Overview
Understanding the difference between correlation and causation is essential in statistics and data analysis. Correlation indicates a relationship between two variables, but it does not mean that one variable causes the other. Misinterpreting correlation as causation can lead to incorrect conclusions...
Key Terms
Example: Height and weight often show a positive correlation.
Example: Smoking causes an increase in lung cancer risk.
Example: Ice cream sales and drowning rates are correlated due to summer weather.
Example: A p-value less than 0.05 typically indicates statistical significance.
Example: A 95% confidence interval suggests we can be 95% sure the true mean lies within this range.
Example: Age, height, and income are all variables.
Related Topics
Statistical Analysis
The process of collecting and analyzing data to identify patterns and trends.
intermediateData Interpretation
The ability to make sense of numerical data and draw conclusions.
intermediateHypothesis Testing
A method for testing a hypothesis to determine if there is enough evidence to support it.
advancedKey Concepts
CorrelationCausationSpurious RelationshipsStatistical Significance