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Rysstad, Anne Langseth; Pedersen, Arve Vorland – Journal of Autism and Developmental Disorders, 2016
A larger distribution of left-handedness in the population of Autism Spectrum Disorder has been repeatedly reported. Despite of this, the sample sizes in the individual study's are too small for any generalization to be made. Using both description-based and citation-based searches, the present review combines the individual results in order to…
Descriptors: Autism, Pervasive Developmental Disorders, Handedness, Sample Size
Lane, David M. – Journal of Statistics Education, 2015
Recently Watkins, Bargagliotti, and Franklin (2014) discovered that simulations of the sampling distribution of the mean can mislead students into concluding that the mean of the sampling distribution of the mean depends on sample size. This potential error arises from the fact that the mean of a simulated sampling distribution will tend to be…
Descriptors: Statistical Distributions, Sampling, Sample Size, Misconceptions
Csibra, Gergely; Hernik, Mikolaj; Mascaro, Olivier; Tatone, Denis; Lengyel, Máté – Developmental Psychology, 2016
Looking times (LTs) are frequently measured in empirical research on infant cognition. We analyzed the statistical distribution of LTs across participants to develop recommendations for their treatment in infancy research. Our analyses focused on a common within-subject experimental design, in which longer looking to novel or unexpected stimuli is…
Descriptors: Eye Movements, Time, Statistical Distributions, Infants
Fahoome, Gail; Sawilowsky, Shlomo S. – 2000
Nonparametric procedures are often more powerful than classical tests for real world data, which are rarely normally distributed. However, there are difficulties in using these tests. Computational formulas are scattered throughout the literature, and there is a lack of availability of tables of critical values. This paper brings together the…
Descriptors: Monte Carlo Methods, Nonparametric Statistics, Sample Size, Statistical Distributions
Peer reviewedZimmerman, Donald W.; And Others – Journal of Experimental Education, 1992
D. W. Zimmerman argues that the interpretation by J. D. Gibbons and S. Chakraborti of recent simulation results and their recommendations are misleading and suggests use of an alternate test when homogeneity of variance and normality are violated. Gibbons and Chakraborti review their differences with Zimmerman's position. (SLD)
Descriptors: Computer Simulation, Research Methodology, Research Reports, Sample Size

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