Randomized experiment

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Flowchart of four phases (enrollment, intervention allocation, follow-up, and data analysis) of a parallel randomized trial of two groups, modified from the CONSORT 2010 Statement[1]

In science, randomized experiments are the experiments that allow the greatest reliability and validity of statistical estimates of treatment effects. Randomization-based inference is especially important in experimental design and in survey sampling.

Overview[edit]

In the statistical theory of design of experiments, randomization involves randomly allocating the experimental units across the treatment 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 is not haphazard. Randomization reduces bias by equalising other factors that have not been explicitly accounted for in the experimental design (according to the law of large numbers). Randomization also produces ignorable designs, which are valuable in model-based statistical inference, especially Bayesian or likelihood-based. In the design of experiments, the simplest design for comparing treatments is the "completely randomized design". Some "restriction on randomization" can occur with blocking 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 the variance of estimators of selected effects.

Randomization of treatment in clinical trials pose ethical problems. In some cases, randomization reduces the therapeutic options for both physician and patient, and so randomization requires clinical equipoise regarding the treatments.

Online Randomized Controlled Experiments[edit]

Web sites can run randomized controlled experiments to create a feedback loop.[2] Key differences between offline experimentation and online experiments include:[2][3]

  • 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.[4]
  • Robots, whether web crawlers from valid sources or malicious internet bots.
  • Ability to ramp-up experiments from low percentages to higher percentages.
  • Ability to use the pre-experiment period as an A/A test to reduce variance.[5]

History[edit]

Randomized experiments were institutionalized in psychology and education in the late eighteen-hundreds, following the invention of randomized experiments by C. S. Peirce.[6][7][8][9] Outside of psychology and education, randomized experiments were popularized by R.A. Fisher in his book Statistical Methods for Research Workers, which also introduced additional principles of experimental design.

Statistical Interpretation[edit]

The Rubin 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. Most commonly, randomized experiments are analyzed using ANOVA, Student's t-test, Regression analysis, or a similar statistical test.

See also[edit]

References[edit]

  1. ^ Schulz KF, Altman DG, Moher D; for the CONSORT Group (2010). "CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials". BMJ 340: c332. doi:10.1136/bmj.c332. PMC 2844940. PMID 20332509. 
  2. ^ a b Kohavi, Ron; Longbotham, Roger; Sommerfield, Dan; Henne, Randal M. (2009). "Controlled experiments on the web: survey and practical guide". Data Mining and Knowledge Discovery (Berlin: Springer) 18 (1): 140–181. doi:10.1007/s10618-008-0114-1. ISSN 1384-5810. 
  3. ^ Kohavi, Ron; Deng, Alex; Frasca, Brian; Longbotham, Roger; Walker, Toby; Xu Ya (2012). "Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining".  |chapter= ignored (help)
  4. ^ 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 Conference on Knowledge Discovery and Data Mining 19 (Chicago, Illinois, USA: ACM): 1168–1176. doi:10.1145/2487575.2488217. 
  5. ^ Deng, Alex; Xu, Ya; Kohavi, Ron; Walker, Toby (2013). "WSDM 2013: Sixth ACM International Conference on Web Search and Data Mining".  |chapter= ignored (help)
  6. ^ Charles Sanders Peirce and Joseph Jastrow (1885). "On Small Differences in Sensation". Memoirs of the National Academy of Sciences 3: 73–83.  http://psychclassics.yorku.ca/Peirce/small-diffs.htm
  7. ^ Hacking, Ian (September 1988). "Telepathy: Origins of Randomization in Experimental Design". Isis 79 (3): 427–451. doi:10.1086/354775. JSTOR 234674. MR 1013489. 
  8. ^ Stephen M. Stigler (November 1992). "A Historical View of Statistical Concepts in Psychology and Educational Research". American Journal of Education 101 (1): 60–70. doi:10.1086/444032. 
  9. ^ Trudy Dehue (December 1997). "Deception, Efficiency, and Random Groups: Psychology and the Gradual Origination of the Random Group Design". Isis 88 (4): 653–673. doi:10.1086/383850. PMID 9519574.