Brief Notes: Statistical Power Analysis and Research Results

1973 ◽  
Vol 10 (3) ◽  
pp. 225-229 ◽  
Author(s):  
Jacob Cohen

I was most pleased by the recent publication by Brewer, “On the Power of Statistical Tests in the American Educational Research Journal” 1972, and understandably delighted with his heavy reliance, in accomplishing his survey, on my power handbook ( Cohen, 1969 ). I strongly agree with his stress on the importance of power analysis. Further, his survey’s confirmation of my finding of a decade ago ( Cohen, 1962 ; 1965 ) that the neglect of power analysis results in generally low power is very useful, although not surprising. Unfortunately, however, some conceptual errors in the article may seriously mislead educational researchers, and undermine our shared goal of promulgating power analysis. Hence, this note.

2013 ◽  
Vol 41 ◽  
pp. 67-72 ◽  
Author(s):  
G.D. Cappon ◽  
D. Potter ◽  
M.E. Hurtt ◽  
G.F. Weinbauer ◽  
C.M. Luetjens ◽  
...  

1990 ◽  
Vol 22 (3) ◽  
pp. 271-282 ◽  
Author(s):  
Michael Borenstein ◽  
Jacob Cohen ◽  
Hannah R. Rothstein ◽  
Simcha Pollack ◽  
John M. Kane

1990 ◽  
Vol 47 (1) ◽  
pp. 2-15 ◽  
Author(s):  
Randall M. Peterman

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.


NeuroImage ◽  
2015 ◽  
Vol 108 ◽  
pp. 95-109 ◽  
Author(s):  
Franziskus Liem ◽  
Susan Mérillat ◽  
Ladina Bezzola ◽  
Sarah Hirsiger ◽  
Michel Philipp ◽  
...  

Biometrics ◽  
1970 ◽  
Vol 26 (3) ◽  
pp. 588 ◽  
Author(s):  
Sylvia Wassertheil ◽  
Jacob Cohen

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