The Power option calculates statistical power and other details about a given hypothesis test.
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LSV (the Least Significant Value) is the value of some parameter or function of parameters that would produce a certain pvalue alpha. Said another way, you want to know how small an effect would be declared significant at some pvalue alpha. The LSV provides a measuring stick for significance on the scale of the parameter, rather than on a probability scale. It shows how sensitive the design and data are.

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LSN (the Least Significant Number) is the total number of observations that would produce a specified pvalue alpha given that the data has the same form. The LSN is defined as the number of observations needed to reduce the variance of the estimates enough to achieve a significant result with the given values of alpha, sigma, and delta (the significance level, the standard deviation of the error, and the effect size). If you need more data to achieve significance, the LSN helps tell you how many more. The LSN is the total number of observations that yields approximately 50% power.

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Power is the probability of getting significance (pvalue < alpha) when a real difference exists between groups. It is a function of the sample size, the effect size, the standard deviation of the error, and the significance level. The power tells you how likely your experiment is to detect a difference (effect size), at a given alpha level.

The Power Details window and reports are the same as those in the general fitting platform launched by the Fit Model platform. For more details about power calculation, see the Fitting Linear Models book.
For each of four columns Alpha, Sigma, Delta, and Number, fill in a single value, two values, or the start, stop, and increment for a sequence of values. See Example of the Power Details Window. Power calculations are performed on all possible combinations of the values that you specify.
Alpha (α)


Sigma (σ)


Delta (δ)

Raw effect size. For details about effect size computations, see the Fitting Linear Models book. The first position is initially set to the square root of the sums of squares for the hypothesis divided by n; that is, .

Number (n)


Solves for the value of the parameter or linear test that produces a pvalue of α. This is a function of α, σ, n, and the standard error of the estimate. This feature is available only when the X factor has two levels and is usually used for individual parameters.


Adjusted power and confidence limits are computed only for the original Delta, because that is where the random variation is.
