about · email me · subscribe
Spurious correlation #11,062 · View random

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 Brad Pitt appeared in and the second variable is Popularity of the 'trollface' meme.  The chart goes from 2006 to 2023, and the two variables track closely in value over that time. Small Image
Download png
, svg

It takes a bit of time to generate AI explanations!
Check back later, or email me if you'd enjoy seeing this work in real-time.

Random correlation

Discover a new correlation

View all correlations

View all research papers

Report an error

Data details

The number of movies Brad Pitt appeared in
Source: The Movie DB
Additional Info: Meet Joe Black (1998); Troy (2004); Mr. & Mrs. Smith (2005); Seven Years in Tibet (1997); Babel (2006); Legends of the Fall (1994); Kalifornia (1993); The Curious Case of Benjamin Button (2008); The Mexican (2001); The Tree of Life (2011); The Dark Side of the Sun (1988); Sinbad: Legend of the Seven Seas (2003); Moneyball (2011); Johnny Suede (1991); Killing Them Softly (2012); World War Z (2013); Fury (2014); Hitting the Apex (2015); Sinbad and the Cyclops Island (2003); Touch of Evil (2011); Inglourious Basterds (2009); Allied (2016); Voyage of Time: The IMAX Experience (2016); War Machine (2017); Two-Fisted Tales (1992); Ad Astra (2019); Bullet Train (2022); Broken: The Incredible Story of Brangelina (2018); Babylon (2022); Unity Temple: Frank Lloyd Wright’s Modern Masterpiece (2022); Rx for Survival: A Global Health Challenge (2005); Ocean's Twelve (2004); A River Runs Through It (1992); Ocean's Thirteen (2007); Fight Club (1999); Interview with the Vampire (1994); Se7en (1995); Spy Game (2001); The Assassination of Jesse James by the Coward Robert Ford (2007); Too Young to Die (1990); Across the Tracks (1991); By the Sea (2015); The Devil's Own (1997); Once Upon a Time… in Hollywood (2019); Brangelina: The Inside Story (2021); A Panoramic Canvas Called 'Babylon' (2023); A Love Letter To Making Movies (2020); Twelve Monkeys (1995); Snatch (2000); Burn After Reading (2008); Cutting Class (1989); The Hamster Factor and Other Tales of 'Twelve Monkeys' (1996); Cool World (1992); Happy Together (1989); The Assassination of Jesse James: Death of an Outlaw (2008); The Audition (2015); Jeff Buckley: Everybody Here Wants You (2002); Fast Times at Ridgemont High: A Virtual Table Read (2020); Ocean's Eleven (2001); Megamind (2010); The Favor (1994); Contact (1992); The Big Short (2015); Voom Portraits (2007); John Lewis: Celebrating a Hero (2020); In the Shadow of the Vampire: The Making of Interview with the Vampire (2000); Sleepers (1996); The Counselor (2013); Moneyball: Playing the Game (2012); The Lost City (2022); Wrath of Briand (2021); Thelma & Louise (1991); A Stoning in Fulham County (1988); The Curious Birth of Benjamin Button (2009); Making 'Snatch' (2001); True Romance (1993); Full Frontal (2002); 'Ocean's Eleven': The Look of the Con (2002); Happy Feet Two (2011); 12 Years a Slave (2013); 8 (2012); His Highness Hollywood (2008); Thelma & Louise: The Last Journey (2003); His Way (2011); Exploring 'The Tree of Life' (2011); Truth of the Situation: Making 'The Counselor' (2014); Hunk (1987); The Image (1990); Hatton (2023); Beyond All Boundaries (2009); Deadpool 2 (2018); Once Upon a Deadpool (2018); Hannibal Hopkins et Sir Anthony (2021); No Way Out (1987); La Classe américaine (2012); Friends: The Reunion (2021); QT8: The First Eight (2019); Close Up (2012); Less Than Zero (1987); Ultimate Fights from the Movies (2002); Confessions of a Dangerous Mind (2002); No Man's Land (1987); Smash His Camera (2010); Final Cut: Ladies and Gentlemen (2012); Abby Singer (2003); Live 8 (2005); America: A Tribute to Heroes (2001); Being John Malkovich (1999); Never Sleep Again: The Elm Street Legacy (2010)

See what else correlates with The number of movies Brad Pitt appeared in

Popularity of the 'trollface' meme
Detailed data title: Relative volume of Google searches for 'trollface meme' (without quotes, in the United States)
Source: Google Trends
Additional Info: Relative search volume is a unique Google thing; the shape of the chart is accurate but the actual numbers are meaningless.

See what else correlates with Popularity of the 'trollface' meme

Correlation r = 0.7228636 (Pearson correlation coefficient)
Correlation is a measure of how much the variables move together. If it is 0.99, when one goes up the other goes up. If it is 0.02, the connection is very weak or non-existent. If it is -0.99, then when one goes up the other goes down. If it is 1.00, you probably messed up your correlation function.

r2 = 0.5225318 (Coefficient of determination)
This means 52.3% of the change in the one variable (i.e., Popularity of the 'trollface' meme) is predictable based on the change in the other (i.e., The number of movies Brad Pitt appeared in) over the 18 years from 2006 through 2023.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0007008. 0.0007008436880861966000000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.72 in 0.07008% of random cases. Said differently, if you correlated 1,427 random variables Which I absolutely did.
with the same 17 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 17 because we have two variables measured over a period of 18 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.39, 0.89 ] 95% correlation confidence interval (using the Fisher z-transformation)
The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.
The number of movies Brad Pitt appeared in (Movie appearances)134336632421333442
Popularity of the 'trollface' meme (Relative popularity)7.416674.727275.8521.557.833359.666723.083313.545516.090918.5833118.833338.454558.5833325.166724.083323.9167

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.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations 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 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.
    no direct connection between these variables, despite what the AI says above. 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. 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 simple Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
    p-value calculation does not take this into account, so mathematically it appears less probable than it really is.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. Step 1: Download and install Python on your computer.

Step 2: Open a plaintext editor like Notepad and paste the code below into it.

Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

Step 7: Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."

# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([1,3,4,3,3,6,6,3,2,4,2,1,3,3,3,4,4,2,])
array_2 = np.array([7.41667,4.72727,5.8,5,21.5,57.8333,59.6667,23.0833,13.5455,16.0909,18.5833,11,8.83333,8.45455,8.58333,25.1667,24.0833,23.9167,])
array_1_name = "The number of movies Brad Pitt appeared in"
array_2_name = "Popularity of the 'trollface' meme"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

Full license details.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:

View another random correlation

How fun was this correlation?

Hats off to you for rating!

Correlation ID: 11062 · Black Variable ID: 26607 · Red Variable ID: 25172
about · subscribe · emailme@tylervigen.com · twitter

CC BY 4.0