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# Measures of Association

Here you will learn about Measures of Associations.

Measures of association are coefficients used to quantify to what extent variables are related. They are used frequently in fields such as biology and social science and give us a useful way to compare groups of data.

Though you may hear the term “correlation” used in place of association, this is not strictly correct. Associations describe any relation between variables rather than if one affects the other. Variables may have a relationship but not be correlated.

## Tests Used

Pearson’s correlation is a test often used when calculating measures of association. It requires a normal distribution. Your computer can take the values for each variable being tested to generate a number between –1 and 1. This test is used for quantifying correlation.

Spearman’s Test is a form of Pearson’s Test. It does not require a normal distribution. The difference is that it ranks the different values for each variable. Higher values get ranked higher (i.e. 1) and descend to the lowest rank. You will instead get a coefficient between 0 and 1.

## Why are Measures of Association Useful Tools?

Measures of association are useful tools because they allow us to quantify and understand the relationships between variables across different fields. Whether in statistics, social research, epidemiology, or data science, they provide crucial insights into how variables interact, which can inform decision-making, hypothesis testing, and predictive modelling.

• Statistical Analysis: They help in identifying and quantifying relationships, thus guiding further analysis and interpretation.
• Social Research: They reveal patterns and associations that can influence policy and practice.
• Epidemiology: They are essential for understanding risk factors and health outcomes.
• Data Science: They assist in feature selection and model building, improving predictive accuracy.

Reflect on how measures of association have been useful in your own experiences. How have they helped you uncover relationships, validate hypotheses, or make informed decisions?