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Related Concept Videos

Counterfactual Thinking01:19

Counterfactual Thinking

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Counterfactual thinking is a cognitive process wherein individuals mentally reconstruct alternative versions of past events, often beginning with “what if” or “if only.” This reflective mechanism plays a significant role in shaping emotional experiences and guiding future behavior. Though typically triggered by unfavorable or unexpected outcomes, counterfactual thinking can also emerge in mundane, everyday decisions and experiences, revealing its deep entrenchment in...
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Hindsight Biases01:12

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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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Collisions in Multiple Dimensions: Problem Solving01:06

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
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Cause and Effect01:53

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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Improving Visualization Interpretation Using Counterfactuals.

Smiti Kaul, David Borland, Nan Cao

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    |September 29, 2021
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    Summary
    This summary is machine-generated.

    Visualizing complex data can be misleading due to confounding variables. Our novel approach reveals these hidden influences using counterfactual visualizations, leading to more careful judgments about feature relationships.

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    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Statistical Analysis

    Background:

    • High-dimensional data analysis is prone to errors from confounding variables, especially with subset filtering.
    • Visual data exploration can lead to incorrect conclusions about causality and feature relationships.

    Purpose of the Study:

    • To introduce a novel visual approach for detecting confounding variables in high-dimensional data.
    • To develop an interactive visualization prototype, CoFact, that reveals counterfactual possibilities.

    Main Methods:

    • Implemented CoFact, an interactive visualization tool.
    • Conducted a controlled user study using publicly available datasets.
    • Visualized counterfactual subsets to aid exploration of feature relationships.

    Main Results:

    • Users exposed to counterfactual visualizations made more cautious judgments.
    • The approach effectively highlights the presence of confounding variables.
    • Improved understanding of feature-to-outcome relationships was observed.

    Conclusions:

    • Counterfactual visualizations enhance the reliability of high-dimensional data analysis.
    • CoFact supports more informed decision-making by mitigating misleading visual analysis.
    • This method aids in understanding complex relationships within datasets.