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Example of direct replication and conceptual replication. There are two main types of replication in statistics. First, there is a type called “exact replication” (also called "direct replication"), which involves repeating the study as closely as possible to the original to see whether the original results can be precisely reproduced. [3]
Balanced repeated replication is a statistical technique for estimating the sampling variability of a statistic obtained by stratified sampling. Outline of the technique
sc is a cross-platform, free, TUI, spreadsheet and calculator application that runs on Unix and Unix-like operating systems. It has also been ported to Windows. It can be accessed through a terminal emulator, and has a simple interface and keyboard shortcuts resembling the key bindings of the Vim text editor. It can be used in a similar manner ...
The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4]
From the definition of ¯ as the average of the jackknife replicates one could try to calculate explicitly. The bias is a trivial calculation, but the variance of x ¯ j a c k {\displaystyle {\bar {x}}_{\mathrm {jack} }} is more involved since the jackknife replicates are not independent.
This is done by generating the reference distribution by Monte Carlo sampling, which takes a small (relative to the total number of permutations) random sample of the possible replicates. The realization that this could be applied to any permutation test on any dataset was an important breakthrough in the area of applied statistics.
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 ...
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.