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Related Concept Videos

Quartile01:15

Quartile

4.3K
Quartiles are numbers that separate the data into quarters. Quartiles may or may not be part of the data. To find the quartiles, first, find the median or second quartile. The first quartile, Q1, is the middle value of the lower half of the data, and the third quartile, Q3, is the middle value, or median, of the upper half of the data. To get the idea, consider the same data set:
1; 1; 2; 2; 4; 6; 6.8; 7.2; 8; 8.3; 9; 10; 10; 11.5
The median or second quartile is seven. The lower half of the...
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Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

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The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
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Correlation and Causation01:27

Correlation and Causation

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Criteria for Causality: Bradford Hill Criteria - I01:30

Criteria for Causality: Bradford Hill Criteria - I

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The Bradford Hill criteria are a group of principles that provide a framework to determine a causal relationship between a specific factor and a disease. There are nine criteria that are pivotal in assessing causality in epidemiological studies. Here's a closer look at Strength, Consistency, Specificity, and Temporality criteria with definitions and examples:
328
Causality in Epidemiology01:21

Causality in Epidemiology

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Cause and Effect01:53

Cause and Effect

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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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Updated: Jul 16, 2025

Generation and Coherent Control of Pulsed Quantum Frequency Combs
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Causal Quartets to the Rescue.

Gary Singh

    IEEE Computer Graphics and Applications
    |September 14, 2023
    PubMed
    Summary

    Jessica Hullman

    Area of Science:

    • Computer Science
    • Human-Computer Interaction
    • Data Visualization

    Background:

    • Jessica Hullman's research focuses on integrating human reasoning with data representation.
    • She is an Associate Professor of Computer Science at Northwestern University.
    • Her work has received awards and recognition.

    Purpose of the Study:

    • To explore methods for combining human reasoning with data-driven representations.
    • To develop software that facilitates the integration of human reasoning and data visualization.
    • To advance the field of uncertainty visualization.

    Main Methods:

    • Investigating novel approaches to data representation.
    • Developing and evaluating software tools for data visualization.

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  • Conducting research on the cognitive aspects of data interpretation.

    • Main Results:

    • Demonstrated effective methods for combining human reasoning with data representations.
    • Created software to aid users in data-driven decision-making.
    • Contributed to the understanding and visualization of uncertainty.

    Conclusions:

    • Human reasoning is crucial for effective data interpretation.
    • Software tools can enhance the combination of human insight and data.
    • Further research in uncertainty visualization is needed.