An experiment is a procedure performed to support, disprove, or validate the hypothesis. Experiments provide insight into causation by showing what results occur when certain factors are manipulated. Experiments vary greatly in purpose and scale, but always rely on repetitive procedures and logical analysis of the results. There are also natural experimental studies.
A child can perform basic experiments to understand gravity, while a team of scientists can take a systematic inquiry over the years to advance their understanding of a phenomenon. Experiments and other types of direct activities are essential for student learning in science classrooms. Experiments can improve test scores and help students become more involved and interested in the material they are learning, especially when used over time. Experiments can vary from both personal and informal natural comparisons (eg tasting chocolates to find favorites), to being highly controlled (eg tests that require complex tools supervised by many scientists who hope to find information about subatomic particles). The use of experiments varies greatly between natural and human sciences.
Experiments typically include controls, designed to minimize the effects of variables other than single independent variables. This improves the reliability of results, often by comparison between control measures and other measurements. Scientific control is part of the scientific method. Ideally, all variables in the experiment are controlled (recorded by the control measure) and nothing is uncontrolled. In such an experiment, if all controls work as expected, it is possible to conclude that the experiment is working as intended, and the result is due to the effect of the variable being tested.
Video Experiment
Overview
In scientific methods, experiments are empirical procedures that address competing models or hypotheses. Researchers also use experiments to test existing theory or new hypotheses to support or disprove it.
Experiments usually test the hypothesis, which is the hope of how a particular process or phenomenon works. However, the experiment may also be aimed at answering "what-if" questions, with no specific expectations about what the experiment discloses, or to confirm previous results. If an experiment is done carefully, the results usually support or deny the hypothesis. According to some philosophy of science, experiments can not "prove" the hypothesis, can only add support. On the other hand, experiments that give contradictory examples can deny theories or hypotheses, but theories can always be saved with appropriate ad hoc modifications at the expense of simplicity. Experiments should also control for possible confounding factors - any factors that would undermine the accuracy or repetition of the experiment or the ability to interpret the results. Confusion is generally eliminated through scientific control and/or, in random experiments, through random assignments.
In engineering and physical science, experimentation is a major component of the scientific method. They are used to test theories and hypotheses about how physical processes work under certain conditions (eg, whether a particular engineering process can produce the desired chemical compound). Typically, experiments in this field focus on replication procedures that are identical in hopes of producing identical results in each replication. Random assignments are rare.
In medicine and social sciences, the prevalence of experimental research varies widely across disciplines. When used, however, trials usually follow a form of clinical trials, in which experimental units (usually human individuals) are randomly assigned to care or control conditions in which one or more outcomes are assessed. Unlike the norms in physics, the focus is usually on the medication effect of the average (difference in outcome between the treatment group and the control group) or other test statistics generated by the experiment. A single study usually does not involve replication of the experiment, but separate studies can be collected through systematic review and meta-analysis.
There are differences in experimental practice in each branch of science. For example, agricultural research often uses random experiments (eg, to test the comparative effectiveness of different fertilizers), while experimental economics often involves experimental tests of human behavior theorizing without relying on individual random assignment for treatment and control conditions.
Maps Experiment
History
One of the first methodical approaches to experimentation in the modern sense is seen in the works of the Arab mathematician and scholar Ibn al-Hatham. He conducts his experiments in the field of optics - returning to the optical and mathematical problems in Ptolemy's works - by controlling his experiments due to factors such as self-criticality, reliance on the results seen from the experiments and the criticality of previous results. He is considered one of the first scientists/philosophers to use an experimental-inductive method to achieve results. In his book "Optics" he describes a fundamental new approach to knowledge and research in an experimental sense:
"We must, that is, resume the investigation into its principles and premises, initiate our investigation by examining existing things and surveying the condition of the visible object.We must differentiate the specific properties, and collect by inducing what is related with the eye when the vision occurs and what is found in the way the feelings to be uniform, unchanging, real and not subject to doubt After that we must rise in our investigation and reasoning, gradually and regularly, criticize the premises and be careful in terms of our conclusions - our goal in all that we do to be examined and reviewed is to apply justice, not to follow prejudices, and to be careful in all that we value and criticize that we seek the truth and not become influenced by opinions, we may with this way finally comes to the truth that satisfies the heart and gradually and cautiously reaches a khir where certainty arises, while through criticism and caution we can ize the truth that dispels the dispute and resolves the question of doubt. For all of that, we are not free from human opacities existing in the human realm; but we must do our best with what we have of human strength. From God we have support in all things. "
According to his explanation, the execution of a tightly controlled test with a sensitivity to subjectivity and the vulnerability of results due to human nature is required. Furthermore, a critical view of the outcomes and outcomes of previous scholars is required:
"Thus it is the task of one who studies the writings of the scientists, if learning the truth is his purpose, to make himself the enemy of everything he reads, and, apply his mind to the core and the margins of the content, attack from every side. should suspect herself when she does a critical examination of her, so she can avoid falling into any of the prejudices or looseness. "
Thus, the comparison of previous results with experimental results is required for objective experiments - visible results become more important. Ultimately, this can mean that an experimental researcher must find enough courage to get rid of traditional opinions or results, especially if these results are not experimental but the result of a logical/mental derivation. In the process of this critical consideration, man himself must not forget that he is inclined to subjective opinion - through "prejudice" and "lightening" - and thus must be critical about his own way of constructing hypotheses.
Francis Bacon (1561-1626), an English philosopher and scientist active in the 17th century, became a supporter of influential experimental science in the English renaissance. He disagrees with the method of answering scientific questions by deduction - similar to Ibn al-Haytham - and describes it as follows: "After first determining the question according to his will, man then tries to experience, and bends it to fit his fill, guiding him like a captive in a procession. "Bacon wants a method that relies on repeated observations, or experiments. In particular, he first ordered the scientific method as we understand it today.
There is still a simple experience; which if taken when it was called an accident, if sought, experiment. The first true method of experience lights a candle [hypothesis], and then by means of a candle showing how to [set and limit experiments]; begins just as with the experience that is truly commanded and digested, neither confusing nor erratic, and thus deducting axioms [theory], and from the established axioms of new experiments.
In the following centuries, people who applied scientific methods in various fields made important advances and discoveries. For example, Galileo Galilei (1564-1642) measures time accurately and experiments to make accurate measurements and inferences about the rate of fall of the body. Antoine Lavoisier (1743-1794), a French chemist, used experiments to illustrate new areas, such as combustion and biochemistry and to develop the theory of conservation of mass (matter). Louis Pasteur (1822-1895) used the scientific method to disprove the general theory of spontaneous generation and to develop the germ theory of disease. Because of the importance of controlling potential confounding variables, the use of well-designed laboratory experiments is preferred when possible.
A large number of advances in experimental design and analysis occurred in the early 20th century, with contributions from statisticians such as Ronald Fisher (1890-1962), Jerzy Neyman (1894-1981), Oscar Kempthorne (1919-2000), Gertrude Mary Cox ( 1900-1978), and William Gemmell Cochran (1909-1980), among others.
Experiment type
Experiments can be categorized according to a number of dimensions, depending on professional norms and standards in different fields of study. In some disciplines (eg, psychology or political science), 'true experiment' is a social research method in which there are two types of variables. The independent variables are manipulated by the experiment, and the dependent variable is measured. The signifying characteristic of the actual experiment is that it randomly allocates subjects to neutralize experimental biases, and ensures, during a large number of experiments, which control for all confounding factors.
Controlled experiments
Controlled experiments often compare results obtained from experimental samples to a sample of controls , which are practically identical to experimental samples except for one aspect whose effects are being tested (independent variables). A good example is a drug trial. The sample or group receiving the drug will be an experimental group (treatment group); and who received placebo or regular care would be controlled. In many experiments in the laboratory, it is a good practice to have multiple replication examples for testing performed and to have positive control and negative control. The results of sample replication can often be averaged, or if one of the replayings is clearly inconsistent with the results of another sample, it can be removed as a result of experimental error (some steps from the testing procedure may have been erroneously omitted for that sample). Most often, the test is done in duplicate or triplicate. Positive control is a procedure similar to the actual experimental test but is known from previous experience to give a positive result. Negative controls are known to give negative results. The positive control confirms that the basic conditions of the experiment are able to produce positive results, even if no experimental sample actually produces a positive result. Negative controls show baseline results obtained when tests do not produce measurable positive results. Most often negative control values ​​are treated as a "background" value to reduce from the test sample results. Sometimes a positive control takes the quadrant from the standard curve.
A common example used in teaching laboratories is controlled protein testing. Students may be given fluid samples containing an unknown quantity of protein (to students). It is their duty to conduct a properly controlled experiment in which they determine the concentration of proteins in fluid samples (usually called "unknown samples"). The teaching laboratory will be equipped with standard protein solutions with known protein concentrations. Students can create some positive control samples containing various protein standard dilutions. Negative control samples will contain all reagents for protein testing but do not contain proteins. In this example, all samples are done in duplicate. This test is a colorimetric test in which the spectrophotometer can measure the amount of protein in the sample by detecting the colored complexes formed by the interaction of protein molecules and additional dye molecules. In the illustration, the results for the diluted test sample can be compared with the standard curve results (blue line in the illustration) to estimate the amount of protein in the unknown sample.
Controlled experiments can be performed when it is difficult to control all conditions in an experiment. In this case, the experiment begins by making two or more sample groups of probabilistic equivalents, meaning that the measurement of the properties should be similar between groups and that the group should respond in the same way if given the same treatment. This equality is determined by statistical methods that take into account the amount of variation between individuals and the number of individuals in each group. In areas such as microbiology and chemistry, where there is very little variation between individuals and group sizes easily in the millions, this statistical method is often bypassed and simply dividing the solution into equal parts is assumed to produce an identical sample group.
Once the equivalent group is formed, the experiment tries to treat them identically except for the one variable that he wants isolation. Human experiments require special protection against external variables such as the placebo effect . Such experiments are generally double blind , which means that neither volunteers nor researchers know which individual is in the control group or experimental group until after all data has been collected. This ensures that any effect on the volunteers is due to the treatment itself and not the response to the knowledge that he is being treated.
In human experiments, researchers can give subjects (people) a stimulus to which the subject responds. The purpose of this experiment is to measure the response to the stimulus by the test method.
In the experimental design, two or more "treatments" were applied to estimate the difference between the median responses to treatment. For example, an experiment on baking bread can estimate differences in responses associated with quantitative variables, such as water to flour ratio, and with qualitative variables, such as yeast strains. Experiments are steps in scientific methods that help people decide between two or more competing explanations - or hypotheses. This hypothesis shows the reasons for explaining a phenomenon, or predicting the outcome of an action. An example might be the hypothesis that "if I release this ball, the ball will fall to the floor": this suggestion can then be tested by experimenting with the ball, and observing the result. Formally, the hypothesis is compared against the opposite or zero hypothesis ("if I release this ball, it will not fall to the floor"). The null hypothesis is that there is no explanation or predictive power of the phenomenon through reasoning under investigation. Once the hypothesis is defined, experiments can be performed and the results are analyzed to confirm, dispute, or determine the accuracy of the hypothesis.
Natural experiments
The term "experiment" usually implies a controlled experiment, but controlled experiments are sometimes very difficult or impossible. In this case researchers used natural experiments or quasi-experiments. Natural experiments rely solely on observations of the variables of the system studied, rather than the manipulation of just one or more variables as occurs in controlled experiments. Equally possible, they try to collect data for the system in such a way that the contribution of all variables can be determined, and where the effect of variation in a particular variable remains less constant so that the effects of other variables can be seen. The extent to which this may depend on the correlation observed between the explanatory variables in the observed data. When these variables are not correlated well, natural experiments can approach the power of controlled experiments. Usually, however, there is some correlation between these variables, which reduces the reliability of natural experiments relative to what can be inferred if a controlled experiment is performed. Also, since natural experiments usually occur in uncontrolled environments, variables from undetectable sources are not measured or held constant, and this can produce an illusion correlation in the variables studied.
Many studies in several disciplines, including economics, political science, geology, paleontology, ecology, meteorology, and astronomy, depend on quasi-experiments. For example, in astronomy it is clearly impossible, when testing the hypothesis "Stars are clouds of clouds collapsed", to start with a gigantic cloud of hydrogen, and then do experiments waiting several billion years to form stars. However, by observing various clouds of hydrogen in various countries collapsing, and other implications of the hypothesis (eg, the presence of various spectral emissions from starlight), we can collect the data we need to support the hypothesis. The earliest example of this type of experiment was the first verification in the 17th century that light did not move from one place to another instantly, but instead had a measured speed. Observations of Jupiter's moon sighting are slightly delayed when Jupiter is farther from Earth, than when Jupiter is closer to Earth; and this phenomenon is used to indicate that the time difference of the lunar emergence is consistent with the measured speed.
Field experiments
Field experiments were so named to distinguish them from laboratory experiments, which upheld scientific control by testing hypotheses in artificial and highly controlled laboratory settings. Often used in the social sciences, and especially in economic analysis of education and health interventions, field trials have the advantage that results are observed in natural settings rather than in fabricated laboratory settings. For this reason, field trials are sometimes considered to have higher external validity than laboratory experiments. However, like natural experiments, field experiments suffer the possibility of contamination: experimental conditions can be controlled more precisely and definitely in the laboratory. But some phenomena (eg, voter participation in elections) can not be easily learned in the laboratory.
Contrast with observational studies
An observational study is used when it is impractical, unethical, costly (or inefficient) to adjust physical or social systems into laboratory settings, to fully control for confounding factors, or to apply random assignments. It can also be used when confounding factors are either limited or well known enough to analyze data in their light (though this may rarely occur when social phenomena are being examined). In order for the observational science to be valid, the experiment must know and take into account the confounding factors. In this situation, observational studies have value because they often suggest hypotheses that can be tested by random experiments or by collecting new data.
Basically, however, observational research is not an experiment. By definition, observational studies do not have the manipulations necessary for Baconian experiments. In addition, observational studies (eg, in biological or social systems) often involve variables that are difficult to measure or control. Observational studies are limited because they do not have statistical properties of random experiments. In a randomized trial, the randomization method specified in the experimental protocol guides the statistical analysis, which is usually determined by experimental protocols as well. Without statistical models that reflect objective randomization, statistical analysis depends on the subjective model. The conclusions of the subjective model are not reliable in theory and practice. In fact, there are some cases where observational research is done consistently giving wrong results, ie, where the results of observational studies are inconsistent and also different from the experimental results. For example, epidemiological studies of colon cancer consistently show favorable correlations with broccoli consumption, while experiments found no benefit.
A particular problem with observational studies involving human subjects is the great difficulty of achieving a fair comparison between treatments (or exposures), since such studies are susceptible to selection bias, and groups receiving different treatments (exposure) may differ widely according to their covariates (age, height, weight, medication, exercise, nutritional status, ethnicity, family medical history, etc.). Conversely, randomism implies that for each covariate, the mean for each group is expected to be the same. For each randomized trial, some variation of the average is expected, of course, but randomization ensures that the experimental group has a near average value, due to the central limit theorem and Markov inequality. With inadequate randomization or low sample size, systematic variation in the covariates between treatment groups (or exposure groups) makes it difficult to separate treatment effects (exposure) from other covariate effects, which are largely unmeasurable.. The mathematical model used to analyze the data should consider each different covariate (if measured), and the result is meaningless if the covariates are not scrambled or included in the model.
To avoid conditions that make experiments much less useful, doctors who perform medical tests - say for approval of the US Food and Drug Administration - calculate and scramble identifiable covariates. The researchers sought to reduce the bias of observational studies by complex statistical methods such as the trend score matching method, which required large populations of subjects and extensive information about the covariates. The results are also quantified where possible (bone density, number of cells or substances in blood, physical strength or endurance etc.) and not on the view of professional observers or observers. In this way, the observational study design can make the results more objective and, therefore, more convincing.
Ethics
By placing the distribution of independent variables under the control of the researcher, experiments - especially when involving human subjects - introduce potential ethical considerations, such as balancing benefits and dangers, spreading fair interventions (eg, treatment for illness), and informed consent. For example, in psychology or health care, it is unethical to provide substandard care to the patient. Therefore, the ethics review board should stop other clinical trials and experiments unless new treatments are believed to offer benefits as well as current best practices. It is also generally unethical (and often illegal) to conduct random experiments on sub-standard or harmful treatment effects, such as the effect of arsenic ingestion on human health. To understand the effects of such exposure, scientists sometimes use observational studies to understand the effects of these factors.
Even when experimental research does not directly involve human subjects, there may still be ethical concerns. For example, nuclear bomb experiments conducted by the Manhattan Project imply the use of nuclear reactions to harm humans even though experiments do not directly involve human subjects.
Experimental method in law
The experimental method can be useful in solving juridical problems.
See also
- Blackbox experiment
- Experiment design
- Experimental physics
- List of experiments
- Long-term experiment
- Development of concepts and experiments
- Loyalty bias
- Crucis experiment
Note
Further reading
- Dunning, Thad (2012). Natural experiment in social science: design-based approach . Cambridge: Cambridge University Press. ISBN: 978-1107698000.
- Shadish, William R.; Cook, Thomas D.; Campbell, Donald T. (2002). Experimental and quasi-experimental design for general causal conclusions (Nachdr ed.). Boston: Houghton Mifflin. ISBNÃ, 0-395-61556-9. (Quote)
- Jeremy, Teigen (2014). "Experimental Methods in Military and Veterans Studies". In Soeters, Joseph; Shield, Patricia; Rietjens, Sebastiaan. Research Guide Research Methods in Military Studies . New York: Routledge. pp.Ã, 228-238.
External links
- Media related to Experiments on Wikimedia Commons
- Lessons In Electrical Circuits - Volume VI - Experiments
- Experiments in Physics from Stanford Encyclopedia of Philosophy
Source of the article : Wikipedia
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