To most people a theory is a hunch. In science, a theory is the framework for observations and facts, Tanner told Live Science. Some of the things we take for granted today were dreamed up on pure brainpower, others by total accident. But just how much do you know about the origin of things? Here, we've invented a quiz about 15 of the world's most useful inventions, from adhesives. The earliest evidence of science can be found in prehistoric times, such as the discovery of fire , invention of the wheel and development of writing.
Early tablets contain numerals and information about the solar system. Science became decidedly more scientific over time, however. His works included the principle that an inquiry must be based on measurable evidence that is confirmed through testing. The artist, scientist and mathematician also gathered information about optics and hydrodynamics. This is a model in which Earth and the other planets revolve around the sun, which is the center of the solar system.
Galileo Gallilei improved on a new invention, the telescope, and used it to study the sun and planets. The s also saw advancements in the study of physics as Isaac Newton developed his laws of motion. He also contributed to the study of oceanography and meteorology. The understanding of chemistry also evolved during this century as Antoine Lavoisier, dubbed the father of modern chemistry, developed the law of conservation of mass.
A process like the scientific method that involves such backing up and repeating is called an iterative process. Whether you are doing a science fair project, a classroom science activity, independent research, or any other hands-on science inquiry understanding the steps of the scientific method will help you focus your scientific question and work through your observations and data to answer the question as well as possible. Diagram of the scientific method. The Scientific Method starts with aquestion, and background research is conducted to try to answer that question.
If you want to find evidence for an answer or an answer itself then you construct a hypothesis and test that hypothesis in an experiment. If the experiment works and the data is analyzed you can either prove or disprove your hypothesis. If your hypothesis is disproved, then you can go back with the new information gained and create a new hypothesis to start the scientific process over again.
For a science fair project some teachers require that the question be something you can measure, preferably with a number. Rather than starting from scratch in putting together a plan for answering your question, you want to be a savvy scientist using library and Internet research to help you find the best way to do things and ensure that you don't repeat mistakes from the past.
A hypothesis is an educated guess about how things work. It is an attempt to answer your question with an explanation that can be tested. State both your hypothesis and the resulting prediction you will be testing. Predictions must be easy to measure. Your experiment tests whether your prediction is accurate and thus your hypothesis is supported or not. It is important for your experiment to be a fair test. You conduct a fair test by making sure that you change only one factor at a time while keeping all other conditions the same.
You should also repeat your experiments several times to make sure that the first results weren't just an accident. Once your experiment is complete, you collect your measurements and analyze them to see if they support your hypothesis or not.
Scientists often find that their predictions were not accurate and their hypothesis was not supported, and in such cases they will communicate the results of their experiment and then go back and construct a new hypothesis and prediction based on the information they learned during their experiment. This starts much of the process of the scientific method over again. Even if they find that their hypothesis was supported, they may want to test it again in a new way.
Professional scientists do almost exactly the same thing by publishing their final report in a scientific journal or by presenting their results on a poster or during a talk at a scientific meeting. In a science fair, judges are interested in your findings regardless of whether or not they support your original hypothesis. A theory is then confirmed by testing, where more facts are brought under the theory, called the Consilience of Inductions.
Whewell felt that this was the method by which the true laws of nature could be discovered: clarification of fundamental concepts, clever invention of explanations, and careful testing. Down-playing the discovery phase would come to characterize methodology of the early 20 th century see section 3.
Mill, in his System of Logic , put forward a narrower view of induction as the essence of scientific method. For Mill, induction is the search first for regularities among events. Among those regularities, some will continue to hold for further observations, eventually gaining the status of laws. One can also look for regularities among the laws discovered in a domain, i. These five methods look for circumstances which are common among the phenomena of interest, those which are absent when the phenomena are, or those for which both vary together.
The methods advocated by Whewell and Mill, in the end, look similar. Both involve inductive generalization to covering laws. They differ dramatically, however, with respect to the necessity of the knowledge arrived at; that is, at the meta-methodological level see the entries on Whewell and Mill entries. The quantum and relativistic revolutions in physics in the early 20 th century had a profound effect on methodology.
Conceptual foundations of both theories were taken to show the defeasibility of even the most seemingly secure intuitions about space, time and bodies. Certainty of knowledge about the natural world was therefore recognized as unattainable. Instead a renewed empiricism was sought which rendered science fallible but still rationally justifiable. Analyses of the reasoning of scientists emerged, according to which the aspects of scientific method which were of primary importance were the means of testing and confirming of theories.
A distinction in methodology was made between the contexts of discovery and justification. The distinction could be used as a wedge between the particularities of where and how theories or hypotheses are arrived at, on the one hand, and the underlying reasoning scientists use whether or not they are aware of it when assessing theories and judging their adequacy on the basis of the available evidence.
By and large, for most of the 20 th century, philosophy of science focused on the second context, although philosophers differed on whether to focus on confirmation or refutation as well as on the many details of how confirmation or refutation could or could not be brought about. By the mid th century these attempts at defining the method of justification and the context distinction itself came under pressure.
During the same period, philosophy of science developed rapidly, and from section 4 this entry will therefore shift from a primarily historical treatment of the scientific method towards a primarily thematic one. Carnap attempted to show that a scientific theory could be reconstructed as a formal axiomatic system—that is, a logic.
That system could refer to the world because some of its basic sentences could be interpreted as observations or operations which one could perform to test them. The rest of the theoretical system, including sentences using theoretical or unobservable terms like electron or force would then either be meaningful because they could be reduced to observations, or they had purely logical meanings called analytic, like mathematical identities.
This has been referred to as the verifiability criterion of meaning. According to the criterion, any statement not either analytic or verifiable was strictly meaningless. Although the view was endorsed by Carnap in , he would later come to see it as too restrictive Carnap Another familiar version of this idea is operationalism of Percy William Bridgman.
In The Logic of Modern Physics Bridgman asserted that every physical concept could be defined in terms of the operations one would perform to verify the application of that concept. Making good on the operationalisation of a concept even as simple as length, however, can easily become enormously complex for measuring very small lengths, for instance or impractical measuring large distances like light years. He pointed out that universal generalizations, such as most scientific laws, were not strictly meaningful on the criterion.
Verifiability and operationalism both seemed too restrictive to capture standard scientific aims and practice. The tenuous connection between these reconstructions and actual scientific practice was criticized in another way. In both approaches, scientific methods are instead recast in methodological roles.
Measurements, for example, were looked to as ways of giving meanings to terms. The aim of the philosopher of science was not to understand the methods per se , but to use them to reconstruct theories, their meanings, and their relation to the world.
When scientists perform these operations, however, they will not report that they are doing them to give meaning to terms in a formal axiomatic system. This disconnect between methodology and the details of actual scientific practice would seem to violate the empiricism the Logical Positivists and Bridgman were committed to.
The view that methodology should correspond to practice to some extent has been called historicism, or intuitionism. We turn to these criticisms and responses in section 3. Positivism also had to contend with the recognition that a purely inductivist approach, along the lines of Bacon-Newton-Mill, was untenable. There was no pure observation, for starters. All observation was theory laden.
Theory is required to make any observation, therefore not all theory can be derived from observation alone. See the entry on theory and observation in science. Even granting an observational basis, Hume had already pointed out that one could not deductively justify inductive conclusions without begging the question by presuming the success of the inductive method.
Likewise, positivist attempts at analyzing how a generalization can be confirmed by observations of its instances were subject to a number of criticisms. Goodman and Hempel both point to paradoxes inherent in standard accounts of confirmation.
Recent attempts at explaining how observations can serve to confirm a scientific theory are discussed in section 4 below. The standard starting point for a non-inductive analysis of the logic of confirmation is known as the Hypothetico-Deductive H-D method. In its simplest form, a sentence of a theory which expresses some hypothesis is confirmed by its true consequences. As noted in section 2 , this method had been advanced by Whewell in the 19 th century, as well as Nicod and others in the 20 th century.
Some hypotheses conflicted with observable facts and could be rejected as false immediately. Others needed to be tested experimentally by deducing which observable events should follow if the hypothesis were true what Hempel called the test implications of the hypothesis , then conducting an experiment and observing whether or not the test implications occurred.
If the experiment showed the test implication to be false, the hypothesis could be rejected. If the experiment showed the test implications to be true, however, this did not prove the hypothesis true. The degree of this support then depends on the quantity, variety and precision of the supporting evidence.
Falsification is deductive and similar to H-D in that it involves scientists deducing observational consequences from the hypothesis under test. For Popper, however, the important point was not the degree of confirmation that successful prediction offered to a hypothesis. The crucial thing was the logical asymmetry between confirmation, based on inductive inference, and falsification, which can be based on a deductive inference.
This simple opposition was later questioned, by Lakatos, among others. See the entry on historicist theories of scientific rationality. Popper stressed that, regardless of the amount of confirming evidence, we can never be certain that a hypothesis is true without committing the fallacy of affirming the consequent.
Instead, Popper introduced the notion of corroboration as a measure for how well a theory or hypothesis has survived previous testing—but without implying that this is also a measure for the probability that it is true. Popper was also motivated by his doubts about the scientific status of theories like the Marxist theory of history or psycho-analysis, and so wanted to demarcate between science and pseudo-science.
Popper saw this as an importantly different distinction than demarcating science from metaphysics. The latter demarcation was the primary concern of many logical empiricists. Popper used the idea of falsification to draw a line instead between pseudo and proper science. Science was science because its method involved subjecting theories to rigorous tests which offered a high probability of failing and thus refuting the theory. A commitment to the risk of failure was important. Avoiding falsification could be done all too easily.
If a consequence of a theory is inconsistent with observations, an exception can be added by introducing auxiliary hypotheses designed explicitly to save the theory, so-called ad hoc modifications. This Popper saw done in pseudo-science where ad hoc theories appeared capable of explaining anything in their field of application. In contrast, science is risky. If observations showed the predictions from a theory to be wrong, the theory would be refuted.
Hence, scientific hypotheses must be falsifiable. The more potential falsifiers of a hypothesis, the more falsifiable it would be, and the more the hypothesis claimed. Conversely, hypotheses without falsifiers claimed very little or nothing at all. Originally, Popper thought that this meant the introduction of ad hoc hypotheses only to save a theory should not be countenanced as good scientific method.
These would undermine the falsifiabililty of a theory. However, Popper later came to recognize that the introduction of modifications immunizations, he called them was often an important part of scientific development.
Responding to surprising or apparently falsifying observations often generated important new scientific insights. The ad hoc hypothesis of an outer planet explained the disagreement and led to further falsifiable predictions. Popper sought to reconcile the view by blurring the distinction between falsifiable and not falsifiable, and speaking instead of degrees of testability Popper 41f. From the s on, sustained meta-methodological criticism emerged that drove philosophical focus away from scientific method.
A brief look at those criticisms follows, with recommendations for further reading at the end of the entry. History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed. See the entry on the Vienna Circle. Kuhn shares with other of his contemporaries, such as Feyerabend and Lakatos, a commitment to a more empirical approach to philosophy of science.
Namely, the history of science provides important data, and necessary checks, for philosophy of science, including any theory of scientific method. The history of science reveals, according to Kuhn, that scientific development occurs in alternating phases. During normal science, the members of the scientific community adhere to the paradigm in place. Their commitment to the paradigm means a commitment to the puzzles to be solved and the acceptable ways of solving them.
Confidence in the paradigm remains so long as steady progress is made in solving the shared puzzles. An important part of a disciplinary matrix is the set of values which provide the norms and aims for scientific method. The main values that Kuhn identifies are prediction, problem solving, simplicity, consistency, and plausibility. An important by-product of normal science is the accumulation of puzzles which cannot be solved with resources of the current paradigm.
Once accumulation of these anomalies has reached some critical mass, it can trigger a communal shift to a new paradigm and a new phase of normal science. Importantly, the values that provide the norms and aims for scientific method may have transformed in the meantime.
Method may therefore be relative to discipline, time or place. Feyerabend also identified the aims of science as progress, but argued that any methodological prescription would only stifle that progress Feyerabend Heroes of science, like Galileo, are shown to be just as reliant on rhetoric and persuasion as they are on reason and demonstration.
Others, like Aristotle, are shown to be far more reasonable and far-reaching in their outlooks then they are given credit for. More generally, even the methodological restriction that science is the best way to pursue knowledge, and to increase knowledge, is too restrictive. Feyerabend suggested instead that science might, in fact, be a threat to a free society, because it and its myth had become so dominant Feyerabend An even more fundamental kind of criticism was offered by several sociologists of science from the s onwards who rejected the methodology of providing philosophical accounts for the rational development of science and sociological accounts of the irrational mistakes.
Instead, they adhered to a symmetry thesis on which any causal explanation of how scientific knowledge is established needs to be symmetrical in explaining truth and falsity, rationality and irrationality, success and mistakes, by the same causal factors see, e. Movements in the Sociology of Science, like the Strong Programme, or in the social dimensions and causes of knowledge more generally led to extended and close examination of detailed case studies in contemporary science and its history.
See the entries on the social dimensions of scientific knowledge and social epistemology. As they saw it therefore, explanatory appeals to scientific method were not empirically grounded. A late, and largely unexpected, criticism of scientific method came from within science itself.
Beginning in the early s, a number of scientists attempting to replicate the results of published experiments could not do so. There may be close conceptual connection between reproducibility and method. For example, if reproducibility means that the same scientific methods ought to produce the same result, and all scientific results ought to be reproducible, then whatever it takes to reproduce a scientific result ought to be called scientific method.
Space limits us to the observation that, insofar as reproducibility is a desired outcome of proper scientific method, it is not strictly a part of scientific method.
See the entry on reproducibility of scientific results. By the close of the 20 th century the search for the scientific method was flagging. Despite the many difficulties that philosophers encountered in trying to providing a clear methodology of conformation or refutation , still important progress has been made on understanding how observation can provide evidence for a given theory. Work in statistics has been crucial for understanding how theories can be tested empirically, and in recent decades a huge literature has developed that attempts to recast confirmation in Bayesian terms.
Here these developments can be covered only briefly, and we refer to the entry on confirmation for further details and references. Statistics has come to play an increasingly important role in the methodology of the experimental sciences from the 19 th century onwards. At that time, statistics and probability theory took on a methodological role as an analysis of inductive inference, and attempts to ground the rationality of induction in the axioms of probability theory have continued throughout the 20 th century and in to the present.
Developments in the theory of statistics itself, meanwhile, have had a direct and immense influence on the experimental method, including methods for measuring the uncertainty of observations such as the Method of Least Squares developed by Legendre and Gauss in the early 19 th century, criteria for the rejection of outliers proposed by Peirce by the mid th century, and the significance tests developed by Gosset a.
These developments within statistics then in turn led to a reflective discussion among both statisticians and philosophers of science on how to perceive the process of hypothesis testing: whether it was a rigorous statistical inference that could provide a numerical expression of the degree of confidence in the tested hypothesis, or if it should be seen as a decision between different courses of actions that also involved a value component. This led to a major controversy among Fisher on the one side and Neyman and Pearson on the other see especially Fisher , Neyman and Pearson , and for analyses of the controversy, e.
Introducing the distinction between the error of rejecting a true hypothesis type I error and accepting a false hypothesis type II error , they argued that it depends on the consequences of the error to decide whether it is more important to avoid rejecting a true hypothesis or accepting a false one.
Hence, Fisher aimed for a theory of inductive inference that enabled a numerical expression of confidence in a hypothesis. To him, the important point was the search for truth, not utility. In contrast, the Neyman-Pearson approach provided a strategy of inductive behaviour for deciding between different courses of action. Here, the important point was not whether a hypothesis was true, but whether one should act as if it was.
Similar discussions are found in the philosophical literature. On the one side, Churchman and Rudner argued that because scientific hypotheses can never be completely verified, a complete analysis of the methods of scientific inference includes ethical judgments in which the scientists must decide whether the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis, which again will depend on the importance of making a mistake in accepting or rejecting the hypothesis.
Others, such as Jeffrey and Levi disagreed and instead defended a value-neutral view of science on which scientists should bracket their attitudes, preferences, temperament, and values when assessing the correctness of their inferences. For more details on this value-free ideal in the philosophy of science and its historical development, see Douglas and Howard For a broad set of case studies examining the role of values in science, see e. Hence, for Bayesians probabilities refer to a state of knowledge, whereas for frequentists probabilities refer to frequencies of events see, e.
Bayesianism aims at providing a quantifiable, algorithmic representation of belief revision, where belief revision is a function of prior beliefs i. The probability that a particular hypothesis is true is interpreted as a degree of belief, or credence, of the scientist. There will also be a probability and a degree of belief that a hypothesis will be true conditional on a piece of evidence an observation, say being true.
Bayesianism proscribes that it is rational for the scientist to update their belief in the hypothesis to that conditional probability should it turn out that the evidence is, in fact, observed see, e. Originating in the work of Neyman and Person, frequentism aims at providing the tools for reducing long-run error rates, such as the error-statistical approach developed by Mayo that focuses on how experimenters can avoid both type I and type II errors by building up a repertoire of procedures that detect errors if and only if they are present.
Both Bayesianism and frequentism have developed over time, they are interpreted in different ways by its various proponents, and their relations to previous criticism to attempts at defining scientific method are seen differently by proponents and critics. The literature, surveys, reviews and criticism in this area are vast and the reader is referred to the entries on Bayesian epistemology and confirmation. Attention to scientific practice, as we have seen, is not itself new.
However, the turn to practice in the philosophy of science of late can be seen as a correction to the pessimism with respect to method in philosophy of science in later parts of the 20 th century, and as an attempted reconciliation between sociological and rationalist explanations of scientific knowledge.
Much of this work sees method as detailed and context specific problem-solving procedures, and methodological analyses to be at the same time descriptive, critical and advisory see Nickles for an exposition of this view.
The following section contains a survey of some of the practice focuses. In this section we turn fully to topics rather than chronology. A problem with the distinction between the contexts of discovery and justification that figured so prominently in philosophy of science in the first half of the 20 th century see section 2 is that no such distinction can be clearly seen in scientific activity see Arabatzis Thus, in recent decades, it has been recognized that study of conceptual innovation and change should not be confined to psychology and sociology of science, but are also important aspects of scientific practice which philosophy of science should address see also the entry on scientific discovery.
Looking for the practices that drive conceptual innovation has led philosophers to examine both the reasoning practices of scientists and the wide realm of experimental practices that are not directed narrowly at testing hypotheses, that is, exploratory experimentation. Examining the reasoning practices of historical and contemporary scientists, Nersessian has argued that new scientific concepts are constructed as solutions to specific problems by systematic reasoning, and that of analogy, visual representation and thought-experimentation are among the important reasoning practices employed.
These ubiquitous forms of reasoning are reliable—but also fallible—methods of conceptual development and change. On her account, model-based reasoning consists of cycles of construction, simulation, evaluation and adaption of models that serve as interim interpretations of the target problem to be solved. Often, this process will lead to modifications or extensions, and a new cycle of simulation and evaluation.
However, Nersessian also emphasizes that. Nersessian Thus, while on the one hand she agrees with many previous philosophers that there is no logic of discovery, discoveries can derive from reasoned processes, such that a large and integral part of scientific practice is.
Drawing largely on cases from the biological sciences, much of their focus has been on reasoning strategies for the generation, evaluation, and revision of mechanistic explanations of complex systems. Addressing another aspect of the context distinction, namely the traditional view that the primary role of experiments is to test theoretical hypotheses according to the H-D model, other philosophers of science have argued for additional roles that experiments can play.
The notion of exploratory experimentation was introduced to describe experiments driven by the desire to obtain empirical regularities and to develop concepts and classifications in which these regularities can be described Steinle , ; Burian ; Waters However the difference between theory driven experimentation and exploratory experimentation should not be seen as a sharp distinction.
Theory driven experiments are not always directed at testing hypothesis, but may also be directed at various kinds of fact-gathering, such as determining numerical parameters.
0コメント