public interface IPowerAnalysis : ICloneable, IFormattable
Public Interface IPowerAnalysis Inherits ICloneable, IFormattable
Thetype exposes the following members.
Gets the effect size of the test.
Gets the power of the test, also known as the (1-Beta error rate) or the test's sensitivity.
Gets the number of samples considered in the test.
Gets the significance level for the test. Also known as alpha.
Gets the test type.
Creates a new object that is a copy of the current instance.(Inherited from .)
Formats the value of the current instance using the specified format.(Inherited from .)
The power of a statistical test is the probability that it correctly rejects the null hypothesis when the null hypothesis is false. That is,
power = P(reject null hypothesis | null hypothesis is false)
It can be equivalently thought of as the probability of correctly accepting the alternative hypothesis when the alternative hypothesis is true - that is, the ability of a test to detect an effect, if the effect actually exists. The power is in general a function of the possible distributions, often determined by a parameter, under the alternative hypothesis. As the power increases, the chances of a Type II error occurring decrease. The probability of a Type II error occurring is referred to as the false negative rate (β) and the power is equal to 1−β. The power is also known as the sensitivity.
Power analysis can be used to calculate the minimum sample size required so that one can be reasonably likely to detect an effect of a given size. Power analysis can also be used to calculate the minimum effect size that is likely to be detected in a study using a given sample size. In addition, the concept of power is used to make comparisons between different statistical testing procedures: for example, between a parametric and a nonparametric test of the same hypothesis. There is also the concept of a power function of a test, which is the probability of rejecting the null when the null is true.