G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of thet, F, and χ2 test families. In addition, it includes power analyses forz tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.

https://link.springer.com/content/pdf/10.3758/BF03193146.pdf

G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

/pdf/statistical-power-analyses-using-g-power-3-1-tests-for-dnxl4mlxpv.pdf

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

A study with low statistical power has a reduced chance of detecting a true effect, but it is less well appreciated that low power also reduces the likelihood that a statistically significant result reflects a true effect. Here, we show that the average statistical power of studies in the neurosciences is very low. The consequences of this include overestimates of effect size and low reproducibility of results. There are also ethical dimensions to this problem, as unreliable research is inefficient and wasteful. Improving reproducibility in neuroscience is a key priority and requires attention to well-established but often ignored methodological principles.

/pdf/power-failure-why-small-sample-size-undermines-the-4ftwwuexp7.pdf

Power failure: why small sample size undermines the reliability of neuroscience

Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA’s such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.

/pdf/calculating-and-reporting-effect-sizes-to-facilitate-4a429kama3.pdf

Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

This work was supported by grants from the National Institutes of Health (R01 AG048814, B.A.B.; RO1 DA15043, B.A.B.; P50 NS38377, V.L.D. and T.M.D.) Christopher and Dana Reeve Foundation (B.A.B.), the Novartis Institute for Biomedical Research (B.A.B.), Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (B.A.B.), the JPB Foundation (B.A.B., T.M.D.), the Cure Alzheimer’s Fund (B.A.B.), the Glenn Foundation (B.A.B.), the Esther B O’Keeffe Charitable Foundation (B.A.B.), the Maryland Stem Cell Research Fund (2013-MSCRFII-0105-00, V.L.D.; 2012-MSCRFII-0268-00, T.M.D.; 2013-MSCRFII-0105-00, T.M.D.; 2014-MSCRFF-0665, M.K.). S.A.L. was supported by a postdoctoral fellowship from the Australian National Health and Medical Research Council (GNT1052961), and the Glenn Foundation Glenn Award. L.E.C. was funded by a Merck Research Laboratories postdoctoral fellowship (administered by the Life Science Research Foundation). W.-S.C. was supported by a career transition grant from NEI (K99EY024690). C.J.B. was supported by a postdoctoral fellowship from Damon Runyon Cancer Research Foundation (DRG-2125-12). L.S. was supported by a postdoctoral fellowship from the German Research Foundation (DFG, SCHI 1330/1-1).

/pdf/neurotoxic-reactive-astrocytes-are-induced-by-activated-33z6oieelb.pdf

Neurotoxic reactive astrocytes are induced by activated microglia

What determines the take-over time? An integrated model approach of driver take-over after automated driving.

The purpose of this paper is to promote statistical power analysis in the behavioral sciences by introducing the easy to use GPower software GPower is a free general power analysis program available in two essentially equivalent versions, one designed for Macintosh OS/OS X and the other for MS‐DOS/Windows platforms Psychological research examples are presented to illustrate the various features of the GPower software In particular, a priori, post‐hoc, and compromise power analyses for t‐tests, F‐tests, and χ2‐tests will be demonstrated For all examples, the underlying statistical concepts as well as the implementation in GPower will be described In the behavioral sciences, we routinely apply statistical tests, but control of statistical power cannot be taken for granted However, neglecting statistical power—the probability of rejecting false null hypotheses—can have severe consequences For example, without control of statistical power it is very difficult to interpret nonsignificant results Statistical tests can produce nonsignificant results because (a) the null hypothesis (H0) holds and is retained correctly or (b) the alternative hypothesis (H1) holds but the test has not been powerful enough to detect the deviations from H0 Obviously, there is no reasonable way to decide between interpretations (a) and

/pdf/a-short-tutorial-of-gpower-5aszwezbpt.pdf

Axel Buchner

Papers

G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

What determines the take-over time? An integrated model approach of driver take-over after automated driving.

A short tutorial of GPower

Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving