Definition
The power of a statistical hypothesis test is the probability of rejecting the null hypothesis given that the null hypothesis is in fact false.
Description
There are four possible outcomes of a statistical hypothesis test: (1) the null hypothesis is maintained given that it is in fact true (a true negative decision); (2) the null hypothesis is rejected even though it is true (a false positive decision or type I error); (3) the null hypothesis is maintained even though it is false (a false negative decision or type II error); and (4) the null hypothesis is rejected given that it is in fact false (a true positive decision). The probabilities of type I and type II errors are often denoted by the Greek letters α and β, respectively. Accordingly, the power (i.e., the probability of a true positive decision, also referred to as the sensitivityof a test) is (1-β), whereas (1-α) denotes the probability of a true...
References
American Psychological Association. (2001). Publication manual of the American Psychological Association (5th ed.). Washington, DC: Author.
Berger, M. P. F., & Wong, W. K. (2009). An introduction to optimal design for social and biomedical research. Chichester: Wiley.
Brandmaier, A. M., von Oertzen, T., Ghisletta, P., Hertzog, C., & Lindenberger, U. (2015). LIFESPAN: A tool for the computer-aided design of longitudinal studies. Frontiers in Psychology, 6, 272. https://doi.org/10.3389/fpsyg.2015.00272.
Champely, S. (2020). pwr: Basic functions for power analysis. (Version 1.3-0). Retrieved from https://CRAN.R-project.org/package=pwr
Cohen, J. (1962). The statistical power of abnormal-social psychological research: A review. The Journal of Abnormal and Social Psychology, 65(3), 145–153. https://doi.org/10.1037/h0045186.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Erlbaum.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. https://doi.org/10.1037/0033-2909.112.1.155.
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Hillsdale: Erlbaum.
Erdfelder, E., Faul, F., Buchner, A., & Cüpper, L. (2010). Effektgröße und Teststärke. In H. Holling & B. Schmitz (Eds.), Handbuch der Psychologischen Methoden und Evaluation (pp. 358–369). Göttingen: Hogrefe.
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146.
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149.
Hoenig, J. M., & Heisey, D. M. (2001). The abuse of power. The American Statistician, 55(1), 19–24. https://doi.org/10.1198/000313001300339897.
Kruschke, J. K., & Liddell, T. M. (2018). The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review, 25(1), 178–206. https://doi.org/10.3758/s13423-016-1221-4.
Liu, X., & Wang, L. (2019). Sample size planning for detecting mediation effects: A power analysis procedure considering uncertainty in effect size estimates. Multivariate Behavioral Research, 54(6), 822–839. https://doi.org/10.1080/00273171.2019.1593814.
Maxwell, S. E. (2004). The persistence of underpowered studies in psychological research: Causes, consequences, and remedies. Psychological Methods, 9(2), 147–163. https://doi.org/10.1037/1082-989X.9.2.147.
Maxwell, S. E., Kelley, K., & Rausch, J. R. (2008). Sample size planning for statistical power and accuracy in parameter estimation. Annual Review of Psychology, 59, 537–563. https://doi.org/10.1146/annurev.psych.59.103006.093735.
Maxwell, S. E., Delaney, H. D., & Kelley, K. (2018). Designing experiments and analyzing data: a model comparison perspective (3rd ed.). New York: Routledge.
Moshagen, M., & Erdfelder, E. (2016). A new strategy for testing structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 23, 54–60. https://doi.org/10.1080/10705511.2014.950896.
Muthén, L. K., & Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling: A Multidisciplinary Journal, 9(4), 599–620. https://doi.org/10.1207/S15328007SEM0904_8.
Onwuegbuzie, A. J., & Leech, N. L. (2004). Post hoc power: A concept whose time has come. Understanding Statistics, 3(4), 201–230. https://doi.org/10.1207/s15328031us0304_1.
Paxton, P., Curran, P. J., Bollen, K. A., Kirby, J., & Chen, F. (2001). Monte Carlo experiments: Design and implementation. Structural Equation Modeling: A Multidisciplinary Journal, 8, 287–312. https://doi.org/10.1207/S15328007SEM0802_7.
R Core Team. (2021). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Retrieved from http://www.R-project.org/.
Schnuerch, M., & Erdfelder, E. (2020). Controlling decision errors with minimal costs: The sequential probability ratio t test. Psychological Methods, 25(2), 206–226. https://doi.org/10.1037/met0000234.
Sedlmeier, P., & Gigerenzer, G. (1989). Do studies of statistical power have an effect on the power of studies? Psychological Bulletin, 105(2), 309–316. https://doi.org/10.1037/0033-2909.105.2.309.
von Oertzen, T. (2010). Power equivalence in structural equation modelling. British Journal of Mathematical and Statistical Psychology, 63, 257–272. https://doi.org/10.1348/000711009X441021.
Wald, A. (1947). Sequential analysis. New York: Wiley.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this entry
Cite this entry
Voelkle, M.C., Erdfelder, E. (2021). Power Analysis. In: Maggino, F. (eds) Encyclopedia of Quality of Life and Well-Being Research. Springer, Cham. https://doi.org/10.1007/978-3-319-69909-7_2230-2
Download citation
DOI: https://doi.org/10.1007/978-3-319-69909-7_2230-2
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69909-7
Online ISBN: 978-3-319-69909-7
eBook Packages: Springer Reference Social SciencesReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences