Spurious correlation #2,683 · 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 bellhops in Minnesota and the second variable is Gasoline pumped in Gambia. The chart goes from 2003 to 2021, 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 bellhops in Minnesota and the second variable is Gasoline pumped in Gambia. The chart goes from 2003 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the bellhops in Minnesota perfected their art of swiftly moving luggage from place to place, they inadvertently increased the state's overall kinetic energy. This energy manifested in the form of very subtle but influential air currents that, over time, wafted all the way to Gambia. Once there, these air currents interacted with the gasoline molecules, giving them a little motivational boost, leading to increased pumping activity. It's like the butterfly effect, but with bellhops and gasoline!

^{Show image generation prompt} Model: dalle-3
Prompt: [ Cutsy art illustration ] Generate an image of a charming hotel lobby in Minnesota filled with a flurry of bellhops helping guests with their luggage. The scene should exude warmth and hospitality, with colorful decor and a sense of excitement. Outside the lobby, show a vibrant gas station in Gambia, where a line of cute, whimsical cars is waiting to pump gasoline. Capture the lively atmosphere and the increasing demand for fuel as more travelers hit the road, creating a sense of bustling activity. Ensure that the illustration has a cheerful and captivating aesthetic that conveys the connection between the bustling hotel and the increasing demand for fuel at the gas station. 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 bellhops in Minnesota and Gasoline pumped in Gambia. 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: Minimalism Scalar art Concept art Cutsy art illustration Whimsical illustration
ChatGPT: Cutsy art illustration

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 bellhops in Minnesota and Gasoline pumped in Gambia, in the following situation: Because as the number of bellhops in Minnesota increases, so does the demand for hotel stays which means more people traveling and running out of gas in Gambia, prompting them to pump more gasoline to keep the trips rolling. After all, when bellhops go up, gas goes down!. Keep the description simple.
ChatGPT: A bustling hotel lobby in Minnesota, with an increasing number of bellhops assisting guests with their luggage. Outside the lobby, a line of cars in Gambia waiting to pump gasoline at a busy gas station, indicating an increased demand for fuel as more travelers hit the road due to the rising number of hotel stays.

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: [ Cutsy art illustration ] and depicting this scene: A bustling hotel lobby in Minnesota, with an increasing number of bellhops assisting guests with their luggage. Outside the lobby, a line of cars in Gambia waiting to pump gasoline at a busy gas station, indicating an increased demand for fuel as more travelers hit the road due to the rising number of hotel stays. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ Cutsy art illustration ] Generate an image of a charming hotel lobby in Minnesota filled with a flurry of bellhops helping guests with their luggage. The scene should exude warmth and hospitality, with colorful decor and a sense of excitement. Outside the lobby, show a vibrant gas station in Gambia, where a line of cute, whimsical cars is waiting to pump gasoline. Capture the lively atmosphere and the increasing demand for fuel as more travelers hit the road, creating a sense of bustling activity. Ensure that the illustration has a cheerful and captivating aesthetic that conveys the connection between the bustling hotel and the increasing demand for fuel at the gas station.

*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 increases in The number of bellhops in Minnesota positively influenced Gasoline pumped in Gambia. Make it feel plausible.

AI academic paper

(Because p < 0.01)

Bellhops in Minnesota and the Bolstering of Gasoline in Gambia: A Bizarrely Bountiful Bond

The Journal of Quirky 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 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 The number of bellhops in Minnesota and Gasoline pumped in Gambia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading CVS receipts.

Here is the title and abstract of the paper:
[[TITLE]]
Bellhops in Minnesota and the Bolstering of Gasoline in Gambia: A Bizarrely Bountiful Bond

[[ABSTRACT]]
This paper delves into the peculiarly potent pairing of the number of bellhops in Minnesota and the amount of gasoline pumped in Gambia. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, a significant correlation coefficient of 0.7847305 (p < 0.01) was observed during the years 2003 to 2021. The findings of this study highlight an unforeseen link between seemingly disparate variables, leading us to ponder the whimsical wonders of statistical serendipity. While the causality behind this relationship remains enigmatic, the implications of this uncanny correlation may rouse both amusement and bemusement within the academic community.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The interplay between seemingly unrelated variables has long been a source of fascination and bemusement within the realm of statistical analysis. In this paper, we endeavor to explore the unexpected connection between the number of bellhops in the state of Minnesota and the volume of gasoline pumped in the Republic of Gambia. At first glance, one may raise an eyebrow at the seemingly bizarre juxtaposition of these two distinct phenomena. However, as we delve into the empirical evidence, a remarkable correlation emerges, prompting us to ponder the whimsical wonders of statistical serendipity.
The impetus for this study stems from the peculiar pairing of these variables, which appears to defy traditional logic and expectations. The Bureau of Labor Statistics (BLS) provided invaluable data on employment figures in Minnesota, while the Energy Information Administration (EIA) furnished comprehensive statistics on gasoline consumption in Gambia. Through rigorous analysis and statistical modeling, a significant correlation coefficient of 0.7847305 (p < 0.01) manifested itself, signifying a robust relationship that surpassed mere chance.
While the concept of cause and effect lies at the heart of scientific inquiry, the enigmatic causality behind this correlation eludes conventional explanation. Nonetheless, the findings of this study invite us to contemplate the implications of this uncanny correlation with a blend of amusement and intellectual intrigue. The unearthing of such an inexplicable link challenges our preconceived notions and inspires us to approach statistical analysis with a sense of whimsy and open-mindedness. As we delve into the labyrinth of data and numbers, we encounter an unexpected marriage of the mundane and the mysterious, evoking an undercurrent of humor and wonder within the academic community.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

Smith (2010) demonstrated the vast implications of seemingly unrelated variables in statistical analysis, laying the groundwork for the exploration of whimsical correlations. Doe (2015) expanded on this notion by emphasizing the potential for unexpected connections to defy conventional scientific understanding, fostering a spirit of intellectual curiosity.

Turning to the specific context of bellhops in Minnesota and gasoline pumped in Gambia, the work of Jones (2018) unveiled unforeseen patterns in diverse datasets, challenging traditional assumptions and beckoning researchers to embark on unconventional analytical journeys. This peculiar pairing of variables has incited a fervor of inquiry, prompting us to sift through an eclectic array of literature to unravel the enigmatic bond between the two.

The non-fiction realm unveils a trove of scholarly works that shed light on tangentially related themes. In "The Economics of Hospitality" by White (2012), the intricate dynamics of employment and service industries offer a lens through which to contemplate the role of bellhops in broader economic contexts. Meanwhile, "Fueling the World: A Global Perspective on Energy Consumption" by Black (2016) provides an insightful exploration of gasoline usage trends, setting the stage for the intersection of empirical data and inexplicable correlations.

On the fringes of relevancy, yet tantalizingly close, lie a collection of fiction works that evoke tantalizing images of improbable connections. "The Bellhop's Dilemma" by Green (2005) weaves a tale of intrigue within a hotel setting, tantalizingly skirting the edges of probability. In a different vein, "Gasoline Dreams" by Brown (2013) plunges the reader into a surreal landscape of speculative fiction, beckoning us to ponder the surreal and the unexplained.

Faced with the task of unraveling a perplexing correlation seemingly plucked from the realm of absurdist fiction, it would be remiss not to acknowledge the unorthodox sources that have contributed to this study's foundation. We confess to perusing an unconventional array of literary artifacts, from ancient tomes of statistical analysis to the esoteric wisdom gleaned from the back of CVS receipts. As we navigate the labyrinthine depths of research, it becomes apparent that the whims of statistical serendipity may take us on a journey not just through academia, but through the absurd and the delightfully confounding.

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 The number of bellhops in Minnesota and Gasoline pumped in Gambia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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 2021

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]]
Bellhops in Minnesota and the Bolstering of Gasoline in Gambia: A Bizarrely Bountiful Bond

[[ABSTRACT]]
This paper delves into the peculiarly potent pairing of the number of bellhops in Minnesota and the amount of gasoline pumped in Gambia. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, a significant correlation coefficient of 0.7847305 (p < 0.01) was observed during the years 2003 to 2021. The findings of this study highlight an unforeseen link between seemingly disparate variables, leading us to ponder the whimsical wonders of statistical serendipity. While the causality behind this relationship remains enigmatic, the implications of this uncanny correlation may rouse both amusement and bemusement within the academic community.

[[INTRODUCTION]]
The interplay between seemingly unrelated variables has long been a source of fascination and bemusement within the realm of statistical analysis. In this paper, we endeavor to explore the unexpected connection between the number of bellhops in the state of Minnesota and the volume of gasoline pumped in the Republic of Gambia. At first glance, one may raise an eyebrow at the seemingly bizarre juxtaposition of these two distinct phenomena. However, as we delve into the empirical evidence, a remarkable correlation emerges, prompting us to ponder the whimsical wonders of statistical serendipity.
The impetus for this study stems from the peculiar pairing of these variables, which appears to defy traditional logic and expectations. The Bureau of Labor Statistics (BLS) provided invaluable data on employment figures in Minnesota, while the Energy Information Administration (EIA) furnished comprehensive statistics on gasoline consumption in Gambia. Through rigorous analysis and statistical modeling, a significant correlation coefficient of 0.7847305 (p < 0.01) manifested itself, signifying a robust relationship that surpassed mere chance.
While the concept of cause and effect lies at the heart of scientific inquiry, the enigmatic causality behind this correlation eludes conventional explanation. Nonetheless, the findings of this study invite us to contemplate the implications of this uncanny correlation with a blend of amusement and intellectual intrigue. The unearthing of such an inexplicable link challenges our preconceived notions and inspires us to approach statistical analysis with a sense of whimsy and open-mindedness. As we delve into the labyrinth of data and numbers, we encounter an unexpected marriage of the mundane and the mysterious, evoking an undercurrent of humor and wonder within the academic community.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The present study utilized data sourced from the Bureau of Labor Statistics (BLS) to obtain information on the number of bellhops employed in the state of Minnesota. Concurrently, the Energy Information Administration (EIA) served as the primary data source, furnishing comprehensive statistics on the volume of gasoline consumption in the Republic of Gambia. The latter involved sifting through an exhaustive array of reports, charts, and databases to extract the relevant variables pertaining to gasoline consumption.

Data Cleaning and Preparation:
The initial phase of data processing involved meticulous cleansing and reconciliation of disparate datasets from both BLS and EIA. This entailed disentangling any ambiguities or discrepancies in the data, akin to navigating the tangled webs of a whimsical labyrinth. Notably, the eccentric nature of the variables under investigation imbued the data cleaning process with an unforeseen charm, prompting the research team to approach it with a blend of fastidiousness and joviality.

Statistical Analysis:
To establish the relationship between the number of bellhops in Minnesota and the volume of gasoline pumped in Gambia, the research team employed a range of statistical techniques. These included Pearson correlation coefficient, regression analysis, and time series modeling, with a subtle nod to the poetic interplay of variables akin to a balletic pas de deux. Furthermore, the researchers ventured into the realm of exploratory data analysis, unraveling the mysteries of the dataset with a combination of levity and scholarly rigor. Notably, the statistical models adopted for this study were tailored to accommodate the idiosyncratic nature of the variables under consideration, elevating the analysis beyond the realm of conventional statistical investigations.

Model Validation:
Following the application of statistical models, the research team embarked on a diligent process of model validation and robustness checks. This involved subjecting the models to a battery of diagnostic tests and sensitivity analyses, akin to scrutinizing the whimsical interplay of variables under a magnifying glass. Notably, the validation process was infused with a tinge of playfulness, mirroring the unconventional nature of the research question at hand.

Sensitivity Testing:
In acknowledgment of the unorthodox nature of the relationship under investigation, the research team subjected the statistical models to sensitivity testing of various forms. This process entailed perturbing the data with simulated scenarios reminiscent of a playful experiment, aimed at gauging the resilience of the observed correlation to potential anomalies and outliers. This lighthearted approach to sensitivity testing sought to capture the capricious essence of the relationship between bellhops in Minnesota and gasoline consumption in Gambia, unveiling a tapestry of statistical robustness with a hint of scholarly whimsy.

Ethical Considerations:
It is imperative to note that the data utilized in this study was sourced from publicly available repositories, safeguarding against any ethical quandaries and upholding the principles of academic integrity and transparency. Additionally, the research team conducted the study with a spirit of scholarly probity, recognizing the ethical responsibilities incumbent upon those unraveling the enigmatic web of statistical relationships.

In summation, the methodology employed in this study reflects a blend of academic rigor and lighthearted curiosity, as befits the exploration of such a delightfully enigmatic correlation.

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 The number of bellhops in Minnesota and Gasoline pumped in Gambia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
Bellhops in Minnesota and the Bolstering of Gasoline in Gambia: A Bizarrely Bountiful Bond

[[ABSTRACT]]
This paper delves into the peculiarly potent pairing of the number of bellhops in Minnesota and the amount of gasoline pumped in Gambia. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, a significant correlation coefficient of 0.7847305 (p < 0.01) was observed during the years 2003 to 2021. The findings of this study highlight an unforeseen link between seemingly disparate variables, leading us to ponder the whimsical wonders of statistical serendipity. While the causality behind this relationship remains enigmatic, the implications of this uncanny correlation may rouse both amusement and bemusement within the academic community.

[[LITERATURE REVIEW]]
Smith (2010) demonstrated the vast implications of seemingly unrelated variables in statistical analysis, laying the groundwork for the exploration of whimsical correlations. Doe (2015) expanded on this notion by emphasizing the potential for unexpected connections to defy conventional scientific understanding, fostering a spirit of intellectual curiosity.
Turning to the specific context of bellhops in Minnesota and gasoline pumped in Gambia, the work of Jones (2018) unveiled unforeseen patterns in diverse datasets, challenging traditional assumptions and beckoning researchers to embark on unconventional analytical journeys. This peculiar pairing of variables has incited a fervor of inquiry, prompting us to sift through an eclectic array of literature to unravel the enigmatic bond between the two.
The non-fiction realm unveils a trove of scholarly works that shed light on tangentially related themes. In "The Economics of Hospitality" by White (2012), the intricate dynamics of employment and service industries offer a lens through which to contemplate the role of bellhops in broader economic contexts. Meanwhile, "Fueling the World: A Global Perspective on Energy Consumption" by Black (2016) provides an insightful exploration of gasoline usage trends, setting the stage for the intersection of empirical data and inexplicable correlations.
On the fringes of relevancy, yet tantalizingly close, lie a collection of fiction works that evoke tantalizing images of improbable connections. "The Bellhop's Dilemma" by Green (2005) weaves a tale of intrigue within a hotel setting, tantalizingly skirting the edges of probability. In a different vein, "Gasoline Dreams" by Brown (2013) plunges the reader into a surreal landscape of speculative fiction, beckoning us to ponder the surreal and the unexplained.
Faced with the task of unraveling a perplexing correlation seemingly plucked from the realm of absurdist fiction, it would be remiss not to acknowledge the unorthodox sources that have contributed to this study's foundation. We confess to perusing an unconventional array of literary artifacts, from ancient tomes of statistical analysis to the esoteric wisdom gleaned from the back of CVS receipts. As we navigate the labyrinthine depths of research, it becomes apparent that the whims of statistical serendipity may take us on a journey not just through academia, but through the absurd and the delightfully confounding.

[[RESULTS]]
The analysis of the data revealed a notable correlation coefficient of 0.7847305 (r-squared = 0.6158020, p < 0.01) between the number of bellhops in Minnesota and the amount of gasoline pumped in Gambia from 2003 to 2021. This finding, while unexpected, raises eyebrows and elicits a bemused chuckle from even the most stoic of statisticians.
Fig. 1 depicts the scatterplot demonstrating the unexpectedly strong relationship between these seemingly incongruous variables. Upon gazing upon this graph, one cannot help but marvel at the bizarrely bountiful bond that has emerged from the depths of our data analysis.
The robustness of the correlation prompts us to ponder the whimsical wonders of statistical serendipity. While the "aha" moment of understanding the causality behind this peculiar connection remains elusive, the implications of this unexpected correlation are undoubtedly thought-provoking and, dare I say, entertaining.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of this study have shed light on a perplexing and unprecedented correlation between the number of bellhops in Minnesota and the amount of gasoline pumped in Gambia. Our findings not only confirm the unexpected connection alluded to in the literature review but also provide empirical support for the whimsical wonders of statistical serendipity.

Returning to the literature review, we recall the work of Jones (2018), who dared to unveil unforeseen patterns in diverse datasets. Our results align with Jones's intrepid spirit of exploration, as we have unraveled a remarkable relationship that defies conventional scientific understanding. Likewise, Doe (2015) emphasized the potential for unexpected connections to challenge traditional assumptions, and our study exemplifies this notion in its examination of the bizarrely bountiful bond between bellhops and gasoline.

Furthermore, our analysis adds a distinctly "playful" spin to the discourse on statistical correlations, embracing the intellectual curiosity fostered by previous scholars. The surprising link between bellhops and gasoline serves as a testament to the eclectic and enigmatic nature of statistical analysis, prompting a jovial reconsideration of seemingly unrelated variables.

The scatterplot, as depicted in Fig. 1, serves as the tangible evidence of this unlikely alliance, captivating the viewer with its whimsical juxtaposition of bellhops and gasoline consumption. It surely elicits a wry smile from even the most stoic of statisticians, as they grapple with the apparent absurdity yet undeniable robustness of this correlation.

In conclusion, our study has not only confirmed the existence of a hitherto unexplored relationship between bellhops in Minnesota and gasoline in Gambia but has also reaffirmed the importance of embracing the unexpected in statistical analysis. As we continue to navigate the labyrinthine depths of research, may we remember that the whims of statistical serendipity may take us not just through academia, but through the absurd and the delightfully confounding.

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 The number of bellhops in Minnesota and Gasoline pumped in Gambia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
Bellhops in Minnesota and the Bolstering of Gasoline in Gambia: A Bizarrely Bountiful Bond

[[ABSTRACT]]
This paper delves into the peculiarly potent pairing of the number of bellhops in Minnesota and the amount of gasoline pumped in Gambia. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, a significant correlation coefficient of 0.7847305 (p < 0.01) was observed during the years 2003 to 2021. The findings of this study highlight an unforeseen link between seemingly disparate variables, leading us to ponder the whimsical wonders of statistical serendipity. While the causality behind this relationship remains enigmatic, the implications of this uncanny correlation may rouse both amusement and bemusement within the academic community.

[[INTRDUCTION]]
The interplay between seemingly unrelated variables has long been a source of fascination and bemusement within the realm of statistical analysis. In this paper, we endeavor to explore the unexpected connection between the number of bellhops in the state of Minnesota and the volume of gasoline pumped in the Republic of Gambia. At first glance, one may raise an eyebrow at the seemingly bizarre juxtaposition of these two distinct phenomena. However, as we delve into the empirical evidence, a remarkable correlation emerges, prompting us to ponder the whimsical wonders of statistical serendipity.
The impetus for this study stems from the peculiar pairing of these variables, which appears to defy traditional logic and expectations. The Bureau of Labor Statistics (BLS) provided invaluable data on employment figures in Minnesota, while the Energy Information Administration (EIA) furnished comprehensive statistics on gasoline consumption in Gambia. Through rigorous analysis and statistical modeling, a significant correlation coefficient of 0.7847305 (p < 0.01) manifested itself, signifying a robust relationship that surpassed mere chance.
While the concept of cause and effect lies at the heart of scientific inquiry, the enigmatic causality behind this correlation eludes conventional explanation. Nonetheless, the findings of this study invite us to contemplate the implications of this uncanny correlation with a blend of amusement and intellectual intrigue. The unearthing of such an inexplicable link challenges our preconceived notions and inspires us to approach statistical analysis with a sense of whimsy and open-mindedness. As we delve into the labyrinth of data and numbers, we encounter an unexpected marriage of the mundane and the mysterious, evoking an undercurrent of humor and wonder within the academic community.

[[RESULTS]]
The analysis of the data revealed a notable correlation coefficient of 0.7847305 (r-squared = 0.6158020, p < 0.01) between the number of bellhops in Minnesota and the amount of gasoline pumped in Gambia from 2003 to 2021. This finding, while unexpected, raises eyebrows and elicits a bemused chuckle from even the most stoic of statisticians.
Fig. 1 depicts the scatterplot demonstrating the unexpectedly strong relationship between these seemingly incongruous variables. Upon gazing upon this graph, one cannot help but marvel at the bizarrely bountiful bond that has emerged from the depths of our data analysis.
The robustness of the correlation prompts us to ponder the whimsical wonders of statistical serendipity. While the "aha" moment of understanding the causality behind this peculiar connection remains elusive, the implications of this unexpected correlation are undoubtedly thought-provoking and, dare I say, entertaining.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation into the peculiarly potent pairing of the number of bellhops in Minnesota and the amount of gasoline pumped in Gambia has led to the unearthing of an unforeseen correlation. The robust correlation coefficient of 0.7847305 (p < 0.01) that emerged from our analysis has left even the most seasoned researchers scratching their heads and chuckling in disbelief.

The unexpectedly strong relationship depicted in the scatterplot, as shown in Fig. 1, serves as a visual testament to the whimsical wonders of statistical serendipity. It appears that the mundane task of bellhopping in the Land of 10,000 Lakes may hold a quixotic power over the gasoline consumption habits in the smiling coast of Gambia.

While the causality behind this relationship remains enigmatic, the implications of this uncanny correlation may rouse both amusement and bemusement within the academic community. It is undoubtedly a testament to the capricious nature of statistical analysis, and it prompts one to approach data with an open-mindedness that anticipates the unexpected.

The findings of this study leave us pondering the whimsical wonders of statistical serendipity, for it seems that even in the realm of dry data and numbers, there is room for whimsy and wonder. It is a reminder that in the world of statistics, truth is often stranger than fiction.

In light of these findings, it seems evident that no further research on this topic is necessary. As the saying goes, "Why bellhop around the bush when the correlation is crystal clear?"

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 Bellhops in Minnesota and the Bolstering of Gasoline in Gambia: A Bizarrely Bountiful Bond. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Quirky 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 Bellhops in Minnesota and the Bolstering of Gasoline in Gambia: A Bizarrely Bountiful Bond. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Quirky Connections in Global Economics and Transportation

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 Bellhops in Minnesota and the Bolstering of Gasoline in Gambia: A Bizarrely Bountiful Bond with an abstract of This paper delves into the peculiarly potent pairing of the number of bellhops in Minnesota and the amount of gasoline pumped in Gambia. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, a significant correlation coefficient of 0.7847305 (p < 0.01) was observed during the years 2003 to 2021. The findings of this study highlight an unforeseen link between seemingly disparate variables, leading us to ponder the whimsical wonders of statistical serendipity. While the causality behind this relationship remains enigmatic, the implications of this uncanny correlation may rouse both amusement and bemusement within the academic community.

ChatGPT:

bellhops, Minnesota, gasoline, Gambia, correlation coefficient, Bureau of Labor Statistics, Energy Information Administration, causality, academic community, statistical serendipity

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

Random correlation

Discover a new correlation

View all correlations

View all research papers

Report an error

Data details

The number of bellhops in Minnesota
Detailed data title: BLS estimate of baggage porters and bellhops in Minnesota
Source: Bureau of Larbor Statistics
See what else correlates with The number of bellhops in Minnesota

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

Correlation r = 0.7847305 (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 = 0.6158020 (Coefficient of determination)
This means 61.6% of the change in the one variable (i.e., Gasoline pumped in Gambia) is predictable based on the change in the other (i.e., The number of bellhops in Minnesota) over the 19 years from 2003 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 6.9E-5.^Show 0.0000693410938792109100000000
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 0.78 in 0.0069% of random cases. Said differently, if you correlated 14,421 random variables^Note Which I absolutely did.
with the same 18 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 18 because we have two variables measured over a period of 19 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.

[ 0.51, 0.91 ] 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
The number of bellhops in Minnesota (Laborers)	410	330	320	310	1380	1370	1310	280	320	550	440	350	320	330	470	430	660	460	270
Gasoline pumped in Gambia (Million Barrels/Day)	0.841315	0.862322	0.864685	0.888055	1.14512	1.1653	1.56578	0.474408	0.521148	0.494087	0.518811	0.574899	0.577235	0.58265	0.593594	0.584247	0.607616	0.580163	0.61217

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([410,330,320,310,1380,1370,1310,280,320,550,440,350,320,330,470,430,660,460,270,])
array_2 = np.array([0.841315,0.862322,0.864685,0.888055,1.14512,1.1653,1.56578,0.474408,0.521148,0.494087,0.518811,0.574899,0.577235,0.58265,0.593594,0.584247,0.607616,0.580163,0.61217,])
array_1_name = "The number of bellhops in Minnesota"
array_2_name = "Gasoline pumped in Gambia"

# 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 bellhops in Minnesota
Line chart for only Gasoline pumped in Gambia
AI-generated correlation image
The spurious research paper: Bellhops in Minnesota and the Bolstering of Gasoline in Gambia: A Bizarrely Bountiful Bond

View another random correlation

How fun was this correlation?

Kudos for rating!

Correlation ID: 2683 · Black Variable ID: 10011 · Red Variable ID: 24440


Problem variable:
Issue:
Additional details: Optional
Confirm you are a human: