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For example, let the design effect, for estimating the population mean based on some sampling design, be 2. If the sample size is 1,000, then the effective sample size will be 500. It means that the variance of the weighted mean based on 1,000 samples will be the same as that of a simple mean based on 500 samples obtained using a simple random ...
Experimental design is the design of all information-gathering exercises where variation is present, whether under the full control of the experimenter or an observational study. The experimenter may be interested in the effect of some intervention or treatment on the subjects in the design.
The use of a sequence of experiments, where the design of each may depend on the results of previous experiments, including the possible decision to stop experimenting, is within the scope of sequential analysis, a field that was pioneered [12] by Abraham Wald in the context of sequential tests of statistical hypotheses. [13]
The Design of Experiments is a 1935 book by the English statistician Ronald Fisher about the design of experiments and is considered a foundational work in experimental design. [ 2 ] [ 3 ] [ 4 ] Among other contributions, the book introduced the concept of the null hypothesis in the context of the lady tasting tea experiment. [ 5 ]
Design and Analysis of Experiments. Handbook of Statistics. Vol. 13. North-Holland. ISBN 978-0-444-82061-7. "Model Robust Designs". Design and Analysis of Experiments. Handbook of Statistics. pp. 1055– 1099. Cheng, C.-S. "Optimal Design: Exact Theory". Design and Analysis of Experiments. Handbook of Statistics. pp. 977– 1006.
In that book he emphasized examples and how to design experiments systematically from a statistical point of view. The mathematical justification of the methods described was not stressed and, indeed, proofs were often barely sketched or omitted altogether ..., a fact which led H. B. Mann to fill the gaps with a rigorous mathematical treatment ...
These two expressions are called the generators of the design. So for example, when the experiment is run and the experimenter estimates the effects for factor D, what is really being estimated is a combination of the main effect of D and the two-factor interaction involving A and B.
The observer-expectancy effect [a] is a form of reactivity in which a researcher's cognitive bias causes them to subconsciously influence the participants of an experiment. Confirmation bias can lead to the experimenter interpreting results incorrectly because of the tendency to look for information that conforms to their hypothesis, and ...