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Implicit pseudoreplication occurs when standard errors (or confidence limits) are estimated within experimental units. As with other sources of pseudoreplication, treatment effects cannot be statistically separated from effects due to variation among experimental units.
In engineering, science, and statistics, replication is the process of repeating a study or experiment under the same or similar conditions. It is a crucial step to test the original claim and confirm or reject the accuracy of results as well as for identifying and correcting the flaws in the original experiment. [1]
Classical definition of probability; Classical test theory – psychometrics; Classification rule; Classifier (mathematics) ... Pseudoreplication; PSPP (free software)
Ignoring these dependencies, the analysis can lead to an inflated sample size or pseudoreplication. While a unit is often the lowest level at which observations are made, in some cases, a unit can be further decomposed as a statistical assembly. Many statistical analyses use quantitative data that have units of measurement. This is a distinct ...
For example, the above definition of effect size is often measured by Cohen's d estimator. The same effect size might have multiple estimators, as they have tradeoffs between efficiency, bias, variance, etc. This further increases the number of possible statistical quantities that can be computed on a single dataset.
Also confidence coefficient. A number indicating the probability that the confidence interval (range) captures the true population mean. For example, a confidence interval with a 95% confidence level has a 95% chance of capturing the population mean. Technically, this means that, if the experiment were repeated many times, 95% of the CIs computed at this level would contain the true population ...
The best example of the plug-in principle, the bootstrapping method. Bootstrapping is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with the purpose of deriving robust estimates of standard errors and confidence intervals of a population parameter like a mean, median, proportion, odds ratio ...
By allowing some of the training data to also be included in the test set – this can happen due to "twinning" in the data set, whereby some exactly identical or nearly identical samples are present in the data set, see pseudoreplication. To some extent twinning always takes place even in perfectly independent training and validation samples.