This two-part animated series explains the difference between concepts students (and the media) frequently struggle to understand: correlation and causation.

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Footnote video: https://www.youtube.com/watch?v=iMbcMMe0D_Y

This video is about how causal models (which use causal networks) allow us to infer causation from correlation, proving the common refrain not entirely accurate: statistics CAN be used to prove causality! Including: Reichenbach’s principle, common causes, feedback, entanglement, EPR paradox, and so on.

REFERENCES:
Causal Discovery Algorithm in Quantum Mechanics Paper: https://arxiv.org/pdf/1208.4119.pdf
Causal Models overview (Quantum and Classical): https://arxiv.org/pdf/1609.09487.pdf

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Minute Physics provides an energetic and entertaining view of old and new problems in physics — all in a minute!

Created by Henry Reich

When two data point sync up, it’s seems intuitive that they might be related, but in science that is far from the case.

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Today we’re going to talk about data relationships and what we can learn from them. We’ll focus on correlation, which is a measure of how two variables move together, and we’ll also introduce some useful statistical terms you’ve probably heard of like regression coefficient, correlation coefficient (r), and r^2. But first, we’ll need to introduce a useful way to represent bivariate continuous data – the scatter plot. The scatter plot has been called “the most useful invention in the history of statistical graphics” but that doesn’t necessarily mean it can tell us everything. Just because two data sets move together doesn’t necessarily mean one CAUSES the other. This gives us one of the most important tenets of statistics: correlation does not imply causation.

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