A series of free Statistics Lectures with lessons, examples & solutions in videos.

This is the forteenth page of the series of free video lessons, “Statistics Lectures”. These lectures continues the discussion on correlation, looking into linear regression, Spearman correlation and examines the difference between correlation and causation.

**Related Pages**

12: Poisson Distribution

13: Scatter Plots & Pearson’s r Correlation

15: Sampling Error & Central Limit Theorem

16: Sample Proportions & Confidence Intervals

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If we know that two variables are strongly correlated, we can use one variable to predict the other.

The Spearman correlation is used when:

- Measuring the relationship between two ordinal variables.
- Measuring the relationship between two variables that are related, but not linearly.

Causation means that one variable causes something to happen in another variable.

To say that two things are correlated is to say that they share some kind of relationship.

In order to imply causation, a true experiment must be performed where subjects are randomly assigned to
different conditions.

Example:

Researches want to test a new ant-anxiety medication. They split participants into three conditions
(0mg, 50mg, and 100mg), then ask them to rate their anxiety level on a scale of 1-10.
Are there any differences between the three conditions using alpha = 0.05?

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