Power is defined as the probability of correctly detecting an effect. It is often noted , where the converse, ̄ is the probability of a type-II error (not rejecting H0 when there is an effect). When planning a new experiment, it is generally recommended to have a power of at least .80. Suppose that your design has very little power and suppose further that you found a significant effect. Since y...

many commonly used statistical methods require that the population distribution be nearly normal. unfortunately, in some papers the one-sample kolmogorov-smirnov test has been used for testing normality while the assumptions of applying this test are not satisfied. to conduct this test, it is assumed that the population distribution is fully specified. in practical situation where the mean and ...

The R function t.test() can be used to perform both one and two sample t-tests on vectors of data. The function contains a variety of options and can be called as follows: > t.test(x, y = NULL, alternative = c("two.sided", "less", "greater"), mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95) Here x is a numeric vector of data values and y is an optional numeric vector of data values...

Many Six Sigma practitioners use “Student’s” independent two-sample t-test when investigating differences in means. This type of test is based upon drawing random samples from two independent normal (Gaussian) distributions. Often, however, the assumption of normality and/or random sampling is violated. This article discusses when and why the two-sample t-test may be considered robust to these ...