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García-Pérez, Miguel A. – Educational and Psychological Measurement, 2017
Null hypothesis significance testing (NHST) has been the subject of debate for decades and alternative approaches to data analysis have been proposed. This article addresses this debate from the perspective of scientific inquiry and inference. Inference is an inverse problem and application of statistical methods cannot reveal whether effects…
Descriptors: Hypothesis Testing, Statistical Inference, Effect Size, Bayesian Statistics
Braham, Hana Manor; Ben-Zvi, Dani – Statistics Education Research Journal, 2017
A fundamental aspect of statistical inference is representation of real-world data using statistical models. This article analyzes students' articulations of statistical models and modeling during their first steps in making informal statistical inferences. An integrated modeling approach (IMA) was designed and implemented to help students…
Descriptors: Foreign Countries, Elementary School Students, Statistical Inference, Mathematical Models
Weller, Susan C. – Field Methods, 2015
This article presents a simple approach to making quick sample size estimates for basic hypothesis tests. Although there are many sources available for estimating sample sizes, methods are not often integrated across statistical tests, levels of measurement of variables, or effect sizes. A few parameters are required to estimate sample sizes and…
Descriptors: Sample Size, Statistical Analysis, Computation, Hypothesis Testing
Watson, Jane; Chance, Beth – Australian Senior Mathematics Journal, 2012
Formal inference, which makes theoretical assumptions about distributions and applies hypothesis testing procedures with null and alternative hypotheses, is notoriously difficult for tertiary students to master. The debate about whether this content should appear in Years 11 and 12 of the "Australian Curriculum: Mathematics" has gone on…
Descriptors: Foreign Countries, Research Methodology, Sampling, Statistical Inference
Eisenhauer, Joseph G. – Teaching Statistics: An International Journal for Teachers, 2009
Very little explanatory power is required in order for regressions to exhibit statistical significance. This article discusses some of the causes and implications. (Contains 2 tables.)
Descriptors: Statistical Significance, Educational Research, Sample Size, Probability
Capraro, Robert M.; Capraro, Mary Margaret – Middle Grades Research Journal, 2009
This study examines two journals specific to the middles grades where original quantitative empirical articles are published, Research in Middle Level Education and Middle Grades Research Journal to determine what quantitative statistics are used, how they are used, and what study designs are used. Important for those who write for the…
Descriptors: Periodicals, Research Methodology, Social Science Research, Effect Size
Rosenthal, James A. – Springer, 2011
Written by a social worker for social work students, this is a nuts and bolts guide to statistics that presents complex calculations and concepts in clear, easy-to-understand language. It includes numerous examples, data sets, and issues that students will encounter in social work practice. The first section introduces basic concepts and terms to…
Descriptors: Statistics, Data Interpretation, Social Work, Social Science Research

Bonett, Douglas G.; Seier, Edith – Journal of Educational and Behavioral Statistics, 2003
Derived a confidence interval for a ratio of correlated mean absolute deviations. Simulation results show that it performs well in small sample sizes across realistically nonnormal distributions and that it is almost as powerful as the most powerful test examined by R. Wilcox (1990). (SLD)
Descriptors: Correlation, Equations (Mathematics), Hypothesis Testing, Sample Size

Finstuen, Kenn; And Others – Educational and Psychological Measurement, 1994
Computation of a one-way analysis of variance (ANOVA) "F" ratio from descriptive statistics in the absence of raw data is corrected from two sources. Means associated with inferential statistical hypotheses are identified as estimable population parameters. (Author)
Descriptors: Analysis of Variance, Computation, Estimation (Mathematics), Hypothesis Testing
Chou, Tungshan; Wang, Lih-Shing – 1992
P. O. Johnson and J. Neyman (1936) proposed a general linear hypothesis testing procedure for testing the null hypothesis of no treatment difference in the presence of some covariates. This is generally known as the Johnson-Neyman (JN) technique. The need for the hypothesis testing step (often omitted) as originally presented and the…
Descriptors: Computer Simulation, Equations (Mathematics), Foreign Countries, Hypothesis Testing
Lipson, Kay – Mathematics Education Research Journal, 2003
Many statistics educators believe that few students develop the level of conceptual understanding essential for them to apply correctly the statistical techniques at their disposal and to interpret their outcomes appropriately. It is also commonly believed that the sampling distribution plays an important role in developing this understanding.…
Descriptors: Statistical Inference, Learning Strategies, Sampling, Statistics
Thompson, Bruce – 1987
This paper evaluates the logic underlying various criticisms of statistical significance testing and makes specific recommendations for scientific and editorial practice that might better increase the knowledge base. Reliance on the traditional hypothesis testing model has led to a major bias against nonsignificant results and to misinterpretation…
Descriptors: Analysis of Variance, Data Interpretation, Editors, Effect Size