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Inferential statistics cannot separate variability due to treatment from variability due to experimental units when there is only one measurement per unit. Sacrificial pseudoreplication (Figure 5b in Hurlbert 1984) occurs when means within a treatment are used in an analysis, and these means are tested over the within unit variance.
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.
Pseudoreplication; PSPP (free software) ... These lists include items which are somehow related to statistics however are not included in this index:
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 ...
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 ...
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.
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 ...
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.