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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 first name Blanca and the second variable is Robberies in Texas.  The chart goes from 1985 to 2022, and the two variables track closely in value over that time. Small Image
View details about correlation #2,498




What else correlates?
Popularity of the first name Blanca · all first names
Robberies 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 The number of movies Amy Poehler appeared in and the second variable is Popularity of the 'whip nae nae' meme.  The chart goes from 2015 to 2023, and the two variables track closely in value over that time. Small Image
View details about correlation #5,865




What else correlates?
The number of movies Amy Poehler appeared in · all films & actors
Popularity of the 'whip nae nae' meme · all memes

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Google searches for 'Taylor Swift' and the second variable is Fossil fuel use in British Virgin Islands.  The chart goes from 2006 to 2021, and the two variables track closely in value over that time. Small Image
View details about correlation #5,825




What else correlates?
Google searches for 'Taylor Swift' · all google searches
Fossil fuel use in British Virgin Islands · all energy

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Number of public school students in 9th grade and the second variable is Bank of America's stock price (BAC).  The chart goes from 2002 to 2022, and the two variables track closely in value over that time. Small Image
View details about correlation #1,828




What else correlates?
Number of public school students in 9th grade · all education
Bank of America's stock price (BAC) · all stocks

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Searches for 'never gonna give you up' and the second variable is Google searches for 'who is elon musk'.  The chart goes from 2006 to 2023, and the two variables track closely in value over that time. Small Image
View details about correlation #4,986




What else correlates?
Searches for 'never gonna give you up' · all memes
Google searches for 'who is elon musk' · 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 physicists in California and the second variable is Michael Schumacher's Formula One Ranking.  The chart goes from 2003 to 2012, and the two variables track closely in value over that time. Small Image
View details about correlation #3,705




What else correlates?
The number of physicists in California · all cccupations
Michael Schumacher's Formula One Ranking · 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 the moon and the second variable is Electricity generation in Japan.  The chart goes from 1980 to 2021, and the two variables track closely in value over that time. Small Image
View details about correlation #2,730




What else correlates?
The distance between Uranus and the moon · all planets
Electricity generation in Japan · all energy

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 History and the second variable is Centene's stock price (CNC).  The chart goes from 2011 to 2021, and the two variables track closely in value over that time. Small Image
View details about correlation #2,709




What else correlates?
Associates degrees awarded in History · all education
Centene's stock price (CNC) · all stocks

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of mobile heavy equipment mechanics in Maine and the second variable is Customer satisfaction with Verizon.  The chart goes from 2004 to 2021, and the two variables track closely in value over that time. Small Image
View details about correlation #1,149




What else correlates?
The number of mobile heavy equipment mechanics in Maine · all cccupations
Customer satisfaction with Verizon · all weird & wacky

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Votes for Libertarian Senators in Kansas and the second variable is Google searches for 'two day shipping'.  The chart goes from 2004 to 2020, and the two variables track closely in value over that time. Small Image
View details about correlation #4,624




What else correlates?
Votes for Libertarian Senators in Kansas · all elections
Google searches for 'two day shipping' · 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 Will Smith appeared in and the second variable is Electricity generation in Kosovo.  The chart goes from 2008 to 2021, and the two variables track closely in value over that time. Small Image
View details about correlation #5,860




What else correlates?
The number of movies Will Smith appeared in · all films & actors
Electricity generation in Kosovo · all energy

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is UFO sightings in Utah 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. Small Image
View details about correlation #1,116




What else correlates?
UFO sightings in Utah · 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 Popularity of the first name Lane and the second variable is The number of merchandise displayers and window trimmers in Alaska.  The chart goes from 2003 to 2021, and the two variables track closely in value over that time. Small Image
View details about correlation #3,239




What else correlates?
Popularity of the first name Lane · all first names
The number of merchandise displayers and window trimmers in Alaska · 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 'balloon boy' meme and the second variable is Wind power generated in Fiji.  The chart goes from 2009 to 2021, and the two variables track closely in value over that time. Small Image
View details about correlation #5,125




What else correlates?
Popularity of the 'balloon boy' meme · all memes
Wind power generated in Fiji · 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 Uranus and the Sun and the second variable is Global count of operating nuclear power plants.  The chart goes from 1975 to 2022, and the two variables track closely in value over that time. Small Image
View details about correlation #1,185




What else correlates?
The distance between Uranus and the Sun · all planets
Global count of operating nuclear power plants · all weird & wacky

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Why this works

  1. 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.” 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.
  2. Lack of causal connection: There is probably no direct connection between these variables, despite what the AI says above. 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.
  3. 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. 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.
  4. 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. 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.
  5. 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.
  6. Outliers: Some datasets here have outliers which drag up the correlation. 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.
  7. Low n: There are not many data points included in some of these charts. 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:

Project by Tyler Vigen
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