Spurious correlation #2,411 · View random

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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 Roger Federer's annual earnings and the second variable is Google searches for 'who is elon musk'. The chart goes from 2005 to 2022, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Roger Federer's annual earnings and the second variable is Google searches for 'who is elon musk'. The chart goes from 2005 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As Roger Federer's wealth served up, more people began to see him as a role model for success. This led to a volley of inspiration, prompting them to aim higher and reach for the stars, just like Elon Musk. In other words, Federer's rising earnings aced as a catalyst for people to serve-iously question, "who is Elon Musk?"

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a tennis court with the lush green surface bathed in warm golden sunlight. In the center of the court, Roger Federer stands proudly, with a giant dollar sign displayed on the surface. The onlookers surrounding him are visibly inspired, with thought bubbles emanating from their heads. These thought bubbles portray financial symbols and represent the inspiration drawn from Federer's wealth. Additionally, a flurry of Google search bars emerges from the onlookers' heads, all displaying the query "who is Elon Musk." This creates a visual representation of Federer's wealth inspiring curiosity and prompting a surge in searches about high-flying entrepreneurs. The entire scene should have a photorealistic quality. 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: Roger Federer's annual earnings and Google searches for 'who is elon musk'. 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: Magic realism Art nouveau a funny image you would see on reddit Cyberpunk Photorealism
ChatGPT: Photorealism

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 Roger Federer's annual earnings and Google searches for 'who is elon musk', in the following situation: Federer's massive bank account serves as a constant reminder to everyone that when it comes to money, you shouldn't "drop the ball." People see his swelling savings and think, "If Roger can net that much cash, maybe I should 'serve' up some financial aces myself." As they ponder their own investment strategies, they realize they want to be on the winning "court" of capitalism, just like Federer. In essence, his wealth serves as a "racket," propelling others to "lob" inquiries into the enigmatic world of billionaires, including "serving" up searches to learn who exactly is Elon Musk. It's a classic case of financial inspiration "serving" up a curiosity-filled "ace"! His earnings are truly making a "net" positive impact on the world's inquisitiveness about high-flying entrepreneurs!. Keep the description simple.
ChatGPT: Imagine a tennis court bathed in golden sunlight, where Roger Federer stands proudly with a giant dollar sign displayed on the court's surface, indicating his annual earnings. Surrounding onlookers are visibly inspired by the sight, with thought bubbles portraying financial symbols and the words "winning court of capitalism." Simultaneously, a flurry of Google search bars emerges from their heads, all displaying the query "who is Elon Musk." It's a visual representation of Federer's wealth inspiring curiosity and prompting a surge in searches about high-flying entrepreneurs.

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: [ Photorealism ] and depicting this scene: Imagine a tennis court bathed in golden sunlight, where Roger Federer stands proudly with a giant dollar sign displayed on the court's surface, indicating his annual earnings. Surrounding onlookers are visibly inspired by the sight, with thought bubbles portraying financial symbols and the words "winning court of capitalism." Simultaneously, a flurry of Google search bars emerges from their heads, all displaying the query "who is Elon Musk." It's a visual representation of Federer's wealth inspiring curiosity and prompting a surge in searches about high-flying entrepreneurs. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a tennis court with the lush green surface bathed in warm golden sunlight. In the center of the court, Roger Federer stands proudly, with a giant dollar sign displayed on the surface. The onlookers surrounding him are visibly inspired, with thought bubbles emanating from their heads. These thought bubbles portray financial symbols and represent the inspiration drawn from Federer's wealth. Additionally, a flurry of Google search bars emerges from the onlookers' heads, all displaying the query "who is Elon Musk." This creates a visual representation of Federer's wealth inspiring curiosity and prompting a surge in searches about high-flying entrepreneurs. The entire scene should have a photorealistic quality.

*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.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
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 Roger Federer's annual earnings caused who is elon musk (Google searches for 'who is elon musk') to increase. Include one good pun.

AI academic paper

(Because p < 0.01)

Roger Federer's Riches and Elon Musk's Mystique: A Marvellous Monetary Match

The Journal of Extraterrestrial Economics

Jan 2024

Reminder: This paper is AI-generated. Not real!

^{Show prompt used to generate this paper} Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between Roger Federer's annual earnings and Google searches for 'who is elon musk'. Make lots of jokes, goofy observations, and puns.

Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.

Make up the lorem and ipsum part, but make it sound related to the topic at hand.

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then you might move on to cartoons and children's shows that you watched for research.

Here is the title and abstract of the paper:
[[TITLE]]
Roger Federer's Riches and Elon Musk's Mystique: A Marvellous Monetary Match

[[ABSTRACT]]
This research paper delves into the peculiar connection between the annual earnings of tennis superstar Roger Federer and the Google searches for "who is Elon Musk," the enigmatic tech titan. Using data from Wikipedia and Google Trends covering the years 2005 to 2022, our research team established a substantial correlation coefficient of 0.9346367 and p < 0.01. It appears that as Roger Federer's earnings rise, so does the curiosity about Elon Musk. It's as if people are serving a double fault - first, wondering "who is this Musk fellow?" and then acing their search for Federer's financial successes. This paper not only reveals an unexpected linkage between sports earnings and tech intrigue but also serves as a delightful rally of puns and whimsical observations.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intertwining of sports and technology may seem as peculiar as a mismatched doubles pair, yet this paper aims to serve up some compelling evidence of a surprising connection between two seemingly disparate entities – Roger Federer's earnings and the public's fascination with none other than the enigmatic Elon Musk. Yes, you read that correctly. We have delved into the data and uncovered a substantial correlation between the annual earnings of the eminent tennis maestro and the Google searches for "who is Elon Musk," the visionary behind some of the most electrifying ventures in the tech world.
As we embark on this gripping match between financial fortunes and technological marvels, we invite you to join us in pondering the delightful irony of this unanticipated association. It's as if the tennis court of economics has collided with the technological baseline, creating a hypnotic back-and-forth of wealth and wonder. We found ourselves reminiscing about the classic quip – is it "love" for Federer or "love" for Musk that prevails in the hearts and minds of the masses?
Our statistical analysis, conducted with all the rigor of a Grand Slam final, has produced a correlation coefficient of 0.9346367, with the p-value dancing below the coveted 0.01 threshold. It seems that as Federer's earnings ascend to stratospheric heights, so too does the curiosity surrounding the man often referred to as the real-life Tony Stark. It is almost as if the financial rallies of Roger Federer prompt spectators to switch their attention from serves to SpaceX, from volleys to electric vehicles. The specter of Musk's mystique seems to be as captivating as Federer's signature one-handed backhand.
Amidst this blend of sports, finance, and technology, we cannot help but revel in the delightful quirkiness of our findings. It is as if we have stumbled upon a captivating dance between two seemingly incongruous variables, twirling and pirouetting through the realm of correlation and causation. As we present our observations, we hope they will serve as a volley of whimsical amusement and perhaps inspire further investigations into the unexpected intersections of curiosity and prosperity.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "Smith et al.," the authors find that the annual earnings of public figures often garner significant public interest and curiosity. This is particularly evident in the case of sports personalities and tech moguls, where the public's fascination with their financial standing can be quite remarkable. Similarly, "Doe and Jones" have investigated the link between public curiosity and the enigmatic aura surrounding certain technological innovators. The correlation between the public's intrigue and the financial success of prominent personalities has been a topic of much discussion in the realm of celebrity and business studies.

Turning our attention to the world of non-fiction literature, "The Wealth of Nations" by Adam Smith provides insights into the dynamics of wealth and public perception, albeit in a decidedly less whimsical manner. In a more contemporary vein, "Elon Musk: Tesla, SpaceX, and the Quest for a Fantastic Future" by Ashlee Vance offers a comprehensive look at the life and ventures of the eponymous tech entrepreneur. As for fictional works that might shed light on the unexpected correlation between sports earnings and tech intrigue, one might humorously consider "Moneyball" by Michael Lewis, although the connection may require some imaginative interpretation. Additionally, "Infinite Jest" by David Foster Wallace, while not directly related to sports or technology, does offer a glimpse into the complexities of human desires and obsessions, which may tangentially relate to our subject matter.

Reaching further into the realms of popular culture, we find that cartoons and children's shows also provide valuable insights into the peculiar interplay between disparate entities. For instance, "Pinky and the Brain" humorously illustrates the adage "What are we going to do tonight? The same thing we do every night, Pinky - try to take over the world!" which, in a whimsical twist, seems oddly reminiscent of the fervor surrounding technological innovators. Moreover, the animated series "SpongeBob SquarePants" humorously portrays the everyday dynamics of curiosity and ambition, albeit in a decidedly aquatic setting. These seemingly unrelated sources serve as whimsical parallels to our investigation, reminding us that unexpected connections can be found in the unlikeliest of places.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between Roger Federer's annual earnings and Google searches for 'who is elon musk'. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Wikipedia and Google Trends . You used data from 2005 to 2022

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
Roger Federer's Riches and Elon Musk's Mystique: A Marvellous Monetary Match

[[ABSTRACT]]
This research paper delves into the peculiar connection between the annual earnings of tennis superstar Roger Federer and the Google searches for "who is Elon Musk," the enigmatic tech titan. Using data from Wikipedia and Google Trends covering the years 2005 to 2022, our research team established a substantial correlation coefficient of 0.9346367 and p < 0.01. It appears that as Roger Federer's earnings rise, so does the curiosity about Elon Musk. It's as if people are serving a double fault - first, wondering "who is this Musk fellow?" and then acing their search for Federer's financial successes. This paper not only reveals an unexpected linkage between sports earnings and tech intrigue but also serves as a delightful rally of puns and whimsical observations.

[[INTRODUCTION]]
The intertwining of sports and technology may seem as peculiar as a mismatched doubles pair, yet this paper aims to serve up some compelling evidence of a surprising connection between two seemingly disparate entities – Roger Federer's earnings and the public's fascination with none other than the enigmatic Elon Musk. Yes, you read that correctly. We have delved into the data and uncovered a substantial correlation between the annual earnings of the eminent tennis maestro and the Google searches for "who is Elon Musk," the visionary behind some of the most electrifying ventures in the tech world.
As we embark on this gripping match between financial fortunes and technological marvels, we invite you to join us in pondering the delightful irony of this unanticipated association. It's as if the tennis court of economics has collided with the technological baseline, creating a hypnotic back-and-forth of wealth and wonder. We found ourselves reminiscing about the classic quip – is it "love" for Federer or "love" for Musk that prevails in the hearts and minds of the masses?
Our statistical analysis, conducted with all the rigor of a Grand Slam final, has produced a correlation coefficient of 0.9346367, with the p-value dancing below the coveted 0.01 threshold. It seems that as Federer's earnings ascend to stratospheric heights, so too does the curiosity surrounding the man often referred to as the real-life Tony Stark. It is almost as if the financial rallies of Roger Federer prompt spectators to switch their attention from serves to SpaceX, from volleys to electric vehicles. The specter of Musk's mystique seems to be as captivating as Federer's signature one-handed backhand.
Amidst this blend of sports, finance, and technology, we cannot help but revel in the delightful quirkiness of our findings. It is as if we have stumbled upon a captivating dance between two seemingly incongruous variables, twirling and pirouetting through the realm of correlation and causation. As we present our observations, we hope they will serve as a volley of whimsical amusement and perhaps inspire further investigations into the unexpected intersections of curiosity and prosperity.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To unravel the tangled web of Roger Federer's earnings and the allure of the enigmatic Elon Musk, our research team embarked on a delightful journey through an assortment of data sources, primarily Wikipedia and Google Trends. Our primary goal was to concoct a rigorous and meticulous methodology that would serve as a robust racquet for volleying statistical precision and whimsy in equal measure.

First, we meticulously scoured Wikipedia for Roger Federer's annual earnings spanning the years 2005 to 2022, aiming to capture the financial ebb and flow of the eminent tennis virtuoso's career. With the precision of a Hawk-Eye challenge, we meticulously documented these earnings, accounting for sponsorships, tournament winnings, and other assorted revenue streams.

Simultaneously, we delved into the curious world of Google searches, specifically examining the frequency of the query "who is Elon Musk" from 2005 to 2022 using Google Trends. This unconventional choice of variable stemmed from our team's conviction that the public's intrigue in the enigmatic tech maverick may hold a surprising mirror to the financial fortunes of Roger Federer. We acknowledge that this selection may appear as unorthodox as executing an underhand serve at Wimbledon, but we remained resolute in our pursuit of unearthing an unexpected association.

With data in hand, we invoked the incantations of statistical wizardry to conjure the Pearson correlation coefficient and assess its mystical dance with Roger Federer's earnings and the communal curiosity about Elon Musk. Our methodology harnessed the powers of SPSS and R, invoking them as our trusty partners in unraveling the numerical wizardry and contemplative contemplations. In a flourish of the most elegant statistical choreography, we derived a correlation coefficient and p-value that satisfied the demanding standards of empirical inquiry while leaving ample room for whimsical interpretation.

In summary, our methodology encapsulated a delightful dance across the realms of sports, technology, and statistical sorcery, all to illuminate the unexpected kinship between the financial trajectory of Roger Federer and the inquisitive musings about the elusive Elon Musk. As with any audacious experiment, our methodology embraced a spirit of adventure and whimsy, aiming to transcend the traditional boundaries of academic inquiry while serving as a visual spectacle fit for the Centre Court of scholarly amusement.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the results section of an academic research paper. You speak in the past tense (you found...).
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the results section of an academic research paper presenting the findings of the connection between Roger Federer's annual earnings and Google searches for 'who is elon musk'. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Wikipedia and Google Trends .

For the time period 2005 to 2022, you found a correlation 0.9346367, r-squared of 0.8735457, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
Roger Federer's Riches and Elon Musk's Mystique: A Marvellous Monetary Match

[[ABSTRACT]]
This research paper delves into the peculiar connection between the annual earnings of tennis superstar Roger Federer and the Google searches for "who is Elon Musk," the enigmatic tech titan. Using data from Wikipedia and Google Trends covering the years 2005 to 2022, our research team established a substantial correlation coefficient of 0.9346367 and p < 0.01. It appears that as Roger Federer's earnings rise, so does the curiosity about Elon Musk. It's as if people are serving a double fault - first, wondering "who is this Musk fellow?" and then acing their search for Federer's financial successes. This paper not only reveals an unexpected linkage between sports earnings and tech intrigue but also serves as a delightful rally of puns and whimsical observations.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intertwining of sports and technology may seem as peculiar as a mismatched doubles pair, yet this paper aims to serve up some compelling evidence of a surprising connection between two seemingly disparate entities – Roger Federer's earnings and the public's fascination with none other than the enigmatic Elon Musk. Yes, you read that correctly. We have delved into the data and uncovered a substantial correlation between the annual earnings of the eminent tennis maestro and the Google searches for "who is Elon Musk," the visionary behind some of the most electrifying ventures in the tech world.
As we embark on this gripping match between financial fortunes and technological marvels, we invite you to join us in pondering the delightful irony of this unanticipated association. It's as if the tennis court of economics has collided with the technological baseline, creating a hypnotic back-and-forth of wealth and wonder. We found ourselves reminiscing about the classic quip – is it "love" for Federer or "love" for Musk that prevails in the hearts and minds of the masses?
Our statistical analysis, conducted with all the rigor of a Grand Slam final, has produced a correlation coefficient of 0.9346367, with the p-value dancing below the coveted 0.01 threshold. It seems that as Federer's earnings ascend to stratospheric heights, so too does the curiosity surrounding the man often referred to as the real-life Tony Stark. It is almost as if the financial rallies of Roger Federer prompt spectators to switch their attention from serves to SpaceX, from volleys to electric vehicles. The specter of Musk's mystique seems to be as captivating as Federer's signature one-handed backhand.
Amidst this blend of sports, finance, and technology, we cannot help but revel in the delightful quirkiness of our findings. It is as if we have stumbled upon a captivating dance between two seemingly incongruous variables, twirling and pirouetting through the realm of correlation and causation. As we present our observations, we hope they will serve as a volley of whimsical amusement and perhaps inspire further investigations into the unexpected intersections of curiosity and prosperity.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The research findings elucidate a remarkable connection between the annual earnings of tennis virtuoso Roger Federer and the public's curiosity about the polymathic entrepreneur, Elon Musk. Our investigation spanning the years 2005 to 2022 yielded a strikingly robust correlation coefficient of 0.9346367 and an r-squared value of 0.8735457, with a p-value less than 0.01. The relationship unearthed between these two seemingly unrelated entities is so strong, it's as if they have engaged in a mesmerizing baseline rally, with Federer's earnings serving as the forceful forehand and the Google searches for "who is Elon Musk" returning equally compelling backhands.

Our team plotted the data on a scatterplot (Fig. 1), which illustrates the awe-inspiring correlation between Federer's earnings and the public's burgeoning interest in Elon Musk. The strength of this relationship is as clear as a perfectly executed drop shot, leaving no room for doubt or skepticism.

It's as if the rise in Federer's earnings acts as the forehand winner that charges toward the net, while the swell in searches for Elon Musk is the unsuspecting backhand return that effortlessly lands across the court. Like a riveting match point, the link we have unveiled between these two variables epitomizes the unpredictability and drama that ensues in the realm of statistical analysis.

This research not only reveals a compelling association between the financial achievements of a sports icon and the inquisitiveness about a tech luminary but also serves as a delightful demonstration of the unexpected and intricate connections that can be uncovered through rigorous analysis and inquiry.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between Roger Federer's annual earnings and Google searches for 'who is elon musk'. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
Roger Federer's Riches and Elon Musk's Mystique: A Marvellous Monetary Match

[[ABSTRACT]]
This research paper delves into the peculiar connection between the annual earnings of tennis superstar Roger Federer and the Google searches for "who is Elon Musk," the enigmatic tech titan. Using data from Wikipedia and Google Trends covering the years 2005 to 2022, our research team established a substantial correlation coefficient of 0.9346367 and p < 0.01. It appears that as Roger Federer's earnings rise, so does the curiosity about Elon Musk. It's as if people are serving a double fault - first, wondering "who is this Musk fellow?" and then acing their search for Federer's financial successes. This paper not only reveals an unexpected linkage between sports earnings and tech intrigue but also serves as a delightful rally of puns and whimsical observations.

[[LITERATURE REVIEW]]
In "Smith et al.," the authors find that the annual earnings of public figures often garner significant public interest and curiosity. This is particularly evident in the case of sports personalities and tech moguls, where the public's fascination with their financial standing can be quite remarkable. Similarly, "Doe and Jones" have investigated the link between public curiosity and the enigmatic aura surrounding certain technological innovators. The correlation between the public's intrigue and the financial success of prominent personalities has been a topic of much discussion in the realm of celebrity and business studies.
Turning our attention to the world of non-fiction literature, "The Wealth of Nations" by Adam Smith provides insights into the dynamics of wealth and public perception, albeit in a decidedly less whimsical manner. In a more contemporary vein, "Elon Musk: Tesla, SpaceX, and the Quest for a Fantastic Future" by Ashlee Vance offers a comprehensive look at the life and ventures of the eponymous tech entrepreneur. As for fictional works that might shed light on the unexpected correlation between sports earnings and tech intrigue, one might humorously consider "Moneyball" by Michael Lewis, although the connection may require some imaginative interpretation. Additionally, "Infinite Jest" by David Foster Wallace, while not directly related to sports or technology, does offer a glimpse into the complexities of human desires and obsessions, which may tangentially relate to our subject matter.
Reaching further into the realms of popular culture, we find that cartoons and children's shows also provide valuable insights into the peculiar interplay between disparate entities. For instance, "Pinky and the Brain" humorously illustrates the adage "What are we going to do tonight? The same thing we do every night, Pinky - try to take over the world!" which, in a whimsical twist, seems oddly reminiscent of the fervor surrounding technological innovators. Moreover, the animated series "SpongeBob SquarePants" humorously portrays the everyday dynamics of curiosity and ambition, albeit in a decidedly aquatic setting. These seemingly unrelated sources serve as whimsical parallels to our investigation, reminding us that unexpected connections can be found in the unlikeliest of places.

[[RESULTS]]
The research findings elucidate a remarkable connection between the annual earnings of tennis virtuoso Roger Federer and the public's curiosity about the polymathic entrepreneur, Elon Musk. Our investigation spanning the years 2005 to 2022 yielded a strikingly robust correlation coefficient of 0.9346367 and an r-squared value of 0.8735457, with a p-value less than 0.01. The relationship unearthed between these two seemingly unrelated entities is so strong, it's as if they have engaged in a mesmerizing baseline rally, with Federer's earnings serving as the forceful forehand and the Google searches for "who is Elon Musk" returning equally compelling backhands.
Our team plotted the data on a scatterplot (Fig. 1), which illustrates the awe-inspiring correlation between Federer's earnings and the public's burgeoning interest in Elon Musk. The strength of this relationship is as clear as a perfectly executed drop shot, leaving no room for doubt or skepticism.
It's as if the rise in Federer's earnings acts as the forehand winner that charges toward the net, while the swell in searches for Elon Musk is the unsuspecting backhand return that effortlessly lands across the court. Like a riveting match point, the link we have unveiled between these two variables epitomizes the unpredictability and drama that ensues in the realm of statistical analysis.
This research not only reveals a compelling association between the financial achievements of a sports icon and the inquisitiveness about a tech luminary but also serves as a delightful demonstration of the unexpected and intricate connections that can be uncovered through rigorous analysis and inquiry.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The investigation into the mesmerizing relationship between Roger Federer's annual earnings and the public's intrigue about Elon Musk has provided compelling insights into the unpredictable connections that can be revealed through statistical analysis. The substantial correlation coefficient of 0.9346367 and p < 0.01, uncovered in our study, supports previous research by Smith et al., which emphasizes the noteworthy public interest and curiosity surrounding the financial status of prominent personalities. This correlation has been demonstrated not only in the realm of sports but also in the expanse of technological innovation. In a delightful rally of puns and whimsical observations, our findings underscore the enigmatic allure of individuals who command attention in the worlds of sports and technology.

It is as if Roger Federer's increasing financial triumphs serve as the assertive forehand, propelling the public's interest in Elon Musk toward a definitive backhand return of escalating Google searches. This unexpected association exemplifies the intricate and unforeseeable linkages that can surface through rigorous statistical analysis. The humorously serious parallels drawn from popular culture, such as "Pinky and the Brain," underscore the quirky yet thought-provoking interplay between disparate entities, resonating with the surprising connection unearthed in our research.

The results of our investigation reflect the quirks and idiosyncrasies that can be encountered in the realm of statistical inquiry, akin to the plot twists in a suspenseful thriller. This unexpected correlation between Roger Federer's financial accomplishments and the public's burgeoning curiosity about Elon Musk serves as a testament to the capricious nature of research and the potential for serendipitous discoveries that can emerge from seemingly unrelated variables. Much like a well-crafted pun, this correlation represents both the unexpected and the delightful in the realm of statistical inquiry.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Roger Federer's annual earnings and Google searches for 'who is elon musk'. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Roger Federer's Riches and Elon Musk's Mystique: A Marvellous Monetary Match

[[ABSTRACT]]
This research paper delves into the peculiar connection between the annual earnings of tennis superstar Roger Federer and the Google searches for "who is Elon Musk," the enigmatic tech titan. Using data from Wikipedia and Google Trends covering the years 2005 to 2022, our research team established a substantial correlation coefficient of 0.9346367 and p < 0.01. It appears that as Roger Federer's earnings rise, so does the curiosity about Elon Musk. It's as if people are serving a double fault - first, wondering "who is this Musk fellow?" and then acing their search for Federer's financial successes. This paper not only reveals an unexpected linkage between sports earnings and tech intrigue but also serves as a delightful rally of puns and whimsical observations.

[[INTRDUCTION]]
The intertwining of sports and technology may seem as peculiar as a mismatched doubles pair, yet this paper aims to serve up some compelling evidence of a surprising connection between two seemingly disparate entities – Roger Federer's earnings and the public's fascination with none other than the enigmatic Elon Musk. Yes, you read that correctly. We have delved into the data and uncovered a substantial correlation between the annual earnings of the eminent tennis maestro and the Google searches for "who is Elon Musk," the visionary behind some of the most electrifying ventures in the tech world.
As we embark on this gripping match between financial fortunes and technological marvels, we invite you to join us in pondering the delightful irony of this unanticipated association. It's as if the tennis court of economics has collided with the technological baseline, creating a hypnotic back-and-forth of wealth and wonder. We found ourselves reminiscing about the classic quip – is it "love" for Federer or "love" for Musk that prevails in the hearts and minds of the masses?
Our statistical analysis, conducted with all the rigor of a Grand Slam final, has produced a correlation coefficient of 0.9346367, with the p-value dancing below the coveted 0.01 threshold. It seems that as Federer's earnings ascend to stratospheric heights, so too does the curiosity surrounding the man often referred to as the real-life Tony Stark. It is almost as if the financial rallies of Roger Federer prompt spectators to switch their attention from serves to SpaceX, from volleys to electric vehicles. The specter of Musk's mystique seems to be as captivating as Federer's signature one-handed backhand.
Amidst this blend of sports, finance, and technology, we cannot help but revel in the delightful quirkiness of our findings. It is as if we have stumbled upon a captivating dance between two seemingly incongruous variables, twirling and pirouetting through the realm of correlation and causation. As we present our observations, we hope they will serve as a volley of whimsical amusement and perhaps inspire further investigations into the unexpected intersections of curiosity and prosperity.

[[RESULTS]]
The research findings elucidate a remarkable connection between the annual earnings of tennis virtuoso Roger Federer and the public's curiosity about the polymathic entrepreneur, Elon Musk. Our investigation spanning the years 2005 to 2022 yielded a strikingly robust correlation coefficient of 0.9346367 and an r-squared value of 0.8735457, with a p-value less than 0.01. The relationship unearthed between these two seemingly unrelated entities is so strong, it's as if they have engaged in a mesmerizing baseline rally, with Federer's earnings serving as the forceful forehand and the Google searches for "who is Elon Musk" returning equally compelling backhands.
Our team plotted the data on a scatterplot (Fig. 1), which illustrates the awe-inspiring correlation between Federer's earnings and the public's burgeoning interest in Elon Musk. The strength of this relationship is as clear as a perfectly executed drop shot, leaving no room for doubt or skepticism.
It's as if the rise in Federer's earnings acts as the forehand winner that charges toward the net, while the swell in searches for Elon Musk is the unsuspecting backhand return that effortlessly lands across the court. Like a riveting match point, the link we have unveiled between these two variables epitomizes the unpredictability and drama that ensues in the realm of statistical analysis.
This research not only reveals a compelling association between the financial achievements of a sports icon and the inquisitiveness about a tech luminary but also serves as a delightful demonstration of the unexpected and intricate connections that can be uncovered through rigorous analysis and inquiry.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has aced the serve in elucidating the captivating correlation between Roger Federer's prodigious fortune and the public's burgeoning fascination with the polymathic giant, Elon Musk. It seems as though spectators of the financial game are not content to merely witness Federer's fiscal forehands and backhands but also serve up a curiosity-ridden return, seeking to unravel the enigma of Musk's ventures.

As we bid adieu to this mesmerizing match of financial flair and technological allure, we cannot help but marvel at the delightful irony of this unanticipated entanglement. It's as if the economic baseline and the technological net have converged, resulting in an electrifying back-and-forth of wealth and wonder that leaves us in awe of the whimsical dance of statistics.

Our findings reveal a correlation coefficient so robust, it would be a shame to deuce it down to mere chance. The relationship is as compelling as an unexpected drop shot, leaving us with no choice but to acknowledge the captivating interplay between Federer's financial prowess and the allure of Musk's endeavors.

In the spirit of scientific inquiry, we have uncovered this unlikely connection, much like finding love in a statistically significant place. Whether it's "love" for Federer's earnings or "love" for Elon Musk that prevails, this unexpected nexus deserves unequivocal admiration for its sheer, unorthodox charm.

Therefore, we confidently assert that no further research is needed in this area, as the findings have served up a winning ace that satisfies the innate curiosity of the scientific and lay communities alike.

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited Roger Federer's Riches and Elon Musk's Mystique: A Marvellous Monetary Match. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Extraterrestrial Economics

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited Roger Federer's Riches and Elon Musk's Mystique: A Marvellous Monetary Match. Respond only with the name of a fictional organization.

ChatGPT:

The International Institute for Celebrity Wealth and Wonderment

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Roger Federer's Riches and Elon Musk's Mystique: A Marvellous Monetary Match with an abstract of This research paper delves into the peculiar connection between the annual earnings of tennis superstar Roger Federer and the Google searches for "who is Elon Musk," the enigmatic tech titan. Using data from Wikipedia and Google Trends covering the years 2005 to 2022, our research team established a substantial correlation coefficient of 0.9346367 and p < 0.01. It appears that as Roger Federer's earnings rise, so does the curiosity about Elon Musk. It's as if people are serving a double fault - first, wondering "who is this Musk fellow?" and then acing their search for Federer's financial successes. This paper not only reveals an unexpected linkage between sports earnings and tech intrigue but also serves as a delightful rally of puns and whimsical observations.

ChatGPT:

Roger Federer, earnings, annual earnings, tennis superstar, Google searches, Elon Musk, who is, Elon Musk, enigmatic tech titan, research, Wikipedia, Google Trends, correlation coefficient, curiosity, financial successes, sports earnings, tech intrigue, puns, whimsical observations

*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.

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Data details

Roger Federer's annual earnings
Source: Wikipedia
See what else correlates with Roger Federer's annual earnings

Google searches for 'who is elon musk'
Detailed data title: Relative volume of Google searches for 'who is elon musk' (Worldwide, without quotes)
Source: Google Trends
Additional Info: ^{See full details} Relative search volume (not absolute numbers)

See what else correlates with Google searches for 'who is elon musk'

Correlation r = 0.9346367 (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 = 0.8735457 (Coefficient of determination)
This means 87.4% of the change in the one variable (i.e., Google searches for 'who is elon musk') is predictable based on the change in the other (i.e., Roger Federer's annual earnings) over the 18 years from 2005 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.4E-8.^Show 0.0000000136299001198637560000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note 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 0.93 in 1.4E-6% of random cases. Said differently, if you correlated 73,368,109 random variables^Note You don't actually need 73 million 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 17 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 17 because we have two variables measured over a period of 18 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.

[ 0.83, 0.98 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} 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: ^Note 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.

	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Roger Federer's annual earnings (Thousands of dollars)	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
Google searches for 'who is elon musk' (Rel. search volume)	0.75	0.0833333	0.0833333	0.0909091	0.111111	0	0	0	0.75	1	1.58333	1.66667	3.66667	8.41667	6.08333	15.4167	26.3333	43

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^Note 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^Note 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. ^{Show the steps to do this.} 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([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_2 = np.array([0.75,0.0833333,0.0833333,0.0909091,0.111111,0,0,0,0.75,1,1.58333,1.66667,3.66667,8.41667,6.08333,15.4167,26.3333,43,])
array_1_name = "Roger Federer's annual earnings"
array_2_name = "Google searches for 'who is elon musk'"

# 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)

Reuseable content

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. ^Note 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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For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:

High resolution line chart ^Note 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 Roger Federer's annual earnings
Line chart for only Google searches for 'who is elon musk'
AI-generated correlation image
The spurious research paper: Roger Federer's Riches and Elon Musk's Mystique: A Marvellous Monetary Match

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Correlation ID: 2411 · Black Variable ID: 84 · Red Variable ID: 1487


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