Spurious correlation #3,445 · 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 boilermakers in California and the second variable is Kerosene used in Sudan. 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 boilermakers in California and the second variable is Kerosene used in Sudan. 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 number of boilermakers in California increased, so did their collective love for crafting oversized kerosene lamps in the shape of popular Hollywood landmarks. These intricate lamps were then exported to Sudan, where they became a must-have item for local social gatherings and camel rodeos, leading to a surge in kerosene consumption. It was a shining example of how Californian expertise and Sudanese style combined to light up the desert nights in a truly extravagant fashion.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image showing a TIME magazine cover with a vibrant California landscape featuring hot air balloons filling the sky. In the foreground, include the silhouette of people in Sudan laboring to fuel the balloons with kerosene, creating a warm, glowing atmosphere against the night sky. The image should capture the energy of California's ballooning enthusiasts and the industriousness of the workers in Sudan, conveying an atmosphere of excitement and diligent activity. 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 boilermakers in California and Kerosene used in Sudan. 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: Whimsical illustration Voxel art Oragami An image that looks like it is a scene from A Disney Movie A TIME magazine cover image
ChatGPT: A TIME magazine cover image

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 boilermakers in California and Kerosene used in Sudan, in the following situation: The increase in boilermakers in California has caused a surge in steamy enthusiasm, leading to a higher demand for kerosene in Sudan to ignite the hot air balloons used for international boilermaker conventions. The Sudanese have been burning the midnight oil to keep up with this unexpected demand!. Keep the description simple.
ChatGPT: In California, the number of enthusiastic boilermakers has skyrocketed, filling the sky with colorful hot air balloons. In Sudan, people are seen working tirelessly, filling the balloons with kerosene to keep up with the unexpected demand, making the night sky glow with their efforts.

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 TIME magazine cover image ] and depicting this scene: In California, the number of enthusiastic boilermakers has skyrocketed, filling the sky with colorful hot air balloons. In Sudan, people are seen working tirelessly, filling the balloons with kerosene to keep up with the unexpected demand, making the night sky glow with their efforts. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image showing a TIME magazine cover with a vibrant California landscape featuring hot air balloons filling the sky. In the foreground, include the silhouette of people in Sudan laboring to fuel the balloons with kerosene, creating a warm, glowing atmosphere against the night sky. The image should capture the energy of California's ballooning enthusiasts and the industriousness of the workers in Sudan, conveying an atmosphere of excitement and diligent activity.

*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 boilermakers in California positively influenced Kerosene used in Sudan. Make it feel plausible.

AI academic paper

(Because p < 0.01)

Boiling Point: Exploring the Surprising Link Between Boilermakers in California and Kerosene Consumption in Sudan

Journal of Global Energy Dynamics

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 boilermakers in California and Kerosene used in Sudan. 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 cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
Boiling Point: Exploring the Surprising Link Between Boilermakers in California and Kerosene Consumption in Sudan

[[ABSTRACT]]
This paper presents a comprehensive analysis of the unexpected correlation between the number of boilermakers in California and the kerosene consumption in Sudan. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration spanning from 2003 to 2021, our research team identified a notable correlation coefficient of 0.7823494 and a statistically significant p-value of less than 0.01. The findings of this study defy conventional expectations and prompt further inquiry into the underlying mechanisms driving this seemingly disparate connection. As we delve into the intricate world of occupational trends and energy consumption, we unravel surprising insights that shed light on the uncharted territory of global socio-economic interconnectivity. This investigation not only highlights the need for interdisciplinary exploration but also underscores the potential for serendipitous findings in seemingly unrelated domains.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The pursuit of knowledge often leads researchers down unexpected paths, unveiling connections that defy conventional wisdom and challenge established paradigms. In the realm of socio-economic analysis, uncovering correlations between seemingly disparate variables can offer valuable insights into the complex web of global interdependencies. Our study ventures into this intriguing terrain, exploring the perplexing relationship between the number of boilermakers in California and kerosene consumption in Sudan.
At first glance, one might be forgiven for assuming that these two variables exist in entirely separate realms, with little to no discernible link between them. After all, the image of industrious boilermakers plying their trade in the Golden State seems worlds apart from the quiet glow of kerosene lamps in the homes of Sudanese families. However, as we have discovered through meticulous data analysis, reality often defies expectations, leaving us to ponder the whims of statistical fate with a mixture of awe and bewilderment.
Our research team embarked on this investigation with an open mind, cognizant of the potential for unexpected discoveries lurking beneath the surface of mundane statistics. The initial revelation of a significant correlation coefficient of 0.7823494 and a p-value that gleefully defied the customary threshold of statistical significance sent ripples of intrigue through our scholarly ranks. As we pored over the data sourced from the Bureau of Labor Statistics and the Energy Information Administration, the improbable link between boilermakers and kerosene consumption emerged as an enigma begging to be unraveled.
While the layperson may be inclined to dismiss this correlation as an anomalous blip in the data, we, as diligent researchers, understand the gravity of such findings. The implications stretch far beyond the narrow confines of industrial occupations and energy sources, beckoning us to delve deeper into the veiled intricacies of global socio-economic dynamics. As we embark on this scholarly expedition, we invite our esteemed colleagues to join us in unraveling the curious tapestry of occupational trends and energy utilization, with a dash of intellectual curiosity and perhaps a sprinkle of whimsy. For in the esoteric world of scholarly inquiry, serendipity often dances hand-in-hand with methodical investigation, yielding unforeseen revelations that challenge our preconceived notions and kindle the flame of intellectual curiosity.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The exploration of the unexpected correlation between the number of boilermakers in California and kerosene consumption in Sudan has prompted a wide-ranging survey of literature encompassing diverse fields, from industrial demographics to energy economics. While conventional wisdom may cast doubt on the plausibility of such a seemingly incongruous connection, the authors find a surprising array of tangentially related research and scholarly commentary that indirectly touches upon the interplay between these two ostensibly unrelated variables.

Smith, in "Industrial Demographics and Occupational Trends," captures the essence of occupational dynamics with insightful analyses of labor trends in various regions. Although Smith's work does not overtly investigate the specific role of boilermakers, the broader context of industrial employment provides a foundational backdrop for understanding the occupational landscape, much like the striking backdrop of a kerosene lamp illuminating the quiet confines of a Sudanese household.

Doe's comprehensive review, "Energy Sources and Utilization Patterns in Developing Nations," sheds light on the complex interweaving of sociopolitical factors and energy consumption. While Doe's focus lies primarily on broader energy sources, the underlying principles resonate with the nuanced dynamics of kerosene utilization in Sudan, much like the gentle glow of kerosene juxtaposed against the myriad energy sources powering the modern world.

In a departure from conventional academia, Jones's treatise, "Boilermakers and Kerosene: A Tale of Serendipitous Synchronicity," delves into the curious nexus of seemingly disparate elements, albeit in the form of an enigmatic allegorical narrative rather than scholarly discourse. While Jones's work may not conform to traditional academic standards, its whimsical musings serve as a thought-provoking divergence in the exploration of unexpected connections, much like a lighthearted anecdote injected into a ponderous discussion.

Transitioning to more unconventional sources, the seminal work of Rowling, "The Tales of Beedle the Boiler-Maker," introduces a fantastical world of enchanting allegories that, despite their fictional nature, embody threads of universal human experience. The parallel between the meticulous craft of boilermaking and the magical allure of kerosene lamps serves as a testament to the pervasive nature of artisanal craftsmanship across disparate cultures, much like the universal appeal of whimsical storytelling that transcends the boundaries of reality.

In an unexpected twist, a serendipitous encounter with social media commentary sheds unanticipated light on the matter at hand. An anonymous post on a popular platform humorously juxtaposed images of industrious boilermakers with the ambiance of a dimly lit Sudanese village, accompanied by the caption "When California meets Sudan: The Unlikely Romance of Boilers and Kerosene." While the post may have been intended as a lighthearted jest, it inadvertently captures the essence of the unexpected correlation under investigation, much like a candid snapshot capturing a profound truth amidst lighthearted banter.

In summary, the diverse array of literature and peripheral sources indirectly converges upon the perplexing relationship between the number of boilermakers in California and kerosene consumption in Sudan, adding layers of complexity to an already enigmatic correlation. As the scholarly journey delves deeper into this uncharted territory of occupational and energy interconnectivity, the authors invite fellow explorers to embrace the inherent whimsy of such unexpected discoveries, navigating the maze of unconventional scholarly contributions with a blend of intellectual inquiry and lighthearted reflection.

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 boilermakers in California and Kerosene used in Sudan. 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]]
Boiling Point: Exploring the Surprising Link Between Boilermakers in California and Kerosene Consumption in Sudan

[[ABSTRACT]]
This paper presents a comprehensive analysis of the unexpected correlation between the number of boilermakers in California and the kerosene consumption in Sudan. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration spanning from 2003 to 2021, our research team identified a notable correlation coefficient of 0.7823494 and a statistically significant p-value of less than 0.01. The findings of this study defy conventional expectations and prompt further inquiry into the underlying mechanisms driving this seemingly disparate connection. As we delve into the intricate world of occupational trends and energy consumption, we unravel surprising insights that shed light on the uncharted territory of global socio-economic interconnectivity. This investigation not only highlights the need for interdisciplinary exploration but also underscores the potential for serendipitous findings in seemingly unrelated domains.

[[INTRODUCTION]]
The pursuit of knowledge often leads researchers down unexpected paths, unveiling connections that defy conventional wisdom and challenge established paradigms. In the realm of socio-economic analysis, uncovering correlations between seemingly disparate variables can offer valuable insights into the complex web of global interdependencies. Our study ventures into this intriguing terrain, exploring the perplexing relationship between the number of boilermakers in California and kerosene consumption in Sudan.
At first glance, one might be forgiven for assuming that these two variables exist in entirely separate realms, with little to no discernible link between them. After all, the image of industrious boilermakers plying their trade in the Golden State seems worlds apart from the quiet glow of kerosene lamps in the homes of Sudanese families. However, as we have discovered through meticulous data analysis, reality often defies expectations, leaving us to ponder the whims of statistical fate with a mixture of awe and bewilderment.
Our research team embarked on this investigation with an open mind, cognizant of the potential for unexpected discoveries lurking beneath the surface of mundane statistics. The initial revelation of a significant correlation coefficient of 0.7823494 and a p-value that gleefully defied the customary threshold of statistical significance sent ripples of intrigue through our scholarly ranks. As we pored over the data sourced from the Bureau of Labor Statistics and the Energy Information Administration, the improbable link between boilermakers and kerosene consumption emerged as an enigma begging to be unraveled.
While the layperson may be inclined to dismiss this correlation as an anomalous blip in the data, we, as diligent researchers, understand the gravity of such findings. The implications stretch far beyond the narrow confines of industrial occupations and energy sources, beckoning us to delve deeper into the veiled intricacies of global socio-economic dynamics. As we embark on this scholarly expedition, we invite our esteemed colleagues to join us in unraveling the curious tapestry of occupational trends and energy utilization, with a dash of intellectual curiosity and perhaps a sprinkle of whimsy. For in the esoteric world of scholarly inquiry, serendipity often dances hand-in-hand with methodical investigation, yielding unforeseen revelations that challenge our preconceived notions and kindle the flame of intellectual curiosity.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To unravel the mysterious connection between the number of boilermakers in California and kerosene consumption in Sudan, our research team employed a multi-faceted approach that marries the rigor of statistical analysis with the unyielding tenacity of scholarly curiosity. The data utilized in this study were primarily sourced from the Bureau of Labor Statistics and the Energy Information Administration, spanning the years from 2003 to 2021.

Firstly, we embarked on a quest for data that would allow us to encapsulate the essence of boilermaker prominence in California. This involved navigating through the labyrinthine archives of occupational statistics, meticulously isolating the numerical representation of individuals engaged in the noble craft of boilermaking within the sun-kissed confines of the Golden State. The Bureau of Labor Statistics served as our guiding beacon in this quest, providing a trove of information that allowed us to quantify the ebb and flow of this venerable occupation over the years.

Simultaneously, our foray into the realm of kerosene-related statistics took us on a journey to the Energy Information Administration's reservoir of knowledge. Here, amid the numerical tapestries of energy consumption, we sought to capture the essence of kerosene utilization in Sudan, a pursuit that demanded an incisive understanding of energy dynamics within this enigmatic African nation. Through a delicate dance of data extraction and refinement, we were able to distill the essence of kerosene consumption into quantifiable metrics that would form the bedrock of our analysis.

With these distinct yet tantalizingly enigmatic datasets in hand, we orchestrated a symphony of statistical analysis that would set the stage for our exploration. Employing the formidable power of correlation analysis, we sought to discern the elusive threads connecting the number of boilermakers in California to the consumption of kerosene in Sudan. The calculated correlation coefficient of 0.7823494 emerged as a beacon of statistical significance, guiding our path through the murky waters of socio-economic interconnectedness.

In addition, we wielded the potent sword of the p-value, unleashing its formidable might to ascertain the robustness of our findings. The p-value, with its immutable aura of statistical significance, stood as a sentinel at the gates of conventional wisdom, compelling us to question established notions and embrace the unorthodox revelations unraveled by our data.

Furthermore, to fortify our analysis and ensure the resilience of our findings, we engaged in a meticulous process of sensitivity analysis, probing the robustness of our results across a spectrum of analytical variables. This painstaking endeavor served as a bulwark against spurious associations, ensuring that our newfound correlation stood steadfast in the face of rigorous scrutiny.

Thus, armed with the mighty arsenal of statistical tools and fortified by the resolute spirit of scholarly inquiry, we ventured forth into the uncharted terrain of socio-economic connections, prepared to confront the unexpected and embrace the whims of statistical fate in our pursuit of knowledge.

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 boilermakers in California and Kerosene used in Sudan. 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]]
Boiling Point: Exploring the Surprising Link Between Boilermakers in California and Kerosene Consumption in Sudan

[[ABSTRACT]]
This paper presents a comprehensive analysis of the unexpected correlation between the number of boilermakers in California and the kerosene consumption in Sudan. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration spanning from 2003 to 2021, our research team identified a notable correlation coefficient of 0.7823494 and a statistically significant p-value of less than 0.01. The findings of this study defy conventional expectations and prompt further inquiry into the underlying mechanisms driving this seemingly disparate connection. As we delve into the intricate world of occupational trends and energy consumption, we unravel surprising insights that shed light on the uncharted territory of global socio-economic interconnectivity. This investigation not only highlights the need for interdisciplinary exploration but also underscores the potential for serendipitous findings in seemingly unrelated domains.

[[LITERATURE REVIEW]]
The exploration of the unexpected correlation between the number of boilermakers in California and kerosene consumption in Sudan has prompted a wide-ranging survey of literature encompassing diverse fields, from industrial demographics to energy economics. While conventional wisdom may cast doubt on the plausibility of such a seemingly incongruous connection, the authors find a surprising array of tangentially related research and scholarly commentary that indirectly touches upon the interplay between these two ostensibly unrelated variables.
Smith, in "Industrial Demographics and Occupational Trends," captures the essence of occupational dynamics with insightful analyses of labor trends in various regions. Although Smith's work does not overtly investigate the specific role of boilermakers, the broader context of industrial employment provides a foundational backdrop for understanding the occupational landscape, much like the striking backdrop of a kerosene lamp illuminating the quiet confines of a Sudanese household.
Doe's comprehensive review, "Energy Sources and Utilization Patterns in Developing Nations," sheds light on the complex interweaving of sociopolitical factors and energy consumption. While Doe's focus lies primarily on broader energy sources, the underlying principles resonate with the nuanced dynamics of kerosene utilization in Sudan, much like the gentle glow of kerosene juxtaposed against the myriad energy sources powering the modern world.
In a departure from conventional academia, Jones's treatise, "Boilermakers and Kerosene: A Tale of Serendipitous Synchronicity," delves into the curious nexus of seemingly disparate elements, albeit in the form of an enigmatic allegorical narrative rather than scholarly discourse. While Jones's work may not conform to traditional academic standards, its whimsical musings serve as a thought-provoking divergence in the exploration of unexpected connections, much like a lighthearted anecdote injected into a ponderous discussion.
Transitioning to more unconventional sources, the seminal work of Rowling, "The Tales of Beedle the Boiler-Maker," introduces a fantastical world of enchanting allegories that, despite their fictional nature, embody threads of universal human experience. The parallel between the meticulous craft of boilermaking and the magical allure of kerosene lamps serves as a testament to the pervasive nature of artisanal craftsmanship across disparate cultures, much like the universal appeal of whimsical storytelling that transcends the boundaries of reality.
In an unexpected twist, a serendipitous encounter with social media commentary sheds unanticipated light on the matter at hand. An anonymous post on a popular platform humorously juxtaposed images of industrious boilermakers with the ambiance of a dimly lit Sudanese village, accompanied by the caption "When California meets Sudan: The Unlikely Romance of Boilers and Kerosene." While the post may have been intended as a lighthearted jest, it inadvertently captures the essence of the unexpected correlation under investigation, much like a candid snapshot capturing a profound truth amidst lighthearted banter.
In summary, the diverse array of literature and peripheral sources indirectly converges upon the perplexing relationship between the number of boilermakers in California and kerosene consumption in Sudan, adding layers of complexity to an already enigmatic correlation. As the scholarly journey delves deeper into this uncharted territory of occupational and energy interconnectivity, the authors invite fellow explorers to embrace the inherent whimsy of such unexpected discoveries, navigating the maze of unconventional scholarly contributions with a blend of intellectual inquiry and lighthearted reflection.

[[RESULTS]]
The results of our investigation revealed a striking correlation between the number of boilermakers in California and kerosene consumption in Sudan, with a correlation coefficient of 0.7823494. This correlation indicates a strong positive relationship between these seemingly unrelated variables, prompting further contemplation on the underlying mechanisms dictating this connection. The r-squared value of 0.6120705 further supported the robustness of this association, indicating that approximately 61.2% of the variability in kerosene consumption in Sudan can be explained by the number of boilermakers in California. Additionally, the statistically significant p-value of less than 0.01 underscored the validity of this correlation, reinforcing the notion that this finding is not merely a fortuitous coincidence.
Furthermore, the scatterplot in Fig. 1 visually depicts the strong correlation between the number of boilermakers in California and kerosene consumption in Sudan, providing a compelling illustration of this unexpected relationship. The scatterplot serves as a visual testament to the indisputable connection unearthed by our rigorous analysis, serving as a striking reminder of the idiosyncrasies that often permeate the intricate tapestry of socio-economic phenomena.
In light of these findings, it becomes evident that the peculiar association between the number of boilermakers in California and kerosene consumption in Sudan defies conventional expectations, serving as a poignant reminder of the serendipitous nature of scholarly inquiry. As we navigate the uncharted waters of interdisciplinary exploration, these results beckon us to reevaluate our assumptions and approach the global socio-economic landscape with a renewed sense of curiosity and open-mindedness.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The unexpected correlation between the number of boilermakers in California and kerosene consumption in Sudan, as unearthed in our investigation, beckons us to unravel the enigmatic tapestry of socio-economic connectivity. Building upon the whimsical musings and unexpected insights gleaned from the literature review, our findings not only substantiate prior conjectures but also propel this paradigm-shifting correlation into the spotlight of scholarly curiosity.

Smith's astute dissection of industrial demographics, albeit not directly delving into the realm of boilermakers, lays the groundwork for comprehending the intricate dance of occupational ebbs and flows. The resonating parallels found within the unassuming pages of Doe's exploration of energy utilization in developing nations provide a soothing symphony of confluence, harmonizing with the unexpected intersection of kerosene consumption in Sudan. Moreover, while Jones's unconventional narrative may initially appear as a departure from traditional scholarship, its clandestine wisdom weaves an unexpected thread of relevance into our investigation, akin to a whimsical allegory suddenly shedding light on an intellectual enigma.

Our robust statistical analysis undeniably reinforces the veracity of this perplexing correlation, further cementing the significance of our empirical revelations. The resolute correlation coefficient of 0.7823494 stands as a testament to the unyielding bond between these seemingly disparate variables, casting a radiant glow of empirical certainty upon this uncharted terrain. The visually compelling scatterplot encapsulates the irrefutable visual evidence of this association, reminding us that scholarly inquiry, much like the unpredictable dance of statistical patterns, often unfurls in unforeseen directions.

As we embark on a scholarly odyssey propelled by the unassuming nexus of boilermakers and kerosene, we are reminded of the inherent whimsy and unforeseen revelations that punctuate the oftentimes austere realm of academic inquiry. Our findings not only tantalize the intellectual palate with their unexpected flavor but also invigorate the vanguard of interdisciplinary exploration, fervently beckoning fellow scholars to traverse this uncharted territory ladened with serendipitous synchronicity.

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 boilermakers in California and Kerosene used in Sudan. 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]]
Boiling Point: Exploring the Surprising Link Between Boilermakers in California and Kerosene Consumption in Sudan

[[ABSTRACT]]
This paper presents a comprehensive analysis of the unexpected correlation between the number of boilermakers in California and the kerosene consumption in Sudan. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration spanning from 2003 to 2021, our research team identified a notable correlation coefficient of 0.7823494 and a statistically significant p-value of less than 0.01. The findings of this study defy conventional expectations and prompt further inquiry into the underlying mechanisms driving this seemingly disparate connection. As we delve into the intricate world of occupational trends and energy consumption, we unravel surprising insights that shed light on the uncharted territory of global socio-economic interconnectivity. This investigation not only highlights the need for interdisciplinary exploration but also underscores the potential for serendipitous findings in seemingly unrelated domains.

[[INTRDUCTION]]
The pursuit of knowledge often leads researchers down unexpected paths, unveiling connections that defy conventional wisdom and challenge established paradigms. In the realm of socio-economic analysis, uncovering correlations between seemingly disparate variables can offer valuable insights into the complex web of global interdependencies. Our study ventures into this intriguing terrain, exploring the perplexing relationship between the number of boilermakers in California and kerosene consumption in Sudan.
At first glance, one might be forgiven for assuming that these two variables exist in entirely separate realms, with little to no discernible link between them. After all, the image of industrious boilermakers plying their trade in the Golden State seems worlds apart from the quiet glow of kerosene lamps in the homes of Sudanese families. However, as we have discovered through meticulous data analysis, reality often defies expectations, leaving us to ponder the whims of statistical fate with a mixture of awe and bewilderment.
Our research team embarked on this investigation with an open mind, cognizant of the potential for unexpected discoveries lurking beneath the surface of mundane statistics. The initial revelation of a significant correlation coefficient of 0.7823494 and a p-value that gleefully defied the customary threshold of statistical significance sent ripples of intrigue through our scholarly ranks. As we pored over the data sourced from the Bureau of Labor Statistics and the Energy Information Administration, the improbable link between boilermakers and kerosene consumption emerged as an enigma begging to be unraveled.
While the layperson may be inclined to dismiss this correlation as an anomalous blip in the data, we, as diligent researchers, understand the gravity of such findings. The implications stretch far beyond the narrow confines of industrial occupations and energy sources, beckoning us to delve deeper into the veiled intricacies of global socio-economic dynamics. As we embark on this scholarly expedition, we invite our esteemed colleagues to join us in unraveling the curious tapestry of occupational trends and energy utilization, with a dash of intellectual curiosity and perhaps a sprinkle of whimsy. For in the esoteric world of scholarly inquiry, serendipity often dances hand-in-hand with methodical investigation, yielding unforeseen revelations that challenge our preconceived notions and kindle the flame of intellectual curiosity.

[[RESULTS]]
The results of our investigation revealed a striking correlation between the number of boilermakers in California and kerosene consumption in Sudan, with a correlation coefficient of 0.7823494. This correlation indicates a strong positive relationship between these seemingly unrelated variables, prompting further contemplation on the underlying mechanisms dictating this connection. The r-squared value of 0.6120705 further supported the robustness of this association, indicating that approximately 61.2% of the variability in kerosene consumption in Sudan can be explained by the number of boilermakers in California. Additionally, the statistically significant p-value of less than 0.01 underscored the validity of this correlation, reinforcing the notion that this finding is not merely a fortuitous coincidence.
Furthermore, the scatterplot in Fig. 1 visually depicts the strong correlation between the number of boilermakers in California and kerosene consumption in Sudan, providing a compelling illustration of this unexpected relationship. The scatterplot serves as a visual testament to the indisputable connection unearthed by our rigorous analysis, serving as a striking reminder of the idiosyncrasies that often permeate the intricate tapestry of socio-economic phenomena.
In light of these findings, it becomes evident that the peculiar association between the number of boilermakers in California and kerosene consumption in Sudan defies conventional expectations, serving as a poignant reminder of the serendipitous nature of scholarly inquiry. As we navigate the uncharted waters of interdisciplinary exploration, these results beckon us to reevaluate our assumptions and approach the global socio-economic landscape with a renewed sense of curiosity and open-mindedness.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation has brought to light a remarkable correlation between the number of boilermakers in California and kerosene consumption in Sudan. The statistically significant correlation coefficient of 0.7823494 and the robust r-squared value of 0.6120705 underscore the compelling nature of this seemingly incongruous relationship. It is evident that the enigmatic link between these variables transcends simplistic explanations, inviting us to contemplate the underlying forces at play. The scatterplot in Fig. 1 serves as a visual testament to this unexpected connection, a graphic representation of the unlikely dance between Californian boilermakers and Sudanese kerosene consumption.

As we wrap up this scholarly odyssey, it is imperative to acknowledge the whims of statistical fate and the tantalizing allure of serendipitous findings. The unearthing of this correlation not only challenges conventional expectations but also highlights the intricate interplay of global socio-economic dynamics. The unsuspecting nature of this connection reminds us to approach our scholarly pursuits with a blend of methodical rigor and an open embrace of the unexpected.

While the temptation to delve deeper into the underlying mechanisms driving this correlation may beckon, we assert, with a twinkle in our scholarly eyes, that the time has come to applaud the quirks of statistical discovery and savor this tantalizing enigma. As we bid adieu to this singular correlation, we declare, with scholarly conviction and a hint of whimsy, that no further research in this area is warranted. For in the tumultuous seas of academic inquiry, some mysteries are best left to waltz in the exquisite realm of statistical randomness.

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 Boiling Point: Exploring the Surprising Link Between Boilermakers in California and Kerosene Consumption in Sudan. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Global Energy Dynamics

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 Boiling Point: Exploring the Surprising Link Between Boilermakers in California and Kerosene Consumption in Sudan. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Interdisciplinary Study of Global Energy Consumption

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 Boiling Point: Exploring the Surprising Link Between Boilermakers in California and Kerosene Consumption in Sudan with an abstract of This paper presents a comprehensive analysis of the unexpected correlation between the number of boilermakers in California and the kerosene consumption in Sudan. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration spanning from 2003 to 2021, our research team identified a notable correlation coefficient of 0.7823494 and a statistically significant p-value of less than 0.01. The findings of this study defy conventional expectations and prompt further inquiry into the underlying mechanisms driving this seemingly disparate connection. As we delve into the intricate world of occupational trends and energy consumption, we unravel surprising insights that shed light on the uncharted territory of global socio-economic interconnectivity. This investigation not only highlights the need for interdisciplinary exploration but also underscores the potential for serendipitous findings in seemingly unrelated domains.

ChatGPT:

boilermakers, California, kerosene consumption, Sudan, correlation, analysis, BLS, EIA, Bureau of Labor Statistics, Energy Information Administration, occupational trends, energy consumption, socio-economic interconnectivity, interdisciplinary exploration, serendipitous findings

*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 boilermakers in California
Detailed data title: BLS estimate of boilermakers in California
Source: Bureau of Larbor Statistics
See what else correlates with The number of boilermakers in California

Kerosene used in Sudan
Detailed data title: Volume of kerosene used consumed in Sudan in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Kerosene used in Sudan

Correlation r = 0.7823494 (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 = 0.6120705 (Coefficient of determination)
This means 61.2% of the change in the one variable (i.e., Kerosene used in Sudan) is predictable based on the change in the other (i.e., The number of boilermakers in California) over the 19 years from 2003 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 7.5E-5.^Show 0.0000754731092275394800000000
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 0.0075% of random cases. Said differently, if you correlated 13,250 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 boilermakers in California (Boilermakers)	710	930	1050	710	770	700	650	550	540	680	510	650	500	430	270	170	570	750	770
Kerosene used in Sudan (Million Barrels/Day)	0.317671	0.316803	0.254137	0.360027	0.254137	0.190082	0.10589	0.0847123	0.0847123	0.0844809	0.0635342	0.0635342	0.148246	0	0	0	0.152082	0.260712	0.260712

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([710,930,1050,710,770,700,650,550,540,680,510,650,500,430,270,170,570,750,770,])
array_2 = np.array([0.317671,0.316803,0.254137,0.360027,0.254137,0.190082,0.10589,0.0847123,0.0847123,0.0844809,0.0635342,0.0635342,0.148246,0,0,0,0.152082,0.260712,0.260712,])
array_1_name = "The number of boilermakers in California"
array_2_name = "Kerosene used in Sudan"

# 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 boilermakers in California
Line chart for only Kerosene used in Sudan
AI-generated correlation image
The spurious research paper: Boiling Point: Exploring the Surprising Link Between Boilermakers in California and Kerosene Consumption in Sudan

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Correlation ID: 3445 · Black Variable ID: 5548 · Red Variable ID: 24947


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