to identify traits and, second, one must select traits that express the deeper essence of the system. 4. Quantification

    Quantification refers to the process of using numbers to express observations rather than relying only on qualitative descriptions. The process has two major values. First, by expressing something in numerical terms the need for translation of verbal meaning is reduced. Second, the use of numbers allows mathematical logic to be applied to attempts to explore, describe and understand nature. For example, consider a situation where one might try to describe the various hair colors of students in a classroom. Try making an accurate and complete description using only qualitative terms.

    At best we might develop groupings based on generic names such as brunette and blonde (I am sure you will recognize these as an example of classification, as described above). The problem we must deal with is that terms such as brunette and blonde are not absolute. Some brunettes are obviously darker than others and some blondes are clearly lighter than others and we need a scheme that will allow us to express such variation. Numbers will allow us to do that. For example, suppose Sally’s hair is the darkest and Jeff’s is the lightest. If we assigned a number such as 10 to Sally and 1 to Jeff a ange has been developed within which all other shades must fall. Incidentally, the range could be reversed with Sally being assigned the 1 and Jeff the 10. It really doesn’t matter and the scheme would work just as well. Either way, by defining color as a number the arithmetic logic of sequencing can be applied to the problem. In so doing, we find that all observers of hair color are playing by the same rules. Everyone is accepting the quantitative logic so that there is no question that haircolor #7 must fall somewhere, probably midway, between #6 and #8. This leaves a lot of room for describing very subtle differences.

    For instance, we can have some idea of the color difference between a 6. 9 and a 7. 2 but try describing that difference in qualitative terms. Consequently the nature of the skill of quantification is one of application where one seeks precision of expression by transferring the logic of mathematics to qualitative problems. 5. Inferring Inferring is an inventive process in which an assumption of cause is generated to explain an observed event. This is a very common function and is influenced by culture and personal theories of nature. Inferences can also influence actions.

    For example, suppose two students receive a poor grade on some project. One student observes the poor grade and infers that the reason he received it was because the teacher does not like him. The second student infers that he did not spend enough time on the project. Would you expect these two students to respond to the poor grade in the same way? In both cases the event was the same but different inferences about the cause of the event would likely lead to very different responses. The nature of this process is inventive within the parameters of cosmology and culture. 6. Predicting

    This process deals with projecting events based upon a body of information. One might project in a future tense, a sort of trend analysis, or one might look for an historical precedent to a current circumstance. In either case, the prediction emerges for a data base rather than being just a guess. A guess is not a prediction. By definition, predictions must also be testable. This means that predictions are accepted or rejected based upon observed criteria. If they are not testable they are not predictions. It is not unusual to find that a data base is not available for a particular system.

    In such cases predictions about that system are not possible. The first step in understanding such a mystery system would be to observe it as objectively as possible with the goal being to acquire the data base necessary to develop predictions. The nature of the skill of predicting is to be able to identify a trend in a body of data and then to project that trend in a way that can be tested. 7. Relationships The process skill of relationships deals with the interaction of variables. This interaction can be thought of as a kind of influence–counter influence occurring among a system’s variables.

    Relationships can occur in multiple or single dimensions. An example of a multiple dimension relationship is speed with distance and time representing the two dimensions. Single dimension relationships can only be expressed relative to something else as in the location in space of some object. It’s location can only be expressed with relative terms such as over, under, near, far, etc. Of course the notion of relationships can be extended into more abstract areas such as values, friendships, marriage, love, and growth, for examples.

    The inherent nature of this skill is that it requires analytical thought in which one seeks to dissect cause from effect. The causal elements are the system’s variables and the effect is the resulting interaction. 8. Communication This process actually refers to a group of skills, all of which represent some form of systematic reporting of data. The most common examples include data display tables, charts and graphs. The process is conceptually fairly simple and is frequently based upon some type of two or three dimensional matrix with the axes representing the system variables and the cells of the matrix representing the interactions.

    The purpose of the communication skills is to represent information in such a way that the maximum amount of data can be reviewed with an eye toward discovering inherent patterns of association. The inherent nature of this process skill involves the ability to see and, consequently, represent information as the interplay among influencing variables. 9. Interpreting data This process refers to the intrinsic ability to recognize patterns and associations within bodies of data. Obviously there is a direct contribution of the previous process, communication, to interpreting data.

    The better the data is represented the more likely one will detect associations within the data. Interpretation probably requires creative thinking that results in the invention of conceptual umbrellas that can encompass the data. 10. Controlling variables This process is also a kind of group process because one may engage in several different behaviors in an attempt to control variables. In general, this skill is any attempt to isolate a single influent of a system so that it’s role can be inferred. The process is an attempt to achieve a circumstance or condition in which the impact of one variable is clearly exposed.

    The use of experimental and control circumstances, standardizing procedures and repeated measures are only a few of the ways in which variables might be controlled. Understanding the nature of the skill requires analytical thinking in which the system under study can be reduced to a set of interacting components. The next step is to establish some circumstance that allows the scientist to observe one component in isolation. 11. Operational definitions An operational definition is one that is made in measurable, or observable terms. An operational definition should not require interpretation of meaning nor is it relative.

    The meaning of the defined term must be explicit and limited to the parameters established for the definition. An operational definition is primarily a research tool and related to the concern for controlling variables. The major function of operational definitions is to establish the parameters of an investigation or conclusion in an attempt to gain a higher degree of objectivity. Consider this example. An investigator suggests that by applying some treatment a class of students will become more intelligent. The problem here lies with the word intelligent. What does it mean?

    And, more to the point, what does the investigator mean with the word? In order to evaluate the treatment intelligence must be defined in a very clear way. Perhaps, in this case, defining intelligence as a score on an IQ test makes sense. Such a definition (intelligence = IQ score) would be an excellent example of an operational definition. In terms of the nature of the skill, we are again dealing with analytical issues. An individual who is skillful a making operational definitions is one who can engage in reductionistic thinking that defines phenomena as a collection of components which interact. 12. Hypothesizing

    Hypothesizing is, again, an intrinsic and creative mental process rather than a more straight forward and obvious behavior. Consequently, developing this ability is probably less a product of linear training but more a function of intuitive thinking that emerges from experience. Defined, an hypothesis in a response, or potential solution, to a specific research question, or problem. For our purposes we will insist upon a rather rigid use of the term and will restrict it to the second step in the classical scientific algorithm as outlined in the next process. The kind of hypothesis one produces is also heavily dependent upon one’s world view.

    For instance consider the individual whose world view is based upon anthropomorphic and supernatural beliefs. This person is likely to develop anthropomorphic and supernatural hypotheses in response to questions so disasters become a function of angered gods and good times result from happier gods. A result of western science has been to replace the supernatural worldview with one steeped in the physics of Newton and the philosophy of Descartes. This has lead to an industrial age cosmology characterized by cause and effect and the separateness of the observed from the observer.

    Therefore current explanations (or hypotheses) are more likely to take the form of a causal chain forged link by link by observations which seem to lead inevitably to a conclusion. The nature of the skill is to recognize that objectively gathered observations are justified into an explanation as a result of having an operational cosmology, or worldview. Secondly, a good hypothesizer recognizes that explanations are inventions rather than discoveries and subject to rejection based upon facts. Beyond this no one is really sure how hypotheses are actually generated.

    No one really knows what goes on in the mind that results in the hypothesis but it seems reasonable to suspect that information, perceptions, and ideas are being combined and recombined until a particular combination seems to make sense. 13. Experimenting This process is a systematic approach to solving a problem. Usually experimenting is synonymous with the algorithm called scientific method which follows these five basic steps: PROBLEM—->HYPOTHESIS—->PREDICTIONS—->TEST OF PREDICTIONS—->EVALUATION OF HYPOTHESIS In experimentation each step emerges from the previous one.

    The purpose of the process is to judge the extent to which an hypothesis might be true and to set a standard whereby that judgement is made. Consequently, scientists tend to think in terms of probabilities of truth rather than absolute correctness. As a term, experimenting is frequently used in a much broader way than described here. It is not unusual to hear teachers applying the term to any activity or demonstration but, strictly speaking, experimentation should be reserved for the process of systematically evaluating hypotheses. THE CHARACTERISTICS OF THE SCIENTIFIC PROCESS Empirical * Systematic * Replication * Search for Causes * Provisional * Objective * Intersubjective Testability Empirical: Information or facts about the world based on sensory experiences. That is direct observation of the world, to see whether scientific theories or speculations agree with the facts. Systematic: All aspects of the research process are carefully planned in advance, and nothing is done in a casual or haphazard fashion. Replication: Repeating studies numerous times to determine if the same results will be obtained.

    Search For Causes: Scientists assume that there is order in the universe, that there are ascertainable reasons for the occurrence of all events, and that science can discover the orderly nature of the world. Provisional: Scientific conclusions are always accepted as tentative and subject to question and possible refutation. Objective: Scientists attempt to remove their bias, belief, preferences, wishes, and values from their scientific research. It means the ability to see and accept facts as they are, not as one might wish them to be.

    Deductive and Inductive Logic: Deductive reasoning (a priori assumption) is where a conclusion is inferred from more abstract premises or propositions (Monette et al, 1994). Inductive reasoning involves the derivation of general principles from direct observation-from particular instances to general principles (Rubin & Babbie, 1997). The scientific method is the system used by scientists to explore data, generate and test hypotheses, develop new theories and confirm or reject earlier results.

    Although the exact methods used in the different sciences vary (for example, physicists and psychologists work in very different ways), they share some fundamental attributes that may be called characteristics of the scientific method. Empirical * The scientific method is empirical. That is, it relies on direct observation of the world, and disdains hypotheses that run counter to observable fact. This contrasts with methods that rely on pure reason (including that proposed by Plato) and with methods that rely on emotional or other subjective factors.

    Replicable * Scientific experiments are replicable. That is, if another person duplicates the experiment, he or she will get the same results. Scientists are supposed to publish enough of their method so that another person, with appropriate training, could replicate the results. This contrasts with methods that rely on experiences that are unique to a particular individual or a small group of individuals. Provisional * Results obtained through the scientific method are provisional; they are (or ought to be) open to question and debate.

    If new data arise that contradict a theory, that theory must be modified. For example, the phlogiston theory of fire and combustion was rejected when evidence against it arose. Objective * The scientific method is objective. It relies on facts and on the world as it is, rather than on beliefs, wishes or desires. Scientists attempt (with varying degrees of success) to remove their biases when making observations. Systematic * Strictly speaking, the scientific method is systematic; that is, it relies on carefully planned studies rather than on random or haphazard observation.

    Nevertheless, science can begin from some random observation. Isaac Asimov said that the most exciting phrase to hear in science is not “Eureka! ” but “That’s funny. ” After the scientist notices something funny, he or she proceeds to investigate it systematically. HYPOTHETICAL DEDUCTIVE MODEL A general model of science (Popper, 1934, 1959; Hempel, 1970) in which science is stated as involving the formulation of hypotheses and theories from which particular occurrences can be deduced and thus also predicted and explained.

    As a model of scientific discovery and explanation the hypothetico-deductive method is advanced as an alternative to Baconian ‘inductive method’ (Bacon 1561-1626) in which the simple accumulation of instances gives rise to generalizations. The model is based on the idea that, rather than the accumulation of facts, hypotheses are essential to science as the basis of proposed generalizations and their empirical testing (cf. ‘falsification’). Philosopher Karl Popper suggested that it is impossible to prove a scientific theory true by means of induction, because no amount of evidence assures us that contrary evidence will not be found.

    Instead, Karl Popper proposed that proper science is accomplished by deduction. Deduction involves the process of falsification. Falsification is a particular specialized aspect of hypothesis testing. It involves stating some output from theory in specific and then finding contrary cases using experiments or observations. The methodology proposed by Popper is commonly known as the hypothetico-deductive method. A model which describes the way in which all the different branches of science work.

    It describes the process of finding out new information about the world. The Hypothetico-deductive model is a model of a method of scientific investigation and reasoning. It is very commonly used without anyone realizing they are using it. In a nutshell if is the way of discovering answers by: Recognizing a hypothesis (theory), and then proposing the expected outcome of an experiment of this hypothesis. You then make observations of the experiment/phenomena and deduce whether your observations disprove or prove your hypothesis and proposed outcome.

    This strays away from the belief that induction or deduction are the only methods of scientific inquiry, because it accepts the resulting answers as probable, or most likely to be true than not. This has been accepted as the best method of inquiry in some instances because sometimes the most probable answer is the best (accurate) answer we can expect to gain. The hypothetical-deductive method (HD method) is a very important method for testing theories or hypotheses. It is sometimes said to be “the scientific method”.

    This is not quite correct because surely there is not just one method being used in science. However, it is true that the HD method is of central importance, because it is one of the more basic methods common to all scientific disciplines, whether it is economics, physics, or biochemistry. Its application can be divided into four stages: 1. Identify the hypothesis to be tested. 2. Generate predications from the hypothesis. 3. Use experiments to check whether predictions are correct. 4. If the predictions are correct, then the hypothesis is confirmed.

    If not, then the hypothesis isdisconfirmed. HD reasoning involves starting with a general theory of all possible factors that might affect an outcome and forming ahypothesis; then deductions are made from that hypothesis to predict what might happen in an experiment. In scientific inquiry, HD reasoning is very important because, in order to solve science problems, you need to make hypotheses. Many hypotheses can’t be tested directly; you have to deduce from a hypothesis and make predictions which can be tested through experiments. HYPOTHETICAL INDUCTIVE MODEL

    About 1600 A. D. , it became apparent to several people – Galileo Galilei in Italy, Francis Bacon in England, Tycho Brahe in Denmark, and others – that there were no subtle logical errors in Aristotle’s use of the deductive method. The problem was that the deductive method, while wildly successful in mathematics, did not fit well with scientific investigations of nature. In order to use the deductive method, you need to start with axioms – simple true statements about the way the world works. Then you use these axioms to build your logical system of nature.

    If your axioms are true, everything that follows will be true, but Galileo and his contemporaries realized that the problem was that it was enormously difficult to determine “simple true statements about the way the world works”. In fact, they realized that it should be the goal of science – not the starting place – to determine what the “simple true statements about the way the world works” really are! Since 1600, the inductive method has been incredibly successful in investigating nature – surely far more successful than its originators could have imagined.

    The inductive method of investigation has become so entrenched in science that it is often referred to as the scientific method. The inductive method (usually called the scientific method) is the deductive method “turned upside down”. The deductive method starts with a few true statements (axioms) with the goal of proving many true statements (theorems) that logically follow from them. The inductive method starts with many observations of nature, with the goal of finding a few, powerful statements about how nature works (laws and theories). In the deductive method, logic is the authority.

    If a statement follows logically from the axioms of the system, it must be true. In the scientific method, observation of nature is the authority. If an idea conflicts with what happens in nature, the idea must be changed or abandoned. The philosophical definition of inductive reasoning is much more nuanced than simple progression from particular/individual instances to broader generalizations. Rather, the premises of an inductive logical argument indicate some degree of support (inductive probability) for the conclusion but do not entail it; that is, they suggest truth but do not ensure it.

    In this manner, there is the possibility of moving from generalizations to individual instances. Inductive reasoning consists of inferring general principles or rules from specific facts. A well-known laboratory example of inductive reasoning works like a guessing game. The participants are shown cards that contain figures differing in several ways, such as shape, number, and color. On each trial, they are given two cards and asked to choose the one that represents a particular concept. After they choose a card, the researcher says “right” or “wrong. Inductive reasoning is probabilistic; it only states that, given the premises, the conclusion is probable. Unlike deductive arguments, inductive reasoning allows for the possibility that the conclusion is false, even if all of the premises are true. [3] Instead of being valid or invalid, inductive arguments are either strong or weak, which describes how probable it is that the conclusion is true. A classical example of an incorrect inductive argument was presented by John Vickers: All of the swans we have seen are white. Therefore, all swans are white.

    Note that this definition of inductive reasoning excludes mathematical induction, which is a form of deductive reasoning. Deductive and Inductive are two methods of logical reasoning. Deductive reasoning works from the more general to the more specific, often referred to as a top down approach. We may start by thinking up a theory about our topic, and then narrow it down to a more specific hypotheses that we can test. We then narrow it down even more where we use observations to address the hypothesis, and then draw our conclusion.

    Inductive reasoning works the other way, moving from more specific observations to broader generalizations and theories. This is often called a bottom up approach. In inductive reasoning, we begin with specific observations and measures, begin to detect patterns and regularities, formulate some tentative hypotheses that we can explore, and finally end up developing some general conclusions or theories. THE MEDIEVAL APPROACH TO SCIENCE Science, as a word in the medieval vocabulary, seems alien as read from a strictly modern context. For the Medieval mind, science served as any body of knowledge that could be systematized.

    Subjects we would not consider science today fell under its auspices, including, most notably, theology. Theology interwove itself through medieval culture and learning, and was not perceived as a truly separate discipline from philosophy or the study of natural phenomena. The field of learning that most nearly resembled modern science was natural philosophy. Natural philosophers sought explanations and causation for substances and actions occurring in the natural world. However, it would be wrong to look for direct synonymy between medieval natural philosophy and modern science.

    Medieval natural philosophers did not engage in the sort of systematic programs of research in which modern scientists participate. They addressed whatever large ranging philosophical questions or trivial pursuits happened to engage their individual attentions. They focused their attention on a range of subjects that do not always directly correspond to modern scientific disciplines. Their interests in astronomy, physics, botany, agriculture, medicine, and mathematics may be roughly comparable to modern subjects, but subjects such as astrology and alchemy evoke a greater sense of dissidence.

    Astrology is a subject now entirely outside of the realm of accepted modern science. After the middle ages, alchemy engendered and was eventually replaced by chemistry as a scientific discipline. The seemingly haphazard approach, the apparently eccentric interests of the Middle Ages, can conceal the avaricious intellectual curiosity and keen analytical approach exhibited by medieval scholarship, particularly in the high Middle Ages. Though the age started in the decline of an empire, and ended in the wake of a devastating plague, the Middle Ages was no fallow time in the development of human thought.

    The natural philosophy, theology, and culture of the middle ages contributed to the formation of the modern sciences. However, it is not always a direct contribution. Throughout the passage of history, ideas may experience radical transformations. This does not negate their place in the history of knowledge. Few intellectual revolutions are so radical that they truly deconstruct all the pieces of the material on which the preceding paradigms were formed. Other ideas wane in one generation only to be reborn in an altered but still recognizable form in another generation.

    The most substantial contribution that medieval philosophy made to modern science is not the legacy of individual ideas, or even the introduction of new disciplines. Instead, medieval philosophers laid down much of the intellectual foundation, and articulated important assumptions on which the edifice of modern science is built. The medieval mind focused on questions that only sometimes resemble modern questions. Some of the abandoned questions seem trivial, based on misconception, or for some other reason no longer relevant to present culture.

    In other instances, the questions and answers are already imbedded in current culture and thought and grow increasingly invisible. (For instance, modern scientists frequently privilege an experimental approach to testing the ideas of scientific hypothesis. However, if the approach should violate the rules of basic Aristotelian reasoning, it’s unlikely that a resulting paper would pass peer review. ) The more profoundly ingrained a concept, the less consciously a culture acknowledges its meaning and consequence.

    With over half a millennia to absorb or reject the intellectual contribution of the Middle Ages, it is hardly surprising that the nature of this contribution has become obscured over time. The path between medieval philosophy and modern science is further convoluted because of the medieval lack of philosophical and disciplinary boundaries by which we now define the sciences. In order to obtain a just understanding of the medieval contribution, one must transgress the modern boundaries, even to the realm of theology. Science is ultimately a historical and cultural endeavor.

    The questions scientists choose to pursue are grounded in values and assumptions of the culture in which a scientist participates. We may take for granted the scientific focus of our own time, but the study of science in historical context emphasizes how cultural mores can influence the pursuit of knowledge. For instance, in the Middle Ages light served as an important theological metaphor. Consequently, natural philosophers exhibited a profound interest in optics and vision. As a result, some of the most modernly coherent medieval natural philosophy focuses on optics (Durant, 1950).

    Science may advance even under the influence of false assumptions. In The Structure of Scientific Revolutions Thomas Kuhn (1996) used the Ptolemaic system as an example of a now discarded scientific paradigm. Kuhn argued that paradigms last as long as they remain sufficiently functional for the cultures that use them. In Ptolemy’s cosmology the sun, moon, planets, and fixed stars rotated around an unmoving earth. This theory was put forward in the second century BC. Not until Copernicus in the 15th did the first serious challenge arise against the Ptolemaic paradigm.

    This was not because no one ever offered alternative opinions to Ptolemy, however. The century before Ptolemy, Aristarchus proposed a heliocentric cosmology. During the Middle Ages two Arabic astronomers raised issues concerning the Ptolemaic system. Abu Ishaq al-Bitruji criticized the epicycles and eccentrics required to explain the movement of the stars. Abu al-Rayhan Muhammad ibn Ahmad al-Biruni suggested that astronomic data could be explained by a daily rotation of the earth on its axis and an annual rotation around the sun (Durant, 1950). Still, Ptolemaic astronomy was sufficient for the needs of the time. Astronomy did ot stagnate for the 16 intervening centauries. Arabic scholars improved on the astronomy that they borrowed from the Greeks. When Arabic astronomy was transmitted to Europe, European scholars continued the work. It was the accumulation of this astronomic knowledge that paved the way to the Copernican revolution. The Arabic science of alchemy was based on premises that have long since been abandoned. Still, working under these premises Arabic and European scholars experimented and obtained a useful working knowledge of chemistry. It helped contribute to a knowledge base from which the modern discipline of chemistry was eventually shaped.

    Much of the intellectual legacy that Medieval Europe passed on to posterity was not wholly original to the time or place. The historical periods of greatest intellectual vitality, both in Arabic and European scholarship, came with exposure to literature of ancient Greece. This exposure to an alien culture brought about the absorption of new ideas and resulted in intensely creative response to the new concepts and knowledge. Both cultures reformed and synthesized the products of philosophy to fit their own needs. It is through the filter of this synthesis that later generations have come to read classical literature.

    Important medieval approaches to the acquisition of knowledge have done much to influence how we approach science in modern times. Naturalism passes on an essential assumption to the project of scientific inquiry. In naturalism we assume a rational, lawful universe, without which there is no purpose in the pursuit of science, because if the universe is truly arbitrary it is also unintelligible. The scholasticism of theologians such as Aquinas, Duns Scotus, and Ockham provide some of the most precise and carefully honed examples of logical and metaphysical reasoning available from history.

    Both logic and metaphysics form an important foundation on which scientific methodology is built. While some Greek philosophers identified mathematics as the language of natural philosophy, it was medieval natural philosophers such as Roger Bacon who first truly began to integrate mathematics into disciplines such as physics. In a further move towards creating a more mathematical science, Duns Scotus and Ockham introduced the idea of applying probability to truth claims. In addition to theory, the Middle Ages introduced an important institution to modern times in the form of the university.

    The practice of science is profoundly influenced by this academic institution. In both theoretical and physical ways, the history of science would be profoundly different without the contribution of the Middle Ages. Towards the end of the Middle Ages the philosophical groundwork for the development of modern science had already been laid. Additional refinements were to be added over time, but all the essential building blocks were there. Yet, it was another several centuries until a truly modern science began to emerge. It is hard to define what exactly lead to this delay.

    Certainly, one factor occurred in the middle of the 14th century, when Bubonic plague broke out. In less than a decade approximately a third of the population of Europe died. Successive, though less devastating outbreaks continued over the century that followed, further reducing the population. It was a difficult, demoralizing time and not conducive to further intellectual advances. Also, it was a time of transition, as the Middle Ages gave way to the Renaissance. The intellectual climate changed, and scholars began shifting their focus to different types of questions (Williams, 2007).

    Many Renaissance scholars disdained the contributions of their medieval predecessors, even as they built new theories on the works of the prior age. For a long time the contribution of medieval philosophy was downplayed or even entirely ignored. Even today, the contributions of the Renaissance and Scientific Revolution are easier to point to, as the works of Galileo, Descarte, Sir Francis Bacon, Newton, Hook and Boyle are more tangible. But without the foundation provided by medieval natural philosophy, it is doubtful that these later contributors could have offered as much as they did to future generations.

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