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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 The number of movies Ralph Fiennes appeared in and the second variable is Votes for Libertarian Senators in Wisconsin.  The chart goes from 1990 to 2016, and the two variables track closely in value over that time. Small Image
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Data details

The number of movies Ralph Fiennes appeared in
Source: The Movie DB
Additional Info: Strange Days (1995); The English Patient (1996); The Constant Gardener (2005); The End of the Affair (1999); Onegin (1999); The Miracle Maker (2000); Quiz Show (1994); The Avengers (1998); Spider (2002); Oscar and Lucinda (1997); Sunshine (1999); The White Countess (2005); Coriolanus (2011); The Grand Budapest Hotel (2014); A Dangerous Man: Lawrence After Arabia (1990); The Invisible Woman (2013); The Cormorant (1993); Land of the Blind (2006); Two Women (2014); Almeida Theatre Live: Richard III (2016); The Forgiven (2022); National Theatre Live: Antony & Cleopatra (2018); The King's Man (2021); Coup 53 (2019); Ten Days to D-Day (2004); Beat the Devil (2021); The Wonderful Story of Henry Sugar (2023); National Theatre Live: Straight Line Crazy (2022); Backstage: Ralph Fiennes Straight Line Crazy (2022); Four Quartets (2022); Wallace & Gromit: The Curse of the Were-Rabbit (2005); The Baby of Mâcon (1993); Maid in Manhattan (2002); The Reader (2008); Wuthering Heights (1992); The Duchess (2008); The Prince of Egypt (1998); Bernard and Doris (2006); André: The Voice of Wine (2017); The Dig (2021); How Proust Can Change Your Life (2000); The Menu (2022); Batmersive VR Experience (2017); Anatomy of a Global Thriller: Behind the Scenes of The Constant Gardener (2006); The Rat Catcher (2023); The Swan (2023); Schindler's List (1993); In Bruges (2008); Red Dragon (2002); Nanny McPhee and the Big Bang (2010); Chromophobia (2006); Page Eight (2011); Wrath of the Titans (2012); Great Expectations (2012); Salting the Battlefield (2014); A Bigger Splash (2015); National Theatre Live: Man and Superman (2015); My Astonishing Self: Gabriel Byrne on George Bernard Shaw (2018); Official Secrets (2019); A Director's Journey: The Making of 'Red Dragon' (2003); Harry Potter and the Deathly Hallows: Part 2 (2011); Skyfall (2012); The Wildest Dream (2010); Muse of Fire (2013); Turks & Caicos (2014); Hail, Caesar! (2016); Spectre (2015); Sea Sorrow (2017); The White Crow (2018); Poison (2023); Cemetery Junction (2010); The Lego Batman Movie (2017); No Time to Die (2021); The Making of The Grand Budapest Hotel (2014); Clash of the Titans (2010); The Prince of Egypt: From Dream to Screen (1999); Harry Potter and the Goblet of Fire (2005); The Hurt Locker (2008); Harry Potter and the Deathly Hallows: Part 1 (2010); Imaginary Witness: Hollywood and the Holocaust (2004); Harry Potter and the Order of the Phoenix (2007); Kubo and the Two Strings (2016); Holmes & Watson (2018); Being James Bond (2021); Butterflies (2016); Designing Bond (2021); Harry Potter 20th Anniversary: Return to Hogwarts (2022); The Chumscrubber (2005); National Theatre Live: 50 Years on Stage (2013); Dolittle (2020); Shakespeare Lives: The Works (2016); Spielberg (2017); The Lego Movie 2: The Second Part (2019); The Good Thief (2003); The Making of 'Gosford Park' (2002)

See what else correlates with The number of movies Ralph Fiennes appeared in

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

Correlation r = 0.9076915 (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 = 0.8239039 (Coefficient of determination)
This means 82.4% of the change in the one variable (i.e., Votes for Libertarian Senators in Wisconsin) is predictable based on the change in the other (i.e., The number of movies Ralph Fiennes appeared in) over the 7 years from 1990 through 2016.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0047. 0.0047286440704641330000000000
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 0.91 in 0.47% of random cases. Said differently, if you correlated 211 random variables Which I absolutely did.
with the same 6 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 6 because we have two variables measured over a period of 7 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.

[ 0.49, 0.99 ] 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.
1992199419982000200420122016
The number of movies Ralph Fiennes appeared in (Movie appearances)1122235
Votes for Libertarian Senators in Wisconsin (Total votes)91471543955912134883676224087531




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.
  4. Very low n: There are not many data points included in this analysis. Even if the p-value is high, we should be suspicious of using so few datapoints in a correlation.




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([1,1,2,2,2,3,5,])
array_2 = np.array([9147,15439,5591,21348,8367,62240,87531,])
array_1_name = "The number of movies Ralph Fiennes appeared in"
array_2_name = "Votes for Libertarian Senators in Wisconsin"

# 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: 13543 · Black Variable ID: 26688 · Red Variable ID: 26337
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