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Accord.NET (logo) IPowerAnalysis Interface
Common interface for power analysis objects.

Namespace:  Accord.Statistics.Testing.Power
Assembly:  Accord.Statistics (in Accord.Statistics.dll) Version: 3.4.0
public interface IPowerAnalysis : ICloneable, 
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The IPowerAnalysis type exposes the following members.

Public propertyEffect
Gets the effect size of the test.
Public propertyPower
Gets the power of the test, also known as the (1-Beta error rate) or the test's sensitivity.
Public propertySamples
Gets the number of samples considered in the test.
Public propertySize
Gets the significance level for the test. Also known as alpha.
Public propertyTail
Gets the test type.
Public methodClone
Creates a new object that is a copy of the current instance.
(Inherited from ICloneable.)
Public methodToString
Formats the value of the current instance using the specified format.
(Inherited from IFormattable.)

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.


  • Wikipedia, The Free Encyclopedia. Statistical power. Available on:

See Also