Spurious correlation #4,775 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Cincinnati and the second variable is Kerosene used in Peru. The chart goes from 1980 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 Air pollution in Cincinnati and the second variable is Kerosene used in Peru. The chart goes from 1980 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

Cincinnati's cleaner air led to an increase in hot air balloon enthusiasts, creating a sudden kerosene demand and subsequent shortage in Peru which heavily relies on hot air balloon transportation. Remember, what goes up must come down, unless there's a kerosene shortage!

^{Show image generation prompt} Model: dalle-3
Prompt: Prompt: Generate an surrealistic image depicting a scene where hot air balloons of vibrant colors float over the city of Cincinnati, while in contrast, the skies of Peru are empty, emphasizing the grounding of the balloons due to kerosene shortage. The people in Peru are shown looking up in dismay as they witness this unusual absence, hinting at the disruption of transportation and daily activities. 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: Air pollution in Cincinnati and Kerosene used in Peru. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: Woodblock painting Symbolism Surrealism Symbolism a funny image macro
ChatGPT: Surrealism

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 Air pollution in Cincinnati and Kerosene used in Peru, in the following situation: Cincinnati's cleaner air led to an increase in hot air balloon enthusiasts, creating a sudden kerosene demand and subsequent shortage in Peru which heavily relies on hot air balloon transportation. Remember, what goes up must come down, unless there's a kerosene shortage!. Keep the description simple.
ChatGPT: In Cincinnati, the sky is filled with colorful hot air balloons, soaring above the city as enthusiasts enjoy the cleaner air. However, in Peru, the usual sky filled with hot air balloons is noticeably empty, with people looking up in disappointment. The shortage of kerosene has grounded the balloons, causing disruptions in transportation and daily activities.

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: [ Surrealism ] and depicting this scene: In Cincinnati, the sky is filled with colorful hot air balloons, soaring above the city as enthusiasts enjoy the cleaner air. However, in Peru, the usual sky filled with hot air balloons is noticeably empty, with people looking up in disappointment. The shortage of kerosene has grounded the balloons, causing disruptions in transportation and daily activities. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Generate an surrealistic image depicting a scene where hot air balloons of vibrant colors float over the city of Cincinnati, while in contrast, the skies of Peru are empty, emphasizing the grounding of the balloons due to kerosene shortage. The people in Peru are shown looking up in dismay as they witness this unusual absence, hinting at the disruption of transportation and daily activities.

*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how a decrease in Air pollution in Cincinnati caused Kerosene used in Peru to decrease. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

Clearing the Air: Unveiling the Surprising Connection between Air Pollution in Cincinnati and Kerosene Consumption in Peru

International Journal of Environmental Anthropology

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 Air pollution in Cincinnati and Kerosene used in Peru. 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 name a few real TV shows that sound like they might be relevant to the topic that you watched as research.

Here is the title and abstract of the paper:
[[TITLE]]
"Clearing the Air: Unveiling the Surprising Connection between Air Pollution in Cincinnati and Kerosene Consumption in Peru"

[[ABSTRACT]]
The study aims to uncover the often overlooked, yet curious correlation between air pollution levels in Cincinnati and the consumption of kerosene in Peru. Utilizing data from the Environmental Protection Agency and Energy Information Administration, our research team identified a strong correlation coefficient of 0.8386880 and a statistically significant p-value of less than 0.01 over the time period from 1980 to 2021. The results highlight an intriguing association that may shed light on the intricate interconnections of global environmental factors. This unexpected linkage between distant geographical regions prompts a whimsical consideration of the intricate dance of industrial emissions and household fuel choices across the globe. The findings garner attention to the bewildering ways in which seemingly unrelated elements can influence each other, much like the curious relationship between a tangled knot and a gust of wind. The implications of this correlation open a realm of possibilities for future research and spark a playful contemplation of the interconnected tapestry of our world.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The interplay between environmental factors and their cascading effects across distant regions has long been a subject of fascination in scientific inquiry. In this study, we set out to explore the unexpected connection between air pollution levels in Cincinnati and the consumption of kerosene in Peru. While the notion of these two seemingly unrelated variables being entangled may initially appear as preposterous as trying to fit a square peg into a round hole, our findings cast light on a surprising relationship worthy of further investigation.
As researchers, we often find ourselves plumbing the depths of datasets and navigating through statistical thickets, seeking correlations that are as elusive as the fabled needle in a haystack. However, the exploration of this uncharted territory has yielded a correlation coefficient of 0.8386880, a figure even more astonishing than the discovery of a unicorn in a field of statistical significance. Moreover, the associated p-value of less than 0.01 provides a robust foundation upon which to stand, akin to the sturdy scaffolding supporting a particularly complex scientific theory.
The rather fortuitous finding of a notable correlation has prompted us to consider the intricate ballet of industrial emissions and household fuel choices across the globe. The relationship between these traditionally distinct geographic regions invites a whimsical contemplation of the interconnectedness of our world, not unlike pondering the convoluted paths of subatomic particles engaged in their quantum waltz.
The implications of this correlation extend beyond mere academic curiosity, opening a veritable Pandora's box of potential implications and applications. Our study invites the scientific community to partake in a playful yet astute consideration of this unanticipated linkage, much like the delighted contemplation of a particularly intricate and unexpected cosmic connection. Let us embark upon this intellectual journey together, as we seek to unravel the mysteries of this captivating conundrum.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The unexpected correlation between air pollution levels in Cincinnati and the consumption of kerosene in Peru has sparked a spirited discussion among researchers and scholars, provoking a whimsical consideration of the intricate dance of industrial emissions and household fuel choices across the globe, not unlike a serendipitous encounter with a pun at a scientific conference.

In "Air Pollution and Health" by Smith et al., the authors find a significant association between air pollution and adverse health outcomes, highlighting the seriousness of this global issue. Meanwhile, Doe's analysis in "Fuel Choices in Developing Countries" sheds light on the prevalence of kerosene use in low-income households. Jones, in "Global Energy Consumption Patterns," provides a comprehensive overview of energy usage trends across different regions, offering a broad perspective on the intersections of energy consumption and environmental factors.

Moving away from the scholarly realm, books such as "The Air We Breathe" and "Kerosene Cowboys" offer unique insights into the human experiences intertwined with these environmental factors. In a lighter tone, fictional works like "The Polluted Peruvian Puzzle" and "Cincinnati Kerosene Chronicles" playfully explore the imagined interactions between these seemingly disparate elements, reminiscent of an inside joke that only scholars of the most peculiar scientific theories would fully appreciate.

Having delved into the realm of popular culture, television shows such as "Air Pollution Files" and "Kerosene Quest" provide a less conventional yet equally intriguing lens through which to consider the interplay between environmental and energy factors, much like stumbling upon an unexpected but oddly relevant meme in the midst of a serious research quest.

The diverse sources and perspectives reviewed here set the stage for an exploration of the unexpected and often perplexing interconnections between air pollution in Cincinnati and kerosene consumption in Peru, inviting the academic community to tackle this quixotic conundrum with a wry smile and an open mind.

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 Air pollution in Cincinnati and Kerosene used in Peru. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Energy Information Administration . You used data from 1980 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]]
"Clearing the Air: Unveiling the Surprising Connection between Air Pollution in Cincinnati and Kerosene Consumption in Peru"

[[ABSTRACT]]
The study aims to uncover the often overlooked, yet curious correlation between air pollution levels in Cincinnati and the consumption of kerosene in Peru. Utilizing data from the Environmental Protection Agency and Energy Information Administration, our research team identified a strong correlation coefficient of 0.8386880 and a statistically significant p-value of less than 0.01 over the time period from 1980 to 2021. The results highlight an intriguing association that may shed light on the intricate interconnections of global environmental factors. This unexpected linkage between distant geographical regions prompts a whimsical consideration of the intricate dance of industrial emissions and household fuel choices across the globe. The findings garner attention to the bewildering ways in which seemingly unrelated elements can influence each other, much like the curious relationship between a tangled knot and a gust of wind. The implications of this correlation open a realm of possibilities for future research and spark a playful contemplation of the interconnected tapestry of our world.

[[INTRODUCTION]]
The interplay between environmental factors and their cascading effects across distant regions has long been a subject of fascination in scientific inquiry. In this study, we set out to explore the unexpected connection between air pollution levels in Cincinnati and the consumption of kerosene in Peru. While the notion of these two seemingly unrelated variables being entangled may initially appear as preposterous as trying to fit a square peg into a round hole, our findings cast light on a surprising relationship worthy of further investigation.
As researchers, we often find ourselves plumbing the depths of datasets and navigating through statistical thickets, seeking correlations that are as elusive as the fabled needle in a haystack. However, the exploration of this uncharted territory has yielded a correlation coefficient of 0.8386880, a figure even more astonishing than the discovery of a unicorn in a field of statistical significance. Moreover, the associated p-value of less than 0.01 provides a robust foundation upon which to stand, akin to the sturdy scaffolding supporting a particularly complex scientific theory.
The rather fortuitous finding of a notable correlation has prompted us to consider the intricate ballet of industrial emissions and household fuel choices across the globe. The relationship between these traditionally distinct geographic regions invites a whimsical contemplation of the interconnectedness of our world, not unlike pondering the convoluted paths of subatomic particles engaged in their quantum waltz.
The implications of this correlation extend beyond mere academic curiosity, opening a veritable Pandora's box of potential implications and applications. Our study invites the scientific community to partake in a playful yet astute consideration of this unanticipated linkage, much like the delighted contemplation of a particularly intricate and unexpected cosmic connection. Let us embark upon this intellectual journey together, as we seek to unravel the mysteries of this captivating conundrum.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The research methodology employed in this study involved a comprehensive analysis of air pollution data in Cincinnati and kerosene consumption data in Peru spanning the years 1980 to 2021. The data were primarily sourced from the Environmental Protection Agency (EPA) and the Energy Information Administration (EIA), regarded as the bulwarks of information in the domain of environmental and energy statistics.

Upon the collection of copious quantities of data, our research team set about the daunting task of wrangling, massaging, and masticating the numbers into a coherent form for analysis. This process bore semblance to the coaxing of stubborn puzzle pieces into their designated slots, as we endeavored to create a comprehensive picture of the relationship between air pollution in Cincinnati and kerosene usage in Peru.

To examine the potential correlation between these seemingly disparate variables, the data underwent rigorous statistical analysis, including but not limited to correlation coefficient calculations, regression models, and trend analyses. The statistical maneuvers performed on the data were executed with the same precision and finesse as a maestro orchestrating a symphony, harmonizing the discordant notes of raw data into a melodious analytical composition.

Following the meticulous statistical analyses, the data underwent an array of sensitivity tests, akin to stress-testing a high-stakes financial model. These tests were conducted to ensure the robustness and reliability of the observed correlation, providing a safeguard against any statistical sleight of hand attempting to obfuscate the true nature of the relationship between air pollution in Cincinnati and kerosene consumption in Peru.

Furthermore, to minimize the potential influence of confounding variables and spurious correlations, a series of control analyses were conducted. This approach aimed to untangle the overlapping threads of causation, much like a determined detective unraveling the complex web of motives in a whodunit mystery.

The culmination of these efforts allowed for the identification of a robust and noteworthy correlation between air pollution levels in Cincinnati and kerosene consumption in Peru, setting the stage for the intriguing findings presented in the subsequent sections of this scholarly exposé.

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

Please make up the results section of an academic research paper presenting the findings of the connection between Air pollution in Cincinnati and Kerosene used in Peru. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Energy Information Administration .

For the time period 1980 to 2021, you found a correlation 0.8386880, r-squared of 0.7033976, and p < 0.01.

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

Here is the title and abstract of the paper:
[[TITLE]]
"Clearing the Air: Unveiling the Surprising Connection between Air Pollution in Cincinnati and Kerosene Consumption in Peru"

[[ABSTRACT]]
The study aims to uncover the often overlooked, yet curious correlation between air pollution levels in Cincinnati and the consumption of kerosene in Peru. Utilizing data from the Environmental Protection Agency and Energy Information Administration, our research team identified a strong correlation coefficient of 0.8386880 and a statistically significant p-value of less than 0.01 over the time period from 1980 to 2021. The results highlight an intriguing association that may shed light on the intricate interconnections of global environmental factors. This unexpected linkage between distant geographical regions prompts a whimsical consideration of the intricate dance of industrial emissions and household fuel choices across the globe. The findings garner attention to the bewildering ways in which seemingly unrelated elements can influence each other, much like the curious relationship between a tangled knot and a gust of wind. The implications of this correlation open a realm of possibilities for future research and spark a playful contemplation of the interconnected tapestry of our world.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The interplay between environmental factors and their cascading effects across distant regions has long been a subject of fascination in scientific inquiry. In this study, we set out to explore the unexpected connection between air pollution levels in Cincinnati and the consumption of kerosene in Peru. While the notion of these two seemingly unrelated variables being entangled may initially appear as preposterous as trying to fit a square peg into a round hole, our findings cast light on a surprising relationship worthy of further investigation.
As researchers, we often find ourselves plumbing the depths of datasets and navigating through statistical thickets, seeking correlations that are as elusive as the fabled needle in a haystack. However, the exploration of this uncharted territory has yielded a correlation coefficient of 0.8386880, a figure even more astonishing than the discovery of a unicorn in a field of statistical significance. Moreover, the associated p-value of less than 0.01 provides a robust foundation upon which to stand, akin to the sturdy scaffolding supporting a particularly complex scientific theory.
The rather fortuitous finding of a notable correlation has prompted us to consider the intricate ballet of industrial emissions and household fuel choices across the globe. The relationship between these traditionally distinct geographic regions invites a whimsical contemplation of the interconnectedness of our world, not unlike pondering the convoluted paths of subatomic particles engaged in their quantum waltz.
The implications of this correlation extend beyond mere academic curiosity, opening a veritable Pandora's box of potential implications and applications. Our study invites the scientific community to partake in a playful yet astute consideration of this unanticipated linkage, much like the delighted contemplation of a particularly intricate and unexpected cosmic connection. Let us embark upon this intellectual journey together, as we seek to unravel the mysteries of this captivating conundrum.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The data analysis revealed a striking correlation between air pollution levels in Cincinnati and the consumption of kerosene in Peru over the period from 1980 to 2021. The correlation coefficient of 0.8386880 suggests a strong positive relationship between these seemingly disparate variables, akin to the surprising camaraderie of a penguin and a polar bear in the realm of statistical significance.

Furthermore, the r-squared value of 0.7033976 indicates that approximately 70.34% of the variation in air pollution levels in Cincinnati can be explained by the consumption of kerosene in Peru. This proportion is as noteworthy as uncovering a rare gem in a vast expanse of statistical gravel.

The associated p-value of less than 0.01 underscores the statistical significance of this relationship, akin to stumbling upon a hidden treasure map leading to the heart of scientific inquiry.

The visual representation of this association is depicted in Figure 1, which prominently showcases the scatterplot displaying the robust correlation between air pollution levels in Cincinnati and kerosene consumption in Peru. The strength of this connection is as clear as a cloudless sky after a storm of data analysis.

In conclusion, the results hint at an unexpected and thought-provoking linkage between air pollution in Cincinnati and kerosene usage in Peru, inviting further exploration and contemplation. This unconventional connection serves as a testament to the whimsical interplay of environmental and human factors on a global scale, much like the intricate patterns woven by the capricious hands of statistical fate.

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 Air pollution in Cincinnati and Kerosene used in Peru. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

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

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

[[TITLE]]
"Clearing the Air: Unveiling the Surprising Connection between Air Pollution in Cincinnati and Kerosene Consumption in Peru"

[[ABSTRACT]]
The study aims to uncover the often overlooked, yet curious correlation between air pollution levels in Cincinnati and the consumption of kerosene in Peru. Utilizing data from the Environmental Protection Agency and Energy Information Administration, our research team identified a strong correlation coefficient of 0.8386880 and a statistically significant p-value of less than 0.01 over the time period from 1980 to 2021. The results highlight an intriguing association that may shed light on the intricate interconnections of global environmental factors. This unexpected linkage between distant geographical regions prompts a whimsical consideration of the intricate dance of industrial emissions and household fuel choices across the globe. The findings garner attention to the bewildering ways in which seemingly unrelated elements can influence each other, much like the curious relationship between a tangled knot and a gust of wind. The implications of this correlation open a realm of possibilities for future research and spark a playful contemplation of the interconnected tapestry of our world.

[[LITERATURE REVIEW]]
The unexpected correlation between air pollution levels in Cincinnati and the consumption of kerosene in Peru has sparked a spirited discussion among researchers and scholars, provoking a whimsical consideration of the intricate dance of industrial emissions and household fuel choices across the globe, not unlike a serendipitous encounter with a pun at a scientific conference.
In "Air Pollution and Health" by Smith et al., the authors find a significant association between air pollution and adverse health outcomes, highlighting the seriousness of this global issue. Meanwhile, Doe's analysis in "Fuel Choices in Developing Countries" sheds light on the prevalence of kerosene use in low-income households. Jones, in "Global Energy Consumption Patterns," provides a comprehensive overview of energy usage trends across different regions, offering a broad perspective on the intersections of energy consumption and environmental factors.
Moving away from the scholarly realm, books such as "The Air We Breathe" and "Kerosene Cowboys" offer unique insights into the human experiences intertwined with these environmental factors. In a lighter tone, fictional works like "The Polluted Peruvian Puzzle" and "Cincinnati Kerosene Chronicles" playfully explore the imagined interactions between these seemingly disparate elements, reminiscent of an inside joke that only scholars of the most peculiar scientific theories would fully appreciate.
Having delved into the realm of popular culture, television shows such as "Air Pollution Files" and "Kerosene Quest" provide a less conventional yet equally intriguing lens through which to consider the interplay between environmental and energy factors, much like stumbling upon an unexpected but oddly relevant meme in the midst of a serious research quest.
The diverse sources and perspectives reviewed here set the stage for an exploration of the unexpected and often perplexing interconnections between air pollution in Cincinnati and kerosene consumption in Peru, inviting the academic community to tackle this quixotic conundrum with a wry smile and an open mind.

[[RESULTS]]
The data analysis revealed a striking correlation between air pollution levels in Cincinnati and the consumption of kerosene in Peru over the period from 1980 to 2021. The correlation coefficient of 0.8386880 suggests a strong positive relationship between these seemingly disparate variables, akin to the surprising camaraderie of a penguin and a polar bear in the realm of statistical significance.
Furthermore, the r-squared value of 0.7033976 indicates that approximately 70.34% of the variation in air pollution levels in Cincinnati can be explained by the consumption of kerosene in Peru. This proportion is as noteworthy as uncovering a rare gem in a vast expanse of statistical gravel.
The associated p-value of less than 0.01 underscores the statistical significance of this relationship, akin to stumbling upon a hidden treasure map leading to the heart of scientific inquiry.
The visual representation of this association is depicted in Figure 1, which prominently showcases the scatterplot displaying the robust correlation between air pollution levels in Cincinnati and kerosene consumption in Peru. The strength of this connection is as clear as a cloudless sky after a storm of data analysis.
In conclusion, the results hint at an unexpected and thought-provoking linkage between air pollution in Cincinnati and kerosene usage in Peru, inviting further exploration and contemplation. This unconventional connection serves as a testament to the whimsical interplay of environmental and human factors on a global scale, much like the intricate patterns woven by the capricious hands of statistical fate.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of the present study echo and amplify the lively discussions and unconventional musings that have permeated the scholarly landscape regarding the enigmatic relationship between air pollution in Cincinnati and kerosene consumption in Peru. The robust correlation coefficient of 0.8386880 and its associated p-value of less than 0.01 lend substantial weight to the existence of a surprisingly strong bond between these seemingly distant variables, much like the magnetic pull between two celestial bodies in the vast expanse of the statistical universe.

Our results align with the scholarly assertions regarding the adverse health outcomes associated with elevated air pollution levels, as elucidated in the work of Smith et al. This association between air pollution and health outcomes underscores the profound impact of environmental factors on human well-being, akin to the indomitable force of a gusty wind rearranging the petals of a delicate flower.

Similarly, the substantial prevalence of kerosene use in low-income households, as highlighted in Doe's analysis, speaks to the vital role of household fuel choices in shaping global energy consumption patterns, not unlike the melodious dance of subatomic particles in the quantum realm. The unexpected link between air pollution in Cincinnati and kerosene consumption in Peru prompts a whimsical reimagining of the intricate tapestry of global energy dynamics, much like a playful jigsaw puzzle waiting to be unraveled.

The visual representation in Figure 1 provides a striking portrayal of the robust correlation, much like a portrait capturing the harmonious convergence of two unrelated but beautifully entwined elements. The r-squared value of 0.7033976 further underlines the substantial proportion of variation in air pollution levels in Cincinnati explained by the consumption of kerosene in Peru, akin to a bright beacon illuminating the path towards understanding this peculiar interconnection.

In exploring the unexpected correlation between air pollution in Cincinnati and kerosene consumption in Peru, this study sparks a playful contemplation of the whimsical interplay between seemingly disparate elements in the global landscape. The intriguing implications of this linkage open a realm of possibilities for future research and invite scholars to join in the adventurous quest of unraveling the intricate mysteries of statistical fate.

The present findings, infused with a hint of scholarly whimsy, stand as a testament to the captivating dance of industrial emissions and household fuel choices across the globe, not unlike a chance encounter with a serendipitous pun at a scientific congress.

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 Air pollution in Cincinnati and Kerosene used in Peru. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"Clearing the Air: Unveiling the Surprising Connection between Air Pollution in Cincinnati and Kerosene Consumption in Peru"

[[ABSTRACT]]
The study aims to uncover the often overlooked, yet curious correlation between air pollution levels in Cincinnati and the consumption of kerosene in Peru. Utilizing data from the Environmental Protection Agency and Energy Information Administration, our research team identified a strong correlation coefficient of 0.8386880 and a statistically significant p-value of less than 0.01 over the time period from 1980 to 2021. The results highlight an intriguing association that may shed light on the intricate interconnections of global environmental factors. This unexpected linkage between distant geographical regions prompts a whimsical consideration of the intricate dance of industrial emissions and household fuel choices across the globe. The findings garner attention to the bewildering ways in which seemingly unrelated elements can influence each other, much like the curious relationship between a tangled knot and a gust of wind. The implications of this correlation open a realm of possibilities for future research and spark a playful contemplation of the interconnected tapestry of our world.

[[INTRDUCTION]]
The interplay between environmental factors and their cascading effects across distant regions has long been a subject of fascination in scientific inquiry. In this study, we set out to explore the unexpected connection between air pollution levels in Cincinnati and the consumption of kerosene in Peru. While the notion of these two seemingly unrelated variables being entangled may initially appear as preposterous as trying to fit a square peg into a round hole, our findings cast light on a surprising relationship worthy of further investigation.
As researchers, we often find ourselves plumbing the depths of datasets and navigating through statistical thickets, seeking correlations that are as elusive as the fabled needle in a haystack. However, the exploration of this uncharted territory has yielded a correlation coefficient of 0.8386880, a figure even more astonishing than the discovery of a unicorn in a field of statistical significance. Moreover, the associated p-value of less than 0.01 provides a robust foundation upon which to stand, akin to the sturdy scaffolding supporting a particularly complex scientific theory.
The rather fortuitous finding of a notable correlation has prompted us to consider the intricate ballet of industrial emissions and household fuel choices across the globe. The relationship between these traditionally distinct geographic regions invites a whimsical contemplation of the interconnectedness of our world, not unlike pondering the convoluted paths of subatomic particles engaged in their quantum waltz.
The implications of this correlation extend beyond mere academic curiosity, opening a veritable Pandora's box of potential implications and applications. Our study invites the scientific community to partake in a playful yet astute consideration of this unanticipated linkage, much like the delighted contemplation of a particularly intricate and unexpected cosmic connection. Let us embark upon this intellectual journey together, as we seek to unravel the mysteries of this captivating conundrum.

[[RESULTS]]
The data analysis revealed a striking correlation between air pollution levels in Cincinnati and the consumption of kerosene in Peru over the period from 1980 to 2021. The correlation coefficient of 0.8386880 suggests a strong positive relationship between these seemingly disparate variables, akin to the surprising camaraderie of a penguin and a polar bear in the realm of statistical significance.
Furthermore, the r-squared value of 0.7033976 indicates that approximately 70.34% of the variation in air pollution levels in Cincinnati can be explained by the consumption of kerosene in Peru. This proportion is as noteworthy as uncovering a rare gem in a vast expanse of statistical gravel.
The associated p-value of less than 0.01 underscores the statistical significance of this relationship, akin to stumbling upon a hidden treasure map leading to the heart of scientific inquiry.
The visual representation of this association is depicted in Figure 1, which prominently showcases the scatterplot displaying the robust correlation between air pollution levels in Cincinnati and kerosene consumption in Peru. The strength of this connection is as clear as a cloudless sky after a storm of data analysis.
In conclusion, the results hint at an unexpected and thought-provoking linkage between air pollution in Cincinnati and kerosene usage in Peru, inviting further exploration and contemplation. This unconventional connection serves as a testament to the whimsical interplay of environmental and human factors on a global scale, much like the intricate patterns woven by the capricious hands of statistical fate.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study unravels the enigmatic nexus between air pollution in Cincinnati and kerosene consumption in Peru, shedding light on the bewildering caprices of interconnected environmental phenomena. The correlation coefficient of 0.8386880 stands as sturdy evidence of the unexpected dance of industrial emissions and household fuel choices, akin to an astrophysicist stumbling upon a celestial tap dance in the heavens. Similarly, the statistically significant p-value of less than 0.01 serves as a beacon, illuminating the curious interplay between these seemingly disparate variables, much like a lighthouse guiding a ship through the choppy waters of statistical inquiry. The implications of this correlation invite a whimsical yet astute consideration, akin to the delighted contemplation of an unexpected cosmic connection. However, it is clear that this research has opened a proverbial can of worms, revealing a universe of possibilities for further exploration and contemplation. Yet, in the spirit of statistical finality, we dare say, further research in this area would be a mere exercise in redundancy.

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 Clearing the Air: Unveiling the Surprising Connection between Air Pollution in Cincinnati and Kerosene Consumption in Peru. Respond only with the name of a fictional academic journal.

ChatGPT:

International Journal of Environmental Anthropology

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 Clearing the Air: Unveiling the Surprising Connection between Air Pollution in Cincinnati and Kerosene Consumption in Peru. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Global Air Quality and Cultural 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 Clearing the Air: Unveiling the Surprising Connection between Air Pollution in Cincinnati and Kerosene Consumption in Peru with an abstract of The study aims to uncover the often overlooked, yet curious correlation between air pollution levels in Cincinnati and the consumption of kerosene in Peru. Utilizing data from the Environmental Protection Agency and Energy Information Administration, our research team identified a strong correlation coefficient of 0.8386880 and a statistically significant p-value of less than 0.01 over the time period from 1980 to 2021. The results highlight an intriguing association that may shed light on the intricate interconnections of global environmental factors. This unexpected linkage between distant geographical regions prompts a whimsical consideration of the intricate dance of industrial emissions and household fuel choices across the globe. The findings garner attention to the bewildering ways in which seemingly unrelated elements can influence each other, much like the curious relationship between a tangled knot and a gust of wind. The implications of this correlation open a realm of possibilities for future research and spark a playful contemplation of the interconnected tapestry of our world.

ChatGPT:

air pollution, Cincinnati air quality, kerosene consumption, Peru energy consumption, correlation between air pollution and kerosene consumption, environmental factors, global interconnections, industrial emissions, household fuel choices, geographical correlations, international environmental impact

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

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Data details

Air pollution in Cincinnati
Detailed data title: Percentage of days 'unhealthy' or worse air quality in Cincinnati, OH-KY-IN
Source: Environmental Protection Agency
See what else correlates with Air pollution in Cincinnati

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

Correlation r = 0.8386880 (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 = 0.7033976 (Coefficient of determination)
This means 70.3% of the change in the one variable (i.e., Kerosene used in Peru) is predictable based on the change in the other (i.e., Air pollution in Cincinnati) over the 42 years from 1980 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 4.11E-12.^Show 0.0000000000041098792977001364
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.84 in 4.11E-10% of random cases. Said differently, if you correlated 243,316,148,131 random variables^Note You don't actually need 243 billion variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.

In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 41 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 41 because we have two variables measured over a period of 42 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.

[ 0.72, 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.

	1980	1981	1982	1983	1984	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Air pollution in Cincinnati (Bad air quality days)	11.7486	6.30137	9.58904	12.8767	5.46448	4.10959	7.39726	13.1507	16.1202	5.47945	6.0274	7.39726	1.63934	4.38356	5.47945	7.39726	5.46448	3.83562	6.30137	8.76712	2.73224	3.83562	8.21918	3.28767	0.546448	5.20548	1.91781	3.28767	1.63934	0	1.09589	1.91781	2.45902	0	0	0.273973	0	0	0.547945	0	0	0
Kerosene used in Peru (Million Barrels/Day)	18.6082	19.1679	19.028	17.349	17.7688	17.489	19	22	22	18	16	15	6.9	15	13	14	14	14	14	14	14.0449	14.8882	14.9517	10.8008	6.84295	4.42622	1.77896	1.16479	0.97153	1.52482	1.41893	1.39775	1.03489	1.14362	1.24951	1	1	0.782137	0.412795	0.0651781	0.0434521	0.0434521

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([11.7486,6.30137,9.58904,12.8767,5.46448,4.10959,7.39726,13.1507,16.1202,5.47945,6.0274,7.39726,1.63934,4.38356,5.47945,7.39726,5.46448,3.83562,6.30137,8.76712,2.73224,3.83562,8.21918,3.28767,0.546448,5.20548,1.91781,3.28767,1.63934,0,1.09589,1.91781,2.45902,0,0,0.273973,0,0,0.547945,0,0,0,])
array_2 = np.array([18.6082,19.1679,19.028,17.349,17.7688,17.489,19,22,22,18,16,15,6.9,15,13,14,14,14,14,14,14.0449,14.8882,14.9517,10.8008,6.84295,4.42622,1.77896,1.16479,0.97153,1.52482,1.41893,1.39775,1.03489,1.14362,1.24951,1,1,0.782137,0.412795,0.0651781,0.0434521,0.0434521,])
array_1_name = "Air pollution in Cincinnati"
array_2_name = "Kerosene used in Peru"

# 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 Air pollution in Cincinnati
Line chart for only Kerosene used in Peru
AI-generated correlation image
The spurious research paper: Clearing the Air: Unveiling the Surprising Connection between Air Pollution in Cincinnati and Kerosene Consumption in Peru

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Correlation ID: 4775 · Black Variable ID: 20464 · Red Variable ID: 24811


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