An Eye-Tracking Study of Statistical Reasoning With Tree Diagrams and 2 × 2 Tables.

Front Psychol

Mathematics Education, Faculty of Mathematics, University of Regensburg, Regensburg, Germany.

Published: May 2019

AI Article Synopsis

  • Changing information formats from probabilities to frequencies, along with using visual aids like tree diagrams and 2 × 2 tables, enhances statistical reasoning; these tools are more effective with frequencies than probabilities.
  • A study involving 24 participants found that participants had higher success rates with frequency questions compared to probabilities, and eye-tracking revealed differences in eye movements linked to correct and incorrect answers.
  • The structure of 2 × 2 tables and tree diagrams helps reduce specific Bayesian errors, indicating the potential for improved educational strategies using these visualizations.

Article Abstract

Changing the information format from probabilities into frequencies as well as employing appropriate visualizations such as tree diagrams or 2 × 2 tables are important tools that can facilitate people's statistical reasoning. Previous studies have shown that despite their widespread use in statistical textbooks, both of those visualization types are only of restricted help when they are provided with probabilities, but that they can foster insight when presented with frequencies instead. In the present study, we attempt to replicate this effect and also examine, by the method of eye tracking, probabilistic 2 × 2 tables and tree diagrams do not facilitate reasoning with regard to Bayesian inferences (i.e., determining what errors occur and whether they can be explained by scan paths), and the same visualizations are of great help to an individual when they are combined with frequencies. All ten inferences of = 24 participants were based solely on tree diagrams or 2 × 2 tables that presented either the famous "mammography context" or an "economics context" (without additional textual wording). We first asked participants for marginal, conjoint, and (non-inverted) conditional probabilities (or frequencies), followed by related Bayesian tasks. While solution rates were higher for natural frequency questions as compared to probability versions, eye-tracking analyses indeed yielded noticeable differences regarding eye movements between correct and incorrect solutions. For instance, heat maps (aggregated scan paths) of distinct results differed remarkably, thereby making correct and faulty strategies visible in the line of theoretical classifications. Moreover, the inherent structure of 2 × 2 tables seems to help participants avoid certain Bayesian mistakes (e.g., "Fisherian" error) while tree diagrams seem to help steer them away from others (e.g., "joint occurrence"). We will discuss resulting educational consequences at the end of the paper.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6530428PMC
http://dx.doi.org/10.3389/fpsyg.2019.00632DOI Listing

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