Search results
Results from the WOW.Com Content Network
Difference between Z-test and t-test: Z-test is used when sample size is large (n>50), or the population variance is known. t-test is used when sample size is small (n<50) and population variance is unknown. There is no universal constant at which the sample size is generally considered large enough to justify use of the plug-in test. Typical ...
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]
The sample size is relatively small (say, n ≤ 10— ¯ and s charts are typically used for larger sample sizes) The sample size is constant; Humans must perform the calculations for the chart; As with the ¯ and s and individuals control charts, the ¯ chart is only valid if the within-sample variability is constant. [4]
This approximate formula is for moderate to large sample sizes; the reference gives the exact formulas for any sample size, and can be applied to heavily autocorrelated time series like Wall Street stock quotes. Moreover, this formula works for positive and negative ρ alike. [12] See also unbiased estimation of standard deviation for more ...
Statistical tests are used to test the fit between a hypothesis and the data. [1] [2] Choosing the right statistical test is not a trivial task. [1]The choice of the test depends on many properties of the research question.
where N is the population size, n is the sample size, m x is the mean of the x variate and s x 2 and s y 2 are the sample variances of the x and y variates respectively. These versions differ only in the factor in the denominator (N - 1). For a large N the difference is negligible.
The Z-factor defines a characteristic parameter of the capability of hit identification for each given assay. The following categorization of HTS assay quality by the value of the Z-Factor is a modification of Table 1 shown in Zhang et al. (1999); [2] note that the Z-factor cannot exceed one.
I edited the top parts of this page to make it more parallel to the t-test page, which defines a t-test as being any test for which the test statistic follows a t-distribution (rather than just covering the one-sample and two-sample t-tests). Nevertheless, I agree that the one-sample/two-sample tests need to be covered in detail.