Experiment using randomness in some aspect, usually to aid in removal of bias
Flowchart of four phases (enrollment, intervention allocation, follow-up, and data analysis) of a parallel randomized trial of two groups, modified from theCONSORT 2010 Statement[1]
Inscience,randomized experiments are theexperiments that allow the greatest reliability and validity of statistical estimates of treatment effects. Randomization-based inference is especially important inexperimental design and insurvey sampling.
In the statistical theory ofdesign of experiments, randomization involves randomly allocating the experimental units across thetreatment groups. For example, if an experiment compares a new drug against a standard drug, then the patients should be allocated to either the new drug or to the standard drug control using randomization.
Randomized experimentation isnot haphazard. Randomization reducesbias by equalising other factors that have not been explicitly accounted for in the experimental design (according to thelaw of large numbers). Randomization also producesignorable designs, which are valuable inmodel-basedstatistical inference, especiallyBayesian orlikelihood-based. In the design of experiments, the simplest design for comparing treatments is the "completely randomized design". Some "restriction on randomization" can occur withblocking and experiments that have hard-to-change factors; additional restrictions on randomization can occur when a full randomization is infeasible or when it is desirable to reduce thevariance of estimators of selected effects.
Randomization of treatment inclinical trials pose ethical problems. In some cases, randomization reduces the therapeutic options for both physician and patient, and so randomization requiresclinical equipoise regarding the treatments.
Web sites can run randomized controlled experiments[2] to create a feedback loop.[3] Key differences between offline experimentation and online experiments include:[3][4]
Logging: user interactions can be logged reliably.
Number of users: large sites, such as Amazon, Bing/Microsoft, and Google run experiments, each with over a million users.
Number of concurrent experiments: large sites run tens of overlapping, or concurrent, experiments.[5]
A controlled experiment appears to have been suggested in the Old Testament'sBook of Daniel. King Nebuchadnezzar proposed that some Israelites eat "a daily amount of food and wine from the king's table." Daniel preferred avegetarian diet, but the official was concerned that the king would "see you looking worse than the other young men your age? The king would then have my head because of you." Daniel then proposed the following controlled experiment: "Test your servants for ten days. Give us nothing but vegetables to eat and water to drink. Then compare our appearance with that of the young men who eat the royal food, and treat your servants in accordance with what you see". (Daniel 1:12–13).[8][9]
Randomized experiments were institutionalized in psychology and education in the late eighteen-hundreds, following the invention of randomized experiments byC. S. Peirce.[10][11][12][13]Outside of psychology and education, randomized experiments were popularized byR.A. Fisher in his bookStatistical Methods for Research Workers, which also introduced additional principles of experimental design.
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TheRubin Causal Model provides a common way to describe a randomized experiment. While the Rubin Causal Model provides a framework for defining the causal parameters (i.e., the effects of a randomized treatment on an outcome), the analysis of experiments can take a number of forms. The model assumes that there are two potential outcomes for each unit in the study: the outcome if the unit receives the treatment and the outcome if the unit does not receive the treatment. The difference between these two potential outcomes is known as the treatment effect, which is the causal effect of the treatment on the outcome. Most commonly, randomized experiments are analyzed usingANOVA,student's t-test,regression analysis, or a similarstatistical test. The model also accounts for potential confounding factors, which are factors that could affect both the treatment and the outcome. By controlling for these confounding factors, the model helps to ensure that any observed treatment effect is truly causal and not simply the result of other factors that are correlated with both the treatment and the outcome.
The Rubin Causal Model is a useful a framework for understanding how to estimate the causal effect of the treatment, even when there are confounding variables that may affect the outcome. This model specifies that the causal effect of the treatment is the difference in the outcomes that would have been observed for each individual if they had received the treatment and if they had not received the treatment. In practice, it is not possible to observe both potential outcomes for the same individual, so statistical methods are used to estimate the causal effect using data from the experiment.
Empirical evidence that randomization makes a difference
Empirically differences between randomized and non-randomized studies,[14][needs update] and between adequately and inadequately randomized trials have been difficult to detect.[15][16]
Directed acyclic graph (DAG) explanation of randomization
Randomization is the cornerstone of many scientific claims. To randomize, means that we can eliminate the confounding factors. Say we study the effect ofA onB. Yet, there are many unobservablesU that potentially affectB and confound our estimate of the finding. To explain these kinds of issues, statisticians or econometricians nowadays usedirected acyclic graph.[needs update]
^Kohavi, Ron; Longbotham, Roger (2015)."Online Controlled Experiments and A/B Tests"(PDF). In Sammut, Claude; Webb, Geoff (eds.).Encyclopedia of Machine Learning and Data Mining. Springer. pp. to appear.
^Kohavi, Ron; Deng Alex; Frasca Brian; Walker Toby; Xu Ya; Nils Pohlmann (2013). "Online controlled experiments at large scale".Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. Vol. 19. Chicago, Illinois, USA: ACM. pp. 1168–1176.doi:10.1145/2487575.2488217.ISBN9781450321747.S2CID13224883.
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