Experiments might be categorized according to a number of dimensions, depending upon professional norms and standards in different fields of study. In some disciplines e. The independent variable is manipulated by the experimenter, and the dependent variable is measured. The signifying characteristic of a true 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.
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 one aspect whose effect is being tested the independent variable. A good example would be a drug trial. The sample or group receiving the drug would be the experimental group treatment group ; and the one receiving the placebo or regular treatment would be the control one.
In many laboratory experiments it is good practice to have several replicate samples for the test being performed and have both a positive control and a negative control. The results from replicate samples can often be averaged, or if one of the replicates is obviously inconsistent with the results from the other samples, it can be discarded as being the result of an experimental error some step of the test 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 test but is known from previous experience to give a positive result. A negative control is known to give a negative result. The positive control confirms that the basic conditions of the experiment were able to produce a positive result, even if none of the actual experimental samples produce a positive result. The negative control demonstrates the base-line result obtained when a test does not produce a measurable positive result.
Most often the value of the negative control is treated as a "background" value to subtract from the test sample results. Sometimes the positive control takes the quadrant of a standard curve.
An example that is often used in teaching laboratories is a controlled protein assay. Students might be given a fluid sample containing an unknown to the student amount of protein. It is their job to correctly perform a controlled experiment in which they determine the concentration of protein in the fluid sample usually called the "unknown sample". The teaching lab would be equipped with a protein standard solution with a known protein concentration. Students could make several positive control samples containing various dilutions of the protein standard. Negative control samples would contain all of the reagents for the protein assay but no protein.
In this example, all samples are performed in duplicate. The assay is a colorimetric assay 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 it is difficult to exactly control all the conditions in an experiment.
In this case, the experiment begins by creating two or more sample groups that are probabilistically equivalent, which means that measurements of traits should be similar among the groups and that the groups should respond in the same manner 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 group. 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 generally double blind , meaning that neither the volunteer nor the researcher knows which individuals are in the control group or the experimental group until after all of the data have been collected.
This ensures that any effects on the volunteer are due to the treatment itself and are not 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 by a test method. In the design of experiments , two or more "treatments" are applied to estimate the difference between the mean responses for the treatments.
For example, an experiment on baking bread could estimate the difference 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 step in the scientific method that helps people decide between two or more competing explanations — or hypotheses. These hypotheses suggest reasons to explain a phenomenon, or predict the results of an action. An example might be the hypothesis that "if I release this ball, it will fall 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 opposite or null 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 the reasoning that is being investigated. Once hypotheses are defined, an experiment can be 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. 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. To the degree possible, they attempt to collect data for the system 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 effects of other variables can be discerned. The degree to which this is possible depends on the observed correlation between explanatory variables in the observed data.
When these variables are not well correlated, 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 could be concluded if a controlled experiment were performed. Also, because natural experiments usually take place in uncontrolled environments, variables from undetected sources are neither measured nor held constant, and these may produce illusory correlations in variables under study.
Much research in several science disciplines, including economics , political science , geology , paleontology , ecology , meteorology , and astronomy , relies on quasi-experiments.
1. Laboratory / Controlled Experiments
For example, in astronomy it is clearly impossible, when testing the hypothesis "Stars are collapsed 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 form a star. However, by observing various clouds of hydrogen in various states of collapse, and other implications of the hypothesis for example, the presence of various spectral emissions from the light of stars , we can collect data we require to support the hypothesis.
An early example of this type of experiment was the first verification in the 17th century that light does not travel from place to place instantaneously, but instead has a measurable speed.
Observation of the appearance of the moons of Jupiter were slightly delayed when Jupiter was farther from Earth, as opposed to when Jupiter was closer to Earth; and this phenomenon was used to demonstrate that the difference in the time of appearance of the moons was consistent with a measurable speed.
Field experiments are so named to distinguish them from laboratory experiments, which enforce scientific control 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 advantage 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.
However, like natural experiments, field experiments suffer from the possibility of contamination: experimental conditions can be controlled with more precision and certainty in the lab. Yet some phenomena e. An observational study 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 confounding factors, or to apply random assignment.
It can also be used when confounding factors are either limited or known well enough to analyze the data in light of them though this may be rare when social phenomena are under examination. For an observational science to be valid, the experimenter must know and account for confounding factors.
In these situations, observational studies have value because they often suggest hypotheses that can be tested with randomized experiments or by collecting fresh data. Fundamentally, however, observational studies are not experiments. By definition, observational studies lack the manipulation required for Baconian experiments. In addition, observational studies e. By day, he's a journalist, software trainer, and sometime Web developer. By night, he's asleep. These days, he writes news stories, columns, and reviews for CNET and other technology sites and publications.
Experimental | Definition of Experimental by Merriam-Webster
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