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Balanced repeated replication is a statistical technique for estimating the sampling variability of a statistic obtained ... Calculate the statistic of interest for ...
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 experiments (those with an equal sample size for each treatment) are relatively easy to interpret; unbalanced experiments offer more complexity. For single-factor (one-way) ANOVA, the adjustment for unbalanced data is easy, but the unbalanced analysis lacks both robustness and power. [ 57 ]
"The bootstrap can be applied to both variance and distribution estimation problems. However, the bootstrap variance estimator is not as good as the jackknife or the balanced repeated replication (BRR) variance estimator in terms of the empirical results. Furthermore, the bootstrap variance estimator usually requires more computations than the ...
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.
Replication [ edit ] By running a different design for each replicate , where a different effect gets confounded each time, the interaction effects are partially confounded instead of completely sacrificing one single effect. [ 4 ]
Reproducibility, closely related to replicability and repeatability, is a major principle underpinning the scientific method.For the findings of a study to be reproducible means that results obtained by an experiment or an observational study or in a statistical analysis of a data set should be achieved again with a high degree of reliability when the study is replicated.