At the End of an Experiment a Scientist Should Perform the Exact Same Experiment Again.

Scientific procedure performed to validate a hypothesis

"Experimental" redirects here. For the musical classification, see Experimental music.

Astronaut David Scott performs a gravity test on the moon with a hammer and feather

Even very young children perform rudimentary experiments to learn nigh the world and how things work.

An experiment is a procedure carried out to support or abnegate a hypothesis, or determine the efficacy or likelihood of something previously untried. Experiments provide insight into cause-and-result by demonstrating what outcome occurs when a particular factor is manipulated. Experiments vary greatly in goal and scale but e'er rely on repeatable procedure and logical analysis of the results. There besides be natural experimental studies.

A kid may acquit out bones experiments to empathise how things fall to the ground, while teams of scientists may take years of systematic investigation to advance their agreement of a phenomenon. Experiments and other types of hands-on activities are very of import to student learning in the science classroom. Experiments can raise examination scores and assist a student become more engaged and interested in the fabric they are learning, peculiarly when used over fourth dimension.^[1] Experiments can vary from personal and informal natural comparisons (east.yard. tasting a range of chocolates to find a favorite), to highly controlled (eastward.g. tests requiring complex appliance overseen by many scientists that hope to notice information near subatomic particles). Uses of experiments vary considerably between the natural and human sciences.

Experiments typically include controls, which are designed to minimize the effects of variables other than the single contained variable. This increases the reliability of the results, often through a comparing between control measurements and the other measurements. Scientific controls are a part of the scientific method. Ideally, all variables in an experiment are controlled (accounted for by the command measurements) and none are uncontrolled. In such an experiment, if all controls piece of work every bit expected, it is possible to conclude that the experiment works as intended, and that results are due to the effect of the tested variables.

Overview [edit]

In the scientific method, an experiment is an empirical procedure that arbitrates competing models or hypotheses.^{[2] [iii]} Researchers also utilize experimentation to test existing theories or new hypotheses to support or disprove them.^{[3] [4]}

An experiment commonly tests a hypothesis, which is an expectation nigh how a particular procedure or phenomenon works. Yet, an experiment may also aim to answer a "what-if" question, without a specific expectation about what the experiment reveals, or to confirm prior results. If an experiment is carefully conducted, the results usually either support or disprove the hypothesis. Co-ordinate to some philosophies of science, an experiment can never "bear witness" a hypothesis, it tin can simply add support. On the other manus, an experiment that provides a counterexample tin disprove a theory or hypothesis, but a theory can ever be salvaged by appropriate advert hoc modifications at the expense of simplicity.

An experiment must besides command the possible confounding factors—whatever factors that would mar the accuracy or repeatability of the experiment or the ability to interpret the results. Misreckoning is commonly eliminated through scientific controls and/or, in randomized experiments, through random consignment.

In technology and the concrete sciences, experiments are a chief component of the scientific method. They are used to examination theories and hypotheses about how physical processes work under particular conditions (e.g., whether a particular engineering process can produce a desired chemical compound). Typically, experiments in these fields focus on replication of identical procedures in hopes of producing identical results in each replication. Random assignment is uncommon.

In medicine and the social sciences, the prevalence of experimental research varies widely across disciplines. When used, however, experiments typically follow the class of the clinical trial, where experimental units (usually private human beings) are randomly assigned to a treatment or control condition where one or more outcomes are assessed.^[5] In contrast to norms in the physical sciences, the focus is typically on the boilerplate treatment issue (the departure in outcomes betwixt the treatment and control groups) or some other test statistic produced by the experiment.^[6] A single written report typically does not involve replications of the experiment, but separate studies may be aggregated through systematic review and meta-analysis.

There are various differences in experimental practice in each of the branches of science. For instance, agricultural enquiry often uses randomized experiments (due east.g., to test the comparative effectiveness of different fertilizers), while experimental economics often involves experimental tests of theorized human being behaviors without relying on random consignment of individuals to handling and control conditions.

History [edit]

Ane of the offset methodical approaches to experiments in the modern sense is visible in the works of the Arab mathematician and scholar Ibn al-Haytham. He conducted his experiments in the field of optics—going back to optical and mathematical problems in the works of Ptolemy—by controlling his experiments due to factors such as self-criticality, reliance on visible results of the experiments also as a criticality in terms of earlier results. He was one of the first scholars to employ an inductive-experimental method for achieving results.^[7] In his Volume of Optics he describes the fundamentally new approach to noesis and research in an experimental sense:

"We should, that is, recommence the inquiry into its principles and premisses, beginning our investigation with an inspection of the things that be and a survey of the weather of visible objects. We should distinguish the backdrop of particulars, and assemble by induction what pertains to the centre when vision takes place and what is constitute in the manner of sensation to be compatible, unchanging, manifest and not subject to dubiousness. Later which we should arise in our inquiry and reasonings, gradually and orderly, criticizing premisses and exercising caution in regard to conclusions—our aim in all that we brand subject to inspection and review being to employ justice, non to follow prejudice, and to take intendance in all that we guess and criticize that nosotros seek the truth and not to be swayed by stance. We may in this way eventually come to the truth that gratifies the center and gradually and advisedly reach the end at which certainty appears; while through criticism and circumspection we may seize the truth that dispels disagreement and resolves doubtful matters. For all that, nosotros are not costless from that human turbidity which is in the nature of man; but nosotros must do our best with what we possess of human being power. From God we derive support in all things." ^[8]

According to his explanation, a strictly controlled test execution with a sensibility for the subjectivity and susceptibility of outcomes due to the nature of man is necessary. Furthermore, a critical view on the results and outcomes of before scholars is necessary:

"It is thus the duty of the man who studies the writings of scientists, if learning the truth is his goal, to make himself an enemy of all that he reads, and, applying his mind to the core and margins of its content, attack information technology from every side. He should besides suspect himself as he performs his critical examination of it, and then that he may avoid falling into either prejudice or leniency." ^[ix]

Thus, a comparison of earlier results with the experimental results is necessary for an objective experiment—the visible results being more important. In the end, this may mean that an experimental researcher must find enough backbone to discard traditional opinions or results, specially if these results are not experimental but results from a logical/ mental derivation. In this process of critical consideration, the man himself should not forget that he tends to subjective opinions—through "prejudices" and "leniency"—and thus has to exist critical about his own way of edifice hypotheses.^{[ citation needed ]}

Francis Bacon (1561–1626), an English philosopher and scientist active in the 17th century, became an influential supporter of experimental science in the English renaissance. He disagreed with the method of answering scientific questions past deduction—like to Ibn al-Haytham—and described it as follows: "Having first adamant the question according to his will, man then resorts to experience, and bending her to conformity with his placets, leads her near like a captive in a procession."^[ten] Salary wanted a method that relied on repeatable observations, or experiments. Notably, he beginning ordered the scientific method as we sympathise it today.

There remains simple experience; which, if taken as it comes, is called accident, if sought for, experiment. The true method of experience first lights the candle [hypothesis], and so by means of the candle shows the style [arranges and delimits the experiment]; commencing as it does with experience duly ordered and digested, not bungling or erratic, and from it deducing axioms [theories], and from established axioms again new experiments.^{[xi] : 101}

In the centuries that followed, people who applied the scientific method in unlike areas made important advances and discoveries. For case, Galileo Galilei (1564–1642) accurately measured fourth dimension and experimented to make accurate measurements and conclusions about the speed of a falling body. Antoine Lavoisier (1743–1794), a French pharmacist, used experiment to describe new areas, such every bit combustion and biochemistry and to develop the theory of conservation of mass (matter).^[12] Louis Pasteur (1822–1895) used the scientific method to disprove the prevailing theory of spontaneous generation and to develop the germ theory of disease.^[xiii] Considering of the importance of controlling potentially misreckoning variables, the use of well-designed laboratory experiments is preferred when possible.

A considerable corporeality of progress on the design and assay of experiments occurred in the early on 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.

Types of experiments [edit]

Experiments might be categorized co-ordinate to a number of dimensions, depending upon professional norms and standards in different fields of study.

In some disciplines (e.1000., psychology or political science), a 'true experiment' is a method of social enquiry in which in that location are two kinds of variables. The independent variable is manipulated by the experimenter, and the dependent variable is measured. The signifying characteristic of a truthful experiment is that it randomly allocates the subjects to neutralize experimenter bias, and ensures, over a large number of iterations of the experiment, that it controls for all confounding factors.^[14]

Depending on the discipline, experiments can exist conducted to accomplish different but not mutually exclusive goals: ^[15] test theories, search for and document phenomena, develop theories, or suggest policymakers. These goals too relate differently to validity concerns.

Controlled experiments [edit]

A controlled experiment often compares the results obtained from experimental samples against control samples, which are practically identical to the experimental sample except for the ane aspect whose effect is beingness tested (the contained variable). A skillful example would be a drug trial. The sample or group receiving the drug would exist the experimental group (handling grouping); and the i receiving the placebo or regular treatment would be the command i. In many laboratory experiments it is good practice to have several replicate samples for the test being performed and take both a positive control and a negative control. The results from replicate samples tin can frequently be averaged, or if ane of the replicates is obviously inconsistent with the results from the other samples, it can exist discarded every bit being the event of an experimental error (some step of the examination procedure may have been mistakenly omitted for that sample). Most often, tests are done in duplicate or triplicate. A positive control is a procedure similar to the actual experimental exam but is known from previous feel to give a positive result. A negative command is known to give a negative result. The positive control confirms that the basic weather of the experiment were able to produce a positive issue, fifty-fifty if none of the bodily experimental samples produce a positive event. The negative control demonstrates the base of operations-line result obtained when a examination does not produce a measurable positive event. Most often the value of the negative control is treated equally a "background" value to subtract from the test sample results. Sometimes the positive control takes the quadrant of a standard bend.

An example that is often used in teaching laboratories is a controlled protein assay. Students might exist given a fluid sample containing an unknown (to the pupil) corporeality of protein. Information technology is their job to correctly perform a controlled experiment in which they determine the concentration of poly peptide in the fluid sample (ordinarily called the "unknown sample"). The teaching lab would exist equipped with a protein standard solution with a known protein concentration. Students could brand several positive control samples containing various dilutions of the poly peptide standard. Negative control samples would contain all of the reagents for the poly peptide analysis but no protein. In this example, all samples are performed in duplicate. The analysis is a colorimetric analysis in which a spectrophotometer can measure the amount of protein in samples by detecting a colored complex formed by the interaction of protein molecules and molecules of an added dye. In the illustration, the results for the diluted test samples can be compared to the results of the standard curve (the blue line in the illustration) to estimate the amount of protein in the unknown sample.

Controlled experiments can be performed when information technology is difficult to exactly control all the weather condition in an experiment. In this case, the experiment begins past creating two or more sample groups that are probabilistically equivalent, which means that measurements of traits should be similar amidst the groups and that the groups should answer in the same fashion if given the same treatment. This equivalency is determined by statistical methods that take into account the amount of variation between individuals and the number of individuals in each grouping. In fields such as microbiology and chemistry, where there is very little variation between individuals and the group size is easily in the millions, these statistical methods are often bypassed and simply splitting a solution into equal parts is assumed to produce identical sample groups.

Once equivalent groups have been formed, the experimenter tries to treat them identically except for the one variable that he or she wishes to isolate. Human experimentation requires special safeguards against outside variables such as the placebo effect. Such experiments are more often than not double blind, meaning that neither the volunteer nor the researcher knows which individuals are in the control group or the experimental grouping until after all of the data have been nerveless. This ensures that any effects on the volunteer are due to the handling itself and are non a response to the knowledge that he is being treated.

In human experiments, researchers may give a subject (person) a stimulus that the subject responds to. The goal of the experiment is to measure the response to the stimulus past a examination method.

In the design of experiments, two or more "treatments" are applied to estimate the deviation betwixt the mean responses for the treatments. For example, an experiment on blistering bread could estimate the departure in the responses associated with quantitative variables, such as the ratio of water to flour, and with qualitative variables, such as strains of yeast. Experimentation is the stride in the scientific method that helps people determine between two or more competing explanations—or hypotheses. These hypotheses suggest reasons to explicate a phenomenon or predict the results of an action. An example might be the hypothesis that "if I release this ball, it will autumn to the floor": this suggestion can then be tested by carrying out the experiment of letting go of the ball, and observing the results. Formally, a hypothesis is compared against its reverse or null hypothesis ("if I release this ball, it will not fall to the floor"). The zippo hypothesis is that there is no caption or predictive power of the phenomenon through the reasoning that is being investigated. Once hypotheses are divers, an experiment tin can exist carried out and the results analysed to confirm, refute, or define the accuracy of the hypotheses.

Experiments can be also designed to estimate spillover effects onto nearby untreated units.

Natural experiments [edit]

The term "experiment" usually implies a controlled experiment, but sometimes controlled experiments are prohibitively difficult or impossible. In this case researchers resort to natural experiments or quasi-experiments. ^[16] Natural experiments rely solely on observations of the variables of the arrangement under study, rather than manipulation of just one or a few variables as occurs in controlled experiments. To the caste possible, they attempt to collect data for the organisation in such a way that contribution from all variables can be determined, and where the effects of variation in certain variables remain approximately constant so that the furnishings of other variables can exist discerned. The caste to which this is possible depends on the observed correlation between explanatory variables in the observed data. When these variables are non well correlated, natural experiments tin approach the ability of controlled experiments. Usually, however, there is some correlation between these variables, which reduces the reliability of natural experiments relative to what could be concluded if a controlled experiment were performed. Too, considering natural experiments usually have identify in uncontrolled environments, variables from undetected sources are neither measured nor held abiding, and these may produce illusory correlations in variables under written report.

Much inquiry in several science disciplines, including economics, human geography, archaeology, sociology, cultural anthropology, geology, paleontology, environmental, meteorology, and astronomy, relies on quasi-experiments. For example, in astronomy it is clearly impossible, when testing the hypothesis "Stars are complanate clouds of hydrogen", to start out with a giant cloud of hydrogen, and then perform the experiment of waiting a few billion years for it to grade a star. However, by observing various clouds of hydrogen in various states of collapse, and other implications of the hypothesis (for case, the presence of various spectral emissions from the lite of stars), nosotros can collect data we require to support the hypothesis. An early example of this blazon of experiment was the commencement verification in the 17th century that lite does not travel from place to place instantaneously, but instead has a measurable speed. Observation of the advent of the moons of Jupiter were slightly delayed when Jupiter was farther from Earth, as opposed to when Jupiter was closer to World; and this phenomenon was used to demonstrate that the deviation in the time of advent of the moons was consequent with a measurable speed.

Field experiments [edit]

Field experiments are so named to distinguish them from laboratory experiments, which enforce scientific command by testing a hypothesis in the artificial and highly controlled setting of a laboratory. Often used in the social sciences, and especially in economic analyses of education and health interventions, field experiments have the reward that outcomes are observed in a natural setting rather than in a contrived laboratory environment. For this reason, field experiments are sometimes seen as having higher external validity than laboratory experiments. Notwithstanding, similar natural experiments, field experiments endure from the possibility of contamination: experimental atmospheric condition tin can exist controlled with more precision and certainty in the lab. Yet some phenomena (eastward.m., voter turnout in an ballot) cannot exist easily studied in a laboratory.

Contrast with observational study [edit]

The black box model for observation (input and output are observables). When there are a feedback with some observer's control, equally illustrated, the observation is also an experiment.

An observational written report is used when it is impractical, unethical, cost-prohibitive (or otherwise inefficient) to fit a physical or social system into a laboratory setting, to completely control misreckoning factors, or to apply random consignment. It can also be used when confounding factors are either express or known well enough to analyze the data in light of them (though this may exist rare when social phenomena are under exam). For an observational scientific discipline to be valid, the experimenter must know and account for misreckoning factors. In these situations, observational studies have value because they oftentimes suggest hypotheses that can be tested with randomized experiments or by collecting fresh data.

Fundamentally, nonetheless, observational studies are not experiments. By definition, observational studies lack the manipulation required for Baconian experiments. In addition, observational studies (eastward.m., in biological or social systems) often involve variables that are hard to quantify or command. Observational studies are limited because they lack the statistical properties of randomized experiments. In a randomized experiment, the method of randomization specified in the experimental protocol guides the statistical assay, which is usually specified also past the experimental protocol.^[17] Without a statistical model that reflects an objective randomization, the statistical analysis relies on a subjective model.^[17] Inferences from subjective models are unreliable in theory and practice.^[xviii] In fact, at that place are several cases where carefully conducted observational studies consistently give wrong results, that is, where the results of the observational studies are inconsistent and too differ from the results of experiments. For instance, epidemiological studies of colon cancer consistently evidence beneficial correlations with broccoli consumption, while experiments find no benefit.^[nineteen]

A detail problem with observational studies involving human subjects is the peachy difficulty attaining fair comparisons between treatments (or exposures), considering such studies are prone to selection bias, and groups receiving different treatments (exposures) may differ greatly according to their covariates (age, meridian, weight, medications, do, nutritional status, ethnicity, family medical history, etc.). In contrast, randomization implies that for each covariate, the mean for each grouping is expected to be the same. For any randomized trial, some variation from the mean is expected, of course, but the randomization ensures that the experimental groups have hateful values that are shut, due to the central limit theorem and Markov's inequality. With inadequate randomization or low sample size, the systematic variation in covariates between the treatment groups (or exposure groups) makes information technology difficult to separate the effect of the treatment (exposure) from the furnishings of the other covariates, near of which have not been measured. The mathematical models used to analyze such data must consider each differing covariate (if measured), and results are not meaningful if a covariate is neither randomized nor included in the model.

To avoid conditions that render an experiment far less useful, physicians conducting medical trials—say for U.S. Nutrient and Drug Administration approval—quantify and randomize the covariates that tin be identified. Researchers attempt to reduce the biases of observational studies with matching methods such equally propensity score matching, which require large populations of subjects and extensive information on covariates. However, propensity score matching is no longer recommended as a technique because it tin increase, rather than decrease, bias.^[20] Outcomes are as well quantified when possible (bone density, the amount of some prison cell or substance in the blood, physical strength or endurance, etc.) and non based on a subject'south or a professional observer'south opinion. In this style, the design of an observational study tin can render the results more objective and therefore, more convincing.

Ethics [edit]

Past placing the distribution of the independent variable(s) under the control of the researcher, an experiment—particularly when information technology involves human subjects—introduces potential ethical considerations, such as balancing benefit and impairment, fairly distributing interventions (e.chiliad., treatments for a disease), and informed consent. For case, in psychology or health intendance, information technology is unethical to provide a substandard treatment to patients. Therefore, ethical review boards are supposed to finish clinical trials and other experiments unless a new treatment is believed to offer benefits as adept every bit electric current best practice.^[21] Information technology is also generally unethical (and often illegal) to acquit randomized experiments on the effects of substandard or harmful treatments, such as the effects of ingesting arsenic on human wellness. To understand the furnishings of such exposures, scientists sometimes utilise observational studies to understand the effects of those factors.

Even when experimental research does not directly involve human subjects, it may still present ethical concerns. For case, the nuclear bomb experiments conducted past the Manhattan Project implied the apply of nuclear reactions to harm human being beings even though the experiments did not directly involve any human subjects.

See also [edit]

• Allegiance bias
• Black box experimentation
• Concept development and experimentation
• Design of experiments
• Experimentum crucis
• Experimental physics
• Empirical research
• List of experiments
• Long-term experiment

Notes [edit]

1. ^ Stohr-Hunt, Patricia (1996). "An Analysis of Frequency of Hands-on Experience and Science Achievement". Journal of Research in Scientific discipline Teaching. 33 (one): 101–109. Bibcode:1996JRScT..33..101S. doi:10.1002/(SICI)1098-2736(199601)33:1<101::Aid-TEA6>3.0.CO;2-Z.
2. ^ Cooperstock, Fred I. (2009). General Relativistic Dynamics: Extending Einstein's Legacy Throughout the Universe (Online-Ausg. ed.). Singapore: World Scientific. p. 12. ISBN978-981-4271-16-5.
3. ^ ^{a b} Griffith, W. Thomas (2001). The physics of everyday phenomena : a conceptual introduction to physics (3rd ed.). Boston: McGraw-Colina. pp. 3–4. ISBN0-07-232837-one.
4. ^ Wilczek, Frank; Devine, Betsy (2006). Fantastic realities : 49 mind journeys and a trip to Stockholm. New Jersey: World Scientific. pp. 61–62. ISBN978-981-256-649-2.
5. ^ Holland, Paul West. (Dec 1986). "Statistics and Causal Inference". Journal of the American Statistical Association. 81 (396): 945–960. doi:10.2307/2289064. JSTOR 2289064.
6. ^ Druckman, James N.; Dark-green, Donald P.; Kuklinski, James H.; Lupia, Arthur, eds. (2011). Cambridge handbook of experimental political scientific discipline. Cambridge: Cambridge University Press. ISBN978-0521174558.
7. ^ El-Bizri, Nader (2005). "A Philosophical Perspective on Alhazen'southward Eyes". Arabic Sciences and Philosophy (Cambridge University Press). 15 (2): 189–218. doi:x.1017/S0957423905000172. S2CID 123057532.
8. ^ Ibn al-Haytham, Abu Ali Al-Hasan. Optics. p. 5.
9. ^ Ibn al-Haytham, Abu Ali Al-Hasan. Dubitationes in Ptolemaeum. p. 3.
10. ^ "Having first determined the question according to his will, human being and then resorts to experience, and bending her to conformity with his placets, leads her about like a captive in a procession." Bacon, Francis. Novum Organum, i, 63. Quoted in Durant 2012, p. 170.
11. ^ Durant, Will (2012). The story of philosophy : the lives and opinions of the great philosophers of the western world (2nd ed.). New York: Simon and Schuster. ISBN978-0-671-69500-2.
12. ^ Bell, Madison Smartt (2005). Lavoisier in the Year Ane: The Nativity of a New Scientific discipline in an Historic period of Revolution. Due west.W. Norton & Company. ISBN978-0393051551.
13. ^ Brock, Thomas D, ed. (1988). Pasteur and Mod Science (New illustrated ed.). Springer. ISBN978-3540501015.
14. ^ "Types of experiments". Department of Psychology, University of California Davis. Archived from the original on 19 December 2014.
15. ^ Lin, Hause; Werner, Kaitlyn M.; Inzlicht, Michael (2021-02-16). "Promises and Perils of Experimentation: The Mutual-Internal-Validity Trouble". Perspectives on Psychological Science. 16 (four): 854–863. doi:x.1177/1745691620974773. ISSN 1745-6916. PMID 33593177. S2CID 231877717.
16. ^ Dunning 2012
17. ^ ^{a b} Hinkelmann, Klaus and Kempthorne, Oscar (2008). Pattern and Analysis of Experiments, Volume I: Introduction to Experimental Design (Second ed.). Wiley. ISBN978-0-471-72756-9. {{cite book}}: CS1 maint: multiple names: authors list (link)
18. ^ Freedman, David; Pisani, Robert; Purves, Roger (2007). Statistics (4th ed.). New York: Norton. ISBN978-0-393-92972-0.
19. ^ Freedman, David A. (2009). Statistical models : theory and practice (Revised ed.). Cambridge: Cambridge Academy Press. ISBN978-0-521-74385-three.
20. ^ Male monarch, Gary; Nielsen, Richard (October 2019). "Why Propensity Scores Should Non Exist Used for Matching". Political Analysis. 27 (four): 435–454. doi:x.1017/pan.2019.11. hdl:1721.1/128459. ISSN 1047-1987.
21. ^ Bailey, R.A. (2008). Design of comparative experiments. Cambridge: Cambridge University Press. ISBN978-0521683579.

Further reading [edit]

• Dunning, Thad (2012). Natural experiments in the social sciences : a pattern-based arroyo. Cambridge: Cambridge Academy Press. ISBN978-1107698000.
• Shadish, William R.; Cook, Thomas D.; Campbell, Donald T. (2002). Experimental and quasi-experimental designs for generalized causal inference (Nachdr. ed.). Boston: Houghton Mifflin. ISBN0-395-61556-9. (Excerpts)
• Jeremy, Teigen (2014). "Experimental Methods in Military and Veteran Studies". In Soeters, Joseph; Shields, Patricia; Rietjens, Sebastiaan (eds.). Routledge Handbook of Research Methods in Armed services Studies. New York: Routledge. pp. 228–238.

External links [edit]

• Media related to Experiments at Wikimedia Commons
• Lessons In Electric Circuits – Book VI – Experiments
• Experiment in Physics from Stanford Encyclopedia of Philosophy

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Source: https://en.wikipedia.org/wiki/Experiment

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