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Brewer, James K. – American Educational Research Journal, 1974
See TM 501 201-3 and EJ 060 883 for related articles. (MLP)
Descriptors: Bayesian Statistics, Hypothesis Testing, Power (Statistics), Statistical Significance

Penick, John E.; Brewer, James K. – Journal of Research in Science Teaching, 1972
Research reports published in a science education research journal were examined to calculate the probability of rejecting true null hypotheses. Recommendations for improving analyses and research in science education are provided. (PS)
Descriptors: Educational Research, Hypothesis Testing, Research Methodology, Science Education

Brewer, James K. – Journal of Educational Statistics, 1985
Six behavioral statistics textbooks listed by their publishers as "bestsellers" during 1982 were reviewed by the author. The intent was to detect the presence of and to discuss the nature of some theoretical inferential inaccuracies, misinterpretations and errors. (Author/LMO)
Descriptors: Higher Education, Hypothesis Testing, Sampling, Statistical Analysis
Asraf, Ratnawati Mohd; Brewer, James K. – Australian Educational Researcher, 2004
This article addresses the importance of obtaining a sample of an adequate size for the purpose of testing hypotheses. The logic underlying the requirement for a minimum sample size for hypothesis testing is discussed, as well as the criteria for determining it. Implications for researchers working with convenient samples of a fixed size are also…
Descriptors: Hypothesis Testing, Sample Size, Sampling, Research Methodology

Brewer, James K. – American Educational Research Journal, 1972
Purpose of this paper is to report the level of power for recent statistical tests reported in the AERJ and to propose alternative reporting schemes relative to hypothesis testing to include power and effect size. (Author)
Descriptors: Hypothesis Testing, Mathematical Applications, Power (Statistics), Research Reviews (Publications)

Brewer, James K. – Florida Journal of Educational Research, 1987
An index that measures the degree of comfort a researcher has relative to statistical inference application is proposed. The index is calculated by selecting and weighting components inherent in the conduct of hypothesis tests and confidence intervals, and its values range from zero to one. To organize the components of the index, inference…
Descriptors: Attitude Measures, Hypothesis Testing, Rating Scales, Research Reports
Keats, John B.; Brewer, James K. – 1971
This paper presents an index of goodness-of-fit for comparing m models over n trials. The index allows for differentiated weighting of the trials as to their importance in the comparison of the models. Several possible weighting schemes are suggested and the conditions on the weights which assure asymptotic normality of the index distribution are…
Descriptors: Goodness of Fit, Hypothesis Testing, Mathematical Models, Nonparametric Statistics

Brewer, James K.; Sindelar, Paul T. – Journal of Special Education, 1988
From a priori and post hoc data collection perspectives, this paper describes the interrelations among (1) power, alpha, effect size, and sample size for hypothesis testing; and (2) precision, confidence, and sample size for interval estimation. Implications for special education researchers working with convenient samples of fixed size are…
Descriptors: Data Collection, Disabilities, Educational Research, Effect Size