Spurious correlation #2,460 · 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 Cleveland 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 Cleveland 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

As air quality improved in Cleveland, the city became less reliant on burning fossil fuels, leading to a lower demand for kerosene. This shift created a 'clean air cascade effect' that wafted all the way to Peru, reducing the need for kerosene there too. It's like Cleveland said, "Kero-see ya later, air pollution!"

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image that resembles a scene from a Pixar movie, showing Cleveland's skyline shrouded in smog, with the city obscured by the hazy, polluted air. In contrast, depict a Peruvian cityscape with locals using kerosene lamps to protest, casting a warm glow against the darkened, smoky skyline. Ensure the contrast between the two locations is evident, emphasizing the impact of pollution and the resilience of the people. 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 Cleveland 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: Parallax art Art nouveau An image that looks like it is a scene from a Pixar movie Scalar art A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s
ChatGPT: An image that looks like it is a scene from a Pixar movie

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 Cleveland and Kerosene used in Peru, in the following situation: The smog in Cleveland was so thick that it traveled all the way to Peru and inspired the locals to start using kerosene lamps as a form of "light pollution" in protest! It seems the pollution in Cleveland really "fueled" a fiery passion for kerosene in Peru. Who knew that the air quality in one city could have such far-reaching "emissions" on another country's choice of lighting? It's like a global game of environmental telephone, where the message is "let's light things up with kerosene"! . Keep the description simple.
ChatGPT: In Cleveland, the smog hangs heavy in the air, obscuring the city skyline and creating a hazy, polluted atmosphere. Meanwhile, in Peru, locals have begun using kerosene lamps as a form of protest, casting a warm glow against the backdrop of a darkened, smoky skyline.

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: [ An image that looks like it is a scene from a Pixar movie ] and depicting this scene: In Cleveland, the smog hangs heavy in the air, obscuring the city skyline and creating a hazy, polluted atmosphere. Meanwhile, in Peru, locals have begun using kerosene lamps as a form of protest, casting a warm glow against the backdrop of a darkened, smoky skyline. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that resembles a scene from a Pixar movie, showing Cleveland's skyline shrouded in smog, with the city obscured by the hazy, polluted air. In contrast, depict a Peruvian cityscape with locals using kerosene lamps to protest, casting a warm glow against the darkened, smoky skyline. Ensure the contrast between the two locations is evident, emphasizing the impact of pollution and the resilience of the people.

*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 Cleveland caused Kerosene used in Peru to decrease. Include one good pun.

AI academic paper

(Because p < 0.01)

The Gasping Connection: Investigating the Relationship Between Air Pollution in Cleveland and Kerosene Consumption in Peru

The Journal of Global Air Quality and Combustion Studies

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 Air pollution in Cleveland 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 cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
"The Gasping Connection: Investigating the Relationship Between Air Pollution in Cleveland and Kerosene Consumption in Peru"

[[ABSTRACT]]
In this research study, we sought to unravel the curious connection between air pollution in Cleveland, Ohio, and kerosene consumption in the remote villages of Peru. Despite the geographical disparity, our findings revealed a surprisingly strong correlation between these two seemingly unrelated phenomena. Leveraging data from the Environmental Protection Agency (EPA) and the Energy Information Administration (EIA), our analysis yielded a correlation coefficient of 0.7707869 with statistical significance (p < 0.01) for the time period spanning from 1980 to 2021.

It became evident, much to our amusement, that the air pollution levels in Cleveland bore an uncanny resemblance to the patterns of kerosene usage in the remote regions of Peru. Our investigation leads us to hypothesize the existence of a "pollution teleconnection," a whimsical web of atmospheric interactions that transcends continents to link the industrialized urban scape of Cleveland with the rustic terrain of Peru.

While the specific mechanisms underlying this connection remain a puzzle, the implications of our findings beckon for further examination. This study showcases the unexpected linkages that can be unveiled through the prism of data analysis, underscoring the mysteries and mirthful surprises that abound in the ever-captivating realm of environmental research.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
Air pollution and its manifold consequences have long captured the attention of researchers and policymakers alike. Its implications for public health, environmental degradation, and regional economies have sparked numerous investigative endeavors, each seeking to unravel the tangle of factors contributing to the precarious state of our atmosphere. Likewise, the utilization of kerosene as a source of energy in remote, off-grid communities has piqued scholarly interest as a socio-economic and environmental concern. These seemingly disparate topics, air pollution in Cleveland and kerosene consumption in Peru, stand as curious curiosities in their own right. However, as we delved into the depths of data analysis, a surprising seam of correlation emerged, prompting us to scrutinize the possibility of a whimsical nexus binding these seemingly distant phenomena.
The juxtaposition of Cleveland, the industrial heartland with a legacy of steel and smokestacks, and remote villages in Peru, imbued with the rugged simplicity of kerosene lamps, may initially appear a tale of two worlds with nothing in common. However, as with many a captivating mystery, a deeper probing has divulged an unexpected correlation that challenges our preconceived notions. It is akin to stumbling upon an intricate relationship between a lumbering elephant and a nimble hummingbird; incongruous yet undeniably intertwined in the dance of ecological causality.
This study embarks on a peculiar quest to untangle the enigmatic enmeshment of air pollution in the great plains of Cleveland and the utilization of kerosene in the secluded enclaves of Peru. Armed with a bevy of data sources and the analytical tools of correlation and regression, we ventured forth to fathom the reality behind this surreal connection. As we ventured deeper into the labyrinth of statistical analysis, we found ourselves oscillating between the monotonous hum of numbers and the tantalizing allure of unraveling a puzzling riddle.
Fueled by a mix of curiosity and sheer bewilderment, we set out to dissect this unusual correlation, hoping to shed light on a captivating conundrum that transcends geographical boundaries and conventional wisdom. The unfolding tale of this empirical odyssey promises to illuminate the quirks and serendipities inherent in the domain of environmental research, reminding us of the whimsicality that lurks within the labyrinthine corridors of data analysis.
So join us, dear reader, as we embark on a journey through the misty miasma of air pollution in the Rock and Roll Capital of the World to the flickering glow of kerosene lamps in the Andean highlands. Together, we shall traverse the terrain of correlation and causality, all the while keeping a keen eye out for the unexpected thrills and wry amusements that await us in the world of scholarly inquiry.
But fear not, for despite the seriousness of our subject matter, we shall endeavor to keep our spirits aloft with an occasional frolic through the field of academic dryness – a whimsical waltz through the world of data analysis and statistical inference.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

A thorough review of existing literature has been conducted in order to contextualize our curious quest to unravel the peculiar linkage between air pollution in Cleveland and kerosene consumption in Peru. The pursuit of understanding this unorthodox coupling has led us through a mosaic of scholarly articles, non-fiction works by authoritative figures, fictional tales that inexplicably seem relevant, and even musings from the captivating cauldron of social media. While the terrain is varied and often unpredictable, our unwavering commitment to discerning the truth behind this captivating correlation has guided our exploration and shaped the tapestry of knowledge we present here.

In "Air Pollution and Its Impacts: A Comprehensive Analysis" by Smith et al., the authors find themselves immersed in the intricate web of factors contributing to air pollution, examining its deleterious effects on public health and the environment. Though captivating, the text regrettably lacks any mention of kerosene consumption in Peru, leaving us with a yearning for a more holistic understanding of the atmospheric interplay at hand.

Doe and Jones, in their seminal work "Kerosene: From Darkness to Light," deliver a thorough examination of kerosene as a primary source of energy in off-grid communities. While their insights are illuminating, the connection to air pollution in a far-off industrial mecca remains obscured, akin to a shadow skulking in the alleys of scholarly curiosity.

Seeking a broader perspective, we turned to non-fiction works such as "The Big Book of Air" by Lorem, and "Kerosene Chronicles" by Ipsum, hoping to glean insights that would shed light on our enigmatic linkage. Alas, while these tomes provide an abundance of knowledge on their respective subjects, the elusive connection between Cleveland's smog and Peru's kerosene lamps continues to evade direct elucidation, much like a hide-and-seek game played by capricious celestial bodies.

Considering the broader implications of environmental interconnectedness, we delved into the world of fiction, where the boundaries of reality blur and the whims of the imagination reign. Works such as "Smoke and Mirrors: A Tale of Urban Air" by Fictionus, and "Luminary Luminance: The Light Within" by Imagina, offer captivating narratives that, while not rooted in factual veracity, tangentially allude to themes that resonate with our investigation. The intersection of reality and imagination, much like the nebulous connection we seek to untangle, holds a certain mystique that cannot be easily dismissed.

The influence of social media musings on our modern intellectual landscape cannot be overlooked. In a serendipitous turn of events, we stumbled upon a tweet by @AirAndLampEnigma, proclaiming, "The dance of particulates from Cleveland to Peru, a ballad sung by the winds! #AtmosphericArias." While undeniably poetic, this cryptic message stokes the fires of intrigue, hinting at a harmonious symphony of atmospheric particles transcending geographic barriers. Whether this digital tidbit holds the key to our conundrum or serves as a mere distraction in the cacophony of social discourse remains to be seen.

As we sift through the kaleidoscope of literature and media, it is undeniable that the jigsaw puzzle of our research topic, though confounding, offers a feast of intellectual delights and whimsical curiosities. In the intricate mosaic of knowledge presented here, we invite the reader to join in our exploration of the unexpected, as we navigate the terrain of scholarly inquiry with equal measures of rigor and irreverence.

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 Air pollution in Cleveland 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. 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 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]]
"The Gasping Connection: Investigating the Relationship Between Air Pollution in Cleveland and Kerosene Consumption in Peru"

[[ABSTRACT]]
In this research study, we sought to unravel the curious connection between air pollution in Cleveland, Ohio, and kerosene consumption in the remote villages of Peru. Despite the geographical disparity, our findings revealed a surprisingly strong correlation between these two seemingly unrelated phenomena. Leveraging data from the Environmental Protection Agency (EPA) and the Energy Information Administration (EIA), our analysis yielded a correlation coefficient of 0.7707869 with statistical significance (p < 0.01) for the time period spanning from 1980 to 2021.

It became evident, much to our amusement, that the air pollution levels in Cleveland bore an uncanny resemblance to the patterns of kerosene usage in the remote regions of Peru. Our investigation leads us to hypothesize the existence of a "pollution teleconnection," a whimsical web of atmospheric interactions that transcends continents to link the industrialized urban scape of Cleveland with the rustic terrain of Peru.

While the specific mechanisms underlying this connection remain a puzzle, the implications of our findings beckon for further examination. This study showcases the unexpected linkages that can be unveiled through the prism of data analysis, underscoring the mysteries and mirthful surprises that abound in the ever-captivating realm of environmental research.

[[INTRODUCTION]]
INTRODUCTION
Air pollution and its manifold consequences have long captured the attention of researchers and policymakers alike. Its implications for public health, environmental degradation, and regional economies have sparked numerous investigative endeavors, each seeking to unravel the tangle of factors contributing to the precarious state of our atmosphere. Likewise, the utilization of kerosene as a source of energy in remote, off-grid communities has piqued scholarly interest as a socio-economic and environmental concern. These seemingly disparate topics, air pollution in Cleveland and kerosene consumption in Peru, stand as curious curiosities in their own right. However, as we delved into the depths of data analysis, a surprising seam of correlation emerged, prompting us to scrutinize the possibility of a whimsical nexus binding these seemingly distant phenomena.
The juxtaposition of Cleveland, the industrial heartland with a legacy of steel and smokestacks, and remote villages in Peru, imbued with the rugged simplicity of kerosene lamps, may initially appear a tale of two worlds with nothing in common. However, as with many a captivating mystery, a deeper probing has divulged an unexpected correlation that challenges our preconceived notions. It is akin to stumbling upon an intricate relationship between a lumbering elephant and a nimble hummingbird; incongruous yet undeniably intertwined in the dance of ecological causality.
This study embarks on a peculiar quest to untangle the enigmatic enmeshment of air pollution in the great plains of Cleveland and the utilization of kerosene in the secluded enclaves of Peru. Armed with a bevy of data sources and the analytical tools of correlation and regression, we ventured forth to fathom the reality behind this surreal connection. As we ventured deeper into the labyrinth of statistical analysis, we found ourselves oscillating between the monotonous hum of numbers and the tantalizing allure of unraveling a puzzling riddle.
Fueled by a mix of curiosity and sheer bewilderment, we set out to dissect this unusual correlation, hoping to shed light on a captivating conundrum that transcends geographical boundaries and conventional wisdom. The unfolding tale of this empirical odyssey promises to illuminate the quirks and serendipities inherent in the domain of environmental research, reminding us of the whimsicality that lurks within the labyrinthine corridors of data analysis.
So join us, dear reader, as we embark on a journey through the misty miasma of air pollution in the Rock and Roll Capital of the World to the flickering glow of kerosene lamps in the Andean highlands. Together, we shall traverse the terrain of correlation and causality, all the while keeping a keen eye out for the unexpected thrills and wry amusements that await us in the world of scholarly inquiry.
But fear not, for despite the seriousness of our subject matter, we shall endeavor to keep our spirits aloft with an occasional frolic through the field of academic dryness – a whimsical waltz through the world of data analysis and statistical inference.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To embark on our quest to decipher the enchanting enigma of the air pollution-kerosene consumption connection, we employed a kaleidoscopic array of research methods, blending the traditional with the avant-garde, much like a master chef creating a fusion dish of analytical approaches. Our primary data founts were the Environmental Protection Agency (EPA) and the Energy Information Administration (EIA), from which we drew copious gallons of statistical nectar. With the aid of these esteemed establishments, we conscripted data stretching from the groovy era of 1980 to the contemporary epoch of 2021, allowing us to witness the evolution of both air quality in Cleveland and the consumption of kerosene in Peru over a charmingly broad timespan.

Like detective sherlocks of the digital age, we engaged in a process of data sleuthing, combing through the labyrinthine archives of the internet to extract information on air pollutant levels in Cleveland and the usage of kerosene in the remotest nooks and crannies of Peru. This veritable treasure hunt demanded the nimbleness of an Olympic gymnast in navigating the digital landscape, as we carefully curated and cross-referenced a trove of datasets that would make even the most ardent hoarder green with envy.

Once armed with this arsenal of data, our next step involved a pas de deux with statistical analyses. We invoked the stalwart measures of correlation and regression to dance around the intricacies of our datasets, aiming to unveil any clandestine connections lurking beneath the surface. Like painters with an elaborate canvas, we splashed the numbers onto the analytical tableau, using mathematical brushes to tease out the patterns and relationships hidden within the data.

To ensure the robustness of our findings, we employed rigorous controls and covariates, akin to a conductor orchestrating a symphony of variables to harmonize with our research questions. By accounting for factors such as population density, economic indices, and geographic variables, we aimed to create a symphony of statistical significance that would leave our hypothesis resoundingly supported, much like a virtuoso performance of a scientific sonata.

In a nod to the whimsical nature of our investigation, we also indulged in a spot of speculative theorizing, proposing the existence of an ethereal "pollution teleconnection" that might serve as the invisible thread weaving together the disparate fates of air pollution in Cleveland and the utilization of kerosene in Peru. While this proposition may seem as fanciful as a unicorn trotting through statistical pastures, we cannot dismiss the possibility of such a wondrous linkage, given the captivating correlation we have unearthed.

Buoyed by a generous sprinkle of caution and an ample dash of statistical rigor, we present our methodology as a quirky yet robust framework for unraveling the enigmatic liaison between air pollution and kerosene consumption, setting the stage for a whimsical yet methodical explication of our research findings.

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 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 Cleveland 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. 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 Environmental Protection Agency and Energy Information Administration .

For the time period 1980 to 2021, you found a correlation 0.7707869, r-squared of 0.5941124, 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]]
"The Gasping Connection: Investigating the Relationship Between Air Pollution in Cleveland and Kerosene Consumption in Peru"

[[ABSTRACT]]
In this research study, we sought to unravel the curious connection between air pollution in Cleveland, Ohio, and kerosene consumption in the remote villages of Peru. Despite the geographical disparity, our findings revealed a surprisingly strong correlation between these two seemingly unrelated phenomena. Leveraging data from the Environmental Protection Agency (EPA) and the Energy Information Administration (EIA), our analysis yielded a correlation coefficient of 0.7707869 with statistical significance (p < 0.01) for the time period spanning from 1980 to 2021.

It became evident, much to our amusement, that the air pollution levels in Cleveland bore an uncanny resemblance to the patterns of kerosene usage in the remote regions of Peru. Our investigation leads us to hypothesize the existence of a "pollution teleconnection," a whimsical web of atmospheric interactions that transcends continents to link the industrialized urban scape of Cleveland with the rustic terrain of Peru.

While the specific mechanisms underlying this connection remain a puzzle, the implications of our findings beckon for further examination. This study showcases the unexpected linkages that can be unveiled through the prism of data analysis, underscoring the mysteries and mirthful surprises that abound in the ever-captivating realm of environmental research.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
INTRODUCTION
Air pollution and its manifold consequences have long captured the attention of researchers and policymakers alike. Its implications for public health, environmental degradation, and regional economies have sparked numerous investigative endeavors, each seeking to unravel the tangle of factors contributing to the precarious state of our atmosphere. Likewise, the utilization of kerosene as a source of energy in remote, off-grid communities has piqued scholarly interest as a socio-economic and environmental concern. These seemingly disparate topics, air pollution in Cleveland and kerosene consumption in Peru, stand as curious curiosities in their own right. However, as we delved into the depths of data analysis, a surprising seam of correlation emerged, prompting us to scrutinize the possibility of a whimsical nexus binding these seemingly distant phenomena.
The juxtaposition of Cleveland, the industrial heartland with a legacy of steel and smokestacks, and remote villages in Peru, imbued with the rugged simplicity of kerosene lamps, may initially appear a tale of two worlds with nothing in common. However, as with many a captivating mystery, a deeper probing has divulged an unexpected correlation that challenges our preconceived notions. It is akin to stumbling upon an intricate relationship between a lumbering elephant and a nimble hummingbird; incongruous yet undeniably intertwined in the dance of ecological causality.
This study embarks on a peculiar quest to untangle the enigmatic enmeshment of air pollution in the great plains of Cleveland and the utilization of kerosene in the secluded enclaves of Peru. Armed with a bevy of data sources and the analytical tools of correlation and regression, we ventured forth to fathom the reality behind this surreal connection. As we ventured deeper into the labyrinth of statistical analysis, we found ourselves oscillating between the monotonous hum of numbers and the tantalizing allure of unraveling a puzzling riddle.
Fueled by a mix of curiosity and sheer bewilderment, we set out to dissect this unusual correlation, hoping to shed light on a captivating conundrum that transcends geographical boundaries and conventional wisdom. The unfolding tale of this empirical odyssey promises to illuminate the quirks and serendipities inherent in the domain of environmental research, reminding us of the whimsicality that lurks within the labyrinthine corridors of data analysis.
So join us, dear reader, as we embark on a journey through the misty miasma of air pollution in the Rock and Roll Capital of the World to the flickering glow of kerosene lamps in the Andean highlands. Together, we shall traverse the terrain of correlation and causality, all the while keeping a keen eye out for the unexpected thrills and wry amusements that await us in the world of scholarly inquiry.
But fear not, for despite the seriousness of our subject matter, we shall endeavor to keep our spirits aloft with an occasional frolic through the field of academic dryness – a whimsical waltz through the world of data analysis and statistical inference.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The culmination of our analytical escapade revealed a substantial correlation between air pollution levels in Cleveland and kerosene consumption in Peru. We obtained a robust correlation coefficient of 0.7707869, indicating a strong positive relationship between these ostensibly unrelated variables. The coefficient of determination (r-squared) of 0.5941124 suggested that approximately 59.41% of the variability in kerosene usage in Peru could be explained by the corresponding variations in air pollution levels in Cleveland.

Our analysis further unveiled a statistically significant relationship, with the p-value being less than 0.01. This finding reinforces the robustness of the observed connection and bolsters our confidence in the validity of the results. In essence, the probability of observing such a strong correlation by sheer chance is incredibly low, adding weight to the credibility of our findings.

Fig. 1 depicts a scatterplot illustrating the pronounced correlation between air pollution in Cleveland and kerosene consumption in Peru. The data points coalesce into a discernible pattern, forming a trajectory that underscores the interwoven nature of these seemingly incongruent phenomena.

The discovery of this substantial correlation, while scientifically intriguing, also beckons us to contemplate the whimsical nature of the environmental world. It is as if the veils of geographical expanse are pierced by an invisible force, drawing together the emissions of an American metropolis and the glow of kerosene lamps in the Peruvian hinterlands. One cannot help but marvel at the peculiar threads that weave the fabric of our global environment, forming a tapestry that defies conventional expectations and beckons for further exploration.

As we bask in the glow of this unexpected correlation, we are reminded of the serendipitous surprises that lurk within the labyrinth of scientific inquiry. The journey from the smoke-laden horizons of Cleveland to the flickering constellations of kerosene lamps in Peru has unearthed a captivating tale of interconnectedness that transcends continents and defies facile explanation. With each step we took through the corridors of data and analysis, we found ourselves drawn into a tale of wonderment and discovery, where the mundane and the extraordinary intertwine in a delightful dance of statistical significance.

Our findings stand as a testament to the enigmatic allure of the environmental world and the unanticipated connections that permeate its vast tapestry. This study unfurls the beguiling tale of a "pollution teleconnection," inviting us to ponder the whimsical dance of causation and correlation that shapes our planet's ecological narrative. The melding of air pollution in Cleveland and kerosene consumption in Peru serves as a poignant reminder of the enduring mysteries that unfold when we peer through the looking glass of data analysis, prompting us to embrace the unexpected with scholarly vigor and a whimsical wink.

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 Air pollution in Cleveland 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. 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]]
"The Gasping Connection: Investigating the Relationship Between Air Pollution in Cleveland and Kerosene Consumption in Peru"

[[ABSTRACT]]
In this research study, we sought to unravel the curious connection between air pollution in Cleveland, Ohio, and kerosene consumption in the remote villages of Peru. Despite the geographical disparity, our findings revealed a surprisingly strong correlation between these two seemingly unrelated phenomena. Leveraging data from the Environmental Protection Agency (EPA) and the Energy Information Administration (EIA), our analysis yielded a correlation coefficient of 0.7707869 with statistical significance (p < 0.01) for the time period spanning from 1980 to 2021.

It became evident, much to our amusement, that the air pollution levels in Cleveland bore an uncanny resemblance to the patterns of kerosene usage in the remote regions of Peru. Our investigation leads us to hypothesize the existence of a "pollution teleconnection," a whimsical web of atmospheric interactions that transcends continents to link the industrialized urban scape of Cleveland with the rustic terrain of Peru.

While the specific mechanisms underlying this connection remain a puzzle, the implications of our findings beckon for further examination. This study showcases the unexpected linkages that can be unveiled through the prism of data analysis, underscoring the mysteries and mirthful surprises that abound in the ever-captivating realm of environmental research.

[[LITERATURE REVIEW]]
A thorough review of existing literature has been conducted in order to contextualize our curious quest to unravel the peculiar linkage between air pollution in Cleveland and kerosene consumption in Peru. The pursuit of understanding this unorthodox coupling has led us through a mosaic of scholarly articles, non-fiction works by authoritative figures, fictional tales that inexplicably seem relevant, and even musings from the captivating cauldron of social media. While the terrain is varied and often unpredictable, our unwavering commitment to discerning the truth behind this captivating correlation has guided our exploration and shaped the tapestry of knowledge we present here.
In "Air Pollution and Its Impacts: A Comprehensive Analysis" by Smith et al., the authors find themselves immersed in the intricate web of factors contributing to air pollution, examining its deleterious effects on public health and the environment. Though captivating, the text regrettably lacks any mention of kerosene consumption in Peru, leaving us with a yearning for a more holistic understanding of the atmospheric interplay at hand.
Doe and Jones, in their seminal work "Kerosene: From Darkness to Light," deliver a thorough examination of kerosene as a primary source of energy in off-grid communities. While their insights are illuminating, the connection to air pollution in a far-off industrial mecca remains obscured, akin to a shadow skulking in the alleys of scholarly curiosity.
Seeking a broader perspective, we turned to non-fiction works such as "The Big Book of Air" by Lorem, and "Kerosene Chronicles" by Ipsum, hoping to glean insights that would shed light on our enigmatic linkage. Alas, while these tomes provide an abundance of knowledge on their respective subjects, the elusive connection between Cleveland's smog and Peru's kerosene lamps continues to evade direct elucidation, much like a hide-and-seek game played by capricious celestial bodies.
Considering the broader implications of environmental interconnectedness, we delved into the world of fiction, where the boundaries of reality blur and the whims of the imagination reign. Works such as "Smoke and Mirrors: A Tale of Urban Air" by Fictionus, and "Luminary Luminance: The Light Within" by Imagina, offer captivating narratives that, while not rooted in factual veracity, tangentially allude to themes that resonate with our investigation. The intersection of reality and imagination, much like the nebulous connection we seek to untangle, holds a certain mystique that cannot be easily dismissed.
The influence of social media musings on our modern intellectual landscape cannot be overlooked. In a serendipitous turn of events, we stumbled upon a tweet by @AirAndLampEnigma, proclaiming, "The dance of particulates from Cleveland to Peru, a ballad sung by the winds! #AtmosphericArias." While undeniably poetic, this cryptic message stokes the fires of intrigue, hinting at a harmonious symphony of atmospheric particles transcending geographic barriers. Whether this digital tidbit holds the key to our conundrum or serves as a mere distraction in the cacophony of social discourse remains to be seen.
As we sift through the kaleidoscope of literature and media, it is undeniable that the jigsaw puzzle of our research topic, though confounding, offers a feast of intellectual delights and whimsical curiosities. In the intricate mosaic of knowledge presented here, we invite the reader to join in our exploration of the unexpected, as we navigate the terrain of scholarly inquiry with equal measures of rigor and irreverence.

[[RESULTS]]
The culmination of our analytical escapade revealed a substantial correlation between air pollution levels in Cleveland and kerosene consumption in Peru. We obtained a robust correlation coefficient of 0.7707869, indicating a strong positive relationship between these ostensibly unrelated variables. The coefficient of determination (r-squared) of 0.5941124 suggested that approximately 59.41% of the variability in kerosene usage in Peru could be explained by the corresponding variations in air pollution levels in Cleveland.
Our analysis further unveiled a statistically significant relationship, with the p-value being less than 0.01. This finding reinforces the robustness of the observed connection and bolsters our confidence in the validity of the results. In essence, the probability of observing such a strong correlation by sheer chance is incredibly low, adding weight to the credibility of our findings.
Fig. 1 depicts a scatterplot illustrating the pronounced correlation between air pollution in Cleveland and kerosene consumption in Peru. The data points coalesce into a discernible pattern, forming a trajectory that underscores the interwoven nature of these seemingly incongruent phenomena.
The discovery of this substantial correlation, while scientifically intriguing, also beckons us to contemplate the whimsical nature of the environmental world. It is as if the veils of geographical expanse are pierced by an invisible force, drawing together the emissions of an American metropolis and the glow of kerosene lamps in the Peruvian hinterlands. One cannot help but marvel at the peculiar threads that weave the fabric of our global environment, forming a tapestry that defies conventional expectations and beckons for further exploration.
As we bask in the glow of this unexpected correlation, we are reminded of the serendipitous surprises that lurk within the labyrinth of scientific inquiry. The journey from the smoke-laden horizons of Cleveland to the flickering constellations of kerosene lamps in Peru has unearthed a captivating tale of interconnectedness that transcends continents and defies facile explanation. With each step we took through the corridors of data and analysis, we found ourselves drawn into a tale of wonderment and discovery, where the mundane and the extraordinary intertwine in a delightful dance of statistical significance.
Our findings stand as a testament to the enigmatic allure of the environmental world and the unanticipated connections that permeate its vast tapestry. This study unfurls the beguiling tale of a "pollution teleconnection," inviting us to ponder the whimsical dance of causation and correlation that shapes our planet's ecological narrative. The melding of air pollution in Cleveland and kerosene consumption in Peru serves as a poignant reminder of the enduring mysteries that unfold when we peer through the looking glass of data analysis, prompting us to embrace the unexpected with scholarly vigor and a whimsical wink.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

In the light of our findings, the discussion turns to the enigmatic connection unveiled between air pollution in Cleveland and kerosene consumption in Peru. The statistically significant correlation we observed aligns with prior research, echoing the persistent murmurings in the literature about the intricate interplay of atmospheric phenomena and energy usage. While earlier works may not have explicitly espoused the whimsical theories we encountered in our literature review, our results provide empirical grounding for these elusive connections.

The curious magnetic pull between the industrial emissions of Cleveland and the radiant glow of kerosene lamps in Peru seems to defy conventional expectations, underscoring the capricious nature of environmental linkages. It is as if the winds themselves carry the serendipitous whispers of causation, intertwining the fumes of an urban landscape with the gentle flicker of rural illumination. This unexpected connection, enshrined in the statistical bedrock of our analysis, imbues the realm of environmental research with a mirthful mystique that elicits both scholarly wonder and bemused fascination.

The mystery of this alliance, akin to a captivating symphony composed by the fickle zephyrs, revives the speculative musings encountered in our literature review, where we encountered the poetic reverberations of @AirAndLampEnigma's cryptic tweet. This harmonious ballet of particulates and luminescence, distilled into the concrete manifestation of a robust correlation coefficient, imparts a sense of whimsy that tantalizes the scientific palate, inviting investigators to contemplate the potential machinations of this atmospheric waltz.

Delving further into the implications of our findings, it becomes evident that our results serve as a compelling testament to the breadth of environmental interconnectedness. The substantial correlation, while grounding our investigation in empirical rigidity, also beckons us to embrace the serendipitous surprises that often emerge from the nexus of data and inquiry. The panorama of scholarly discovery, rife with unexpected connections and beguiling revelations, enriches the discourse on environmental linkages with a wink and a nod to the whimsical stirrings that saturate our world.

Taken collectively, our study signals the profound allure of venturing beyond the confines of conventional expectations and embracing the unforeseen with scholarly rigor and a playful spirit. The unanticipated affinity between air pollution in Cleveland and kerosene consumption in Peru serves as a beckoning call to explore the frontiers of environmental interconnectedness, not merely as guideposts of empiricism, but as treasure maps to the whimsical wonders that persist in the ever-captivating dominion of environmental research.

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 Air pollution in Cleveland 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. 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]]
"The Gasping Connection: Investigating the Relationship Between Air Pollution in Cleveland and Kerosene Consumption in Peru"

[[ABSTRACT]]
In this research study, we sought to unravel the curious connection between air pollution in Cleveland, Ohio, and kerosene consumption in the remote villages of Peru. Despite the geographical disparity, our findings revealed a surprisingly strong correlation between these two seemingly unrelated phenomena. Leveraging data from the Environmental Protection Agency (EPA) and the Energy Information Administration (EIA), our analysis yielded a correlation coefficient of 0.7707869 with statistical significance (p < 0.01) for the time period spanning from 1980 to 2021.

It became evident, much to our amusement, that the air pollution levels in Cleveland bore an uncanny resemblance to the patterns of kerosene usage in the remote regions of Peru. Our investigation leads us to hypothesize the existence of a "pollution teleconnection," a whimsical web of atmospheric interactions that transcends continents to link the industrialized urban scape of Cleveland with the rustic terrain of Peru.

While the specific mechanisms underlying this connection remain a puzzle, the implications of our findings beckon for further examination. This study showcases the unexpected linkages that can be unveiled through the prism of data analysis, underscoring the mysteries and mirthful surprises that abound in the ever-captivating realm of environmental research.

[[INTRDUCTION]]
INTRODUCTION
Air pollution and its manifold consequences have long captured the attention of researchers and policymakers alike. Its implications for public health, environmental degradation, and regional economies have sparked numerous investigative endeavors, each seeking to unravel the tangle of factors contributing to the precarious state of our atmosphere. Likewise, the utilization of kerosene as a source of energy in remote, off-grid communities has piqued scholarly interest as a socio-economic and environmental concern. These seemingly disparate topics, air pollution in Cleveland and kerosene consumption in Peru, stand as curious curiosities in their own right. However, as we delved into the depths of data analysis, a surprising seam of correlation emerged, prompting us to scrutinize the possibility of a whimsical nexus binding these seemingly distant phenomena.
The juxtaposition of Cleveland, the industrial heartland with a legacy of steel and smokestacks, and remote villages in Peru, imbued with the rugged simplicity of kerosene lamps, may initially appear a tale of two worlds with nothing in common. However, as with many a captivating mystery, a deeper probing has divulged an unexpected correlation that challenges our preconceived notions. It is akin to stumbling upon an intricate relationship between a lumbering elephant and a nimble hummingbird; incongruous yet undeniably intertwined in the dance of ecological causality.
This study embarks on a peculiar quest to untangle the enigmatic enmeshment of air pollution in the great plains of Cleveland and the utilization of kerosene in the secluded enclaves of Peru. Armed with a bevy of data sources and the analytical tools of correlation and regression, we ventured forth to fathom the reality behind this surreal connection. As we ventured deeper into the labyrinth of statistical analysis, we found ourselves oscillating between the monotonous hum of numbers and the tantalizing allure of unraveling a puzzling riddle.
Fueled by a mix of curiosity and sheer bewilderment, we set out to dissect this unusual correlation, hoping to shed light on a captivating conundrum that transcends geographical boundaries and conventional wisdom. The unfolding tale of this empirical odyssey promises to illuminate the quirks and serendipities inherent in the domain of environmental research, reminding us of the whimsicality that lurks within the labyrinthine corridors of data analysis.
So join us, dear reader, as we embark on a journey through the misty miasma of air pollution in the Rock and Roll Capital of the World to the flickering glow of kerosene lamps in the Andean highlands. Together, we shall traverse the terrain of correlation and causality, all the while keeping a keen eye out for the unexpected thrills and wry amusements that await us in the world of scholarly inquiry.
But fear not, for despite the seriousness of our subject matter, we shall endeavor to keep our spirits aloft with an occasional frolic through the field of academic dryness – a whimsical waltz through the world of data analysis and statistical inference.

[[RESULTS]]
The culmination of our analytical escapade revealed a substantial correlation between air pollution levels in Cleveland and kerosene consumption in Peru. We obtained a robust correlation coefficient of 0.7707869, indicating a strong positive relationship between these ostensibly unrelated variables. The coefficient of determination (r-squared) of 0.5941124 suggested that approximately 59.41% of the variability in kerosene usage in Peru could be explained by the corresponding variations in air pollution levels in Cleveland.
Our analysis further unveiled a statistically significant relationship, with the p-value being less than 0.01. This finding reinforces the robustness of the observed connection and bolsters our confidence in the validity of the results. In essence, the probability of observing such a strong correlation by sheer chance is incredibly low, adding weight to the credibility of our findings.
Fig. 1 depicts a scatterplot illustrating the pronounced correlation between air pollution in Cleveland and kerosene consumption in Peru. The data points coalesce into a discernible pattern, forming a trajectory that underscores the interwoven nature of these seemingly incongruent phenomena.
The discovery of this substantial correlation, while scientifically intriguing, also beckons us to contemplate the whimsical nature of the environmental world. It is as if the veils of geographical expanse are pierced by an invisible force, drawing together the emissions of an American metropolis and the glow of kerosene lamps in the Peruvian hinterlands. One cannot help but marvel at the peculiar threads that weave the fabric of our global environment, forming a tapestry that defies conventional expectations and beckons for further exploration.
As we bask in the glow of this unexpected correlation, we are reminded of the serendipitous surprises that lurk within the labyrinth of scientific inquiry. The journey from the smoke-laden horizons of Cleveland to the flickering constellations of kerosene lamps in Peru has unearthed a captivating tale of interconnectedness that transcends continents and defies facile explanation. With each step we took through the corridors of data and analysis, we found ourselves drawn into a tale of wonderment and discovery, where the mundane and the extraordinary intertwine in a delightful dance of statistical significance.
Our findings stand as a testament to the enigmatic allure of the environmental world and the unanticipated connections that permeate its vast tapestry. This study unfurls the beguiling tale of a "pollution teleconnection," inviting us to ponder the whimsical dance of causation and correlation that shapes our planet's ecological narrative. The melding of air pollution in Cleveland and kerosene consumption in Peru serves as a poignant reminder of the enduring mysteries that unfold when we peer through the looking glass of data analysis, prompting us to embrace the unexpected with scholarly vigor and a whimsical wink.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation into the puzzling connection between air pollution in Cleveland and kerosene consumption in Peru has yielded a bountiful harvest of statistical insights and unfurled the enigmatic tale of a "pollution teleconnection" that transcends continents. The robust correlation coefficient of 0.7707869, coupled with a statistically significant p-value, serves as a testament to the unexpected interplay between these seemingly incongruent phenomena. While we may still be scratching our heads about the precise mechanisms underlying this whimsical nexus, our findings underscore the whimsicality and serendipitous surprises that abound in the realm of environmental research.

The discovery of this correlation, though scientifically intriguing, hints at the playful caprice that often permeates the vast tapestry of environmental phenomena. Like an intricate game of connect-the-dots, our study has unveiled a curiously captivating link that defies our conventional expectations, much like stumbling upon a polar bear on a tropical island – utterly surprising yet undeniably there. While the dance of causation and correlation may remain shrouded in mystery, our findings exhort us to embrace the tangible quirks and uncertainties inherent in the pursuit of scholarly inquiry.

Hence, with the unveiling of this utterly perplexing connection, we dare say that our investigation has reached its zenith, much like a peregrine falcon soaring through the azure sky – majestic and resolute. No further research is needed in this area, as we trust that future scholars will be hard-pressed to match the sheer whimsy and wonderment that our foray has elucidated. And with that, we bid adieu to this peculiar pilgrimage, content in the knowledge that the nexus of air pollution in Cleveland and kerosene consumption in Peru shall remain a delightful enigma for the scholarly world to ponder for generations to come.

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 The Gasping Connection: Investigating the Relationship Between Air Pollution in Cleveland and Kerosene Consumption in Peru. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Global Air Quality and Combustion Studies

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 The Gasping Connection: Investigating the Relationship Between Air Pollution in Cleveland and Kerosene Consumption in Peru. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Global Air Quality and Energy Consumption Analysis

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 The Gasping Connection: Investigating the Relationship Between Air Pollution in Cleveland and Kerosene Consumption in Peru with an abstract of In this research study, we sought to unravel the curious connection between air pollution in Cleveland, Ohio, and kerosene consumption in the remote villages of Peru. Despite the geographical disparity, our findings revealed a surprisingly strong correlation between these two seemingly unrelated phenomena. Leveraging data from the Environmental Protection Agency (EPA) and the Energy Information Administration (EIA), our analysis yielded a correlation coefficient of 0.7707869 with statistical significance (p < 0.01) for the time period spanning from 1980 to 2021.
It became evident, much to our amusement, that the air pollution levels in Cleveland bore an uncanny resemblance to the patterns of kerosene usage in the remote regions of Peru. Our investigation leads us to hypothesize the existence of a "pollution teleconnection," a whimsical web of atmospheric interactions that transcends continents to link the industrialized urban scape of Cleveland with the rustic terrain of Peru.
While the specific mechanisms underlying this connection remain a puzzle, the implications of our findings beckon for further examination. This study showcases the unexpected linkages that can be unveiled through the prism of data analysis, underscoring the mysteries and mirthful surprises that abound in the ever-captivating realm of environmental research.

ChatGPT:

air pollution, kerosene consumption, Cleveland, Ohio, Peru, correlation, EPA data, EIA data, pollution teleconnection, atmospheric interactions, data analysis, environmental research

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

Random correlation

Discover a new correlation

View all correlations

View all research papers

Report an error

Data details

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

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 = 0.7707869 (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 = 0.5941124 (Coefficient of determination)
This means 59.4% 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 Cleveland) over the 42 years from 1980 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.4E-9.^Show 0.0000000023581277579261883000
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 0.77 in 2.4E-7% of random cases. Said differently, if you correlated 424,065,234 random variables^Note You don't actually need 424 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 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.

[ 0.61, 0.87 ] 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 Cleveland (Bad air quality days)	3.00546	4.93151	4.65753	9.31507	4.64481	3.83562	3.56164	4.38356	12.8415	4.65753	2.46575	8.21918	2.18579	4.65753	5.47945	5.75342	4.64481	3.28767	5.20548	6.0274	2.18579	6.57534	7.94521	2.46575	0.546448	4.65753	1.09589	1.36986	0.819672	0.547945	1.09589	1.09589	3.00546	0.273973	0.273973	0	0	0	0	0	0.273224	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([3.00546,4.93151,4.65753,9.31507,4.64481,3.83562,3.56164,4.38356,12.8415,4.65753,2.46575,8.21918,2.18579,4.65753,5.47945,5.75342,4.64481,3.28767,5.20548,6.0274,2.18579,6.57534,7.94521,2.46575,0.546448,4.65753,1.09589,1.36986,0.819672,0.547945,1.09589,1.09589,3.00546,0.273973,0.273973,0,0,0,0,0,0.273224,0,])
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 Cleveland"
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 Cleveland
Line chart for only Kerosene used in Peru
AI-generated correlation image
The spurious research paper: The Gasping Connection: Investigating the Relationship Between Air Pollution in Cleveland and Kerosene Consumption in Peru

View another random correlation

How fun was this correlation?

Your dedication to rating warms my heart!

Correlation ID: 2460 · Black Variable ID: 20474 · Red Variable ID: 24811


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