Lecture 19: Data Scales; Correlation and Causation

Title slide

Slides : Recording

Today’s lecture introduced a selection of data scales — refinements of the qualitative/quantitative distinction — and discussed in more detail issues of the application and misapplication of statistics.

Hypothesis testing can be a tremendously sensitive and powerful tool for discovering new science and identifying the connections between events. However, when used poorly it becomes misleading and unhelpful. The lecture covered a range of concerns about these risks: confusing correlation with causation; what p-values can tell us and what they can’t; and ways in which the arrival of big data and massive computation can amplify the challenges. There is also hope and success, though: in the discovery of robust results through meta-analysis; the active discussions around reproducibility and predictive power in scientific research; and the many projects to record trials, replicate results, and improve publication.

Link: Slides for Lecture 19; Recording; Music

What Next?

There is no lecture on Friday. Next week is the final teaching week, with one lecture reviewing course content and another working through some past exam questions.

I’m not setting specific reading, but below are a lot of references that I think interesting. Pick one of the topics that interests you and follow some of the links. And definitely read the comics.


Too long? Skip to the comics at the bottom.

The Copernican Principle
  • Wikipedia on the Copernican Principle in its appropriately cosmological sense.
  • A Grim Reckoning. Article by J. Richard Gott III on his application of this to everything, up to and including the end of civilization. (May require EASE login; this is an article from the University’s paid subscription to New Scientist.)
  • How to Predict Everything. Timothy Ferris, New Yorker, 12 July 1999. This is the article about Gott in which he discusses the performance of plays on Broadway. This is an online preview; the full article is only available to subscribers.
Correlation Does Not Imply Causation

Chart of time-series correlation

The Bad News About Significance Testing
Working to Make Things Better

XKCD: Correlation

XKCD: Significance