Experimental Design

The design of experiments (DOE, DOX, or experimental design) is the design of any task that aims to describe and explain the variation of information under conditions that are hypothesized to reflect the variation. The term is generally associated with experiments in which the design introduces conditions that directly affect the variation, but may also refer to the design of quasi-experiments, in which natural conditions that influence the variation are selected for observation.In its simplest form, an experiment aims at predicting the outcome by introducing a change of the preconditions, which is represented by one or more independent variables, also referred to as 'input variables' or 'predictor variables.' The change in one or more independent variables is generally hypothesized to result in a change in one or more dependent variables, also referred to as 'output variables' or 'response variables.' The experimental design may also identify control variables that must be held constant to prevent external factors from affecting the results. Experimental design involves not only the selection of suitable independent, dependent, and control variables, but planning the delivery of the experiment under statistically optimal conditions given the constraints of available resources. There are multiple approaches for determining the set of design points (unique combinations of the settings of the independent variables) to be used in the experiment.Main concerns in experimental design include the establishment of validity, reliability, and replicability. For example, these concerns can be partially addressed by carefully choosing the independent variable, reducing the risk of measurement error, and ensuring that the documentation of the method is sufficiently detailed. Related concerns include achieving appropriate levels of statistical power and sensitivity.Correctly designed experiments advance knowledge in the natural and social sciences and engineering. Other applicat
相关学科: Reading ComprehensionSMAActive LearningGaussian ProcessLinear RegressionRobust DesignCausal InferenceComputational Engineering, Finance and ScienceModel SelectionML









Douglas G. Altman

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Zhong Lin Wang

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Geoffrey E. Hinton

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Rob Knight

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Steven L. Salzberg

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Donald B. Rubin

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Karl J. Friston

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Ruedi Aebersold

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Jeffrey I. Gordon

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Peter M. Bentler

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