![]() Power calculations are generally used in two ways:ġ) A priori - Calculation of the minimum sample size needed to achieve a specified Power to detect an effect of a given size at a given alpha. ![]() One very convenient and easy-to-use downloadable Power calculator called G-Power is available at the following link at the time of this writing: ![]() Fortunately there are a number of free utilities available online that can calculate a test’s Power or the sample size needed to achieve a specified Power. If any three of the four related factors (Power, alpha, sample size, and effect size) are known, the fourth factor can be calculated. The larger the specified effect size, the larger is a test’s Power because a larger effect size is more likely to be detected by a statistical test. The lower the Level of Confidence needed, the more likely a statistical test will detect an effect. The larger alpha is, the larger is a test’s Power because a larger alpha reduces the amount of confidence needed to validate a statistical test’s result. The larger the sample size, the larger is a test’s Power because a larger sample size increases a statistical test’s accuracy. The Power of a statistical test is related with alpha, sample size, and effect size in the following ways: The term Power can be described as the accuracy of a statistical test. Power is the probability of detecting a real effect of a given size at a given Level of Significance (alpha) at a given total sample size and number of groups. Β = probability of not detecting a real effectġ - Β = probability of detecting a real effect Β (“beta”) = probability of a type 2 error (a false negative) Α = probability of detecting an effect where there is none Α = probability of a type 1 error (a false positive) Α (“alpha”) = Level of Significance = 1 – Level of Confidence A statistical test’s Power is the probability that the test will detect an effect of a given size at a given level of significance (alpha). The accuracy of a statistical test is specified as the Power of the test. The larger the sample size, the more reliable will be the test’s results. The accuracy of a statistical test is very dependent upon the sample size. Scheirer-Ray-Hare Test Alternative For 2-Factor ANOVA With Replication Shapiro-Wilk Normality Test in Excel For 2-Factor ANOVA With ReplicationĢ-Factor ANOVA With Replication Effect Size in Excel 2010 and Excel 2013Įxcel Post Hoc Tukey’s HSD Test For 2-Factor ANOVA With ReplicationĢ-Factor ANOVA With Replication – Test Power With G-Power Utility Variance Tests: Levene’s and Brown-Forsythe For 2-Factor ANOVA in Excel 2010 and Excel 2013 Two-Factor ANOVA With Replication in 5 Steps in Excel 2010 and Excel 2013 This is one of the following seven articles on Two-Factor ANOVA With Replication in Excel
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