EFFECTS OF PARASITISM BY TOMICOBIA TIBIALIS ASHMEAD (HYMENOPTERA: PTEROMALIDAE) ON REPRODUCTIVE PARAMETERS OF FEMALE PINE ENGRAVERS, IPS PINI (SAY)

1992 ◽  
Vol 124 (3) ◽  
pp. 509-513 ◽  
Author(s):  
Susan E. Senger ◽  
Bernard D. Roitberg

AbstractThe effects of Tomicobia tibialis Ashmead on the reproductive potential of adult Ips pini (Say) females were examined by comparing reproductive indices of parasitized and healthy females maintained in groups of three with healthy males. Parasitized females produced 50% fewer offspring than healthy females, and these offspring were distributed differently in the maternal gallery. Maternal gallery length and larval survival were not significantly different between the two groups, but a statistical power analysis (1 − β) shows this result to be equivocal. The potential to use T. tibialis as a biocontrol agent against I. pini is discussed.

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 ◽  
...  

2001 ◽  
Vol 88 (3_suppl) ◽  
pp. 1194-1198 ◽  
Author(s):  
F. Stephen Bridges ◽  
C. Bennett Williamson ◽  
Donna Rae Jarvis

Of 75 letters “lost” in the Florida Panhandle, 33 (44%) were returned in the mail by the finders (the altruistic response). Addressees' affiliations were significantly associated with different rates of return; fewer emotive Intercontinental Gay and Lesbian Outdoors Organization addressees were returned than nonemotive ones. The technique for power analysis by Gillett (1996) was applied to data from an earlier study and indicated our sample of 75 subjects would still yield a desired power level, i.e., 80, for the likely effect sizes. Statistical power was .83, and the effect was medium in size at .34.


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

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