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Pseudoreplication was originally defined in 1984 by Stuart H. Hurlbert [2] as the use of inferential statistics to test for treatment effects with data from experiments where either treatments are not replicated (though samples may be) or replicates are not statistically independent.
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Replication in statistics evaluates the consistency of experiment results across different trials to ensure external validity, while repetition measures precision and internal consistency within the same or similar experiments. [5] Replicates Example: Testing a new drug's effect on blood pressure in separate groups on different days.
Let a be the value of our statistic as calculated from the full sample; let a i (i = 1,...,n) be the corresponding statistics calculated for the half-samples. (n is the number of half-samples.) Then our estimate for the sampling variance of the statistic is the average of (a i − a) 2. This is (at least in the ideal case) an unbiased estimate ...
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Repeated measures design is a research design that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or over two or more time periods. [1]
Pseudoreplication is defined as the use of inferential statistics to test for treatment effects with data from experiments where either treatments are not replicated (though samples may be) or replicates are not statistically independent.
The standard approach in statistics, where data is split into a training and a validation set, is resisted because test subjects are expensive to acquire. [ 150 ] [ 204 ] One possible solution is cross-validation , which allows model validation while also allowing the whole dataset to be used for model-fitting.