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

2007 ◽  
Vol 39 (2) ◽  
pp. 175-191 ◽  
Author(s):  
Franz Faul ◽  
Edgar Erdfelder ◽  
Albert-Georg Lang ◽  
Axel Buchner
Keyword(s):  
Power Analysis ◽  
Statistical Power ◽  
Analysis Program ◽  
Biomedical Sciences ◽  
Statistical Power Analysis ◽  
The Social

Sensitivity of male reproductive endpoints in nonhuman primate toxicity studies: A statistical power analysis

2013 ◽  
Vol 41 ◽  
pp. 67-72 ◽  
Author(s):  
G.D. Cappon ◽  
D. Potter ◽  
M.E. Hurtt ◽  
G.F. Weinbauer ◽  
C.M. Luetjens ◽  
...  
Keyword(s):  
Nonhuman Primate ◽  
Power Analysis ◽  
Statistical Power ◽  
Statistical Power Analysis ◽  
Toxicity Studies

Statistical power analysis for one-way analysis of variance: A computer program

1990 ◽  
Vol 22 (3) ◽  
pp. 271-282 ◽  
Author(s):  
Michael Borenstein ◽  
Jacob Cohen ◽  
Hannah R. Rothstein ◽  
Simcha Pollack ◽  
John M. Kane
Keyword(s):  
Computer Program ◽  
Analysis Of Variance ◽  
Power Analysis ◽  
Statistical Power ◽  
Statistical Power Analysis

Statistical Power Analysis can Improve Fisheries Research and Management

1990 ◽  
Vol 47 (1) ◽  
pp. 2-15 ◽  
Author(s):  
Randall M. Peterman
Keyword(s):  
Sample Size ◽  
Power Analysis ◽  
Statistical Power ◽  
Small Sample Size ◽  
Industrial Effluent ◽  
Small Sample ◽  
Detectable Effect ◽  
Type Ii ◽  
Sampling Variability ◽  
Statistical Power Analysis

Ninety-eight percent of recently surveyed papers in fisheries and aquatic sciences that did not reject some null hypothesis (H0) failed to report β, the probability of making a type II error (not rejecting H0 when it should have been), or statistical power (1 – β). However, 52% of those papers drew conclusions as if H0 were true. A false H0 could have been missed because of a low-power experiment, caused by small sample size or large sampling variability. Costs of type II errors can be large (for example, for cases that fail to detect harmful effects of some industrial effluent or a significant effect of fishing on stock depletion). Past statistical power analyses show that abundance estimation techniques usually have high β and that only large effects are detectable. I review relationships among β, power, detectable effect size, sample size, and sampling variability. I show how statistical power analysis can help interpret past results and improve designs of future experiments, impact assessments, and management regulations. I make recommendations for researchers and decision makers, including routine application of power analysis, more cautious management, and reversal of the burden of proof to put it on industry, not management agencies.

Reliability and statistical power analysis of cortical and subcortical FreeSurfer metrics in a large sample of healthy elderly

NeuroImage ◽  
2015 ◽  
Vol 108 ◽  
pp. 95-109 ◽  
Author(s):  
Franziskus Liem ◽  
Susan Mérillat ◽  
Ladina Bezzola ◽  
Sarah Hirsiger ◽  
Michel Philipp ◽  
...  
Keyword(s):  
Power Analysis ◽  
Statistical Power ◽  
Statistical Power Analysis ◽  
Large Sample ◽  
Healthy Elderly

Statistical Power Analysis for the Behavioral Sciences

Biometrics ◽  
1970 ◽  
Vol 26 (3) ◽  
pp. 588 ◽  
Author(s):  
Sylvia Wassertheil ◽  
Jacob Cohen
Keyword(s):  
Power Analysis ◽  
Statistical Power ◽  
Behavioral Sciences ◽  
Statistical Power Analysis

Statistical power analysis to estimate how many months of data are required to identify PACU staffing to minimize delays in admission from ORs

2002 ◽  
Vol 17 (2) ◽  
pp. 84-88 ◽  
Author(s):  
Richard H. Epstein ◽  
Franklin Dexter ◽  
Rodney D. Traub
Keyword(s):  
Power Analysis ◽  
Statistical Power ◽  
Statistical Power Analysis
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