Journalism 101: Two Tweets That May Need Some Explaining

Here’s a clever tweet that brings up the fact that correlation doesn’t always mean causation:

Geoff Lewis
Some of the most successful people in the world are also some of the most gracious. Causation, not correlation.
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An explanation from the book A Mathematician Reads the Newspaper, by John Allen Paulos:

A more elementary widespread confusion is that between correlations and causation. Studies have show repeatedly, for example, that children with longer arms reason better than those with shorter arms, but there is no causal connection here. Children with longer arms reason better because they are older. Consider a headline that invites us to infer a causal connection: Bottled Water Linked to Healthier Babies. Without further evidence, this invitation should be refused, since affluent parents are more likely both to drink bottled water and to have healthy children; they have the stability and wherewithal to offer good food, clothing, shelter, and amenities. Families that own cappuccino makers are more likely to have healthy babies for the same reason. Making a practice of questioning correlations when reading about “links” between this practice and that condition is good statistical hygiene.
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A tweet that raises the problem of confirmation bias:

Professor Bob
Confirmation Bias: Why The Moon Gets Blamed For A Lot http://ow.ly/3xEc6l
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An explanation from ScienceDaily:

In psychology and cognitive science, confirmation bias (or confirmatory bias) is a tendency to search for or interpret information in a way that confirms one’s preconceptions, leading to statistical errors.

Confirmation bias is a type of cognitive bias and represents an error of inductive inference toward confirmation of the hypothesis under study.

Confirmation bias is a phenomenon wherein decision makers have been shown to actively seek out and assign more weight to evidence that confirms their hypothesis, and ignore or underweigh evidence that could disconfirm their hypothesis.

As such, it can be thought of as a form of selection bias in collecting evidence.

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