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.

scholarly journals Comparison of statistical methods for analysis of small sample sizes for detecting the differences in efficacy between treatments for knee osteoarthritis

2020 ◽  
Author(s):  
Chia-Lung Shih ◽  
Te-Yu Hung
Keyword(s):  
Knee Osteoarthritis ◽  
Sample Size ◽  
Statistical Methods ◽  
Statistical Power ◽  
Permutation Test ◽  
Small Sample Size ◽  
Small Sample ◽  
T Test ◽  
Sample Sizes ◽  
Knee Oa

Abstract Background A small sample size (n < 30 for each treatment group) is usually enrolled to investigate the differences in efficacy between treatments for knee osteoarthritis (OA). The objective of this study was to use simulation for comparing the power of four statistical methods for analysis of small sample size for detecting the differences in efficacy between two treatments for knee OA. Methods A total of 10,000 replicates of 5 sample sizes (n=10, 15, 20, 25, and 30 for each group) were generated based on the previous reported measures of treatment efficacy. Four statistical methods were used to compare the differences in efficacy between treatments, including the two-sample t-test (t-test), the Mann-Whitney U-test (M-W test), the Kolmogorov-Smirnov test (K-S test), and the permutation test (perm-test). Results The bias of simulated parameter means showed a decreased trend with sample size but the CV% of simulated parameter means varied with sample sizes for all parameters. For the largest sample size (n=30), the CV% could achieve a small level (<20%) for almost all parameters but the bias could not. Among the non-parametric tests for analysis of small sample size, the perm-test had the highest statistical power, and its false positive rate was not affected by sample size. However, the power of the perm-test could not achieve a high value (80%) even using the largest sample size (n=30). Conclusion The perm-test is suggested for analysis of small sample size to compare the differences in efficacy between two treatments for knee OA.

scholarly journals Statistical Power Analysis

2017 ◽  
Author(s):  
Chris Aberson
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
Power Analysis ◽  
Statistical Power ◽  
Type I Error ◽  
Type I ◽  
Statistical Power Analysis ◽  
Emerging Trends ◽  
Optimal Probability ◽  
Innovative Work

Preprint of Chapter appearing as: Aberson, C. L. (2015). Statistical power analysis. In R. A. Scott &amp; S. M. Kosslyn (Eds.) Emerging trends in the behavioral and social sciences. Hoboken, NJ: Wiley.Statistical power refers to the probability of rejecting a false null hypothesis (i.e., finding what the researcher wants to find). Power analysis allows researchers to determine adequate sample size for designing studies with an optimal probability for rejecting false null hypotheses. When conducted correctly, power analysis helps researchers make informed decisions about sample size selection. Statistical power analysis most commonly involves specifying statistic test criteria (Type I error rate), desired level of power, and the effect size expected in the population. This article outlines the basic concepts relevant to statistical power, factors that influence power, how to establish the different parameters for power analysis, and determination and interpretation of the effect size estimates for power. I also address innovative work such as the continued development of software resources for power analysis and protocols for designing for precision of confidence intervals (a.k.a., accuracy in parameter estimation). Finally, I outline understudied areas such as power analysis for designs with multiple predictors, reporting and interpreting power analyses in published work, designing for meaningfully sized effects, and power to detect multiple effects in the same study.

Biomarker discovery in endometrial carcinoma using novel proteomic methods

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 5034-5034
Author(s):  
J. A. Borgia ◽  
C. Frankenberger ◽  
S. E. McCormack ◽  
K. A. Kaiser ◽  
L. Usha ◽  
...  
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Biomarker Discovery ◽  
Small Sample Size ◽  
Tumor Resection ◽  
Small Sample ◽  
Serum Samples ◽  
Ovarian Carcinomas ◽  
Post Surgery ◽  
Operative Specimen

5034 Background: A practical serum-based screening test for endometrial and ovarian carcinomas could greatly improve their early diagnosis, but developing such a test has proved elusive. Herein, we describe methods offering increased statistical power over conventional ‘batch’ analyses, via paired patient serum samples to identify biomarkers specific for uterine endometriod carcinomas (UEA). Methods: Paired serum samples (collected pre- and post- surgery) were prepared from patients undergoing UEA resection. A Ciphergen SELDI-TOF mass spectrometer was used to generate the patient serum proteomic profiles with data acquisition optimized to the 5,000–40,000 m/z (mass) range. Spectra quality was assessed using a specific function within the R-project statistical platform (v2.2.0), whereas data analyses were performed using Bioconductor, an extension of the R statistical platform; all in a blinded manner. Raw spectra were processed as follows: spectra pair normalization, baseline subtraction, and differential peak detection (with calibrated alignment). Aligned peaks were sorted into groups, based on tumor pathology and pre- vs. post-operative specimen type, and compared using a two tailed homoscedastic t-test in Microsoft Excel 2003. Results: Pre- and post-operative serum from 10 patients with UEA had their serum proteomic profiles evaluated for peaks lost after surgery. These values were then compared with a set of ‘normal’ samples (i.e. patients undergoing surgery for benign gynecologic disease). Even with our small sample size we identified 16 serum components that were significantly reduced or absent (p < 0.04) after tumor resection; 10 unique to UEA (p <0.04). From this group, species at m/z values of 4291.9, 4414.2, 5036.1, 9,615.0, and 25,508.8 seem most promising, based on their high level of significance (p < 0.02), for 10/10 patients. Conclusions: These results validate the power of our pre-and post-operative sample sets for the identification of new serum biomarker candidates for UEA. Work is in progress to increase the patient sample size, expand the study to patients with serous uterine and ovarian carcinomas, and identify the reported biomarker candidates via tandem mass spectrometry No significant financial relationships to disclose.

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