"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."
Different types of experimental designs such as randomized controlled trials and quasi-experimental designs.
Hypothesis testing: The process of systematically testing a hypothesis to determine whether a certain statement can be accepted or rejected.
Sampling methods: The methods used to select a representative sample from a population, including random sampling, stratified sampling, and cluster sampling.
Control groups: A group that is kept unchanged in an experiment to evaluate the effect of the experimental treatment.
Randomization: The process of assigning subjects to different groups in an experiment to avoid bias and obtain a representative sample.
Factorial designs: The use of multiple factors in experimental design to investigate their independent and combined effects on the outcome.
Blocking: A method of controlling the variability in an experiment by grouping subjects into blocks based on certain characteristics.
Power analysis: A statistical method to determine the sample size required to detect a certain effect size with a given level of confidence and statistical power.
Analysis of variance (ANOVA): A statistical method used to analyze the differences between two or more groups in a study.
Regression analysis: A statistical technique used to analyze the relationship between two or more variables and to make predictions based on that relationship.
Crossover designs: A type of experimental design where each subject receives multiple treatments in a random order to minimize the effect of variability in treatment response.
Counterbalancing: A method to avoid the order effect in a crossover design by systematically varying the treatment order.
Confounding variables: Extraneous variables that affect the outcome of a study but are not accounted for in the experimental design.
Placebo effect: The effect of psychological or physiological factors on the response to treatment in a study.
Blinding and double-blinding: A method to eliminate bias and placebo effect in a study by concealing the treatment assignment from the subjects and the researchers.
Ethics in experimental design: The principles of ethical conduct in designing and conducting experiments involving human or animal subjects.
Completely randomized design: This is the simplest type of experimental design, in which treatments are assigned randomly to experimental units with no restrictions.
Randomized block design: This design is used when there is a known source of variability that may affect the response variable, and the experimental units are assigned to blocks based on this factor.
Latin square design: This design is used when there are two factors that may affect the response variable, and each treatment is assigned once to each row and once to each column of a square matrix.
Factorial design: This design allows for the simultaneous evaluation of two or more factors, and their interactions, on the response variable.
Split-plot design: This design is used when the experimental units can only be manipulated in a limited way, such as time or space, and different treatments are applied to each unit.
Repeated measures design: This design is used when the same experimental units are measured multiple times, under different treatments or conditions.
Crossover design: This design is used when each experimental unit is subject to more than one treatment, and the order in which the treatments are applied is randomized to eliminate potential confounding effects.
Nested design: This design is used when experimental units are naturally nested within larger units, such as individuals within households or plots within fields, and treatments can be applied at different levels.
Factorial nested design: This design combines the features of the factorial and nested designs, allowing for the simultaneous evaluation of multiple factors at different levels of nesting.
Response surface design: This design is used to identify the optimal levels of multiple factors that maximize or minimize the response variable, often using regression analysis to model the relationships between the factors and the response variable.
"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."