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__Data details__

**The number of movies Dwayne Johnson appeared in**

**Source:**The Movie DB

**Additional Info:**The Rundown (2003); Southland Tales (2006); Walking Tall (2004); Tooth Fairy (2010); Doom (2005); The Scorpion King (2002); Gridiron Gang (2006); The Game Plan (2007); Race to Witch Mountain (2009); Faster (2010); WWE Survivor Series 2001 (2001); Planet 51 (2009); Journey 2: The Mysterious Island (2012); The Rock: The Epic Journey of Dwayne Johnson (2012); WWE WrestleMania XXVIII (2012); WWE Royal Rumble 2002 (2002); WWE Royal Rumble 1999 (1999); Snitch (2013); WWE WrestleMania 29 (2013); Hercules (2014); WWE Elimination Chamber 2013 (2013); WWE Unforgiven 2000 (2000); WWE WrestleMania X-Seven (2001); WWE Unforgiven 1999 (1999); WWE WrestleMania XV (1999); WWE Backlash 2000 (2000); WWE Rebellion 1999 (1999); WWE SummerSlam 2000 (2000); WWF Fully Loaded 2000 (2000); WWE Insurrextion 2000 (2000); WWE Vengeance 2002 (2002); WWE Global Warning (2002); WWE Backlash 2003 (2003); WWE No Way Out 2003 (2003); WWF: The Rock - The People's Champ (2000); The Rock - Just Bring It! (2002); WWF: The Rock - Know Your Role (1998); WWF: Funniest Moments (2002); Rampage (2018); WWE: Attitude Era: Vol. 2 (2014); Jungle Cruise (2021); WWE Collection Volume 2: Know Your Role (2015); Central Intelligence (2016); Baywatch (2017); San Andreas (2015); Jumanji: Welcome to the Jungle (2017); WWE: The Rock: The Most Electrifying Man in Sports Entertainment - Vol. 3 (2011); WWE: The Rock: The Most Electrifying Man in Sports Entertainment (2008); WWE: The Rock: The Most Electrifying Man in Sports Entertainment - Vol. 2 (2010); G.I. Joe: Retaliation (2013); Skyscraper (2018); Fast & Furious Presents: Hobbs & Shaw (2019); Black Adam (2022); Rock and a Hard Place (2017); Jumanji: The Next Level (2019); Red Notice (2021); WWF: Best of Raw - Vol. 1 (1998); Mickey’s 90th Spectacular (2018); The Rock's Most Electrifying Matches (2020); WWE: The Attitude Era (2012); WWE Halftime Heat (1999); DC League of Super-Pets (2022); WWE: Monday Night War Vol. 2: Know Your Role (2015); Black Adam: Saviour or Destroyer? (2022); WWE: Greatest Wrestling Stars of the '90s (2009); WWE SummerSlam 2002 (2002); Pain & Gain (2013); Empire State (2013); WWE No Way Out 2001 (2001); WWE Survivor Series 1999 (1999); WWE Backlash: In Your House (1999); WWE No Mercy 2000 (2000); WWE Rebellion 2000 (2000); WWE Judgment Day 2000 (2000); WWE SummerSlam 2001 (2001); WWE Rebellion 2001 (2001); WWE Vengeance 2001 (2001); WWE: The Rock vs John Cena: Once in a Lifetime (2012); Brock Lesnar: Best of the Beast (2014); Moana (2016); Gone Fishing (2017); WWF: Best of Raw - Vol. 1&2 (2001); WWF: Chris Jericho - Break Down the Walls (2000); My Way: The Life and Legacy of Pat Patterson (2021); The Mummy Returns (2001); Get Smart (2008); Fast & Furious 6 (2013); WWE Wrestlemania XIX (2003); WWE Royal Rumble 2000 (2000); WWE WrestleMania 2000 (2000); WWE Unforgiven 2001 (2001); WWE SummerSlam 1998 (1998); WWE Over the Edge (1999); WWE Armageddon 2000 (2000); WWF: Mick Foley - Hard Knocks & Cheap Pops (2001); WWF: Undertaker The Phenom (1998); WWE: The Videos Ramped Up Vol. 1 (2002); The Fate of the Furious (2017); WWE: Top 50 Superstars of All Time (2010); WWE: Triple H: Thy Kingdom Come (2013); WWE: Best Pay-Per-View Matches of 2013 (2013); WWE: Monday Night War Vol. 1: Shots Fired (2015); Voice of the Islands (2017); WWE: Greatest Stars Of The 90's (2013); For All Mankind - The Life and Career of Mick Foley (2013); WWE WrestleMania XXVII (2011); Summer Game Fest 2022 (2022); Angle (2023); WWE Rivals: Steve Austin vs. The Rock (2022); WWE Survivor Series 2011 (2011); WWE Judgment Day: In Your House (1998); WWE Survivor Series 1998 (1998); WWE D-Generation X: In Your House (1997); WWE St. Valentine's Day Massacre: In Your House (1999); WWE Rock Bottom: In Your House (1998); WWE Fully Loaded 1999 (1999); WWE No Way Out 2000 (2000); WWE King of the Ring 2000 (2000); The Sheik (2014); Escape from Calypso Island (2016); Biography: “Stone Cold” Steve Austin (2021); Be Cool (2005); WWE King of the Ring 1998 (1998); WWE Capital Carnage (1998); WWE King of the Ring 1999 (1999); WWE SummerSlam 1999 (1999); WWE No Mercy 2001 (2001); WWE No Way Out 2002 (2002); WWF: Stone Cold Steve Austin: What? (2002); WWE's Biggest Knuckleheads (2011); WWE: 30 Years of SummerSlam (2018); WWE: Raw 10th Anniversary (2003); The Top 100 Moments In Raw History (2012); Meeting Stone Cold (2021); WWE Tagged Classics: Austin 3:16 Uncensored / Three Faces Of Foley / Chris Jericho: Break Down The Walls / Kurt Angle: Its True (2012); Biography: Kurt Angle (2022); USIDent TV: Surveilling the Southland (2008); WWE: 50 Greatest Finishing Moves in WWE History (2012); WWE: Triple H - That Damn Good (2002); Fighting with My Family (2019); WWE: The Best of King of the Ring (2011); Kurt Angle: The Essential Collection (2017); Rock Bottom Riser (2021); Never Forget: WWE Returns After 9/11 (2021); WWE Royal Rumble 1998 (1998); WWE In Your House 14: Revenge of the Taker (1997); WWE Fully Loaded: In Your House (1998); WWE Armageddon 1999 (1999); WWE: Greatest Superstars of the 21st Century (2011); WWE: John Cena's Greatest Rivalries (2014); Stone Cold Steve Austin: The Bottom Line on the Most Popular Superstar of All Time (2011); Randy Orton: The Evolution of a Predator (2011); The True Story of WrestleMania (2011); WWE: Brock Lesnar: Here Comes the Pain (2003); Superfan: The Story of Vladimir (2023); The Other Guys (2010); WWE Royal Rumble 2013 (2013); WWE Survivor Series 1996 (1996); Furious 7 (2015); WWE: The Best of Raw - After the Show (2014); WWE: The True Story of The Royal Rumble (2016); WWE: Best of the 2000's (2017); WWE Wrestlemania X8 (2002); WWE In Your House 13: Final Four (1997); Elvis: Viva Las Vegas (2008); WWE: Best Pay-Per-View Matches 2012 (2012); WWE: Hell in a Cell - The Greatest Hell in a Cell Matches of All Time (2008); WWE WrestleMania XIV (1998); WWE In Your House 15: A Cold Day in Hell (1997); WWE: The Best of Raw 15th Anniversary (2007); WWE: The Ladder Match (2007); 1997: Dawn of the Attitude (2017); Millennials: The Musical (2016); Iron and Beyond (2002); Operation Filmmaker (2008); Why Did I Get Married Too? (2010); WWE: 150 Best Pay-Per-View Matches, Vol 2 (2014); Starrcast V: The Roast of Ric Flair (2022); Hart & Soul - The Hart Family Anthology (2010); Fast Five (2011); WWE Over the Edge: In Your House (1998); WWE Survivor Series 1997 (1997); WWE: The Legacy of Stone Cold Steve Austin (2008); WWE: The Best of Raw & SmackDown 2012 (2013); Hollywood Hulk Hogan: Hulk Still Rules (2002); WWE In Your House 12: It's Time (1996); WWE Breakdown: In Your House (1998); WWE: Wrestlemania Recall (2005); Breaking the Code: Behind the Walls of Chris Jericho (2010); WWE: Tombstone - The History of the Undertaker (2005); The Ladder Match 2: Crash & Burn (2011); WWE RAW 1000 (2012); WWE Survivor Series 2000 (2000); The Road is Jericho: Epic Stories and Rare Matches from Y2J (2015); WWE: 30 Years of Survivor Series (2017); Beyond the Mat (1999); Owen Hart of Gold (2015); WWE: The Best of the Intercontinental Championship (2005); WWE No Mercy 1999 (1999); WWE: WrestleMania Monday (2017); WWE No Way Out of Texas: In Your House (1998); The Words That Built America (2017); The History of WWE: 50 Years of Sports Entertainment (2013); WWE Mayhem in Manchester (1998); 30 Rock: A One-Time Special (2020); WWE WrestleMania 13 (1997); WWE Unforgiven: In Your House (1998); WWE: The History Of The World Heavyweight Championship (2009); WWE Badd Blood: In Your House (1997); WWE: The History Of The Intercontinental Championship (2008); WWE: Mick Foley's Greatest Hits & Misses - A Life in Wrestling (2004); The History of The WWE Hardcore Championship (2016); WWE: The History Of The WWE Championship (2006); Free Guy (2021); WWE WrestleMania 31 (2015); WWE Royal Rumble 2001 (2001); Longshot (2001); Bret Hart: The Dungeon Collection (2013); Trish & Lita – Best Friends, Better Rivals (2019); WWE WrestleMania 32 (2016); WWE: The Big Show - A Giant's World (2011); WWE: Hulk Hogan: The Ultimate Anthology (2006); WWE: Falls Count Anywhere: The Greatest Street Fights and Other Out of Control Matches (2012); WWE: The Best Of In Your House (2013); WWE: OMG! The Top 50 Incidents in WWE History (2011); WWE King of the Ring 2002 (2002); Straight Outta Dudleyville: The Legacy of the Dudley Boyz (2016); Once Upon a Studio (2023); WWE Royal Rumble 1997 (1997); WWE: The Best of SmackDown - 10th Anniversary, 1999-2009 (2012); Jem and the Holograms (2015); Fast X (2023); WWE: Best of WWE at Madison Square Garden (2013); You Again (2010); WWE Royal Rumble 2015 (2015); WWE WrestleMania XX (2004); WWE: 150 Best Pay-Per-View Matches, Vol 1 (2014); Reno 911!: Miami (2007); WWE WrestleMania XXX (2014)

*See what else correlates with*

**The number of movies Dwayne Johnson appeared in****Google searches for 'how to cure a hangover'**

**Detailed data title:**Relative volume of Google searches for 'how to cure a hangover' (Worldwide, without quotes)

**Source:**Google Trends

**Additional Info:**Relative search volume (not absolute numbers)

*See what else correlates with*

**Google searches for 'how to cure a hangover'****Correlation r = 0.7455884**(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}= 0.5559020This means

**55.6%**of the change in the one variable

*(i.e., Google searches for 'how to cure a hangover')*is predictable based on the change in the other

*(i.e., The number of movies Dwayne Johnson appeared in)*over the 20 years from 2004 through 2023.

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

The

*p*-value is 0.00016. 0.0001610925162702611000000000

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 0.016% of random cases. Said differently, if you correlated 6,208 random variables Which I absolutely did.

with the same 19 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is

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

**20 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.

**[ 0.45, 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 | 2021 | 2022 | 2023 | |

The number of movies Dwayne Johnson appeared in (Movie appearances) | 3 | 5 | 4 | 4 | 8 | 4 | 9 | 13 | 12 | 17 | 9 | 10 | 8 | 12 | 4 | 4 | 2 | 8 | 7 | 4 |

Google searches for 'how to cure a hangover' (Rel. search volume) | 19.5833 | 17 | 20.1667 | 16.1667 | 20.3333 | 29.75 | 47.75 | 74.75 | 63.8333 | 60.6667 | 56.75 | 49.8333 | 46.0833 | 49.5 | 45.75 | 43.6667 | 38 | 43.25 | 35.1667 | 30 |

__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([3,5,4,4,8,4,9,13,12,17,9,10,8,12,4,4,2,8,7,4,])
array_2 = np.array([19.5833,17,20.1667,16.1667,20.3333,29.75,47.75,74.75,63.8333,60.6667,56.75,49.8333,46.0833,49.5,45.75,43.6667,38,43.25,35.1667,30,])
array_1_name = "The number of movies Dwayne Johnson appeared in"
array_2_name = "Google searches for 'how to cure a hangover'"
# 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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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.

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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 Dwayne Johnson appeared in* - Line chart for only
*Google searches for 'how to cure a hangover'*

**How fun was this correlation?**

Your correlation rating is out of this world!

Correlation ID: 10565 · Black Variable ID: 26563 · Red Variable ID: 1347