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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.
The Bonferroni correction compensates for that increase by testing each individual hypothesis at a significance level of , where is the desired overall alpha level and is the number of hypotheses. [4] For example, if a trial is testing hypotheses with a desired overall , then the Bonferroni correction would test each individual hypothesis at .
Pearson's correlation coefficient, when applied to a sample, is commonly represented by and may be referred to as the sample correlation coefficient or the sample Pearson correlation coefficient. We can obtain a formula for r x y {\displaystyle r_{xy}} by substituting estimates of the covariances and variances based on a sample into the formula ...
This formula is valid only if the eight values with which we began form the complete population. If the values instead were a random sample drawn from some large parent population (for example, they were 8 students randomly and independently chosen from a class of 2 million), then one divides by 7 (which is n − 1) instead of 8 (which is n) in the denominator of the last formula, and the ...
Decay correction is one way of working out what the radioactivity would have been at the time it was taken, rather than at the time it was tested. For example, the isotope copper-64, commonly used in medical research, has a half-life of 12.7 hours. If you inject a large group of animals at "time zero", but measure the radioactivity in their ...
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For significance testing, the degrees of freedom for this test is 2n − 2, where n is sample size. Equal or unequal sample sizes, similar variances (1 / 2 < s X 1 / s X 2 < 2) This test is used only when it can be assumed that the two distributions have the same variance (when this assumption is violated, see below).
It can be used in calculating the sample size for a future study. When measuring differences between proportions, Cohen's h can be used in conjunction with hypothesis testing . A " statistically significant " difference between two proportions is understood to mean that, given the data, it is likely that there is a difference in the population ...