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 Popularity of the 'all your base' meme and the second variable is Google searches for 'black holes'. The chart goes from 2006 to 2023, and the two variables track closely in value over that time.

View details about correlation #4,843

^{Show scatterplot}

What else correlates?
Popularity of the 'all your base' meme · all memes
Google searches for 'black holes' · all google searches

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 Precision production and the second variable is The number of parking enforcement workers in New Jersey. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.

View details about correlation #1,529

^{Show scatterplot}

What else correlates?
Bachelor's degrees awarded in Precision production · all education
The number of parking enforcement workers in New Jersey · all cccupations

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Yogurt consumption and the second variable is The Bank of Nova Scotia's stock price (BNS). The chart goes from 2003 to 2021, and the two variables track closely in value over that time.

View details about correlation #2,638

^{Show scatterplot}

What else correlates?
Yogurt consumption · all food
The Bank of Nova Scotia's stock price (BNS) · all stocks

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Super Bowl point difference and the second variable is Highest sale price for a single-family home in Connecticut. The chart goes from 2006 to 2021, and the two variables track closely in value over that time.

View details about correlation #3,979

^{Show scatterplot}

What else correlates?
Super Bowl point difference · all sports
Highest sale price for a single-family home in Connecticut · 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 Neptune and the Sun and the second variable is Popularity of the first name Andrea. The chart goes from 1975 to 2022, and the two variables track closely in value over that time.

View details about correlation #1,222

^{Show scatterplot}

What else correlates?
The distance between Neptune and the Sun · all planets
Popularity of the first name Andrea · all first names

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Oklahoma City and the second variable is Google searches for 'funny cat videos'. The chart goes from 2004 to 2012, and the two variables track closely in value over that time.

View details about correlation #2,187

^{Show scatterplot}

What else correlates?
Air pollution in Oklahoma City · all weather
Google searches for 'funny cat videos' · all google searches

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 Ewan McGregor appeared in and the second variable is The number of ophthalmic medical technicians in Connecticut. The chart goes from 2012 to 2022, and the two variables track closely in value over that time.

View details about correlation #5,869

^{Show scatterplot}

What else correlates?
The number of movies Ewan McGregor appeared in · all films & actors
The number of ophthalmic medical technicians in Connecticut · all cccupations

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the 'starter pack' meme and the second variable is Automotive recalls for issues with the Air Bags. The chart goes from 2006 to 2022, and the two variables track closely in value over that time.

View details about correlation #4,967

^{Show scatterplot}

What else correlates?
Popularity of the 'starter pack' meme · all memes
Automotive recalls for issues with the Air Bags · 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 Uranus and Saturn and the second variable is UFO sightings in Texas. The chart goes from 1975 to 2021, and the two variables track closely in value over that time.

View details about correlation #1,607

^{Show scatterplot}

What else correlates?
The distance between Uranus and Saturn · all planets
UFO sightings in Texas · all random state specific

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 Mathematics and statistics and the second variable is Google searches for 'dollar store near me'. The chart goes from 2011 to 2021, and the two variables track closely in value over that time.

View details about correlation #1,783

^{Show scatterplot}

What else correlates?
Associates degrees awarded in Mathematics and statistics · all education
Google searches for 'dollar store near me' · all google searches

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 Tom Cruise appeared in and the second variable is Votes for the Democratic Presidential candidate in Montana. The chart goes from 1981 to 2020, and the two variables track closely in value over that time.

View details about correlation #5,862

^{Show scatterplot}

What else correlates?
The number of movies Tom Cruise appeared in · all films & actors
Votes for the Democratic Presidential candidate in Montana · all elections

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 Theodore and the second variable is Fossil fuel use in Burundi. The chart goes from 1980 to 2021, and the two variables track closely in value over that time.

View details about correlation #2,393

^{Show scatterplot}

What else correlates?
Popularity of the first name Theodore · all first names
Fossil fuel use in Burundi · all energy

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is xkcd comics published about literature and the second variable is Robberies in Vermont. The chart goes from 2007 to 2022, and the two variables track closely in value over that time.

View details about correlation #1,202

^{Show scatterplot}

What else correlates?
xkcd comics published about literature · all weird & wacky
Robberies in Vermont · all random state specific

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 Constellation Brands' stock price (STZ). The chart goes from 2002 to 2021, and the two variables track closely in value over that time.

View details about correlation #1,100

^{Show scatterplot}

What else correlates?
American cheese consumption · all food
Constellation Brands' stock price (STZ) · 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 Tallahassee and the second variable is The number of postal service machine operators in Florida. The chart goes from 2003 to 2020, and the two variables track closely in value over that time.

View details about correlation #1,423

^{Show scatterplot}

What else correlates?
Air pollution in Tallahassee · all weather
The number of postal service machine operators in Florida · all cccupations

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 Engineering technologies and the second variable is Hydopower energy generated in Vietnam. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.

View details about correlation #2,011

^{Show scatterplot}

What else correlates?
Master's degrees awarded in Engineering technologies · all education
Hydopower energy generated in Vietnam · all energy

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The distance between Neptune and Earth and the second variable is Burglaries in Kansas. The chart goes from 1985 to 2022, and the two variables track closely in value over that time.

View details about correlation #2,428

^{Show scatterplot}

What else correlates?
The distance between Neptune and Earth · all planets
Burglaries in Kansas · all random state specific

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 Russell Crowe appeared in and the second variable is Customer satisfaction with Walgreens. The chart goes from 2005 to 2020, and the two variables track closely in value over that time.

View details about correlation #5,941

^{Show scatterplot}

What else correlates?
The number of movies Russell Crowe appeared in · all films & actors
Customer satisfaction with Walgreens · all weird & wacky

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Manchester, New Hampshire and the second variable is Google searches for 'how to treat internal bleeding'. The chart goes from 2004 to 2011, and the two variables track closely in value over that time.

View details about correlation #1,427

^{Show scatterplot}

What else correlates?
Air pollution in Manchester, New Hampshire · all weather
Google searches for 'how to treat internal bleeding' · 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 'mocking spongebob' meme and the second variable is Liquefied petroleum gas used in Kyrgyzstan. The chart goes from 2006 to 2021, and the two variables track closely in value over that time.

View details about correlation #4,949

^{Show scatterplot}

What else correlates?
Popularity of the 'mocking spongebob' meme · all memes
Liquefied petroleum gas used in Kyrgyzstan · 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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