Comparison Wise Error Rate
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the simple question posed by an analysis of variance - do at least two treatment means differ? It may be that embedded in a group of experiment wise error rate treatments there is only one "control" treatment to which every other treatment experimentwise error rate should be compared, and comparisons among the non-control treatments may be uninteresting. One may also, after performing an analysis comparisonwise error rate of variance and rejecting the null hypothesis of equality of treatment means want to know exactly which treatments or groups of treatments differ. To answer these kinds of questions requires careful
Comparison Wise Error Rate Definition
consideration of the hypotheses of interest both before and after an experiment is conducted, the Type I error rate selected for each hypothesis, the power of each hypothesis test, and the Type I error rate acceptable for the group of hypotheses as a whole. Comparisons or Contrasts If we let represent a treatment mean and ci a weight associated with the ith family wise error rate treatment mean then a comparison or contrast can be represented as: , where It can be seen that this contrast is a linear combination of treatment means (other contrasts such as quadratic and cubic are also possible). All of the following are possible comparisons: because they are weighted linear combinations of treatment means and the weights sum to zero. For example, previously we have performed comparisons between two treatment means using the t - statistic: with (n1 + n2) - 2 degrees of freedom. This statistic is a "contrast." The numerator of this expression follows the general form of the contrast outlined above with the weights c1 and c2 equal to 1 and -1, respectively: However, we also see that this contrast is divided by the pooled within cell or within group variation. So, a contrast is actually the ratio of a linear combination of weighted means to an estimate of the pooled within cell or error variation in the experiment: with degrees of freedom. For a non - directional null h
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Family Wise Error Rate R
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How To Calculate Family Wise Error Rate
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