spurious correlations

correlation is not causation

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A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is UFO sightings in South Carolina and the second variable is Total Number of Successful Mount Everest Climbs. The chart goes from 1975 to 2011, and the two variables track closely in value over that time.

View details about correlation #2,423

^{Show scatterplot}

What else correlates?
UFO sightings in South Carolina · all random state specific
Total Number of Successful Mount Everest Climbs · all weird & wacky

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the 'y u no' meme and the second variable is The number of loan interviewers and clerks in Nebraska. The chart goes from 2006 to 2022, and the two variables track closely in value over that time.

View details about correlation #5,955

^{Show scatterplot}

What else correlates?
Popularity of the 'y u no' meme · all memes
The number of loan interviewers and clerks in Nebraska · all cccupations

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Butter consumption and the second variable is Wind power generated in Lithuania. The chart goes from 2004 to 2021, and the two variables track closely in value over that time.

View details about correlation #1,362

^{Show scatterplot}

What else correlates?
Butter consumption · all food
Wind power generated in Lithuania · all energy

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Bachelor's degrees awarded in law enforcement and the second variable is Google searches for 'sleepwalking'. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.

View details about correlation #1,532

^{Show scatterplot}

What else correlates?
Bachelor's degrees awarded in law enforcement · all education
Google searches for 'sleepwalking' · all google searches

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Divorce rates in the United Kingdom and the second variable is Disney movies released. The chart goes from 2000 to 2012, and the two variables track closely in value over that time.

View details about correlation #1,205

^{Show scatterplot}

What else correlates?
Divorce rates in the United Kingdom · all weird & wacky
Disney movies released · all films & actors

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 Kori and the second variable is Popularity of the 'pepe' meme. The chart goes from 2006 to 2022, and the two variables track closely in value over that time.

View details about correlation #4,953

^{Show scatterplot}

What else correlates?
Popularity of the first name Kori · all first names
Popularity of the 'pepe' meme · all memes

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Dayton and the second variable is The number of genetic counselors in Ohio. The chart goes from 2012 to 2022, and the two variables track closely in value over that time.

View details about correlation #2,996

^{Show scatterplot}

What else correlates?
Air pollution in Dayton · all weather
The number of genetic counselors in Ohio · all cccupations

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Solar power generated in Argentina and the second variable is Google searches for 'do vaccines work'. The chart goes from 2004 to 2021, and the two variables track closely in value over that time.

View details about correlation #1,598

^{Show scatterplot}

What else correlates?
Solar power generated in Argentina · all energy
Google searches for 'do vaccines work' · all google searches

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Votes for the Libertarian Presidential candidate in Florida and the second variable is Automotive recalls for issues with the Parking Brake. The chart goes from 1980 to 2020, and the two variables track closely in value over that time.

View details about correlation #5,818

^{Show scatterplot}

What else correlates?
Votes for the Libertarian Presidential candidate in Florida · all elections
Automotive recalls for issues with the Parking Brake · all weird & wacky

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the 'cicada 3301' meme and the second variable is Total likes of LEMMiNO YouTube videos. The chart goes from 2012 to 2023, and the two variables track closely in value over that time.

View details about correlation #5,126

^{Show scatterplot}

What else correlates?
Popularity of the 'cicada 3301' meme · all memes
Total likes of LEMMiNO YouTube videos · all YouTube

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 Cruz and the second variable is Associates degrees awarded in Nursing. The chart goes from 2011 to 2021, and the two variables track closely in value over that time.

View details about correlation #1,830

^{Show scatterplot}

What else correlates?
Popularity of the first name Cruz · all first names
Associates degrees awarded in Nursing · all education

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Google searches for 'Baroque Obama' and the second variable is The number of furniture finishers in Missouri. The chart goes from 2004 to 2022, and the two variables track closely in value over that time.

View details about correlation #4,260

^{Show scatterplot}

What else correlates?
Google searches for 'Baroque Obama' · all google searches
The number of furniture finishers in Missouri · all cccupations

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of movies Emma Watson appeared in and the second variable is Votes for Democratic Senators in Michigan. The chart goes from 2001 to 2020, and the two variables track closely in value over that time.

View details about correlation #5,845

^{Show scatterplot}

What else correlates?
The number of movies Emma Watson appeared in · all films & actors
Votes for Democratic Senators in Michigan · all elections

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 Customer satisfaction with HP. The chart goes from 1994 to 2021, and the two variables track closely in value over that time.

View details about correlation #1,133

^{Show scatterplot}

What else correlates?
The distance between Saturn and the Sun · all planets
Customer satisfaction with HP · all weird & wacky

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 Rylee and the second variable is GMO use in soybeans in Indiana. The chart goes from 2000 to 2022, and the two variables track closely in value over that time.

View details about correlation #1,887

^{Show scatterplot}

What else correlates?
Popularity of the first name Rylee · all first names
GMO use in soybeans in Indiana · all food

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the 'call me maybe' meme and the second variable is Kerosene used in Panama. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.

View details about correlation #4,876

^{Show scatterplot}

What else correlates?
Popularity of the 'call me maybe' meme · all memes
Kerosene used in Panama · all energy

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Google searches for 'that is sus' and the second variable is MSCI Inc.'s stock price (MSCI). The chart goes from 2008 to 2023, and the two variables track closely in value over that time.

View details about correlation #1,639

^{Show scatterplot}

What else correlates?
Google searches for 'that is sus' · all google searches
MSCI Inc.'s stock price (MSCI) · all stocks

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The distance between Jupiter and Earth and the second variable is Bachelor's degrees awarded in consumer sciences. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.

View details about correlation #2,364

^{Show scatterplot}

What else correlates?
The distance between Jupiter and Earth · all planets
Bachelor's degrees awarded in consumer sciences · all education

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of dietetic technicians in North Carolina and the second variable is Viewership count for Days of Our Lives. The chart goes from 2003 to 2021, and the two variables track closely in value over that time.

View details about correlation #2,550

^{Show scatterplot}

What else correlates?
The number of dietetic technicians in North Carolina · all cccupations
Viewership count for Days of Our Lives · all weird & wacky

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 Eleanor and the second variable is Wind power generated in Poland. The chart goes from 1995 to 2021, and the two variables track closely in value over that time.

View details about correlation #2,239

^{Show scatterplot}

What else correlates?
Popularity of the first name Eleanor · all first names
Wind power generated in Poland · all energy

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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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