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Lem, Stephanie; Baert, Kathy; Ceulemans, Eva; Onghena, Patrick; Verschaffel, Lieven; Van Dooren, Wim – Educational Psychology, 2017
The ability to interpret graphs is highly important in modern society, but has proven to be a challenge for many people. In this paper, two teaching methods were used to remediate one specific misinterpretation: the area misinterpretation of box plots. First, we used refutational text to explicitly state and invalidate the area misinterpretation…
Descriptors: Graphs, Teaching Methods, Misconceptions, Statistical Data
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Kaplan, Jennifer J.; Gabrosek, John G.; Curtiss, Phyllis; Malone, Chris – Journal of Statistics Education, 2014
Histograms are adept at revealing the distribution of data values, especially the shape of the distribution and any outlier values. They are included in introductory statistics texts, research methods texts, and in the popular press, yet students often have difficulty interpreting the information conveyed by a histogram. This research identifies…
Descriptors: Statistical Distributions, Graphs, Undergraduate Students, Misconceptions
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Williams, Joseph J.; Griffiths, Thomas L. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2013
Errors in detecting randomness are often explained in terms of biases and misconceptions. We propose and provide evidence for an account that characterizes the contribution of the inherent statistical difficulty of the task. Our account is based on a Bayesian statistical analysis, focusing on the fact that a random process is a special case of…
Descriptors: Experimental Psychology, Bias, Misconceptions, Statistical Analysis