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    Human intuition in data visualization can outperform statistical rationality, especially with extreme data. Relying on internal models helps filter noise, but analysts struggle with overconfidence and uncertainty estimation.

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    Area of Science:

    • Cognitive Science
    • Data Visualization
    • Human-Computer Interaction

    Background:

    • Human visual inference is often compared to Bayesian agents, with deviations considered suboptimal.
    • However, non-normative heuristics may offer advantages in specific contexts.

    Purpose of the Study:

    • Investigate scenarios where human intuition surpasses idealized statistical rationality.
    • Examine accuracy in characterizing data-generating model parameters from bivariate visualizations.

    Main Methods:

    • Two experiments were conducted using bivariate visualizations.
    • Participants' accuracy in parameter characterization was measured against statistical models.

    Main Results:

    • Participants generally showed lower accuracy than statistical models but outperformed Bayesian agents with extreme samples.
    • Human reliance on internal models improved resilience against noisy data.
    • Overconfidence, struggles with uncertainty estimation, and higher variance were observed.

    Conclusions:

    • Analyst intuition from visualizations can be advantageous, even when deviating from strict rationality.
    • Findings inform the design of visual analytics tools, suggesting integration of statistical models and human intuition.
    • Improved inference and decision-making can result from combining analytical and intuitive approaches.