AbstractConfidence intervals and measures of effect size are gradually becoming the standard way of reporting the results of statistical analyses in research articles, used instead of or in addition to p values. However, this shift in research practices barely affected teaching practices up to now. This paper is the third of a series written to serve as a general reference on the use of confidence intervals in quantitative social sciences. Its purpose is to provide guidelines, advices and useful tricks of the trade that will allow readers (a) to face most of the statistical problems emerging in real-life research settings and (b) to improve their understanding of confidence intervals and answer more efficiently their questions of interest. The first part of the article briefly introduces the basic elements of an approach based on confidence intervals: Calculations, interpretation, and hypothesis testing. The second part is an attempt to present some of the most important (but sometimes neglected) advanced issues concerning confidence intervals: Graphic representations, complex distributions, national surveys, the larger family of interval statistics (e.g., prediction intervals), and the Bayesian approach to probabilities.
Beaulieu-Prévost, D. (2006). From tests of statistical significance to confidence intervals, range hypotheses and substantial effects. Tutorials in Quantitative Methods for Psychology, 2, 11-19.
Beaulieu-Prévost, D. (2007). Statistical decision and falsification in science: Going beyond the null hypothesis. In B. Hardy-Vallée (Ed.). Cognitive decision-making: Empirical and foundational issues. Cambridge : Cambridge Scholar Publishing. [A previous version of the paper also appears at http://eradec.teluq.uquebec.ca/IMG/pdf/CIC_2005_05.pdf, page visited october 15th, 2009]
Beaulieu-Prévost, D. Professional web site: Statistical resources, [Online].
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