Editing Design of experiments (section)

{{short description|Design of tasks}}
{{original research|date=December 2020}}

{{Use dmy dates|date=December 2020}}
[[File:Response surface metodology.jpg|thumb|Design of experiments with full [[factorial design]] (left), [[response surface]] with second-degree polynomial (right)]]

The '''design of experiments''' ('''DOE'''),<ref>{{cite web |title=What Is Design of Experiments (DOE)? |url=https://asq.org/quality-resources/design-of-experiments?srsltid=AfmBOoqGNe13QlU1WGcx1ABznp_0sVoAdwVX3jHd_Hq_a9iaqVTQ9p1u |website=asq.org |publisher=American Society for Quality |access-date=20 February 2025}}</ref> also known as '''experiment design''' 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-experiment]]s, in which [[naturalistic observation|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 [[Dependent and independent variables|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 and independent variables|dependent variables]], also referred to as "output variables" or "response variables." The experimental design may also identify [[Control variable|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 (statistics)|validity]], [[Reliability (statistics)|reliability]], and [[Reproducibility|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 and specificity|sensitivity]].

Correctly designed experiments advance knowledge in the natural and social sciences and engineering, with design of experiments methodology recognised as a key tool in the successful implementation of a [[Quality by design|Quality by Design]] (QbD) framework.<ref>{{Cite web |title=The Sequential Nature of Classical Design of Experiments {{!}} Prism |url=https://prismtc.co.uk/resources/blogs-and-articles/the-sequential-nature-of-classical-design-of-experiments |access-date=2023-03-10 |website=prismtc.co.uk}}</ref> Other applications include marketing and policy making. The study of the design of experiments is an important topic in [[metascience]].