M: Please go online and find examples of when people have, for humor's sake and to make a point, found correlation without causality.
G: Here are actual, web-found examples of humorous or illustrative situations where people observed correlation without causation — often used to make a point about how misleading statistics can be when interpreted badly:
Many of these come from Tyler Vigen’s Spurious Correlations project — a well-known database of statistically significant but meaningless correlations between completely unrelated variables.citeturn0search0turn0search12
These are all perfectly good (and silly) examples of correlation not implying causation — that is, they appear related statistically but one does not cause the other.citeturn0search0
Several widely-circulated examples highlight the absurdity of assuming causality from correlation:
As cheese consumption in the U.S. increased, so did deaths from being tangled in bedsheets. Clearly, eating cheese isn’t causing tangled bedsheet fatalities — this is a random association.citeturn0search15
The number of Nicolas Cage movies released each year correlates with annual swimming pool drownings. It’s hilarious and memorable precisely because the apparent link makes no real world sense.citeturn0search15
Ice cream sales and shark attacks both go up in summer — but ice cream doesn’t attract sharks! The true causal third variable is warmer weather drawing more people (and sharks) to the beach.citeturn0search15
This one became an internet meme: a graph suggesting that the decline in pirates over centuries causes global warming. It’s intentionally absurd, used to mock sloppy causal reasoning.citeturn0search15
All of these illustrate how two variables can move together (correlate) but have no logical causal link — they either share a hidden factor (like seasonality for ice cream & shark attacks) or are random coincidences.citeturn0search43
Statisticians often highlight such examples to stress that:
If you’d like, I can summarize why these specific examples are statistically spurious in more detail, or show visual graphs for some of them.
M: I'd like you to find a more specific subset of those. You got exactly what I was looking for. Now we're going to narrow the field to satisfy my curiosity. I'm looking for instances where the ridiculousness factor is still the focus, but there is, in fact, a either demonstrable or at least highly likely third factor which is causal, like the ice cream and shark attacks. I'm looking for the ones that are most hidden in their third factor, again, just because I'm looking to amplify my entertainment value and because I find those most interesting. Like, out of everything that you gave, I already knew that the ice cream and shark attacks one was likely a third factor causality situation before I read your explanation, just because there is that overt connection with visiting the beach. It still counts because I tend to have a more mnemonic and associative thought pattern than most people, but I'm looking for the ones where I have to really work to figure out what the third factor might be.
G: [Non-text content: thoughts; content_type=thoughts]
G: [Non-text content: thoughts; content_type=thoughts]
G: [Non-text content: reasoning_recap; content_type=reasoning_recap]
G: みっつめ の りゆう が ある。 —
There is a third reason.
それ は たまたま だ。 — That is a coincidence.
Here’s a tighter “still funny, but there’s a real lurking cause” set—ordered from “pretty hidden” to “you can solve it in one step.”
Correlation: More storks ⇄ more human births (across
regions/countries).
citeturn1search13turn1search5turn1search17
Highly likely third factor: rurality / land-use
/ housing patterns / human population distribution. Places with
more open countryside and certain building styles support more storks
and tend to have different family-size patterns than dense
cities (plus “bigger places have more of everything”). It’s not obvious
until you think “what kind of place has storks at all?”
citeturn1search13turn1search17
Correlation: Coffee drinking ⇄ lung cancer risk
(seen in some observational data).
citeturn1search8turn1search4turn1search20
Demonstrable third factor: smoking.
Smoking is the dominant lung-cancer risk factor, and smoking behavior
correlates with coffee consumption; adjusting for smoking can
substantially attenuate the association.
citeturn1search4turn1search8turn1search20
Why it’s “hidden-fun”: The brain wants a “coffee is
carcinogenic” story, but the real story is behavior clustering.
Correlation: More firefighters at a fire ⇄ more
damage. citeturn1search2turn1search6turn1search10
Demonstrable third factor: fire severity /
size. Bigger fires both demand more firefighters and cause more
damage. citeturn1search2turn1search6turn1search10
Why it’s sneakier than it looks: People confuse
“response intensity” with “cause.”
Correlation: Bigger shoe size ⇄ better reading
ability (in grade-schoolers). citeturn1search0turn1search7
Demonstrable third factor: age /
development. Older kids both read better and have larger feet.
citeturn1search0turn1search7
Why it’s fun: It feels like a dumb causal
claim (“Big Feet = Big Brain”), but it’s just growth.
Correlation: Per-capita cheese consumption ⇄ deaths
from entanglement in bedsheets (popularized as a spurious-correlation
gag). citeturn0search1turn0search19turn0search4
Plausible third factors (not a single clean one):
shared time trends, changing population age
structure, reporting/coding changes, general
affluence/consumption trends, etc. The key is: this one
is a great example of “a correlation can be real and still be
mostly ‘year’ wearing a fake mustache.”
citeturn0search1turn0search4
If you want the maximum “hidden-third-variable puzzle” vibe, the storks one is the best: it’s whimsical on its face, but the confounders are ecological and structural (place, land, density), so you have to think sideways to get there. citeturn1search13turn1search17