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Spurious correlation #23,194 · 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 Meryl Streep appeared in and the second variable is Votes for Democratic Senators in Virginia.  The chart goes from 1976 to 2020, and the two variables track closely in value over that time. Small Image
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Data details

The number of movies Meryl Streep appeared in
Source: The Movie DB
Additional Info: The House of the Spirits (1993); Lions for Lambs (2007); Dancing at Lughnasa (1998); She-Devil (1989); Postcards from the Edge (1990); It's Complicated (2009); Mamma Mia! (2008); Julie & Julia (2009); The River Wild (1994); Silkwood (1983); Music of the Heart (1999); The French Lieutenant's Woman (1981); A Prairie Home Companion (2006); ...First Do No Harm (1997); Marvin's Room (1996); Evil Angels (1988); Heartburn (1986); Plenty (1985); Doubt (2008); Before and After (1996); Sophie's Choice (1982); Hurricane on the Bayou (2006); Higglety Pigglety Pop! or There Must Be More to Life (2010); The Living Sea (1995); Stolen Childhoods (2005); The Iron Lady (2011); One True Thing (1998); Hope Springs (2012); Wings of Life (2011); A Fierce Green Fire (2013); To the Arctic 3D (2012); Theater of War (2008); Alice at the Palace (1982); Into the Woods (2014); Florence Foster Jenkins (2016); Little Ears: The Velveteen Rabbit (1984); Ricki and the Flash (2015); Kiss Me, Petruchio (1981); Auschwitz (2015); Solving the Mysteries of Leonardo da Vinci's First Known Portrait (1999); Vermeer Master of Light (2001); Secret Service (1977); Shout Gladi Gladi (2015); The Music of Regret (2006); The Post (2017); Five Came Back (2017); The Laundromat (2019); Age 7 in America (1991); An Old Fashioned Love Story: Making 'The Bridges of Madison County' (2008); Out of Print (2013); Museum Town (2019); The Prom (2020); Sky Island (2010); Keeping the Promise: AHF 30 Years Documentary (2018); Let Them All Talk (2020); Here We Are: Notes for Living on Planet Earth (2020); Everybody Knows... Elizabeth Murray (2016); Chrysanthemum (1999); An Introduction to the Ketogenic Diet (1994); Ribbon of Sand (2008); New York at the Movies (2002); Out of Africa (1985); The Bridges of Madison County (1995); The Devil Wears Prada (2006); Prime (2005); Fantastic Mr. Fox (2009); Adaptation. (2002); Kramer vs. Kramer (1979); Still of the Night (1982); Defending Your Life (1991); The Ant Bully (2006); Ironweed (1987); The Manchurian Candidate (2004); Falling in Love (1984); August: Osage County (2013); Mike Nichols: An American Master (2016); This Changes Everything (2019); Emotional Uncertainties (2015); Dear Elizabeth (2021); Karen Blixen: Out of This World (2005); Birthing Mamma Mia! (2008); The Hours (2002); Rendition (2007); Death Becomes Her (1992); I Knew It Was You: Rediscovering John Cazale (2009); The Seduction of Joe Tynan (1979); Dark Matter (2008); Everybody Rides the Carousel (1975); Secret Ingredients: Creating Julie & Julia (2009); Mamma Mia! Here We Go Again (2018); The Deadliest Season (1977); Don't Look Up (2021); There's Only One Paul McCartney (2002); Doubt: Stage to Screen (2009); Lemony Snicket's A Series of Unfortunate Events (2004); The Giver (2014); Everything Is Copy (2015); 100 Years (2017); We Will Rise: Michelle Obama's Mission to Educate Girls Around the World (2018); Finding the Truth: The Making of 'Kramer vs. Kramer' (2001); Manhattan (1979); The Deer Hunter (1978); Suffragette (2015); Girl Rising (2013); Paul McCartney | Live at Grand Central Station (2018); Clint Eastwood: The Last Legend (2022); Clint Eastwood: Out of the Shadows (2000); Meryl Streep: Mystery and Metamorphosis (2020); Julia (1977); Eastwood Directs: The Untold Story (2013); The Female Lead - A Selection of Portraits (2017); Little Women (2019); Rose McGowan: Being Brave (2019); Uncommon Women and Others (1979); A Song of Africa (2000); Joe Papp in Five Acts (2012); A.I. Artificial Intelligence (2001); Evening (2007); Mary Poppins Returns (2018); The Homesman (2014); Stuck on You (2003); Stanislavski: Lust for Life (2021); Wrestling with Angels: Playwright Tony Kushner (2006); The Concert for Valor (2014); Stuck Together: Bringing Stuck on You to the Screen (2004); In Search of the Sanderson Sisters: A Hocus Pocus Hulaween Takeover (2020); Clint Eastwood: A Cinematic Legacy (2021); Radioman (2012); Beyond Boundaries: The Harvey Weinstein Scandal (2018); From Star Wars to Star Wars: The Story of Industrial Light & Magic (1999); Alan Pakula: Going for Truth (2019); The Words That Built America (2017); La Classe américaine (2012); AFI Life Achievement Award: 50th Anniversary Special (2023); Close Up (2012); Every Act of Life (2018); Final Cut: Ladies and Gentlemen (2012); The Earth Day Special (1990); Starring Austin Pendleton (2016); Roman Polanski: Wanted and Desired (2008); Noi siamo cinema (2021); Voices That Care (1990); Take Me to the World: A Sondheim 90th Birthday Celebration (2020); And the Oscar Goes to... (2014)

See what else correlates with The number of movies Meryl Streep appeared in

Votes for Democratic Senators in Virginia
Detailed data title: Total number of votes cast for Federal Democrat Senate candidates in Virginia
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for Democratic Senators in Virginia

Correlation r = 0.7526357 (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 = 0.5664605 (Coefficient of determination)
This means 56.6% of the change in the one variable (i.e., Votes for Democratic Senators in Virginia) is predictable based on the change in the other (i.e., The number of movies Meryl Streep appeared in) over the 13 years from 1976 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.003. 0.0029889394658863797000000000
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 0.75 in 0.3% of random cases. Said differently, if you correlated 335 random variables Which I absolutely did.
with the same 12 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 12 because we have two variables measured over a period of 13 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.

[ 0.34, 0.92 ] 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.
1978198219841988199419962000200620082012201420182020
The number of movies Meryl Streep appeared in (Movie appearances)1321222687576
Votes for Democratic Senators in Virginia (Total votes)608511690839601142147409093837611159801296090117561023693302010070107367019103702466500




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([1,3,2,1,2,2,2,6,8,7,5,7,6,])
array_2 = np.array([608511,690839,601142,1474090,938376,1115980,1296090,1175610,2369330,2010070,1073670,1910370,2466500,])
array_1_name = "The number of movies Meryl Streep appeared in"
array_2_name = "Votes for Democratic Senators in Virginia"

# 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)



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Correlation ID: 23194 · Black Variable ID: 26583 · Red Variable ID: 26327
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