Meta-analysis of proportions is conceptually simple: Faced with a binary outcome in multiple studies, we seek inference on some overall proportion of successes/failures. Under common effect models, exact inference has long been available, but is not when we more realistically allow for heterogeneity of the proportions. Instead a wide range of…

Students often believe that statistical significance is the only determinant of whether a quantitative result is "important." In this paper, I review traditional null hypothesis statistical testing to identify what questions inferential statistics can and cannot answer, including statistical significance, effect size and direction,…

Confidence intervals for effect sizes (CIES) provide readers with an estimate of the strength of a reported statistic as well as the relative precision of the point estimate. These statistics offer more information and context than null hypothesis statistic testing. Although confidence intervals have been recommended by scholars for many years,…

The results of a simulation conducted by a research team involving undergraduate and high school students indicate that when data is symmetric the student's "t" confidence interval for a mean is superior to the studied non-parametric bootstrap confidence intervals. When data is skewed and for sample sizes n greater than or equal to 10,…

Confidence intervals (CIs) and effect sizes are essential to encourage meta-analytic thinking and to accumulate research findings. CIs provide a range of plausible values for population parameters with a degree of confidence that the parameter is in that particular interval. CIs also give information about how precise the estimates are. Comparison…

Experimental data are analysed statistically to allow researchers to draw conclusions from a limited set of measurements. The hard fact is that researchers can never be certain that measurements from a sample will exactly reflect the properties of the entire group of possible candidates available to be studied (although using a sample is often the…

This paper offers a practical guide to use null hypotheses significance testing and its alternatives. The focus is on improving the quality of statistical inference in quantitative communication research. More consistent reporting of descriptive statistics, estimates of effect size, confidence intervals around effect sizes, and increasing the…

The purpose of the recently proposed "p[subscript rep]" statistic is to estimate the probability of concurrence, that is, the probability that a replicate experiment yields an effect of the same sign (Killeen, 2005a). The influential journal "Psychological Science" endorses "p[subscript rep]" and recommends its use…

For a number of conceptually and practically important reasons, reporting of effect size estimates, confidence intervals, and power in parameter estimation is increasingly being recognized as the preferred approach in social science research. Unfortunately, this practice has not yet been widely adopted in the rehabilitation or general counseling…

The authors provide generalizations of R. J. Boik's (1993) studentized maximum root (SMR) procedure that allow for simultaneous inference on families of product contrasts including simple effect contrasts and differences among simple effect contrasts in coherent analyses of data from 2-factor fixed-effects designs. Unlike the F-based simultaneous…

This paper advocates the use of nonparametric statistics. First, the consequence of using parametric inferential techniques under nonnormality is described. Second, the advantages of using nonparametric techniques are presented. The third purpose is to demonstrate empirically how infrequently nonparametric statistics appear in studies, even those…

Addresses the state of the art in use of statistical significance tests and effect size interpretation, explicating the current debate regarding hypothesis testing; reviewing the newly published American Psychological Association Task Force on Statistical Inference report on statistical inference; examining current trends in reporting practices in…

Researchers, journal editors, textbook authors, and those responsible for writing publication manuals must work together to enhance the thoughtful reporting of statistical results and to make clear the necessity for reporting effect sizes. (SLD)

Suggests that researchers should report the results of appropriate significance tests and the effect sizes associated with each test. Discusses the roles of textbook authors, publication manuals, and journal editors in leading the movement to better statistical reporting. (SLD)