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__Data details__

**The number of movies Scarlett Johansson appeared in**

**Source:**The Movie DB

**Additional Info:**Scoop (2006); Girl with a Pearl Earring (2003); Vicky Cristina Barcelona (2008); The Nanny Diaries (2007); An American Rhapsody (2001); My Brother the Pig (1999); Under the Skin (2014); Manny & Lo (1996); Lucy (2014); Deep Down (2014); Rough Night (2017); Ghost in the Shell (2017); Black Widow (2021); Jeff Koons (2017); North Star (2023); Drive-Thru Records: Vol. 1 (2002); Penglai (2022); Noi siamo cinema (2021); Lost in Translation (2003); Ghost World (2001); The Spirit (2008); The Other Boleyn Girl (2008); The Black Dahlia (2006); A Love Song for Bobby Long (2004); A Good Woman (2004); We Bought a Zoo (2011); Captain America: The Winter Soldier (2014); Her (2013); Don Jon (2013); Match Point (2005); The Island (2005); Marriage Story (2019); Marriage Story: From the Pages to the Performances (2019); Moneymaker: Behind the Black Widow (2021); Asteroid City (2023); Come Home (2021); Escape from the World's Most Dangerous Place (2012); In Good Company (2004); The Perfect Score (2004); Woody Allen: A Documentary (2011); Hitchcock (2012); Marvel Studios: Assembling a Universe (2014); Captain America: Civil War (2016); Building the Dream: Assembling the Avengers (2012); Jojo Rabbit (2019); Art as Dialogue (2017); Sing 2 (2021); VOMO: Vote or Miss Out (2020); The Horse Whisperer (1998); The Prestige (2006); He's Just Not That Into You (2009); Eight Legged Freaks (2002); Yes We Can (2008); Translating History to Screen (2008); Marvel: 75 Years, from Pulp to Pop! (2014); Iron Man 2 (2010); Sing (2016); The Avengers: A Visual Journey (2012); Chadwick Boseman: A Tribute for a King (2020); Ultimate Iron Man: The Making of Iron Man 2 (2010); The Avengers (2012); Avengers: Age of Ultron (2015); Lost on Location: Behind the Scenes of 'Lost in Translation' (2004); Chef (2014); The Jungle Book (2016); Hail, Caesar! (2016); Avengers: Infinity War (2018); Avengers: Endgame (2019); Just Cause (1995); If Lucy Fell (1996); The Man Who Wasn't There (2001); Fall (1997); Catching Fire: The Story of Anita Pallenberg (2023); The Director's Notebook: The Cinematic Sleight of Hand of Christopher Nolan (2007); Bert Stern: Original Madman (2011); The SpongeBob SquarePants Movie (2004); Home Alone 3 (1997); Floyd Norman: An Animated Life (2016); Saturday Night Live: The Best of Amy Poehler (2009); Isle of Dogs (2018); Her: Love in the Modern Age (2014); Marvel Studios Assembled: The Making of Hawkeye (2022); Celebrating Marvel's Stan Lee (2019); Final Cut: Ladies and Gentlemen (2012); Captain Marvel (2019); Thor: Ragnarok (2017); North (1994)

*See what else correlates with*

**The number of movies Scarlett Johansson appeared in****Cumulative goals scored by Vincent Kompany in domestic matches**

**Detailed data title:**Cumulative goals scored by Vincent Kompany in domestic matches for Anderlecht, Hamburg SV, and Manchester City

**Source:**Wikipedia

*See what else correlates with*

**Cumulative goals scored by Vincent Kompany in domestic matches****Correlation r = 0.7234424**(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.

**r**(Coefficient of determination)

^{2}= 0.5233689This means

**52.3%**of the change in the one variable

*(i.e., Cumulative goals scored by Vincent Kompany in domestic matches)*is predictable based on the change in the other

*(i.e., The number of movies Scarlett Johansson appeared in)*over the 17 years from 2004 through 2020.

**p < 0.01,**which is statistically significant(Null hypothesis significance test)

The

*p*-value is 0.00103. 0.0010295163150075370000000000

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 0.103% of random cases. Said differently, if you correlated 971 random variables Which I absolutely did.

with the same 16 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is

**16**because we have two variables measured over a period of

**17 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.

**[ 0.37, 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.

2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |

The number of movies Scarlett Johansson appeared in (Movie appearances) | 6 | 2 | 3 | 2 | 5 | 2 | 2 | 3 | 6 | 2 | 8 | 1 | 5 | 5 | 2 | 6 | 2 |

Cumulative goals scored by Vincent Kompany in domestic matches (Goals scored) | 2 | 2 | 2 | 0 | 3 | 1 | 2 | 0 | 3 | 1 | 4 | 0 | 2 | 3 | 1 | 1 | 1 |

__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.” 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.**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.**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.**Outlandish outliers:**There are "outliers" in this data. 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.

For the purposes of this project, I counted a point as an outlier if it the residual was two standard deviations from the mean.

(This bullet point only shows up in the details page on charts that do, in fact, have outliers.)

They stand out on the scatterplot above: notice the dots that are far away from any other dots. I intentionally mishandeled outliers, which makes the correlation look extra strong.

__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([6,2,3,2,5,2,2,3,6,2,8,1,5,5,2,6,2,])
array_2 = np.array([2,2,2,0,3,1,2,0,3,1,4,0,2,3,1,1,1,])
array_1_name = "The number of movies Scarlett Johansson appeared in"
array_2_name = "Cumulative goals scored by Vincent Kompany in domestic matches"
# 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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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.

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**Download images for these variables:**

- High resolution line chart
The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up
*beyond the size of the observable universe*without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.

If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.

Alternatively you can use a tool like Inkscape. - High resolution line chart, optimized for mobile
- Alternative high resolution line chart
- Scatterplot
- Portable line chart (png)
- Portable line chart (png), optimized for mobile
- Line chart for only
*The number of movies Scarlett Johansson appeared in* - Line chart for only
*Cumulative goals scored by Vincent Kompany in domestic matches*

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Correlation ID: 12098 · Black Variable ID: 26635 · Red Variable ID: 306