"The design of experiments (DOE or DOX), 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."
Methods used to design and conduct experiments, such as randomized controlled trials and factorial experiments.
Hypothesis testing: The process of testing a hypothesis about a research question to determine its statistical significance.
Statistical inference: The process of using sample data to make inferences about a population.
Research design: The plan for conducting a research study, including the selection of participants, measures, and procedures.
Sampling: The process of selecting a representative subset of individuals from a larger population.
Random assignment: The process of randomly assigning participants to different treatment groups in an experiment.
Independent and dependent variables: The independent variable is the variable that is manipulated in an experiment, while the dependent variable is the outcome that is observed.
Control groups: The group in an experiment that receives no treatment or a placebo.
Experimental groups: The group in an experiment that receives a treatment.
Confounding variables: Variables that can impact the dependent variable but are not accounted for in the study design.
Data collection: The process of collecting data through observation, surveys, and other methods.
Data analysis: The process of analyzing and interpreting data collected in a study.
Statistical significance: The level of confidence that a result is not due to chance.
Power analysis: The process of determining the minimum sample size required to detect a statistically significant effect.
Type I and Type II errors: Type I errors occur when the null hypothesis is rejected even though it is true, while Type II errors occur when the null hypothesis is not rejected even though it is false.
Repeated measures design: A research design where each participant is tested more than once.
Factorial design: A design that tests the effects of two or more independent variables on a dependent variable.
Cross-sectional design: A study design that collects data from a single point in time.
Longitudinal design: A design that follows the same participants over a period of time.
Quasi-experimental design: A design that lacks full experimental control.
Counterbalancing: A method used to control the effects of order on the dependent variable.
Completely randomized design: Subjects or experimental units are randomly assigned to different treatment groups. This design is often used when there is no control group and when all treatment groups have one common factor or variable.
Randomized block design: Subjects are divided into blocks based on a common characteristic or variable, and then randomly assigned to different treatments within each block. This design reduces the variability due to the blocking variable that is not of interest and focuses solely on the treatment variable.
Factorial design: In this design, two or more independent variables are studied simultaneously, and their effects on a response variable are evaluated. The interaction between the independent variables is also analyzed, which helps to determine if the two variables have additive or multiplicative effects on the response variable.
Latin square design: It is a special type of randomized block design where subjects or experimental units are arranged in a square or rectangular grid, and treatments are assigned to each cell at random. This design is used to control the effects of two variables that might interact in the same way.
Nested design: In this design, experimental units are nested within one or more factors, and the effect of each factor is studied separately. This design is often used in experimental settings where it is not feasible to randomly assign subjects or experimental units.
Split-plot design: This design is a variation of the randomized block design where some factors are assigned at the whole-plot level, and others are assigned at the sub-plot level. This design is often used when the levels of one or more factors are difficult or expensive to change during the experiment.
Repeated measures design: This design involves measuring the same subjects or experimental units multiple times under different conditions. This design is used to reduce the variability due to individual differences and to increase the power of the study.
Crossover design: This design involves randomly assigning subjects or experimental units to different sequences of treatments over time. This design is used when the carry-over effects of a treatment need to be evaluated over multiple time periods.
Response surface design: In this design, a set of treatments is chosen to sample the response surface of a system or process, and then response data is collected at these points. This design is used to optimize the response variable by finding the maximum or minimum point on the surface.
Factorial time series design: In this design, two or more independent variables are studied over time, and their effects on a response variable are evaluated. The interaction between the independent variables is also analyzed over time. This design is often used in longitudinal studies.
"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, with design of experiments methodology recognized as a key tool in the successful implementation of a Quality by Design (QbD) framework."
"Other applications include marketing and policy making."
"The study of the design of experiments is an important topic in metascience."