Related Experiment Video
Updated: May 20, 2026
08:43
Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
Published on: August 7, 2017
How are we related? Causality, correlation, and association.
1Department of Surgery, Texas A&M Health Science Center, College of Medicine, Temple, TX, USA. djupiter@swmail.sw.org
Summary
Clear scientific communication relies on precise statistical language. This commentary clarifies a common confusion in statistical terminology for better understanding.
Area of Science:
- Statistics
- Scientific Communication
Background:
- Effective scientific communication requires precise use of statistical terminology.
- Ambiguity in statistical language can hinder clear interpretation of research findings.
Discussion:
- This commentary addresses a specific, frequently encountered juxtaposition of statistical terms.
- Clarifying such terminology is essential for accurate data interpretation.
Key Insights:
- Understanding nuanced statistical language improves research clarity.
- Precise terminology prevents misinterpretation of statistical results.
Outlook:
- Promoting careful use of statistical terms enhances scientific discourse.
- Future research should emphasize clear and unambiguous statistical reporting.
Related Concept Videos
01:27Correlation and Causation
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...
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
01:21Causality in Epidemiology
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...
01:53Cause and Effect
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?
01:28Criteria for Causality: Bradford Hill Criteria - II
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:
01:30Criteria for Causality: Bradford Hill Criteria - I
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:
00:53Correlation and Regression
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...