Spurious correlation #5,506 · 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 New York City and the second variable is Petroluem consumption in Bulgaria. 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 New York City and the second variable is Petroluem consumption in Bulgaria. The chart goes from 1980 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

Every time a New Yorker takes a deep breath of cleaner air, a Bulgarian car magically sputters and consumes less petroluem. It's like an unintentional, transcontinental game of environmental 'Simon Says'.

^{Show image generation prompt} Model: dalle-3
Prompt: [ Hyper-realistic ] In a bustling New York City street, the scene captures a person standing amidst the swirling energy of the city as they inhale deeply, with a sense of tranquility contrasting the bustling atmosphere. Across the ocean in Bulgaria, a car is depicted in a detailed manner, emitting a mesmerizing glow as it appears to be sputtering less petroleum, symbolizing a magical transformation. The distant events are depicted in a singular frame, visually connected by a subtle, silent dance of unintentional environmental impact. 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 New York City and Petroluem consumption in Bulgaria. 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: Sci-fi fantacy Concept art Hyper-realistic Psychedelic Glitch art
ChatGPT: Hyper-realistic

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 New York City and Petroluem consumption in Bulgaria, in the following situation: Every time a New Yorker takes a deep breath of cleaner air, a Bulgarian car magically sputters and consumes less petroluem. It's like an unintentional, transcontinental game of environmental 'Simon Says'.. Keep the description simple.
ChatGPT: In a bustling New York City street, a person takes a deep breath of cleaner air, while across the ocean in Bulgaria, a car magically sputters and consumes less petroleum. The two distant events are connected in a silent dance of unintentional environmental impact.

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: [ Hyper-realistic ] and depicting this scene: In a bustling New York City street, a person takes a deep breath of cleaner air, while across the ocean in Bulgaria, a car magically sputters and consumes less petroleum. The two distant events are connected in a silent dance of unintentional environmental impact. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ Hyper-realistic ] In a bustling New York City street, the scene captures a person standing amidst the swirling energy of the city as they inhale deeply, with a sense of tranquility contrasting the bustling atmosphere. Across the ocean in Bulgaria, a car is depicted in a detailed manner, emitting a mesmerizing glow as it appears to be sputtering less petroleum, symbolizing a magical transformation. The distant events are depicted in a singular frame, visually connected by a subtle, silent dance of unintentional environmental impact.

*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 New York City caused Petroluem consumption in Bulgaria to decrease.

AI academic paper

(Because p < 0.01)

The Smog and the Furious: Uncovering the Relationship Between Air Pollution in New York City and Petroleum Consumption in Bulgaria

Journal of Environmental Quirks and Curiosities

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 New York City and Petroluem consumption in Bulgaria. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.

Make up the lorem and ipsum part, but make it sound related to the topic at hand.

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading the backs of shampoo bottles.

Here is the title and abstract of the paper:
[[TITLE]]
"The Smog and the Furious: Uncovering the Relationship Between Air Pollution in New York City and Petroleum Consumption in Bulgaria"

[[ABSTRACT]]
In this study, we endeavor to delve into the often overlooked, yet undeniably fascinating, link between air pollution in the bustling metropolis of New York City and the rather unexpected realm of petroleum consumption in the charming country of Bulgaria. Using comprehensive data from the Environmental Protection Agency and the Energy Information Administration, we conducted a thorough analysis covering the years 1980 to 2021. Our findings revealed a striking correlation coefficient of 0.9007602 and a p-value of less than 0.01, indicating a strong and statistically significant relationship between these seemingly disparate factors. We unpack the complex web of connections between these variables, shedding light on the intertwined nature of global environmental and economic patterns. Our study not only contributes to the body of knowledge in environmental and economic research, but also offers a whimsical perspective on the interconnectedness of the world, linking the streets of New York to the streets of Sofia in an unexpected twist of fate.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
As the world grapples with the pressing issues of air pollution and petroleum consumption, we find ourselves drawn to the unlikely pairing of New York City's smog-choked streets and Bulgaria's reliance on petroleum. This peculiar connection, much like a mad scientist's experiment gone awry, piqued our curiosity and led us down the rabbit hole of data analysis and statistical wizardry.
The great urban jungle of New York City, with its towering skyscrapers and bustling streets, stands as a symbol of human ambition and progress. Meanwhile, nestled in the charming landscapes of Bulgaria, petroleum consumption quietly hums along, much like a background melody in an orchestra of economic activity. These two seemingly incongruent elements come together in a dance of correlation and causation, showing us that the world of scientific research is full of unexpected waltzes and tangoes.
In this study, we aim to unravel the intricate tango between these variables, examining their relationship with the precision of a scientist in a lab, albeit with a touch of whimsy and a sprinkle of statistical fairy dust. Our analysis encompasses a time span from 1980 to 2021, allowing us to witness the ebb and flow of air pollution in the Big Apple and the delicate fluctuations of petroleum consumption in the Balkan gem of Bulgaria.
Armed with comprehensive data from the Environmental Protection Agency and the Energy Information Administration, we embarked on a journey akin to explorers charting new territories, armed with the compass of scientific inquiry and the map of economic indicators. Our findings, much like buried treasure on a scientific expedition, revealed a striking correlation coefficient of 0.9007602, a diamond in the rough amidst the sea of statistical noise. Add a p-value of less than 0.01 to the mix, and we have a recipe for a statistical feast that would make the most discerning data gourmands salivate.
But let us not lose sight of the forest for the trees, or the molecules for the atoms if you will. Beyond the numbers and calculations lies a tale of interconnectedness—a narrative of how the tendrils of air pollution in one corner of the world reach out to intertwine with the patterns of petroleum consumption in another, like a cosmic game of cat's cradle played by the forces of environmental impact and economic activity.
In the spirit of scientific inquiry and a dash of levity, our study not only unravels these tendrils but also offers a glimpse into the whimsical nature of the world, where the streets of New York City and the boulevards of Sofia meet in an unexpected narrative of environmental and economic symbiosis. So, dear reader, buckle up your intellectual seatbelts as we embark on this scholarly rollercoaster ride, where data meets humor, and science meets serendipity.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The relationship between air pollution and petroleum consumption has garnered significant attention in the fields of environmental science and economics. Smith et al. (2018) conducted a comprehensive study on the environmental impact of air pollution in urban centers, emphasizing the need for sustainable policies to mitigate its detrimental effects. Similarly, Doe and Jones (2020) explored the economic and geopolitical factors influencing petroleum consumption in Eastern Europe, delving into the intricate web of global energy markets. These serious scholarly works lay the groundwork for our inquiry into the unexpected connection between air pollution in New York City and petroleum consumption in Bulgaria.

In "Air Pollution and Human Health" by Johnson and Smith, the authors find compelling evidence linking air pollution to respiratory diseases, reminding us that the air we breathe is not just a blend of oxygen, nitrogen, and other gases, but also a cocktail of pollutants that can wreak havoc on our well-being. On the economic front, "The Economics of Petroleum" by Adams and Davis provides an insightful exploration of the complex dynamics shaping petroleum trade and consumption, shedding light on the critical role of energy resources in global economies.

Turning to the realm of fiction, J.K. Rowling's "Harry Potter and the Sorcerer's Stone" might seem like an unlikely candidate for relevance, but consider the scene where Harry is transported from the bustling streets of London to the enchanting alleys of Diagon Alley. Much like this magical transition, our study aims to uncover the invisible channels connecting the urban landscapes of New York City to the economic tapestry of Bulgaria. Meanwhile, in "The Hobbit" by J.R.R. Tolkien, the unexpected journey undertaken by Bilbo Baggins mirrors our own foray into the uncharted territory of interdisciplinary research, where the quest for knowledge is filled with twists, turns, and encounters with unexpected connections.

As we venture further into the literary abyss, let us not overlook the valuable insights lurking in the unlikeliest of places. The backs of shampoo bottles, with their tantalizing promises of silky-smooth hair and exotic fragrances, may seem trivial at first glance. However, upon closer inspection, we find a hidden allegory of the interconnectedness of human activity and environmental impact—a reminder that even the most ordinary objects can offer profound revelations. And so, in the spirit of scholarly exploration, we embrace the whimsy of unconventional sources, uncovering the thread that weaves together air pollution in New York City and petroleum consumption in Bulgaria in a tapestry of scientific curiosity and unexpected hilarity.

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 New York City and Petroluem consumption in Bulgaria. 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]]
"The Smog and the Furious: Uncovering the Relationship Between Air Pollution in New York City and Petroleum Consumption in Bulgaria"

[[ABSTRACT]]
In this study, we endeavor to delve into the often overlooked, yet undeniably fascinating, link between air pollution in the bustling metropolis of New York City and the rather unexpected realm of petroleum consumption in the charming country of Bulgaria. Using comprehensive data from the Environmental Protection Agency and the Energy Information Administration, we conducted a thorough analysis covering the years 1980 to 2021. Our findings revealed a striking correlation coefficient of 0.9007602 and a p-value of less than 0.01, indicating a strong and statistically significant relationship between these seemingly disparate factors. We unpack the complex web of connections between these variables, shedding light on the intertwined nature of global environmental and economic patterns. Our study not only contributes to the body of knowledge in environmental and economic research, but also offers a whimsical perspective on the interconnectedness of the world, linking the streets of New York to the streets of Sofia in an unexpected twist of fate.

[[INTRODUCTION]]
As the world grapples with the pressing issues of air pollution and petroleum consumption, we find ourselves drawn to the unlikely pairing of New York City's smog-choked streets and Bulgaria's reliance on petroleum. This peculiar connection, much like a mad scientist's experiment gone awry, piqued our curiosity and led us down the rabbit hole of data analysis and statistical wizardry.
The great urban jungle of New York City, with its towering skyscrapers and bustling streets, stands as a symbol of human ambition and progress. Meanwhile, nestled in the charming landscapes of Bulgaria, petroleum consumption quietly hums along, much like a background melody in an orchestra of economic activity. These two seemingly incongruent elements come together in a dance of correlation and causation, showing us that the world of scientific research is full of unexpected waltzes and tangoes.
In this study, we aim to unravel the intricate tango between these variables, examining their relationship with the precision of a scientist in a lab, albeit with a touch of whimsy and a sprinkle of statistical fairy dust. Our analysis encompasses a time span from 1980 to 2021, allowing us to witness the ebb and flow of air pollution in the Big Apple and the delicate fluctuations of petroleum consumption in the Balkan gem of Bulgaria.
Armed with comprehensive data from the Environmental Protection Agency and the Energy Information Administration, we embarked on a journey akin to explorers charting new territories, armed with the compass of scientific inquiry and the map of economic indicators. Our findings, much like buried treasure on a scientific expedition, revealed a striking correlation coefficient of 0.9007602, a diamond in the rough amidst the sea of statistical noise. Add a p-value of less than 0.01 to the mix, and we have a recipe for a statistical feast that would make the most discerning data gourmands salivate.
But let us not lose sight of the forest for the trees, or the molecules for the atoms if you will. Beyond the numbers and calculations lies a tale of interconnectedness—a narrative of how the tendrils of air pollution in one corner of the world reach out to intertwine with the patterns of petroleum consumption in another, like a cosmic game of cat's cradle played by the forces of environmental impact and economic activity.
In the spirit of scientific inquiry and a dash of levity, our study not only unravels these tendrils but also offers a glimpse into the whimsical nature of the world, where the streets of New York City and the boulevards of Sofia meet in an unexpected narrative of environmental and economic symbiosis. So, dear reader, buckle up your intellectual seatbelts as we embark on this scholarly rollercoaster ride, where data meets humor, and science meets serendipity.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

In this section, we outline the convoluted yet meticulously crafted research methods employed to untangle the enigmatic connection between air pollution in New York City and petroleum consumption in Bulgaria. Our approach, much like a wily detective solving a perplexing case, involved a blend of quantitative analysis, data wrangling, and a hint of statistical sorcery.

Data Collection:
Our intrepid research team scoured the vast expanse of the internet, sifting through a myriad of websites and databases like archaeologists on a quest for ancient artifacts. In our pursuit, we focused primarily on data provided by the Environmental Protection Agency and the Energy Information Administration, akin to eager treasure hunters seeking elusive riches. The data spanned the years 1980 to 2021, offering a panoramic view of the evolving landscapes of air pollution and petroleum consumption.

Quantitative Analysis:
Armed with mountains of data and enough statistical software to make the most dedicated number-cruncher weak in the knees, we delved into the heart of quantitative analysis. Our trusty tools of the trade included regression analysis, time series modeling, and correlation calculations, akin to a craftsman deftly shaping raw materials into a work of art. These methods allowed us to unveil the hidden relationships and patterns concealed within the labyrinth of numerical values, much like deciphering an ancient code using the language of mathematics.

Normalization and Transformation:
In our quest for scientific truth, we encountered the need to normalize and transform our data, much like alchemists harnessing the powers of transmutation. Through the magical art of normalization, we set out to bring order to the chaos, ensuring that our variables danced to the same statistical tune. Meanwhile, the transformative powers of logarithmic and exponential functions lent us a hand in taming the wild fluctuations and unleashing the underlying trends buried within the data, not unlike a scientist coaxing a shy subject into revealing its secrets.

Cross-Cultural Contextualization:
As we delved into the intriguing interplay between air pollution in the bustling streets of New York and the ebb and flow of petroleum consumption in the charming byways of Bulgaria, we recognized the need for cross-cultural contextualization. Much like intrepid explorers immersed in unfamiliar lands, we sought to understand the nuanced nuances of environmental and economic dynamics in these disparate locales. By leveraging insights from atmospheric science and economic geology, we bridged the divide between these seemingly distant domains, weaving a narrative tapestry that connected the dots between urban smog and energy consumption with the finesse of a seasoned storyteller.

Validation and Sensitivity Analysis:
In our scientific odyssey, we ventured into the domain of validation and sensitivity analysis, akin to fearless voyagers navigating treacherous waters in search of truth. Through rigorous validation exercises and sensitivity tests, we sought to ensure the robustness and reliability of our findings, much like stress-testing the structural integrity of a scientific hypothesis. By subjecting our models to a barrage of tests and challenges, we emerged with a newfound confidence in the validity of our results, akin to a gallant knight emerging victorious from the trials of scientific rigor.

Experimental Controls (or Lack Thereof):
It is worth noting that, much like a mad scientist caught up in the throes of unorthodox experimentation, our study grappled with the inherent challenge of experimental controls. In the realm of observational data from real-world phenomena, controlling for all possible confounding variables proved to be a Herculean task, not unlike herding cats or balancing chemical equations with an ever-changing set of elements. As such, our findings should be interpreted within the context of the complex web of environmental and economic factors at play, with a healthy dose of scientific humility and a pinch of statistical skepticism.

Ethical Considerations:
Amidst our scientific exploits, we upheld the ethical principles of research integrity and data privacy, much like guardians of an intellectual citadel entrusted with the safeguarding of knowledge. Our utilization of publicly available data sources adhered to the tenets of academic transparency and intellectual honesty, ensuring that our scholarly pursuits contributed to the advancement of knowledge while respecting the boundaries of data stewardship.

By navigating this convoluted labyrinth of research methods with equal parts scientific rigor and whimsical curiosity, we endeavored to shed light on the mesmerizing intersection of air pollution in New York City and petroleum consumption in Bulgaria, casting a ray of statistical illumination on the interconnected dance of environmental impact and economic activity.

In the forthcoming section, we unveil the mesmerizing findings of our intrepid scientific journey, where the waltz of data meets the tango of statistical discovery. Prepare to be amazed, amused, and perhaps even enlightened, as we unravel the mysteries hidden within the fabric of air pollution and petroleum consumption in this captivating tale of scientific inquiry.

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 New York City and Petroluem consumption in Bulgaria. 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 0.9007602, r-squared of 0.8113689, 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 Smog and the Furious: Uncovering the Relationship Between Air Pollution in New York City and Petroleum Consumption in Bulgaria"

[[ABSTRACT]]
In this study, we endeavor to delve into the often overlooked, yet undeniably fascinating, link between air pollution in the bustling metropolis of New York City and the rather unexpected realm of petroleum consumption in the charming country of Bulgaria. Using comprehensive data from the Environmental Protection Agency and the Energy Information Administration, we conducted a thorough analysis covering the years 1980 to 2021. Our findings revealed a striking correlation coefficient of 0.9007602 and a p-value of less than 0.01, indicating a strong and statistically significant relationship between these seemingly disparate factors. We unpack the complex web of connections between these variables, shedding light on the intertwined nature of global environmental and economic patterns. Our study not only contributes to the body of knowledge in environmental and economic research, but also offers a whimsical perspective on the interconnectedness of the world, linking the streets of New York to the streets of Sofia in an unexpected twist of fate.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
As the world grapples with the pressing issues of air pollution and petroleum consumption, we find ourselves drawn to the unlikely pairing of New York City's smog-choked streets and Bulgaria's reliance on petroleum. This peculiar connection, much like a mad scientist's experiment gone awry, piqued our curiosity and led us down the rabbit hole of data analysis and statistical wizardry.
The great urban jungle of New York City, with its towering skyscrapers and bustling streets, stands as a symbol of human ambition and progress. Meanwhile, nestled in the charming landscapes of Bulgaria, petroleum consumption quietly hums along, much like a background melody in an orchestra of economic activity. These two seemingly incongruent elements come together in a dance of correlation and causation, showing us that the world of scientific research is full of unexpected waltzes and tangoes.
In this study, we aim to unravel the intricate tango between these variables, examining their relationship with the precision of a scientist in a lab, albeit with a touch of whimsy and a sprinkle of statistical fairy dust. Our analysis encompasses a time span from 1980 to 2021, allowing us to witness the ebb and flow of air pollution in the Big Apple and the delicate fluctuations of petroleum consumption in the Balkan gem of Bulgaria.
Armed with comprehensive data from the Environmental Protection Agency and the Energy Information Administration, we embarked on a journey akin to explorers charting new territories, armed with the compass of scientific inquiry and the map of economic indicators. Our findings, much like buried treasure on a scientific expedition, revealed a striking correlation coefficient of 0.9007602, a diamond in the rough amidst the sea of statistical noise. Add a p-value of less than 0.01 to the mix, and we have a recipe for a statistical feast that would make the most discerning data gourmands salivate.
But let us not lose sight of the forest for the trees, or the molecules for the atoms if you will. Beyond the numbers and calculations lies a tale of interconnectedness—a narrative of how the tendrils of air pollution in one corner of the world reach out to intertwine with the patterns of petroleum consumption in another, like a cosmic game of cat's cradle played by the forces of environmental impact and economic activity.
In the spirit of scientific inquiry and a dash of levity, our study not only unravels these tendrils but also offers a glimpse into the whimsical nature of the world, where the streets of New York City and the boulevards of Sofia meet in an unexpected narrative of environmental and economic symbiosis. So, dear reader, buckle up your intellectual seatbelts as we embark on this scholarly rollercoaster ride, where data meets humor, and science meets serendipity.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

Our analysis of the relationship between air pollution in New York City and petroleum consumption in Bulgaria yielded remarkable insights that not only electrified our scientific senses but also tickled our statistical fancies. We harnessed the power of data from the Environmental Protection Agency and the Energy Information Administration to unravel the mysterious dance of correlation between these seemingly disparate variables.

Delving into the numerical nitty-gritty, we uncovered a correlation coefficient of 0.9007602, indicative of a strong positive linear relationship. This correlation coefficient was as sturdy as a high-rise building in Manhattan, standing tall and proud in the face of statistical scrutiny. The R-squared value of 0.8113689 further supported this robust association, signifying that over 80% of the variation in petroleum consumption in Bulgaria could be explained by the levels of air pollution in New York City. It's as if the Big Apple cast a shadow over the oil barrels in Bulgaria, creating an intricate web of environmental and economic interplay that didn't fail to captivate our scientific imaginations.

In the realm of statistical significance, our findings did not disappoint, boasting a p-value of less than 0.01. This result was more rock-solid than a Bulgarian fortress, providing compelling evidence that the relationship between air pollution in New York City and petroleum consumption in Bulgaria was not a mere statistical fluke. The odds of this correlation occurring by random chance were slimmer than a microbe's waistline, reinforcing the notion that there's more to this connection than meets the eye.

Now, to visually capture the essence of this remarkable relationship, we present Figure 1, a scatterplot that paints a vivid picture of the tight bond between air pollution in New York City and petroleum consumption in Bulgaria (Fig. 1). Behold as the data points form a mesmerizing constellation, telling a tale of environmental impact and economic activity intertwining in a dance of correlation that could rival the elegance of a Viennese waltz-off.

Our results not only contribute to the scientific understanding of global environmental and economic patterns but also serve as a whimsical reminder of the interconnectedness of our world, where the streets of New York City and the highways of Bulgaria converge in a statistical symphony that defies conventional research expectations. With these findings, the bond between air pollution and petroleum consumption emerges as a fascinating subplot in the grand narrative of environmental and economic symbiosis, reminding us that in the world of science, as in life, unexpected connections often lead to the most intriguing discoveries.

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 New York City and Petroluem consumption in Bulgaria. 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]]
"The Smog and the Furious: Uncovering the Relationship Between Air Pollution in New York City and Petroleum Consumption in Bulgaria"

[[ABSTRACT]]
In this study, we endeavor to delve into the often overlooked, yet undeniably fascinating, link between air pollution in the bustling metropolis of New York City and the rather unexpected realm of petroleum consumption in the charming country of Bulgaria. Using comprehensive data from the Environmental Protection Agency and the Energy Information Administration, we conducted a thorough analysis covering the years 1980 to 2021. Our findings revealed a striking correlation coefficient of 0.9007602 and a p-value of less than 0.01, indicating a strong and statistically significant relationship between these seemingly disparate factors. We unpack the complex web of connections between these variables, shedding light on the intertwined nature of global environmental and economic patterns. Our study not only contributes to the body of knowledge in environmental and economic research, but also offers a whimsical perspective on the interconnectedness of the world, linking the streets of New York to the streets of Sofia in an unexpected twist of fate.

[[LITERATURE REVIEW]]
The relationship between air pollution and petroleum consumption has garnered significant attention in the fields of environmental science and economics. Smith et al. (2018) conducted a comprehensive study on the environmental impact of air pollution in urban centers, emphasizing the need for sustainable policies to mitigate its detrimental effects. Similarly, Doe and Jones (2020) explored the economic and geopolitical factors influencing petroleum consumption in Eastern Europe, delving into the intricate web of global energy markets. These serious scholarly works lay the groundwork for our inquiry into the unexpected connection between air pollution in New York City and petroleum consumption in Bulgaria.
In "Air Pollution and Human Health" by Johnson and Smith, the authors find compelling evidence linking air pollution to respiratory diseases, reminding us that the air we breathe is not just a blend of oxygen, nitrogen, and other gases, but also a cocktail of pollutants that can wreak havoc on our well-being. On the economic front, "The Economics of Petroleum" by Adams and Davis provides an insightful exploration of the complex dynamics shaping petroleum trade and consumption, shedding light on the critical role of energy resources in global economies.
Turning to the realm of fiction, J.K. Rowling's "Harry Potter and the Sorcerer's Stone" might seem like an unlikely candidate for relevance, but consider the scene where Harry is transported from the bustling streets of London to the enchanting alleys of Diagon Alley. Much like this magical transition, our study aims to uncover the invisible channels connecting the urban landscapes of New York City to the economic tapestry of Bulgaria. Meanwhile, in "The Hobbit" by J.R.R. Tolkien, the unexpected journey undertaken by Bilbo Baggins mirrors our own foray into the uncharted territory of interdisciplinary research, where the quest for knowledge is filled with twists, turns, and encounters with unexpected connections.
As we venture further into the literary abyss, let us not overlook the valuable insights lurking in the unlikeliest of places. The backs of shampoo bottles, with their tantalizing promises of silky-smooth hair and exotic fragrances, may seem trivial at first glance. However, upon closer inspection, we find a hidden allegory of the interconnectedness of human activity and environmental impact—a reminder that even the most ordinary objects can offer profound revelations. And so, in the spirit of scholarly exploration, we embrace the whimsy of unconventional sources, uncovering the thread that weaves together air pollution in New York City and petroleum consumption in Bulgaria in a tapestry of scientific curiosity and unexpected hilarity.

[[RESULTS]]
Our analysis of the relationship between air pollution in New York City and petroleum consumption in Bulgaria yielded remarkable insights that not only electrified our scientific senses but also tickled our statistical fancies. We harnessed the power of data from the Environmental Protection Agency and the Energy Information Administration to unravel the mysterious dance of correlation between these seemingly disparate variables.
Delving into the numerical nitty-gritty, we uncovered a correlation coefficient of 0.9007602, indicative of a strong positive linear relationship. This correlation coefficient was as sturdy as a high-rise building in Manhattan, standing tall and proud in the face of statistical scrutiny. The R-squared value of 0.8113689 further supported this robust association, signifying that over 80% of the variation in petroleum consumption in Bulgaria could be explained by the levels of air pollution in New York City. It's as if the Big Apple cast a shadow over the oil barrels in Bulgaria, creating an intricate web of environmental and economic interplay that didn't fail to captivate our scientific imaginations.
In the realm of statistical significance, our findings did not disappoint, boasting a p-value of less than 0.01. This result was more rock-solid than a Bulgarian fortress, providing compelling evidence that the relationship between air pollution in New York City and petroleum consumption in Bulgaria was not a mere statistical fluke. The odds of this correlation occurring by random chance were slimmer than a microbe's waistline, reinforcing the notion that there's more to this connection than meets the eye.
Now, to visually capture the essence of this remarkable relationship, we present Figure 1, a scatterplot that paints a vivid picture of the tight bond between air pollution in New York City and petroleum consumption in Bulgaria (Fig. 1). Behold as the data points form a mesmerizing constellation, telling a tale of environmental impact and economic activity intertwining in a dance of correlation that could rival the elegance of a Viennese waltz-off.
Our results not only contribute to the scientific understanding of global environmental and economic patterns but also serve as a whimsical reminder of the interconnectedness of our world, where the streets of New York City and the highways of Bulgaria converge in a statistical symphony that defies conventional research expectations. With these findings, the bond between air pollution and petroleum consumption emerges as a fascinating subplot in the grand narrative of environmental and economic symbiosis, reminding us that in the world of science, as in life, unexpected connections often lead to the most intriguing discoveries.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our study firmly cement the unexpected link between air pollution in New York City and petroleum consumption in Bulgaria, illustrating that these seemingly disparate variables are intertwined in a dance of correlation that would make even the most seasoned statistician do a double take. Our findings not only corroborate the prior scholarly works by Smith et al. (2018) and Doe and Jones (2020), but also echo the subtle whimsy lurking in unexpected places, such as the enigmatic worlds crafted by J.K. Rowling and J.R.R. Tolkien.

The literary references to "Harry Potter and the Sorcerer's Stone" and "The Hobbit" may appear to be humorous asides, yet they tantalizingly reflect the surprising nature of our findings. Much like the magical transition from London to Diagon Alley, our study aims to unveil the invisible threads connecting urban landscapes to economic tapestries, painting a vivid picture of the interconnectedness of global environmental and economic patterns. Similarly, the unexpected journey undertaken by Bilbo Baggins mirrors our own foray into the uncharted territory of interdisciplinary research, where the quest for knowledge is replete with twists, turns, and encounters with unexpected connections - much like navigating the labyrinth of statistical analyses.

Moreover, the mention of shampoo bottles might seem like an absurd diversion, but it serves as a poignant metaphor for our study's revelation of the interconnectedness of human activity and environmental impact. Just as the back of a shampoo bottle holds hidden allegories, our analysis uncovers the delicate balance between air pollution in New York City and petroleum consumption in Bulgaria, weaving a tale of scientific curiosity and statistical hilarity.

Our findings not only electrify our scientific senses, but also tickle our statistical fancies, showcasing a robust correlation coefficient and a p-value that's more solid than a Bulgarian fortress. The captivating correlation revealed by our scatterplot is a visual symphony that defies conventional research expectations, highlighting the elegant dance of environmental impact and economic activity.

In the grand narrative of environmental and economic symbiosis, our study reminds us that unexpected connections often lead to the most intriguing discoveries. As we continue to navigate the unknown waters of research, let us not forget the enchanting allure of unconventional sources and the delightful surprises hidden within the tangled web of statistical analyses and scientific exploration.

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 New York City and Petroluem consumption in Bulgaria. 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]]
"The Smog and the Furious: Uncovering the Relationship Between Air Pollution in New York City and Petroleum Consumption in Bulgaria"

[[ABSTRACT]]
In this study, we endeavor to delve into the often overlooked, yet undeniably fascinating, link between air pollution in the bustling metropolis of New York City and the rather unexpected realm of petroleum consumption in the charming country of Bulgaria. Using comprehensive data from the Environmental Protection Agency and the Energy Information Administration, we conducted a thorough analysis covering the years 1980 to 2021. Our findings revealed a striking correlation coefficient of 0.9007602 and a p-value of less than 0.01, indicating a strong and statistically significant relationship between these seemingly disparate factors. We unpack the complex web of connections between these variables, shedding light on the intertwined nature of global environmental and economic patterns. Our study not only contributes to the body of knowledge in environmental and economic research, but also offers a whimsical perspective on the interconnectedness of the world, linking the streets of New York to the streets of Sofia in an unexpected twist of fate.

[[INTRDUCTION]]
As the world grapples with the pressing issues of air pollution and petroleum consumption, we find ourselves drawn to the unlikely pairing of New York City's smog-choked streets and Bulgaria's reliance on petroleum. This peculiar connection, much like a mad scientist's experiment gone awry, piqued our curiosity and led us down the rabbit hole of data analysis and statistical wizardry.
The great urban jungle of New York City, with its towering skyscrapers and bustling streets, stands as a symbol of human ambition and progress. Meanwhile, nestled in the charming landscapes of Bulgaria, petroleum consumption quietly hums along, much like a background melody in an orchestra of economic activity. These two seemingly incongruent elements come together in a dance of correlation and causation, showing us that the world of scientific research is full of unexpected waltzes and tangoes.
In this study, we aim to unravel the intricate tango between these variables, examining their relationship with the precision of a scientist in a lab, albeit with a touch of whimsy and a sprinkle of statistical fairy dust. Our analysis encompasses a time span from 1980 to 2021, allowing us to witness the ebb and flow of air pollution in the Big Apple and the delicate fluctuations of petroleum consumption in the Balkan gem of Bulgaria.
Armed with comprehensive data from the Environmental Protection Agency and the Energy Information Administration, we embarked on a journey akin to explorers charting new territories, armed with the compass of scientific inquiry and the map of economic indicators. Our findings, much like buried treasure on a scientific expedition, revealed a striking correlation coefficient of 0.9007602, a diamond in the rough amidst the sea of statistical noise. Add a p-value of less than 0.01 to the mix, and we have a recipe for a statistical feast that would make the most discerning data gourmands salivate.
But let us not lose sight of the forest for the trees, or the molecules for the atoms if you will. Beyond the numbers and calculations lies a tale of interconnectedness—a narrative of how the tendrils of air pollution in one corner of the world reach out to intertwine with the patterns of petroleum consumption in another, like a cosmic game of cat's cradle played by the forces of environmental impact and economic activity.
In the spirit of scientific inquiry and a dash of levity, our study not only unravels these tendrils but also offers a glimpse into the whimsical nature of the world, where the streets of New York City and the boulevards of Sofia meet in an unexpected narrative of environmental and economic symbiosis. So, dear reader, buckle up your intellectual seatbelts as we embark on this scholarly rollercoaster ride, where data meets humor, and science meets serendipity.

[[RESULTS]]
Our analysis of the relationship between air pollution in New York City and petroleum consumption in Bulgaria yielded remarkable insights that not only electrified our scientific senses but also tickled our statistical fancies. We harnessed the power of data from the Environmental Protection Agency and the Energy Information Administration to unravel the mysterious dance of correlation between these seemingly disparate variables.
Delving into the numerical nitty-gritty, we uncovered a correlation coefficient of 0.9007602, indicative of a strong positive linear relationship. This correlation coefficient was as sturdy as a high-rise building in Manhattan, standing tall and proud in the face of statistical scrutiny. The R-squared value of 0.8113689 further supported this robust association, signifying that over 80% of the variation in petroleum consumption in Bulgaria could be explained by the levels of air pollution in New York City. It's as if the Big Apple cast a shadow over the oil barrels in Bulgaria, creating an intricate web of environmental and economic interplay that didn't fail to captivate our scientific imaginations.
In the realm of statistical significance, our findings did not disappoint, boasting a p-value of less than 0.01. This result was more rock-solid than a Bulgarian fortress, providing compelling evidence that the relationship between air pollution in New York City and petroleum consumption in Bulgaria was not a mere statistical fluke. The odds of this correlation occurring by random chance were slimmer than a microbe's waistline, reinforcing the notion that there's more to this connection than meets the eye.
Now, to visually capture the essence of this remarkable relationship, we present Figure 1, a scatterplot that paints a vivid picture of the tight bond between air pollution in New York City and petroleum consumption in Bulgaria (Fig. 1). Behold as the data points form a mesmerizing constellation, telling a tale of environmental impact and economic activity intertwining in a dance of correlation that could rival the elegance of a Viennese waltz-off.
Our results not only contribute to the scientific understanding of global environmental and economic patterns but also serve as a whimsical reminder of the interconnectedness of our world, where the streets of New York City and the highways of Bulgaria converge in a statistical symphony that defies conventional research expectations. With these findings, the bond between air pollution and petroleum consumption emerges as a fascinating subplot in the grand narrative of environmental and economic symbiosis, reminding us that in the world of science, as in life, unexpected connections often lead to the most intriguing discoveries.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has successfully unraveled the enigmatic tango between air pollution in New York City and petroleum consumption in Bulgaria, shedding light on the interconnected web of global environmental and economic phenomena. Our findings not only dance to the beat of statistical significance, but they also add a touch of whimsy to the symphony of scientific inquiry.

The correlation coefficient of 0.9007602 stands as a shining star in the statistical constellation, illuminating the strong positive linear relationship between these unlikely bedfellows. Like two peas in a statistical pod, they waltz to the rhythm of environmental impact and economic activity, showcasing a partnership as captivating as Batman and Robin, but with a tad more scientific flair.

The p-value of less than 0.01 serves as a sturdy pillar supporting this remarkable relationship, akin to a scientific Tower of Pisa that refuses to succumb to the winds of random chance. The odds of this correlation occurring by mere happenstance are slimmer than a test tube in a chemistry lab, solidifying the notion that there's no smoke without fire in this captivating nexus of air pollution and petroleum consumption.

Our study not only unlocks the potential for further exploration of these interconnected variables but also serves as a reminder that in the world of science, unexpected connections often lead to the most delightful discoveries. However, we dare say that the connection between air pollution in New York City and petroleum consumption in Bulgaria has been thoroughly probed and prodded, leaving little room for additional scrutiny. It's time to bid farewell to this peculiar pairing and redirect our scientific gaze toward new frontiers, where fresh correlations and scatterplots await their turn in the limelight.

In the grand, whimsical narrative of research, we raise our statistical hats to the dance of air pollution and petroleum consumption, reminiscing on the unexpected twists and turns of this scholarly rollercoaster ride. As we exit stage left, we leave behind a tale of correlation, causation, and a touch of scientific serendipity, paving the way for the next merry band of researchers to unveil their own delightful connections. No more research is needed in this area, for now we embark on our next statistical adventure, armed with curiosity and a generous sprinkle of analytical fairy dust.

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 Smog and the Furious: Uncovering the Relationship Between Air Pollution in New York City and Petroleum Consumption in Bulgaria. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Environmental Quirks and Curiosities

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 Smog and the Furious: Uncovering the Relationship Between Air Pollution in New York City and Petroleum Consumption in Bulgaria. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Transcontinental Air Quality Studies

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled The Smog and the Furious: Uncovering the Relationship Between Air Pollution in New York City and Petroleum Consumption in Bulgaria with an abstract of In this study, we endeavor to delve into the often overlooked, yet undeniably fascinating, link between air pollution in the bustling metropolis of New York City and the rather unexpected realm of petroleum consumption in the charming country of Bulgaria. Using comprehensive data from the Environmental Protection Agency and the Energy Information Administration, we conducted a thorough analysis covering the years 1980 to 2021. Our findings revealed a striking correlation coefficient of 0.9007602 and a p-value of less than 0.01, indicating a strong and statistically significant relationship between these seemingly disparate factors. We unpack the complex web of connections between these variables, shedding light on the intertwined nature of global environmental and economic patterns. Our study not only contributes to the body of knowledge in environmental and economic research, but also offers a whimsical perspective on the interconnectedness of the world, linking the streets of New York to the streets of Sofia in an unexpected twist of fate.

ChatGPT:

air pollution, New York City, petroleum consumption, Bulgaria, environmental protection agency data, energy information administration data, correlation coefficient, statistical significance, global environmental patterns, economic research, interconnectedness of the world

*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 New York City
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in New York-Newark-Jersey City, NY-NJ-PA
Source: Environmental Protection Agency
See what else correlates with Air pollution in New York City

Petroluem consumption in Bulgaria
Detailed data title: Volume of petroluem consumption consumed in Bulgaria in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Petroluem consumption in Bulgaria

Correlation r = 0.9007602 (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 = 0.8113689 (Coefficient of determination)
This means 81.1% of the change in the one variable (i.e., Petroluem consumption in Bulgaria) is predictable based on the change in the other (i.e., Air pollution in New York City) over the 42 years from 1980 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 4.5E-16.^Show 0.0000000000000004502749031789
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 0.9 in 4.5E-14% of random cases. Said differently, if you correlated 2,220,865,504,473,136 random variables^Note You don't actually need 2 quadrillion 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.

[ 0.82, 0.95 ] 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 New York City (Bad air quality days)	45.9016	41.6438	41.6438	41.6438	47.2678	52.0548	44.1096	40.8219	31.4208	30.137	24.3836	29.0411	16.9399	23.0137	23.2877	19.726	18.306	17.8082	22.1918	22.7397	17.7596	24.3836	23.5616	16.7123	13.9344	19.4521	17.8082	18.0822	14.2077	9.58904	16.7123	9.0411	10.6557	5.75342	4.38356	10.411	8.19672	5.20548	7.39726	4.38356	3.00546	5.75342
Petroluem consumption in Bulgaria (Million Barrels/Day)	306	294	295	295	294	290	286.1	276.4	278.2	235.9	192	120.9	129.2	116.9	126.5	130.7	118.3	106.5	102.721	97.1	89.019	93.97	95.583	101.032	102.938	109.702	110.908	106.517	104.987	100.844	89.0614	84.0473	86.2991	80.9185	88.9967	95	97	91.0524	92.773	97.7618	89.7788	94.4061

Why this works

Data dredging: I have 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 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([45.9016,41.6438,41.6438,41.6438,47.2678,52.0548,44.1096,40.8219,31.4208,30.137,24.3836,29.0411,16.9399,23.0137,23.2877,19.726,18.306,17.8082,22.1918,22.7397,17.7596,24.3836,23.5616,16.7123,13.9344,19.4521,17.8082,18.0822,14.2077,9.58904,16.7123,9.0411,10.6557,5.75342,4.38356,10.411,8.19672,5.20548,7.39726,4.38356,3.00546,5.75342,])
array_2 = np.array([306,294,295,295,294,290,286.1,276.4,278.2,235.9,192,120.9,129.2,116.9,126.5,130.7,118.3,106.5,102.721,97.1,89.019,93.97,95.583,101.032,102.938,109.702,110.908,106.517,104.987,100.844,89.0614,84.0473,86.2991,80.9185,88.9967,95,97,91.0524,92.773,97.7618,89.7788,94.4061,])
array_1_name = "Air pollution in New York City"
array_2_name = "Petroluem consumption in Bulgaria"

# 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 New York City
Line chart for only Petroluem consumption in Bulgaria
AI-generated correlation image
The spurious research paper: The Smog and the Furious: Uncovering the Relationship Between Air Pollution in New York City and Petroleum Consumption in Bulgaria

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Correlation ID: 5506 · Black Variable ID: 20976 · Red Variable ID: 24241


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