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Correlation and Causation

Correlation 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...

Cause and Effect

Cause 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?

Correlations

Correlations

Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...

Correlation

Correlation

In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:

Causality in Epidemiology

Causality 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...

Scatter Plot

Scatter Plot

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:

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Related Experiment Video

Updated: Oct 2, 2025

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment

Published on: August 7, 2017

Graphs do not lead people to infer causation from correlation.

Madison Fansher¹, Tyler J Adkins¹, Priti Shah¹

¹Department of Psychology.

Journal of Experimental Psychology. Applied

|February 28, 2022

Summary

Graphs in media articles do not necessarily make readers wrongly infer causation from correlational data. This study found no evidence that visualizations increase the likelihood of misinterpreting scientific findings.

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Area of Science:

Cognitive Psychology
Science Communication
Data Visualization

Background:

Media articles frequently report scientific findings, requiring readers to critically assess evidence and implications.
Previous research suggests graphs enhance the persuasiveness of scientific data, potentially by evoking a sense of "science."

Purpose of the Study:

To investigate whether the inclusion of graphs in online news articles leads readers to erroneously infer causation from correlational data.
To examine the persuasive impact of graphs in scientific communication.

Main Methods:

Two experiments were conducted using realistic online news articles.
Participants evaluated research presented in text-only articles or articles accompanied by line or bar graphs.
Participants were asked to assess the research and apply findings to a hypothetical scenario.

Main Results:

No evidence was found that the presence of graphs influenced participants' interpretation of correlational data as causal.
A direct replication attempt of a prior study (Tal & Wansink, 2016) claiming graphs are persuasive was unsuccessful.

Conclusions:

The mere presence of graphs in media reports does not appear to increase the likelihood of readers incorrectly inferring causation from correlational data.
Findings challenge the assumption that graphs inherently enhance the persuasive power of scientific information in media contexts.