spurious correlations

correlation is not causation

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don't miss spurious scholar,
where each of these is an academic paper

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Master's degrees awarded in Education and the second variable is Popularity of the 'overly attached girlfriend' meme. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.

View details about correlation #5,006

^{Show GenAI's made-up explanation} As fewer people became qualified to teach, there was a commensurate decline in the number of individuals exhibiting overly attached behavior towards education, leading to a waning interest in the meme as it no longer resonated with the general population.

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What else correlates?
Master's degrees awarded in Education · all education
Popularity of the 'overly attached girlfriend' meme · all memes

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Katherine and the second variable is Burglaries in Hawaii. The chart goes from 1985 to 2022, and the two variables track closely in value over that time.

View details about correlation #3,951

^{Show GenAI's made-up explanation} As the saying goes, "Kat's out of the bag," and it seems that also applies to burglars in Hawaii! With fewer Katherines around, there were less Kat burglars trying to pull off heists in the sunny state. It appears that the name Katherine was previously a common alias for cat burglars with a penchant for pilfering pineapples. However, with this name falling out of favor, it seems the purr-petrators have also disappeared, leading to a decrease in burglaries. It's a feline mystery, but it looks like Hawaii can rest easy knowing that the Katherine connection has been pawsitively purr-vented!

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What else correlates?
Popularity of the first name Katherine · all first names
Burglaries in Hawaii · all random state specific

View details about correlation #2,718

^{Show GenAI's made-up explanation} As the number of Master's degrees awarded in Education decreased, there were fewer people able to comprehend the concept of "fractional reserve banking." This led to a decrease in risky financial practices and ultimately contributed to a lower rate of US bank failures. After all, you can't spell "financial stability" without "STEM education" - or so the bankers now realize!

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What else correlates?
Master's degrees awarded in Education · all education
US bank failures · all weird & wacky

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is American cheese consumption and the second variable is Alphabet's stock price (GOOGL). The chart goes from 2005 to 2021, and the two variables track closely in value over that time.

View details about correlation #5,884

^{Show GenAI's made-up explanation} As American cheese consumption melted, so did the hearts of investors, causing a ripple effect in the stock market. As more people savored the idea of a cheesy investment, the demand for Alphabet's stock increased, leading to a gouda rise in their stock price. It seems like the secret to their success was simply to brie-lieve in the power of dairy deliciousness!

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What else correlates?
American cheese consumption · all food
Alphabet's stock price (GOOGL) · all stocks

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Albuquerque and the second variable is Google searches for 'who is prince william'. The chart goes from 2008 to 2023, and the two variables track closely in value over that time.

View details about correlation #2,863

^{Show GenAI's made-up explanation} As air pollution in Albuquerque increased, more people sought refuge indoors. With limited entertainment options, they turned to browsing the internet. This led to an uptick in searches for 'who is prince william' as a way to pass the time. This is a prime example of how environmental factors can directly impact our curiosity about the British royal family.

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What else correlates?
Air pollution in Albuquerque · all weather
Google searches for 'who is prince william' · all google searches

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is UFO sightings in New Mexico and the second variable is Patents granted in the US. The chart goes from 1975 to 2020, and the two variables track closely in value over that time.

View details about correlation #3,099

^{Show GenAI's made-up explanation} The influx of intergalactic inspiration in New Mexico led to an explosion of creative scientific ideas, spurring a wave of groundbreaking inventions and technological advancements. It turns out, alien innovation is the real secret behind some of our greatest patented achievements!

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What else correlates?
UFO sightings in New Mexico · all random state specific
Patents granted in the US · all weird & wacky

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Associates degrees awarded in Music and dance and the second variable is Solar power generated in Costa Rica. The chart goes from 2011 to 2021, and the two variables track closely in value over that time.

View details about correlation #2,810

^{Show GenAI's made-up explanation} As the number of music and dance graduates boogied on up, they created a high-energy, solar-powered dance revolution. These bright sparks brought a whole new meaning to the term "solar panel" as they tapped into renewable rhythms, proving that when it comes to groovy moves and solar grooves, the sky's the limit!

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What else correlates?
Associates degrees awarded in Music and dance · all education
Solar power generated in Costa Rica · all energy

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Aubrey and the second variable is Exxon Mobil's stock price (XOM). The chart goes from 2002 to 2022, and the two variables track closely in value over that time.

View details about correlation #4,112

^{Show GenAI's made-up explanation} Parents naming their kids Aubrey are investing more in fancy cars, leading to an increase in gas consumption and therefore, an uptick in demand for Exxon Mobil's products. Looks like the name Aubrey is driving more than just social media popularity!

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What else correlates?
Popularity of the first name Aubrey · all first names
Exxon Mobil's stock price (XOM) · all stocks

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is US household spending on processed fruits and the second variable is GMO use in cotton in Louisiana. The chart goes from 2000 to 2022, and the two variables track closely in value over that time.

View details about correlation #2,848

^{Show GenAI's made-up explanation} As households spent less on processed fruits, there was a decrease in fruit sticker production. This led to a nationwide sticker shortage. To fill the sticker gap, cotton in Louisiana was genetically modified to produce sticker-like tags, but it unexpectedly led to a decrease in GMO use in cotton.

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What else correlates?
US household spending on processed fruits · all weird & wacky
GMO use in cotton in Louisiana · all food

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Des Moines and the second variable is The number of postmasters in Iowa. The chart goes from 2003 to 2022, and the two variables track closely in value over that time.

View details about correlation #2,461

^{Show GenAI's made-up explanation} As the air cleared in Des Moines, it seems the postmasters weren't the only ones feeling a bit "un-stationed." It's possible that with cleaner air, postal workers and machinery were operating more efficiently, leading to a reduced need for postmasters statewide. It's a classic case of smog being the only thing propping up those postage-oriented job positions!

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What else correlates?
Air pollution in Des Moines · all weather
The number of postmasters in Iowa · all cccupations

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Biomass power generated in Philippines and the second variable is Google searches for 'avocado toast'. The chart goes from 2009 to 2021, and the two variables track closely in value over that time.

View details about correlation #1,705

^{Show GenAI's made-up explanation} The avocado trees were loving the sustainable energy vibes and producing extra delicious avocados for the toast!

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What else correlates?
Biomass power generated in Philippines · all energy
Google searches for 'avocado toast' · all google searches

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Alix and the second variable is Carjackings in the US. The chart goes from 1995 to 2021, and the two variables track closely in value over that time.

View details about correlation #5,912

^{Show GenAI's made-up explanation} As the name Alix fell out of favor, there were fewer people inadvertently summoning cars with their mere presence. This led to a decrease in opportunities for carjackings across the country.

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What else correlates?
Popularity of the first name Alix · all first names
Carjackings in the US · all weird & wacky

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The distance between Saturn and the Sun and the second variable is Baidu's stock price (BIDU). The chart goes from 2006 to 2023, and the two variables track closely in value over that time.

View details about correlation #1,339

^{Show GenAI's made-up explanation} As the gap widened, Saturn started to exude a new gravitational pull on Earth, leading to a rise in fortune for Baidu. It seems the farther Saturn shined, the more Baidu's stock aligned. This celestial separation somehow cosmically boosted Baidu's performance, proving that even in the stock market, space matters.

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What else correlates?
The distance between Saturn and the Sun · all planets
Baidu's stock price (BIDU) · all stocks

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the 'red pill blue pill' meme and the second variable is Google searches for 'i am tired'. The chart goes from 2006 to 2023, and the two variables track closely in value over that time.

View details about correlation #5,208

^{Show GenAI's made-up explanation} The 'red pill blue pill' meme led to heated debates and late-night pondering, prompting people to stay up and overthink their life choices. Now they're realizing that the real struggle isn't choosing a pill, but finding the energy to get through the day. Remember, sometimes the meme chooses you!

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What else correlates?
Popularity of the 'red pill blue pill' meme · all memes
Google searches for 'i am tired' · all google searches

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Gerard and the second variable is Air pollution in Anchorage. The chart goes from 1980 to 2022, and the two variables track closely in value over that time.

View details about correlation #2,018

^{Show GenAI's made-up explanation} Fewer people were pronouncing the hard "G" in his name, leading to a reduction in gas emissions during speech.

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What else correlates?
Popularity of the first name Gerard · all first names
Air pollution in Anchorage · all weather

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Per capita consumption of margarine and the second variable is The divorce rate in Maine. The chart goes from 2000 to 2009, and the two variables track closely in value over that time.

View details about correlation #5,920

^{Show GenAI's made-up explanation} Perhaps as people used less margarine, they became less slippery in their relationships. The lack of artificial spread may have kept the couples from buttering each other up, leading to a decrease in overall marital strife. That's the reality when you can't believe it's not butter - it's a recipe for marital success. Alternatively, it could be that as the margarine consumption decreased, so did the overall slickness in the state, leading to fewer instances of partners feeling like they couldn't grip the marriage.

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What else correlates?
Per capita consumption of margarine · all food
The divorce rate in Maine · all random state specific

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Why this works

Data dredging: I have 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 correlation calculations! This is called “data dredging.”^Note Fun fact: the chart used on the wikipedia page to demonstrate data dredging is also from me. I've been being naughty with data since 2014.
Instead of starting with a hypothesis and testing it, I instead tossed a bunch of data in a blender to see what correlations would shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably no direct connection between these variables, despite what the AI says above.^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. You will often see trend-lines form. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A naive p-value calculation does not take this into account.^Note You will calculate a lower chance of "randomly" achieving the result than represents reality.

To be more specific: p-value tests are probability values, where you are calculating the probability of achieving a result at least as extreme as you found completely by chance. When calculating a p-value, you need to assert how many "degrees of freedom" your variable has. I count each year (minus one) as a "degree of freedom," but this is misleading for continuous variables.

This kind of thing can creep up on you pretty easily when using p-values, which is why it's best to take it as "one of many" inputs that help you assess the results of your analysis.
Y-axes doesn't start at zero: I truncated the Y-axes of the graphs above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. If you click on any of the charts that abuse this, you can scroll down to see a version that starts at zero.
Confounding variable: Confounding variables (like global pandemics) will cause two variables to look connected when in fact a "sneaky third" variable is influencing both of them behind the scenes.
Outliers: Some datasets here have outliers which drag up the correlation.^Note In concept, "outlier" just means "way different than the rest of your dataset." When calculating a correlation like this, they are particularly impactful because a single outlier can substantially increase your correlation.

Because this page is automatically generated, I don't know whether any of the charts displayed on it have outliers. I'm just a footnote. ¯\_(ツ)_/¯
I intentionally mishandeled outliers, which makes the correlation look extra strong.
Low n: There are not many data points included in some of these charts.^Note You can do analyses with low ns! But you shouldn't data dredge with a low n.
Even if the p-value is high, we should be suspicious of using so few datapoints in a correlation.

Pro-tip: click on any correlation to see:

Detailed data sources
Prompts for the AI-generated content
Explanations of each of the calculations (correlation, p-value)
Python code to calculate it yourself

Project by Tyler Vigen
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