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The Power of Replicated Measures to Increase Statistical Power
Advances in Methods and Practices in Psychological Science, 2019When running statistical tests, researchers can commit a Type II error, that is, fail to reject the null hypothesis when it is false. To diminish the probability of committing a Type II error (β), statistical power must be augmented.
Marc-André Goulet, D. Cousineau
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Statistical power and analytical quantification
Journal of Chromatography B, 2007It is suggested that power analysis should be formally incorporated into quantification experiment reports in order to substantiate the conclusions derived from experimental data more effectively. The article addressed the issues of power analysis calculation, sample size estimation and appropriate data reporting in quantitative analytical comparisons.
Livar Frøyland, Pedro Araujo
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2020
In this chapter, we focus on approximation problems motivated by studies on the asymptotic behavior of power-divergence family of statistics. These statistics are the goodness-of-fit test statistics and include, in particular, the Pearson chi-squared statistic, the Freeman–Tukey statistic, and the log-likelihood ratio statistic.
Yasunori Fujikoshi, Vladimir V. Ulyanov
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In this chapter, we focus on approximation problems motivated by studies on the asymptotic behavior of power-divergence family of statistics. These statistics are the goodness-of-fit test statistics and include, in particular, the Pearson chi-squared statistic, the Freeman–Tukey statistic, and the log-likelihood ratio statistic.
Yasunori Fujikoshi, Vladimir V. Ulyanov
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Statistical Power and Sample Size
2000Figure 6.1 shows two graphs of t-distributions. The lower graph (H1) could be a probability distribution of a sample of data or of a sample of paired differences between two observations. N = 20 and so 95% of the observations is within 2.901 ± 2.101 standard errors of the mean (SEMs) on the x-axis (usually called z-axis in statistics).
Toine F. Cleophas +2 more
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Statistics: Discovering Its Power.
Journal of the American Statistical Association, 1983Statistics: Discovering its Power. By T. H. Wonnacott and R. J. Wonnacott. London, Ontario, Wiley, 1982. xviii, 378 p. 24.5 cm. £16.45 (hardbound), £7.20 (paperbound). (Wiley Series in Probability and Mathematical Statistics.)
T. H. Wonnacott +2 more
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Understanding type I and type II errors, statistical power and sample size
Acta paediatrica, 2016The results of a clinical trial may be subject to random error because of the variability in the measured data, which arises purely by chance. There are two types of random error – type I error and type II error.
A. Akobeng
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The 'Power' of Sound Statistics
JAMA: The Journal of the American Medical Association, 1990During the dozen years or so that I have served as a biostatistical consultant to the editors of JAMA , the most frequent comment I have made in my reviews is the simple, straightforward question, "Can the authors provide any rationale or statistical power considerations underlying their choice of sample size?" From this experience, I infer that lack ...
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Powers, Probability and Statistics
2020Regularity is often taken as the starting point of our causal knowledge. But pure constant conjunctions are not what science finds. Even in randomised controlled trials, we do not discover a regular frequency of occurrence of some effect. The dispositionalist is able to explain the evidence of science in terms of the ontology of real causal powers ...
Stephen Mumford +2 more
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Statistical Power In Nursing Research
Nursing Research, 1990A power analysis was performed on 62 articles that were published in Nursing Research and Research in Nursing and Health during 1989. The analysis revealed that when effects were small, the mean power of the statistical tests being performed to test research hypotheses was .26, indicating a very high risk of committing a Type II error.
Denise F. Polit, Robert E. Sherman
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Statistical power in biological psychiatry
Psychiatry Research, 1981The use of statistical power and power analysis in both the design and evaluation of experiments in biological psychiatry is described. The possible consequences of low power investigations are discussed, and guidelines are provided to facilitate the application of power analysis.
Richard C. Mohs +5 more
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