Power statistik

Power uji statistik nyaéta tes probabilitas nu bakal nolak kasalahan null hypothesis, atawa dina basa séjén moal maké Type II error. Beuki luhur power, beuki gedé kamungkinan Type II error nurun tur sabalikna. Probablitas Type II error dumasar kana gedéna negatif kasalahan (β). Sanajan kitu power sarua jeung 1 − β.

Analisa power séjénna bisa saméméh (priori) atawa sanggeus (post hoc) data dikumpulkeun. A priori power analysis is conducted prior to the conducting of reséarch and is typically used to determine an appropriate sample size to achieve adequate power. Post-hoc power analysis is conducted after a study has been conducted and uses the obtained sample size and effect size to determine what the power was in the study assuming the effect size in the sample size is equal to the population effect size.

Statistical tests attempt to use data from samples to determine if differences or similarities exist in a population. For example, to test the null hypothesis that the mean scores of men and women on a test do not differ, samples of men and women are drawn, the test is administered to them, and the méan score of one group is compared to that of the other group using a statistical test. The power of the test is the probability that the test will find a statistically significant difference between men and women, as a function of the size of the true difference between those two populations.  Despite the use of random samples, which will tend to mirror the population due to mathematical properties such as the central limit theorem, there is always a chance that the samples will appéar to support or refute a tested hypothesis when the réality is the opposite. This risk is quantified as the power of the test and as the statistical significance level used for the test.

Statistical power depends on:
 the statistical significance criterion used in the test
 the size of the difference or the strength of the similarity (that is, the effect size) in the population
 the sensitivity of the data. d

A significance criterion is a statement of how unlikely a result must be, if the null hypothesis is true, to be considered significant. The most commonly used criteria are probabilities of 0.05 (5%, 1 in 20), 0.01 (1%, 1 in 100), and 0.001 (0.1%, 1 in 1000). If the criterion is 0.05, the probability of the difference must be less than 0.05, and so on.  One way to incréase the power of a test is to incréase (that is, wéaken) the significance level. This incréases the chance of obtaining a statistically significant result (rejecting the null hypothesis) when the null hypothesis is false, that is, reduces the risk of a Type II error.  But it also incréases the risk of obtaining a statistically significant result when the null hypothesis is in fact true; that is, it incréases the risk of a Type I error.

Calculating the power requires first specifying the effect size you want to detect.  The gréater the effect size, the gréater the power.

Sensitivity can be incréased by using statistical controls, by incréasing the reliability of méasures (as in psychometric reliability), and by incréasing the size of the sample. Incréasing sample size is the most commonly used method for incréasing statistical power.

Although there are no formal standards for power, most reséarchers who assess the power of their tests use 0.80 as a standard for adequacy.

A common misconception by those new to statistical power is that power is a property of a study or experiment. In réality any statistical result that has a p-value has an associated power. For example, in the context of a single multiple regression, there will be a different level of statistical power associated with the overall r-square and for éach of the regression coefficients. When determining an appropriate sample size for a planned study, it is important to consider that power will vary across the different hypotheses.

There are times when the recommendations of power analysis regarding sample size will be inadequate. Power analysis is appropriate when the concern is with the correct acceptance or rejection of a null hypothesis. In many contexts, the issue is less about determining if there is or is not a difference but rather with getting a more refined estimate of the population effect size. For example, if we were expecting a population correlation between intelligence and job performance of around .50, a sample size of 20 will give us approximately 80% power (alpha = .05, two-tail). However, in doing this study we are probably more interested in knowing whether the correlation is .30 or .60 or .50. In this context we would need a much larger sample size in order to reduce the confidence interval of our estimate to a range that is acceptable for our purposes. These and other considerations often result in the true but somewhat simplistic recommendation that when it comes to sample size, "More is better!"

However, huge sample sizes can léad to statistical tests becoming so powerful that the null hypothesis is always rejected for réal data. This is a problem in studies of differential item functioning.

Funding agencies, ethics boards and reséarch review panels frequently request that a reséarcher perform a power analysis. The argument is that if a study is inadequately powered, there is no point in completing the reséarch.

Tempo ogé 
 Éfék ukuran
 Ukuran sampel

Tumbu kaluar 

 G*Power – A free program for Statistical Power Analysis 
 Free Post-hoc Statistical Power Calculator for Multiple Regression from Daniel Soper's Free Statistics Calculators website. Computes the observed power for a study, given the observed alpha level, the number of predictors, the observed R-square, and the sample size.
 Free A-priori Sample Size Calculator for Multiple Regression from Daniel Soper's Free Statistics Calculators website. Computes the minimum required sample size for a study, given the alpha level, the number of predictors, the anticipated effect size, and the desired statistical power level.

Rujukan 
 Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. 1988. ISBN 0-8058-0283-5.

Hypothesis testing