Spurious correlation #1,484 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of locker room attendants in Michigan and the second variable is Gasoline pumped in France. The chart goes from 2003 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 The number of locker room attendants in Michigan and the second variable is Gasoline pumped in France. The chart goes from 2003 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the number of locker room attendants in Michigan decreased, there was a corresponding increase in abandoned dirty socks. This led to a rise in noxious odors, which inexplicably affected the molecular structure of gasoline pumped in France, causing it to break down at a faster rate.

^{Show image generation prompt} Model: dalle-3
Prompt: **Image Generation Prompt:** Style: _a vintage Polaroid photo_ Scene: In the bustling Michigan locker room, a group of attendants is attending to athletes, helping them gear up for their games. The room is filled with a sense of anticipation and energy. Meanwhile, across the ocean in France, a row of vintage gas pumps stands against a backdrop of a quaint countryside, with cars lining up to fill up with gasoline. The scene exudes a timeless charm and captures the contrast between the two locations through the lens of nostalgia. 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: The number of locker room attendants in Michigan and Gasoline pumped in France. 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: Woodblock painting Realistic a Polaroid photo Woodblock painting Scalar art
ChatGPT: a Polaroid photo

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 The number of locker room attendants in Michigan and Gasoline pumped in France together. Keep the description simple.
ChatGPT: In a bustling Michigan locker room, a group of attendants is helping athletes get ready for their games. Meanwhile, across the ocean in France, a row of gas pumps is busy filling up cars with gasoline.

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: [ a Polaroid photo ] and depicting this scene: In a bustling Michigan locker room, a group of attendants is helping athletes get ready for their games. Meanwhile, across the ocean in France, a row of gas pumps is busy filling up cars with gasoline. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: **Image Generation Prompt:** Style: _a vintage Polaroid photo_ Scene: In the bustling Michigan locker room, a group of attendants is attending to athletes, helping them gear up for their games. The room is filled with a sense of anticipation and energy. Meanwhile, across the ocean in France, a row of vintage gas pumps stands against a backdrop of a quaint countryside, with cars lining up to fill up with gasoline. The scene exudes a timeless charm and captures the contrast between the two locations through the lens of nostalgia.

*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 a decrease in The number of locker room attendants in Michigan caused Gasoline pumped in France to decrease.

AI academic paper

(Because p < 0.01)

Fuel for Thought: Exploring the Unlikely Link Between Locker Room Attendants in Michigan and Gasoline Consumption in France

Journal of Unorthodox Connections

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 goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 The number of locker room attendants in Michigan and Gasoline pumped in France. 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 name a couple popular internet memes that are related to one of the topics.

Here is the title and abstract of the paper:
[[TITLE]]
Fuel for Thought: Exploring the Unlikely Link Between Locker Room Attendants in Michigan and Gasoline Consumption in France

[[ABSTRACT]]
Pump up the laughter and rev up the curiosity, as we delve into the unexpected and seemingly inexplicable connection between the number of locker room attendants in Michigan and the gasoline pumped in France. In this study, we analyzed data from the Bureau of Labor Statistics and the Energy Information Administration to uncover a correlation coefficient of 0.8426159 and p < 0.01 for the period spanning from 2003 to 2022. While many may raise their eyebrows or furrow their brows at the mere mention of this correlation, our findings suggest there may be more to these seemingly disparate elements than meets the eye. So, grab your sense of humor and fasten your seatbelts (pun intended), as we embark on a whimsical yet illuminating journey through the world of unconventional correlations.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Imagine the sheer bewilderment when one stumbles upon a statistical connection between the number of locker room attendants in Michigan and the amount of gasoline pumped in France. While it may sound as mismatched as wearing flip-flops to a formal dinner, our research has led us down a rabbit hole of absurdity and enlightenment. The correlation coefficient of 0.8426159 and a p-value less than 0.01, obtained from diverse data sources, have left us scratching our heads and shaking our funny bones simultaneously.
Certainly, the notion of locker room attendants and gasoline pumps striking up a clandestine romance seems akin to a sitcom plotline, but our rigorous analysis suggests otherwise. It's as if the universe decided to play a cosmic joke, intertwining entirely unrelated entities in a statistical pas de deux that challenges conventional wisdom and tickles our statistical fancies.
In this paper, we not only aim to unravel the statistical significance of this correlation but also provide a lighthearted expedition into the realm of unconventional correlations. We invite you to journey with us as we navigate this whimsical landscape, where the baffling meets the amusing and the data-driven meets the downright ludicrous. So, buckle up (pun most definitely intended), and let's dive into this comedic but intriguing tango between locker room attendants and gasoline consumption.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The improbable link between the number of locker room attendants in Michigan and the gasoline consumption in France may seem like a farce fit for the comedy stage, but as we comb through the annals of research, a few unexpected nuggets catch our eye. While the serious scholars Smith, Doe, and Jones have largely gazed in other directions, focusing on more conventional correlations, a sprinkling of light-hearted whimsy and oddball statistics permeates our investigation.

In "The Dynamics of Labor and Employment in Modern Society," Smith undertakes a comprehensive examination of labor trends, but regrettably overlooks the comical potential of correlating employment in locker rooms with cross-continental fuel consumption – an opportunity sorely missed, if you ask us. Doe's "The Impact of Energy Policies on Global Markets" offers a thorough analysis of energy consumption patterns but fails to entertain the offbeat notion that a locker room halfway across the world could be tipping the fuel scales in favor of the French.

Delving into non-fiction tomes with a tenuous yet potentially tangential tether to our subject matter, we encounter "Full Throttle: From the Pump to the Pistons," a gripping exposé on the petroleum industry. While it may not shed direct light on our curious correlation, the mention of pump-related matters teases the realm of gasoline, if only with a wink and a nod. Proceeding into the fictional wilds of literature, "The Locker Room Mysteries" and "The Gasoline Diaries" beckon us in with their enigmatic titles, promising a cornucopia of secrets waiting to be unraveled, even if those secrets amount to little more than sweaty socks and fuel-soaked memos.

In the digital sphere, the internet meme "Gasoline Greta" sashays its way into our discussions with sardonic charm, her image plastered next to snarky captions and indignant rants about petrol prices. While her relevance to our academic pursuits may be as thin as a low-octane fuel, we embrace the interplay of levity and ludicrousness that the meme exudes, if only to dilute the gravity of our scholarly endeavors.

As we wade through the currents of literature, our aim is not simply to unearth academic gems, but to inject a dose of mirth and whimsy into the serious business of scholarly inquiry. With these unexpected companions by our side, we press on in our quest to unravel the curious interplay between locker room attendants and gasoline consumption.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 The number of locker room attendants in Michigan and Gasoline pumped in France. Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Larbor Statistics and Energy Information Administration . You used data from 2003 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]]
Fuel for Thought: Exploring the Unlikely Link Between Locker Room Attendants in Michigan and Gasoline Consumption in France

[[ABSTRACT]]
Pump up the laughter and rev up the curiosity, as we delve into the unexpected and seemingly inexplicable connection between the number of locker room attendants in Michigan and the gasoline pumped in France. In this study, we analyzed data from the Bureau of Labor Statistics and the Energy Information Administration to uncover a correlation coefficient of 0.8426159 and p < 0.01 for the period spanning from 2003 to 2022. While many may raise their eyebrows or furrow their brows at the mere mention of this correlation, our findings suggest there may be more to these seemingly disparate elements than meets the eye. So, grab your sense of humor and fasten your seatbelts (pun intended), as we embark on a whimsical yet illuminating journey through the world of unconventional correlations.

[[INTRODUCTION]]
Imagine the sheer bewilderment when one stumbles upon a statistical connection between the number of locker room attendants in Michigan and the amount of gasoline pumped in France. While it may sound as mismatched as wearing flip-flops to a formal dinner, our research has led us down a rabbit hole of absurdity and enlightenment. The correlation coefficient of 0.8426159 and a p-value less than 0.01, obtained from diverse data sources, have left us scratching our heads and shaking our funny bones simultaneously.
Certainly, the notion of locker room attendants and gasoline pumps striking up a clandestine romance seems akin to a sitcom plotline, but our rigorous analysis suggests otherwise. It's as if the universe decided to play a cosmic joke, intertwining entirely unrelated entities in a statistical pas de deux that challenges conventional wisdom and tickles our statistical fancies.
In this paper, we not only aim to unravel the statistical significance of this correlation but also provide a lighthearted expedition into the realm of unconventional correlations. We invite you to journey with us as we navigate this whimsical landscape, where the baffling meets the amusing and the data-driven meets the downright ludicrous. So, buckle up (pun most definitely intended), and let's dive into this comedic but intriguing tango between locker room attendants and gasoline consumption.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To uncover the mysterious ties between the number of locker room attendants in Michigan and the gasoline consumption in France, our research employed a series of convoluted and circuitous methods to make sense of this seemingly nonsensical correlation. We pored over data from the Bureau of Labor Statistics and the Energy Information Administration, using a combination of statistical analyses, computational models, and a healthy dose of whimsy to extract meaning from this improbable connection.

Data Collection:
Our data collection process resembled a mad dash through an obstacle course, as we scoured the internet far and wide for any morsel of information related to locker room attendants and gasoline consumption. We gathered data from the years 2003 to 2022, sourcing statistics, reports, and any tidbits of relevance with the fervor of treasure hunters in pursuit of an elusive prize. Much like a dedicated food critic seeking the perfect pairing of wine and cheese, we sought the perfect pairing of statistical outliers and quirky correlations.

Statistical Analysis:
Upon amassing a trove of data that would make a hoarder blush, we subjected it to rigorous statistical scrutiny. We utilized correlation coefficients, regression analyses, and various statistical tests, akin to wielding a magician's wand to coax sense out of the inexplicable. Our statistical toolbox resembled a Swiss army knife, with each analysis serving as a quirky gadget in our quest to decipher the hidden relationship between locker room attendants and gasoline consumption. It's safe to say that our data danced to the tune of statistical significance in a manner akin to an unexpected flash mob performance in a bustling city square.

Computational Modeling:
In addition to traditional statistical methods, we delved into the realm of computational modeling to simulate and visualize the intricate interplay between locker room attendants and gasoline pumps. Our models invoked the spirit of creativity, as we crafted virtual landscapes that felt like the unlikely lovechild of a Jackson Pollock painting and a Rubik's Cube. These models helped us traverse the labyrinthine pathways of correlation, offering insight into the bizarre dynamics at play between these disparate variables. It was as if we had unleashed an army of digital Sherlock Holmeses to unravel this statistical mystery, complete with deerstalker hats and amusing accents.

Humor-Driven Analysis:
Recognizing the inherent peculiarity of our research, we infused our analytical process with a healthy dose of humor. Like spirited jesters in the royal court of statistics, we allowed humor to play a pivotal role in shaping our interpretations and conclusions. With puns, whimsical analogies, and the occasional knock-knock joke, we endeavored to breathe levity into an otherwise perplexing intersection of data. After all, what better way to navigate the absurdity of our findings than with a hearty laugh and a raised eyebrow?

In summary, our methodology embarked on a rollicking adventure through the hallowed halls of data analysis, blending traditional statistical techniques with computational wizardry and a liberal sprinkling of whimsy. It was a journey that would make even the most stoic of mathematicians crack a smile and ponder the delightful absurdity of unconventional correlations.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 The number of locker room attendants in Michigan and Gasoline pumped in France. Make lots of jokes, goofy observations, and puns.

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]]
Fuel for Thought: Exploring the Unlikely Link Between Locker Room Attendants in Michigan and Gasoline Consumption in France

[[ABSTRACT]]
Pump up the laughter and rev up the curiosity, as we delve into the unexpected and seemingly inexplicable connection between the number of locker room attendants in Michigan and the gasoline pumped in France. In this study, we analyzed data from the Bureau of Labor Statistics and the Energy Information Administration to uncover a correlation coefficient of 0.8426159 and p < 0.01 for the period spanning from 2003 to 2022. While many may raise their eyebrows or furrow their brows at the mere mention of this correlation, our findings suggest there may be more to these seemingly disparate elements than meets the eye. So, grab your sense of humor and fasten your seatbelts (pun intended), as we embark on a whimsical yet illuminating journey through the world of unconventional correlations.

[[LITERATURE REVIEW]]
The improbable link between the number of locker room attendants in Michigan and the gasoline consumption in France may seem like a farce fit for the comedy stage, but as we comb through the annals of research, a few unexpected nuggets catch our eye. While the serious scholars Smith, Doe, and Jones have largely gazed in other directions, focusing on more conventional correlations, a sprinkling of light-hearted whimsy and oddball statistics permeates our investigation.
In "The Dynamics of Labor and Employment in Modern Society," Smith undertakes a comprehensive examination of labor trends, but regrettably overlooks the comical potential of correlating employment in locker rooms with cross-continental fuel consumption – an opportunity sorely missed, if you ask us. Doe's "The Impact of Energy Policies on Global Markets" offers a thorough analysis of energy consumption patterns but fails to entertain the offbeat notion that a locker room halfway across the world could be tipping the fuel scales in favor of the French.
Delving into non-fiction tomes with a tenuous yet potentially tangential tether to our subject matter, we encounter "Full Throttle: From the Pump to the Pistons," a gripping exposé on the petroleum industry. While it may not shed direct light on our curious correlation, the mention of pump-related matters teases the realm of gasoline, if only with a wink and a nod. Proceeding into the fictional wilds of literature, "The Locker Room Mysteries" and "The Gasoline Diaries" beckon us in with their enigmatic titles, promising a cornucopia of secrets waiting to be unraveled, even if those secrets amount to little more than sweaty socks and fuel-soaked memos.
In the digital sphere, the internet meme "Gasoline Greta" sashays its way into our discussions with sardonic charm, her image plastered next to snarky captions and indignant rants about petrol prices. While her relevance to our academic pursuits may be as thin as a low-octane fuel, we embrace the interplay of levity and ludicrousness that the meme exudes, if only to dilute the gravity of our scholarly endeavors.
As we wade through the currents of literature, our aim is not simply to unearth academic gems, but to inject a dose of mirth and whimsy into the serious business of scholarly inquiry. With these unexpected companions by our side, we press on in our quest to unravel the curious interplay between locker room attendants and gasoline consumption.

[[RESULTS]]
The analysis of our data revealed a surprisingly robust correlation between the number of locker room attendants in Michigan and the amount of gasoline pumped in France, with a correlation coefficient of 0.8426159, a r-squared value of 0.7100015, and a p-value less than 0.01. It's as if the universe decided to play a cosmic joke, intertwining entirely unrelated entities in a statistical pas de deux that challenges conventional wisdom and tickles our statistical fancies.
In Figure 1, we present the scatterplot illustrating the connection between these two seemingly incongruent variables. The strong positive correlation is evident, with each data point seemingly whispering a secret joke about the curious link between locker room attendants and gasoline consumption. Perhaps the attendants were providing some kind of fuel-related encouragement – "You can do it, gas pump, I believe in you!" – or maybe the gasoline pumps were offering life advice to the attendants – "Pump up the volume of laughter, folks!" The possibilities are as intriguing as they are nonsensical.
The robustness of the correlation suggests a potential avenue for further investigation, though we must also acknowledge the possibility of spurious correlation. After all, just because two things are correlated does not mean that one causes the other. In this case, we cannot definitively claim that locker room attendants in Michigan directly influence the gasoline consumption in France, no matter how amusing that mental image may be.
Nonetheless, these findings pique our curiosity and provoke a symphony of giggles in the scientific community. If nothing else, this unexpected correlation demonstrates the peculiar and often whimsical nature of statistical analysis, reminding us to always approach data with a healthy dose of skepticism – and humor.
Overall, our results not only highlight the statistical significance of this correlation but also serve as a timely reminder of the delightfulness that can be found in the world of unconventional correlations. We invite readers to embrace the unexpected and join us in exploring the comically surreal landscape where the absurd meets the empirical, and the data-driven meets the whimsical.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The robust correlation we unearthed between the number of locker room attendants in Michigan and the amount of gasoline pumped in France echoes the unexpected whimsy we encountered in our literature review. Our findings not only align with the lighthearted spirit of our exploration but also highlight the intriguing interplay between seemingly unrelated variables. It's as if statistical analysis donned a clown nose and pointed us toward a carnival of correlations, daring us to ponder the peculiarities of our data with a mix of amusement and skepticism.

While the serious scholars of labor trends and energy consumption may have sidestepped this curious correlation, we chose to embrace the inexplicable dance between locker room attendants and fuel consumption with open arms, or perhaps open locker doors. Our results support the contention that statistical analysis, much like a stand-up comedy routine, often thrives on the unexpected and the absurd. Who knew that the jocular antics of Michigan's locker room attendants could stealthily influence the pumping habits of gasoline in France? It seems that statistical analysis has a sense of humor, or maybe it's just playing a cosmic prank on us.

In the grand tradition of academic research, we must recognize the possibility of a spurious correlation and resist the urge to attribute causation where causation may not dwell. Alas, as much as we'd love to envision Michigan locker room attendants whispering words of encouragement to French gasoline pumps, our findings merely hint at a correlation rather than a causal relationship. Nevertheless, the strength of this correlation serves as a testament to the capricious nature of statistics, reminding us that beneath the layers of data lies a treasure trove of whimsy waiting to be unraveled.

Our findings beckon us to embrace the unexpected and celebrate the comically surreal landscape where the whimsical meets the empirical. While we cannot conclusively declare that Michigan's locker room attendants hold the keys to France's gasoline consumption, our results invite us to revel in the delightful frivolity that can emerge from unconventional correlations. So, let's raise a toast to the absurdity of statistical analysis and savor the whimsical twists and turns it unveils along the way.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 The number of locker room attendants in Michigan and Gasoline pumped in France. Make lots of jokes, goofy observations, and puns.

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]]
Fuel for Thought: Exploring the Unlikely Link Between Locker Room Attendants in Michigan and Gasoline Consumption in France

[[ABSTRACT]]
Pump up the laughter and rev up the curiosity, as we delve into the unexpected and seemingly inexplicable connection between the number of locker room attendants in Michigan and the gasoline pumped in France. In this study, we analyzed data from the Bureau of Labor Statistics and the Energy Information Administration to uncover a correlation coefficient of 0.8426159 and p < 0.01 for the period spanning from 2003 to 2022. While many may raise their eyebrows or furrow their brows at the mere mention of this correlation, our findings suggest there may be more to these seemingly disparate elements than meets the eye. So, grab your sense of humor and fasten your seatbelts (pun intended), as we embark on a whimsical yet illuminating journey through the world of unconventional correlations.

[[INTRDUCTION]]
Imagine the sheer bewilderment when one stumbles upon a statistical connection between the number of locker room attendants in Michigan and the amount of gasoline pumped in France. While it may sound as mismatched as wearing flip-flops to a formal dinner, our research has led us down a rabbit hole of absurdity and enlightenment. The correlation coefficient of 0.8426159 and a p-value less than 0.01, obtained from diverse data sources, have left us scratching our heads and shaking our funny bones simultaneously.
Certainly, the notion of locker room attendants and gasoline pumps striking up a clandestine romance seems akin to a sitcom plotline, but our rigorous analysis suggests otherwise. It's as if the universe decided to play a cosmic joke, intertwining entirely unrelated entities in a statistical pas de deux that challenges conventional wisdom and tickles our statistical fancies.
In this paper, we not only aim to unravel the statistical significance of this correlation but also provide a lighthearted expedition into the realm of unconventional correlations. We invite you to journey with us as we navigate this whimsical landscape, where the baffling meets the amusing and the data-driven meets the downright ludicrous. So, buckle up (pun most definitely intended), and let's dive into this comedic but intriguing tango between locker room attendants and gasoline consumption.

[[RESULTS]]
The analysis of our data revealed a surprisingly robust correlation between the number of locker room attendants in Michigan and the amount of gasoline pumped in France, with a correlation coefficient of 0.8426159, a r-squared value of 0.7100015, and a p-value less than 0.01. It's as if the universe decided to play a cosmic joke, intertwining entirely unrelated entities in a statistical pas de deux that challenges conventional wisdom and tickles our statistical fancies.
In Figure 1, we present the scatterplot illustrating the connection between these two seemingly incongruent variables. The strong positive correlation is evident, with each data point seemingly whispering a secret joke about the curious link between locker room attendants and gasoline consumption. Perhaps the attendants were providing some kind of fuel-related encouragement – "You can do it, gas pump, I believe in you!" – or maybe the gasoline pumps were offering life advice to the attendants – "Pump up the volume of laughter, folks!" The possibilities are as intriguing as they are nonsensical.
The robustness of the correlation suggests a potential avenue for further investigation, though we must also acknowledge the possibility of spurious correlation. After all, just because two things are correlated does not mean that one causes the other. In this case, we cannot definitively claim that locker room attendants in Michigan directly influence the gasoline consumption in France, no matter how amusing that mental image may be.
Nonetheless, these findings pique our curiosity and provoke a symphony of giggles in the scientific community. If nothing else, this unexpected correlation demonstrates the peculiar and often whimsical nature of statistical analysis, reminding us to always approach data with a healthy dose of skepticism – and humor.
Overall, our results not only highlight the statistical significance of this correlation but also serve as a timely reminder of the delightfulness that can be found in the world of unconventional correlations. We invite readers to embrace the unexpected and join us in exploring the comically surreal landscape where the absurd meets the empirical, and the data-driven meets the whimsical.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has illuminated the surprising and, dare we say, rib-tickling correlation between the number of locker room attendants in Michigan and the amount of gasoline pumped in France. It's as if these two seemingly unrelated entities decided to shake hands at a vibrant statistical soiree and ended up performing an unexpected statistical tango. One can almost imagine the attendants cheering on the gas pumps, "You can do it, pump those liters like there's no tomorrow!", or the pumps offering quirky life advice, "Keep pumping up the laughs, folks!"
While the robust correlation coefficient of 0.8426159 and a p-value less than 0.01 hints at a potential link, we mustn't jump to hasty conclusions. As much as we adore the mental image of Michigan's locker room attendants orchestrating France's gasoline consumption, correlation does not imply causation – much to our statistical chagrin.
Nevertheless, our findings have certainly fueled a symphony of giggles in the scientific community and emphasize the delightful, albeit bizarre, nature of statistical analysis. However, given the sheer absurdity of this correlation, we assert that no further research is needed in this comically surreal arena of unconventional correlations. It's been a wild ride, but it's time to park this statistical circus tent and move on to more conventional correlations. After all, there are only so many improbable statistical flings one can investigate before the laughter turns into statistical bemusement.

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 Fuel for Thought: Exploring the Unlikely Link Between Locker Room Attendants in Michigan and Gasoline Consumption in France. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Unorthodox Connections

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 Fuel for Thought: Exploring the Unlikely Link Between Locker Room Attendants in Michigan and Gasoline Consumption in France. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Cross-Cultural Energy Studies

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 Fuel for Thought: Exploring the Unlikely Link Between Locker Room Attendants in Michigan and Gasoline Consumption in France with an abstract of Pump up the laughter and rev up the curiosity, as we delve into the unexpected and seemingly inexplicable connection between the number of locker room attendants in Michigan and the gasoline pumped in France. In this study, we analyzed data from the Bureau of Labor Statistics and the Energy Information Administration to uncover a correlation coefficient of 0.8426159 and p < 0.01 for the period spanning from 2003 to 2022. While many may raise their eyebrows or furrow their brows at the mere mention of this correlation, our findings suggest there may be more to these seemingly disparate elements than meets the eye. So, grab your sense of humor and fasten your seatbelts (pun intended), as we embark on a whimsical yet illuminating journey through the world of unconventional correlations.

ChatGPT:

locker room attendants, Michigan, gasoline consumption, France, correlation coefficient, Bureau of Labor Statistics, Energy Information Administration, unconventional correlations

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

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Data details

The number of locker room attendants in Michigan
Detailed data title: BLS estimate of locker room, coatroom, and dressing room attendants in Michigan
Source: Bureau of Larbor Statistics
See what else correlates with The number of locker room attendants in Michigan

Gasoline pumped in France
Detailed data title: Volume of gasoline pumped consumed in France in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Gasoline pumped in France

Correlation r = 0.8426159 (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 = 0.7100015 (Coefficient of determination)
This means 71% of the change in the one variable (i.e., Gasoline pumped in France) is predictable based on the change in the other (i.e., The number of locker room attendants in Michigan) over the 20 years from 2003 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.1E-6.^Show 0.0000031312012480681000000000
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 0.84 in 0.00031% of random cases. Said differently, if you correlated 319,366 random variables^Note You don't actually need 319 thousand 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 19 degrees of freedom, ^Note 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.

[ 0.64, 0.94 ] 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.

	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
The number of locker room attendants in Michigan (Laborers)	1120	1070	930	690	790	690	690	420	410	440	500	530	600	570	580	410	350	150	830	820
Gasoline pumped in France (Million Barrels/Day)	280.54	266.134	250.997	236.436	224.773	206.934	199.214	184.91	181.775	172.12	167.288	168.748	171.175	175.137	182.704	189.523	203.364	174.025	207.449	232.54

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.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

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([1120,1070,930,690,790,690,690,420,410,440,500,530,600,570,580,410,350,150,830,820,])
array_2 = np.array([280.54,266.134,250.997,236.436,224.773,206.934,199.214,184.91,181.775,172.12,167.288,168.748,171.175,175.137,182.704,189.523,203.364,174.025,207.449,232.54,])
array_1_name = "The number of locker room attendants in Michigan"
array_2_name = "Gasoline pumped in France"

# 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.

Full license details.
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 The number of locker room attendants in Michigan
Line chart for only Gasoline pumped in France
AI-generated correlation image
The spurious research paper: Fuel for Thought: Exploring the Unlikely Link Between Locker Room Attendants in Michigan and Gasoline Consumption in France

View another random correlation

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Correlation ID: 1484 · Black Variable ID: 9737 · Red Variable ID: 24425


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