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AI explanation
This can be attributed to the lesser-known "Stevie Wonder Effect." As the number of parents naming their children after the musical legend Stevie Wonder, there has been a mysterious, but statistically significant, rise in exceptional talent and success in various fields. This has directly led to an increase in overall human achievement, including the awe-inspiring dominance of sports icon Roger Federer. It's as if these individuals are infused with a touch of superstardom from birth, propelling them to greatness and subsequently elevating their financial rewards. The correlation is uncanny, but it seems the world is simply a better (and more lucrative) place with more individuals named Stevie making their mark. It's a wondrous phenomenon, indeed!Model: dalle-3
Prompt: [ Futuristic ] In a holographic stadium filled with vibrant colors and neon lights, the cheers are amplified to a digital symphony as a hologram of Roger Federer effortlessly dominates the tennis court. His illuminated smile reflects off the surrounding holographic crowd, creating a mesmerizing spectacle. Outside, a bustling city street is electrified with neon signs and bustling hovercars as parents proudly call out "Stevie" to their talented children. The atmosphere is charged with a luminous energy, embodying the shared prosperity and achievement that seems to accompany those exceptional individuals named Stevie, creating a futuristic and awe-inspiring scene. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.
This image generation prompt was also generated by AI, using the following sequence:
Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: Popularity of the first name Stevie and Roger Federer's annual earnings. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: Data cyber art a meme Cute 2D animation Charcoal Futuristic
ChatGPT: Futuristic
Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Popularity of the first name Stevie and Roger Federer's annual earnings, in the following situation: This can be attributed to the lesser-known "Stevie Wonder Effect." As the number of parents naming their children after the musical legend Stevie Wonder, there has been a mysterious, but statistically significant, rise in exceptional talent and success in various fields. This has directly led to an increase in overall human achievement, including the awe-inspiring dominance of sports icon Roger Federer. It's as if these individuals are infused with a touch of superstardom from birth, propelling them to greatness and subsequently elevating their financial rewards. The correlation is uncanny, but it seems the world is simply a better (and more lucrative) place with more individuals named Stevie making their mark. It's a wondrous phenomenon, indeed!. Keep the description simple.
ChatGPT: In a packed stadium, the cheers are deafening as Roger Federer effortlessly dominates the tennis court, his winning smile reflecting the adoration of the crowd. Just outside, a bustling city street teems with energy as parents proudly call out "Stevie" to their talented children, who seem to exude an air of natural brilliance. Mingled with the sounds of success, an electric atmosphere fills the air, carrying with it the shared prosperity and achievement that seem to follow in the wake of these exceptional individuals named Stevie.
Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ Futuristic ] and depicting this scene: In a packed stadium, the cheers are deafening as Roger Federer effortlessly dominates the tennis court, his winning smile reflecting the adoration of the crowd. Just outside, a bustling city street teems with energy as parents proudly call out "Stevie" to their talented children, who seem to exude an air of natural brilliance. Mingled with the sounds of success, an electric atmosphere fills the air, carrying with it the shared prosperity and achievement that seem to follow in the wake of these exceptional individuals named Stevie. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ Futuristic ] In a holographic stadium filled with vibrant colors and neon lights, the cheers are amplified to a digital symphony as a hologram of Roger Federer effortlessly dominates the tennis court. His illuminated smile reflects off the surrounding holographic crowd, creating a mesmerizing spectacle. Outside, a bustling city street is electrified with neon signs and bustling hovercars as parents proudly call out "Stevie" to their talented children. The atmosphere is charged with a luminous energy, embodying the shared prosperity and achievement that seems to accompany those exceptional individuals named Stevie, creating a futuristic and awe-inspiring scene.
*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.
I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in Popularity of the first name Stevie caused Roger Federer's annual earnings to increase. Make it feel plausible.
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Data details
Popularity of the first name StevieDetailed data title: Babies of all sexes born in the US named Stevie
Source: US Social Security Administration
See what else correlates with Popularity of the first name Stevie
Roger Federer's annual earnings
Source: Wikipedia
See what else correlates with Roger Federer's annual earnings
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 = 0.8407340 (Coefficient of determination)
This means 84.1% of the change in the one variable (i.e., Roger Federer's annual earnings) is predictable based on the change in the other (i.e., Popularity of the first name Stevie) over the 25 years from 1998 through 2022.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.2E-10. 0.0000000001189071856445753300
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 0.92 in 1.2E-8% of random cases. Said differently, if you correlated 8,409,920,684 random variables You don't actually need 8 billion variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.
p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.
In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.
Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 24 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 24 because we have two variables measured over a period of 25 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.
[ 0.82, 0.96 ] 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.
1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | |
Popularity of the first name Stevie (Babies born) | 307 | 243 | 247 | 236 | 217 | 232 | 215 | 211 | 252 | 229 | 217 | 240 | 210 | 227 | 254 | 260 | 318 | 312 | 379 | 444 | 473 | 629 | 801 | 1147 | 1217 |
Roger Federer's annual earnings (Thousands of dollars) | 27.955 | 225.139 | 623.782 | 865.425 | 1995.03 | 4000.68 | 6357.55 | 6137.02 | 8343.88 | 10130.6 | 5886.88 | 8768.11 | 7698.29 | 6369.58 | 8584.84 | 3203.64 | 2338.45 | 7802890 | 1527270 | 13054900 | 8629230 | 8716980 | 10600000 | 90000000 | 90000000 |
Why this works
- Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 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([307,243,247,236,217,232,215,211,252,229,217,240,210,227,254,260,318,312,379,444,473,629,801,1147,1217,])
array_2 = np.array([27.955,225.139,623.782,865.425,1995.03,4000.68,6357.55,6137.02,8343.88,10130.6,5886.88,8768.11,7698.29,6369.58,8584.84,3203.64,2338.45,7802890,1527270,13054900,8629230,8716980,10600000,90000000,90000000,])
array_1_name = "Popularity of the first name Stevie"
array_2_name = "Roger Federer's annual earnings"
# 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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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 Popularity of the first name Stevie
- Line chart for only Roger Federer's annual earnings
- AI-generated correlation image
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Correlation ID: 4186 · Black Variable ID: 3190 · Red Variable ID: 84