Models of scientific inquiry

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}} In the philosophy of science, the scientific inquiry model has two functions: first, to provide a descriptive account of how scientific investigations are conducted in practice, and second, to provide an explanation of > why scientific inquiry works as it seems to be done to gain genuine knowledge.

The search for scientific knowledge ended long ago. At some point in the past, at least in the time of Aristotle, the philosophers recognize that fundamental differences must be drawn between two types of scientific knowledge - roughly, the knowledge that and why the why . One thing to note is that every planet periodically reverses its course of motion by paying attention to the background of a fixed star; it is a very different thing to know why . Knowledge of the first type is descriptive; knowledge of the latter type is explanation. It is an explanatory knowledge that provides a scientific understanding of the world. (Salmon, 1990)

"Scientific investigation refers to the various ways in which scientists study the natural world and propose explanations based on evidence derived from their work."

Video Models of scientific inquiry

Scientific inquiry account

Classic model

The classical model of scientific research derived from Aristotle, which distinguishes precise forms of reasoning and reasoning, establishes a threefold scheme of abductive, deductive, and inductive conclusions, and also treats compound forms such as reasoning by analogy.

Pragmatic model

Logical Empiricism

Wesley Salmon (1990) initiated his historical survey of scientific explanations with what he calls the accepted view, as received from Hempel and Oppenheim in the years beginning with their Study in the Logic of Explanations (1948) and culminated in the Hempel Scientific Aspects of Explanation (1965). Salmon concluded his analysis of this development using the following Table.

In this classification, the deductive-nomological (D-N) explanation of an event is a valid deduction which concludes that the outcome to be described actually occurs. The deductive argument is called explanation , the premise is called explanans (L: explains ) and the conclusion is called eksplanandum (L: to explain ). Depending on a number of additional qualifications, annotations may be ranked on a scale from potential to true .

Not all explanations in science are D-N types. The explanation of inductive-statistics (I-S) describes the occurrence by putting it into statistical law, not categorical or universal law, and the mode of subsumption itself is inductive rather than deductive. The D-type N can be seen as a case of a more general limitation of the type I-S, the size of the involved certainty being complete, or probability 1, in the previous case, whereas it is incomplete, the probability of & lt; 1, in the latter case.

In this view, the way of D-N reasoning, in addition to being used to describe a particular event, can also be used to explain general order, simply by deducing it from the more general law.

Finally, the deductive-statistics type of explanation (D-S), which is assumed to be true as a subclass of the D-N type, describes statistical regularity with the deduction of a more comprehensive statistical law. (Salmon 1990, pp. 8-9).

Such is the view of receiving from scientific explanations from the standpoint of logical empiricism, that Salmon says "held power" during the third quarter of the last century (Salmon, p.10).

Maps Models of scientific inquiry

Theoretical options

During the course of history, one theory has succeeded the other, and some have suggested further work while others seem satisfied just to explain the phenomenon. The reason why one theory has replaced the other is not always clear or simple. The philosophy of science includes the question: What criteria are met by 'good' theory. This question has a long history, and many scientists, as well as philosophers, have considered it. The goal is to be able to choose one theory as better than another without introducing a cognitive bias. Some of the criteria that are often proposed are summarized by Colyvan. A good theory:

Elegant (formal elegance; no ad hoc modifications)
Contains some arbitrary or adjustable elements (simplicity/stinginess)
Agree with and explain all existing observations (uncertain powers)
Make detailed predictions about future observations that can disprove or falsify a model if it is not covered.
Helpful: Colyvan's emphasis is not only on prediction and forgery, but also on the theory seminality in suggesting future work.

Stephen Hawking supports point 1-4, but does not mention the fruit. On the other hand, Kuhn emphasized the importance of seminality.

The goal here is to make a choice between the less arbitrary theories. However, this criterion contains a subjective element, and is more heuristic than part of the scientific method. Also, such criteria do not always decide between alternative theories. Citing Birds:

"They [the criterion] can not determine the scientific choice First, which features of the theory meet this criterion can be debated (eg i) what is the simplicity of the ontological commitment of the theory or the mathematical form?) Second" This criterion not right, so there is room for disagreement about the extent to which they are holding. Thirdly, there can be disagreement about how they should be given relative weights to one another, especially when they are at odds. "

It can also be debated whether existing scientific theories meet all these criteria, which may represent an unachieved goal. For example, the explanatory power over all observations (criterion 3) is met by no single theory today.

Whatever may be the ultimate goal of some scientists, science, as it is currently practiced, depends on some of the world's overlapping descriptions, each of which has an application domain. In some cases, this domain is very large, but in other countries quite small.

The desiderata of the "good" theory has been debated for centuries, returning perhaps even earlier than Occam's razor, which is often taken as an attribute of good theory. Occam's razor probably falls under the title of "elegance", the first item on the list, but the overly eager application is warned by Albert Einstein: "Everything should be as simple as possible, but not simpler." You could say that parsimony and elegance "usually draw a different direction". The falsifiability items in the list pertain to the criteria proposed by Popper as the demarcation of scientific theories of theory such as astrology: both "explain" observations, but scientific theories take the risk of making predictions that decide whether it is right or wrong:

"That must be possible for the empirical scientific system denied by experience."
"Those of us who do not want to expose their ideas to the dangers of refutation do not take part in the game of science."

Thomas Kuhn argues that the change of the scientist's view of reality contains not only subjective elements, but the result of group dynamics, "revolution" in scientific practice that produces a paradigm shift. For example, Kuhn suggested that the heliocentric "Kopernic Revolution" replaces Ptolemy's geocentric view not because of empirical failure, but because of a new "paradigm" that uses control over what scientists feel as a more useful way of pursuing their goals..

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Aspects of scientific questions

Deduction and induction

The deductive logic and inductive logic are very different in their approach.

deductive logic is the reason for proof, or logical implication. This is the logic used in mathematics and other axiomatic systems such as formal logic. In a deductive system, there will be unproven axioms (postulates). Indeed, they can not be proven without circularity. There will also be primitive terms that are not defined, because they can not be defined without a circle. For example, one can define a line as a set of points, but to then define a point as the intersection of the two lines will be circular. Due to the interesting characteristics of this formal system, Bertrand Russell with humor calls mathematics "the field in which we do not know what we are talking about, or whether or not what we say is true". All the theorems and consequences are evidenced by exploring the implications of axiomata and other theorems previously developed. New terms are defined using primitive terms and other derivative definitions based on these primitive terms.

In a deductive system, one can correctly use the term "proof", as it applies to the theorem. To say that a proved theorem means it is impossible for the axioms to be right and the theorem to be wrong. For example, we can perform simple syllogism as follows:

Arches National Park is located within the state of Utah.
I'm standing in Arches National Park.
Therefore, I stand in the state of Utah.

Note that it is not possible (assuming all the trivial qualifying criteria are given) to be in Arches and not in Utah. However, one can be in Utah while not in Arches National Park. The implications only work in one direction. The statement (1) and (2) the joint statement implies (3). The statement (3) does not imply anything about the statement (1) or (2). Notice that we have not proven statement (3), but we have shown that statements (1) and (2) imply a joint statement (3). In mathematics, what is proved is not the truth of a particular theorem, but that the axiom of the system implies a theorem. In other words, it is impossible for the axioms to become reality and the theorems to be wrong. The power of a deductive system is that they are sure of the outcome. The disadvantage is that they are abstract constructs which, unfortunately, are one step removed from the physical world. They are very useful, however, as mathematics has given great insight to the natural sciences by providing useful models of natural phenomena. One result is the development of products and processes that are beneficial to mankind.

Learning about the physical world requires the use of inductive logic . This is the logic of theory development. This is useful in very different companies such as science jobs and crime detectives. One makes a series of observations, and tries to explain what one sees. The observer forms the hypothesis in an attempt to explain what he has observed. The hypothesis will have implications, which will lead to certain other observations that will naturally result from repeated experiments or make more observations of slightly different circumstances. If predicted observations are correct, people are happy that they may be on the right track. However, the hypothesis has not been proven. The hypothesis implies that certain observations must follow, but positive observations do not imply hypotheses. They just make it more trustworthy. It is likely that several other hypotheses can also explain known observations, and may be better with future experiments. The implications flow in only one direction, as in the syllogism used in the discussion of deduction. Therefore, it is never true to say that scientific principles or hypotheses/theories have been proven. (At least, not in the strict sense of evidence used in deductive systems.)

The classic example of this is the study of gravity. Newton established the law for gravity which states that the force of gravity is directly proportional to the product of the two masses and inversely proportional to the square of the distance between them. For more than 170 years, all observations seem to validate the similarities. However, the telescope eventually becomes powerful enough to see little difference in the orbit of Mercury. Scientists try everything imaginable to explain the difference, but they can not do it using objects that will be in the orbit of Mercury. Finally, Einstein developed his theory of general relativity and explains the orbit of Mercury and all other observations known to be related to gravity. Over a long period of time when scientists made observations that seemed to validate Newton's theory, they did not, in fact, prove his theory to be true. However, it seems that at that time they did it. Just take one counterexample (Mercury orbit) to prove that something is wrong with the theory.

This is typical of inductive logic. All observations that seem to validate the theory, do not prove its truth. But one counter example can prove it wrong. That means deductive logic is used in the evaluation of theory. In other words, if A implies B, then B does not imply A. General Relativity Theory of Einstein has been supported by many observations using the best scientific instruments and experiments. However, his theory now has the same status with Newton's theory of gravity before seeing the problem in Mercury's orbit. It's very credible and validated with everything we know, but it's not proven. Only the best we have today.

Another example of true scientific reasoning is shown in the current search for the Higgs boson. Scientists at the Compact Muon Solenoid experiment at the Large Hadron Collider have experimented with data showing the presence of the Higgs boson. However, realizing that the results may be explained as background fluctuations and not the Higgs boson, they are cautious and await further data from future experiments. Guido Tonelli says:

"We can not rule out the presence of the Higgs Standard Model between 115 and 127 GeV because the few superfluous events in this mass region that appear, quite consistently, in five independent channels [...] As today what we see is consistently good with background fluctuations or by the presence of the boson. "

A brief description of the scientific method will then contain these steps as a minimum:

Create a set of observations about the phenomenon being studied.
Form a hypothesis that might explain the observation. (Inductive Step)
Identify the implications and results to be followed, if the hypothesis is correct.
Experiment or other observations to see if any predicted results fail.
If the predicted result fails, the hypothesis is proved wrong because if A implies B, then B does not imply A. (Deductive Logic) It is necessary to change the hypothesis and return to step 3. If predicted results are confirmed, the hypothesis is not proven, but can be said to be consistent with known data.

When a hypothesis has persisted in a sufficient number of tests, it can be promoted to a scientific theory. Theory is a hypothesis that survives many tests and seems consistent with other scientific theories. Because theory is a promoted hypothesis, it is the same 'logical' species and has the same logical limitations. Just as the hypothesis can not be proved but arguable, the same applies to theory. It is a difference of degree, not kind.

The argument of the analogy is another type of inductive reasoning. In arguing of the analogy, one concludes that because two things are similar in some respects, they tend to be the same in another. This is, of course, an assumption. It is natural to try to find an equation between two phenomena and wonder what can be learned from that equation. However, to note that the two things of sharing attributes in some ways do not imply similarity in anything else. It is possible that the observer has taken account of all shared attributes and other attributes will be different. The argument of analogies is an unreliable method of reasoning that can lead to erroneous conclusions, and thus can not be used to establish scientific facts.

src: abt.ucpress.edu

Source

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External links

For an interesting explanation of the orbit of Mercury and General Relativity, the following links are useful:

Mercury perihelion precession
Confrontation between General Relativity and Experiments

Source of the article : Wikipedia

Models of scientific inquiry

Jumat, 15 Juni 2018

Models of scientific inquiry

Video Models of scientific inquiry