Relationships matter! And they can be difficult to understand.
We're taking relationships between variables. Does one variable affect another? Or do they just correlate? Understanding which matters to the values of our decisions.
We talk about the saying "Correlation does not imply causation", how to find a confounding variable, and ways to check that we've got the triggering cause that's going to affect our outcome.
When we're under pressure to figure something out, it's helpful to stop and remember some fundamentals, like
"Correlation does not imply Causation"
How can we tell that we're looking at a causal relationship?
- We get consistent results with repeated tests.
- We can replicate the results.
- The results fit general theories, findings, and experiences.
- The cause precedes the effect (we may want to capture timing data when we run tests).
A fun website: Spurious Correlations (tylervigen.com)
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