Assessment 
Biopsychology 
Comparative 
Cognitive 
Developmental 
Language 
Individual differences 
Personality 
Philosophy 
Social 
Methods 
Statistics 
Clinical 
Educational 
Industrial 
Professional items 
World psychology 
Statistics: Scientific method · Research methods · Experimental design · Undergraduate statistics courses · Statistical tests · Game theory · Decision theory
.
The first statistician to consider a methodology for the design of experiments was Sir Ronald A. Fisher. He described how to test the hypothesis that a certain lady could distinguish by flavor alone whether the milk or the tea was first placed in the cup. While this sounds like a frivolous application, it allowed him to illustrate the most important means of experimental design:
 Randomization  The process of making something random
 Replication  repeating the creation of a phenomenon, so that the variability associated with the phenomenon can be estimated
 Blocking  the arranging of experimental units in groups (blocks) which are similar to one another
 Orthogonality  Means perpendicular, at right angles or statistically normal.
 Use of factorial experiments instead of the onefactoratatime method
Analysis of the design of experiments was built on the foundation of the analysis of variance, a collection of models in which the observed variance is partitioned into components due to different factors which are estimated and/or tested.
Some efficient designs for estimating several main effects simultaneously were found by Raj Chandra Bose and K. Kishen in 1940 at the Indian Statistical Institute, but remained little known until the PlackettBurman designs were published in Biometrika in 1946.
In 1950, Gertrude Mary Cox and William Cochran published the book Experimental Design which became the major reference work on the design of experiments for statisticians for years afterwards.
Developments of the theory of linear models have encompassed and surpassed the cases that concerned early writers. Today, the theory rests on advanced topics in abstract algebra and combinatorics.
As with all other branches of statistics, there is both classical and Bayesian experimental design.
Contents
Example[edit  edit source]
This example is attributed to Harold Hotelling in ^{[1]}. Although very simple, it conveys at least some of the flavor of the subject.
The weights of eight objects are to be measured using a pan balance that measures the difference between the weight of the objects in the two pans. Each measurement has a random error. The average error is zero; the standard deviations of the probability distribution of the errors is the same number σ on different weighings; and errors on different weighings are independent. Denote the true weights by
We consider two different experiments:
 Weigh each object in one pan, with the other pan empty. Call the measured weight of the ith object X_{i} for i = 1, ..., 8.
 Do the eight weighings according to the following schedule and let Y_{i} be the measured difference for i = 1, ..., 8:
 Then the estimated value of the weight θ_{1} is
The question of design of experiments is: which experiment is better?
The variance of the estimate X_{1} of θ_{1} is σ^{2} if we use the first experiment. But if we use the second experiment, the variance of the estimate given above is σ^{2/8. Thus the second experiment gives us 8 times as much precision. }
Many problems of the design of experiments involve combinatorial designs, as in this example.
Types of design[edit  edit source]
Some of the most popular designs are sorted below, with the ones at the top being the most powerful at reducing observerexpectancy effect but also most expensive, and in some cases introducing ethical concerns. The ones at the bottom are the most affordable, and are frequently used earlier in the research cycle, to develop strong hypotheses worth testing with the more expensive research approaches.
Experimental[edit  edit source]
 Randomized controlled trial
 Nonrandomized controlled trial
 Randomized database studies
Nonexperimental[edit  edit source]
 Cohort study
 Prospective studies
 Retrospective studies
 Nested cohort
 Timetrend study
 Casecohort study
 Casecontrol study (case series)
 Crosssectional study
Descriptive[edit  edit source]
 Community survey
Ordering of conditions[edit  edit source]
An important aspect of some experiment designs is the ordering of different experimental conditions.
Important considerations[edit  edit source]
When choosing a study design, many factors must be taken into account. Different types of studies are subject to different types of bias. For example, recall bias is likely to occur in crosssectional or casecontrol studies where subjects are asked to recall exposure to risk factors. Subjects with the relevant condition (e.g. breast cancer) may be more likely to recall the relevant exposures that they had undergone (e.g. hormone replacement therapy) than subjects who don't have the condition.
The ecological fallacy may occur when analyses are done on ecological (groupbased) data rather than individual data. The nature of this type of analysis tends to overestimate the degree of association between variables.
Other terms[edit  edit source]
 A "retrospective study" looks at past behavior, while a "prospective study" looks at future behavior.
 "Superiority trials" are designed to demonstrate that one treatment is more effective than another.
 "Noninferiority trials" are designed to demonstrate that a treatment is at least not appreciably worse than another.
 "Equivalence trials" are designed to demonstrate that one treatment is as effective as another.
 When using "parallel groups", each patient receives one treatment; in a "crossover study", each patient receives several treatments.
 A longitudinal study studies a few subjects for a long period of time, while a crosssectional study involves many subjects measured at once.
See also[edit  edit source]
 Animal models
 Between groups design
 Case study in psychology
 Conjoint measurement
 Clinical trial
 Cohort analysis
 Debriefing (experimental)
 Design of experiments
 Epidemiological methods
 Experimental control
 Experimental methods
 Experiment volunteers
 Experimentation
 Followup studies
 Hypothesis testing
 Metaanalysis
 Population
 Psychometrics
 Repeated measures
 Research setting
 Sampling (experimental)
 Statistical analysis
 Statistical variables
 Test construction
References[edit  edit source]
 ↑ Herman Chernoff, Sequential Analysis and Optimal Design, SIAM Monograph, 1972.
External links[edit  edit source]
 Epidemiologic.org Epidemiologic Inquiry online weblog for epidemiology researchers
 Epidemiology Forum An epidemiology discussion and forum community to foster debates and collaborations in epidemiology
 Some aspects of study design Tufts University web site
 [1] Truman State University Political Science Research Design Handbook
 Description of how to design experiments
 Articles on Design of Experiments
 Czitrom (1999) "OneFactorataTime Versus Designed Experiments", American Statistician, 53, 2.
 SAS Examples for Experimental Design
Please copy and paste this prompt to other appropriate areas. Feel free to edit as necessary
Instructions_for_archiving_academic_and_professional_materials
Research design: Academic support materials
 Research design: Academic: Lecture slides
 Research design: Academic: Lecture notes
 Research design: Academic: Lecture handouts
 Research design: Academic: Multimedia materials
 Research design: Academic: Other academic support materials
Design of experiments  

Scientific Method 

Treatment & Blocking 

Models & Inference 

Designs: Completely Randomized 

* Glossary 
This page uses Creative Commons Licensed content from Wikipedia (view authors). 