Spurious correlation #2,338 · 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 Atlanta and the second variable is Gasoline pumped in Norway. The chart goes from 1980 to 2022, 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 Atlanta and the second variable is Gasoline pumped in Norway. The chart goes from 1980 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The cleaner air in Atlanta led to a decrease in demand for gas, prompting Norwegian suppliers to cut back on production. After all, it's the latest twist in international air and fuel relations!

^{Show image generation prompt} Model: dalle-3
Prompt: An image that looks like it is a scene from 'Star Wars': Amidst the futuristic cityscape of Atlanta, sleek, aerodynamic bicycles race along the streets with citizens in sleek, space-like attire, moving freely amidst the dwindling gas masks and discarded rubber, symbolizing the transition from pollution to environmental sustainability. In the backdrop, the industrial renaissance of tire production is depicted, with factories churning out the surplus bicycle tires, juxtaposed against the grandeur of Norway's landscape, emulating a utopian vision of clean, peach-scented air. 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 Atlanta and Gasoline pumped in Norway. 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: An image that looks like it is a scene from 'Star Wars' a Polaroid photo Brutalism A TIME magazine cover image An image that looks like it is a scene from A Disney Movie
ChatGPT: An image that looks like it is a scene from 'Star Wars'

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 Atlanta and Gasoline pumped in Norway, in the following situation: The decrease in air pollution in Atlanta led to a decrease in gas mask sales. This, in turn, freed up more rubber for the production of tires in the US. With an increase in the availability of tires, Norway saw a surge in the production of bicycle tires. As a result, fewer people in Norway were using cars, leading to a decreased demand for gasoline, especially the premium, Atlanta-exported, peach-scented gasoline.. Keep the description simple.
ChatGPT: In Atlanta, the air cleared up as fewer cars emitted pollutants, and as a result, the demand for gas masks decreased. This led to an overflow of rubber, which was used to produce more tires in the US. With more tires available, the production of bicycle tires surged in Norway as fewer people were using cars. Consequently, the demand for premium, peach-scented gasoline from Atlanta decreased in Norway.

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 'Star Wars' ] and depicting this scene: In Atlanta, the air cleared up as fewer cars emitted pollutants, and as a result, the demand for gas masks decreased. This led to an overflow of rubber, which was used to produce more tires in the US. With more tires available, the production of bicycle tires surged in Norway as fewer people were using cars. Consequently, the demand for premium, peach-scented gasoline from Atlanta decreased in Norway. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: An image that looks like it is a scene from 'Star Wars': Amidst the futuristic cityscape of Atlanta, sleek, aerodynamic bicycles race along the streets with citizens in sleek, space-like attire, moving freely amidst the dwindling gas masks and discarded rubber, symbolizing the transition from pollution to environmental sustainability. In the backdrop, the industrial renaissance of tire production is depicted, with factories churning out the surplus bicycle tires, juxtaposed against the grandeur of Norway's landscape, emulating a utopian vision of clean, peach-scented air.

*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 Atlanta caused Gasoline pumped in Norway to decrease. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

Smoggy Connections: Uncovering the Correlation Between Atlanta Air Pollution and Norwegian Gasoline Consumption

The Journal of Atmospheric Oddities

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 Atlanta and Gasoline pumped in Norway. 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 you might move on to cartoons and children's shows that you watched for research.

Here is the title and abstract of the paper:
[[TITLE]]
"Smoggy Connections: Uncovering the Correlation Between Atlanta Air Pollution and Norwegian Gasoline Consumption"

[[ABSTRACT]]
The relationship between air pollution and gasoline consumption has long been a subject of scholarly curiosity, though one might expect a connection as clear as smog. In this study, we venture into the intriguing world of environmental and energy data to unravel the mysterious entanglement between air pollution in Atlanta, Georgia, and the seemingly unrelated gasoline consumption trends in Norway. Utilizing extensive datasets from the Environmental Protection Agency and the Energy Information Administration, our research team sought to shed light on this enigmatic correlation.

Our findings reveal a surprisingly robust correlation coefficient of 0.8367424 and a statistically significant p-value of less than 0.01 spanning the years 1980 to 2022. The undeniable connection between air pollution in Atlanta and gasoline consumption in Norway prompts a reevaluation of the oft-overlooked global impact of seemingly local phenomena. As we delve deeper into the web of environmental and energy dynamics, it becomes abundantly clear that the air we breathe and the fuel we use are intertwined in ways we are only beginning to comprehend.

This study not only offers a compelling demonstration of the interplay between distant environmental factors, but also serves as a reminder that even the most unexpected connections can lurk beneath the surface, much like lurking air pollutants. Further exploration into this unexpected relationship may unearth insights that could fuel innovative strategies for addressing air quality and energy consumption challenges on a global scale.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intricate dance between air pollution and gasoline consumption has captivated the minds of scholars and researchers for decades. The notion of unraveling the enigmatic connection between these seemingly disparate variables is enough to make one ponder the very essence of scientific inquiry - much like pondering the essence of why we always seem to find something missing in a cluttered lab. In this paper, we present the results of an extensive investigation into the surprising correlation between the air pollution levels in Atlanta, Georgia, and the consumption of gasoline in the distant land of Norway – a connection as unlikely as a penguin in a tropical rainforest.
The dynamics of environmental and energy data often resemble a complex maze, with twists and turns that can confound even the most seasoned researchers, not unlike conducting a statistical analysis after a sleepless night. Our quest to disentangle the relationships between air quality and fuel consumption led us to wade through oceans of data from the Environmental Protection Agency and the Energy Information Administration. Delving into these datasets felt akin to searching for a needle in a haystack, only to realize that there were multiple needles, each with its own point – much like finding significant correlations between variables in a sea of noise.
As we sifted through the mountain of data, we observed a striking correlation coefficient of 0.8367424, which jumped out at us like a lab rat in a maze. The statistically significant p-value of less than 0.01 sent waves of excitement through our research team, akin to the thrill of discovering a particularly elusive Pokémon. With these compelling statistical indicators in hand, we embarked on the arduous task of interpreting the implications of these findings – much like deciphering an ancient manuscript written in a cryptic language.
Our investigation sheds light on the intricate interplay between the air we breathe and the fuel that propels our modern world. The discovery of a substantial correlation between air pollution in Atlanta and gasoline consumption in Norway serves as a reminder that in the realm of environmental and energy dynamics, the dots may be more interconnected than they initially appear, much like connecting the dots in a cosmic game of celestial pinball.
In the subsequent sections of this paper, we will delve further into the unexpected relationship between air pollution in Atlanta and gasoline consumption in Norway, examining potential mechanisms and implications that may lead to a greater understanding of these seemingly disparate variables and the interlaced tapestry of our global ecosystem.
So, dear reader, buckle up and prepare to embark on a scientific journey filled with surprises, much like finding a unicorn – or perhaps in our case, a unicycle – in the realm of data and statistical analyses.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In their seminal work, Smith et al. (2010) explored the complex relationship between air pollution and gasoline consumption, shedding light on the intricate web of environmental and energy dynamics. Their findings hinted at the potential interconnectedness of seemingly disparate variables, much like finding a pair of mismatched socks in the laundry - seemingly unrelated, yet undeniably connected.

Furthermore, Doe and Jones (2015) delved into the global impact of air pollution, emphasizing its far-reaching implications on climate and public health. Their study illuminated the pervasive nature of air pollutants, much like a persistent earworm of a song that refuses to leave one's mind.

Turning to broader environmental and energy literature, "The Clean Air Act" by Smith and "Fueling Our Future" by Doe provide insightful analyses of air quality regulations and energy consumption patterns, offering valuable context for our exploration of the air pollution-gasoline consumption nexus. These works laid the foundation for our investigation, much like the sturdy groundwork beneath a towering skyscraper.

In a delightful departure from non-fiction, "Blowing in the Wind: A Tale of Airborne Adventures" by John Doe and "Fueling Fantasies: The Mystic Mysteries of Gasoline" by Jane Smith offer whimsical yet thought-provoking narratives that touch upon the seemingly whimsical yet inexplicably linked relationship between air pollution and gasoline consumption.

With a nod to the unexpected, the researchers also turned to more unconventional sources of inspiration, skimming through cartoons and children's shows to unearth metaphors and analogies that encapsulate the enigmatic connection between air pollution in Atlanta and gasoline consumption in Norway. The idiosyncratic adventures of "Captain Planet" and the playful antics of "The Magic School Bus" provided unexpected insights, akin to stumbling upon a hidden treasure map in the midst of a scholarly exploration.

As we journey through the scholarly landscape, it becomes apparent that the intersection of air pollution and gasoline consumption is not merely a dry subject of statistical analysis, but a rich tapestry woven with unexpected connections and delightful surprises. With these diverse voices and sources as our guides, we turn our attention to the empirical studies that paved the way for our own investigation, eager to uncover the secrets hidden within the seemingly disparate realms of pollution and petroleum.

Well, dear reader, much like a skilled chef adding a pinch of spice to an otherwise mundane dish, we aim to infuse this literature review with a dash of humor and a sprinkle of eccentricity, for no scholarly pursuit is devoid of a touch of whimsy and wonder. Onward we go, ready to uncover the smoggy connections that underlie the interplay between air pollution in Atlanta and Norwegian gasoline consumption.

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 Atlanta and Gasoline pumped in Norway. 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 2022

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]]
"Smoggy Connections: Uncovering the Correlation Between Atlanta Air Pollution and Norwegian Gasoline Consumption"

[[ABSTRACT]]
The relationship between air pollution and gasoline consumption has long been a subject of scholarly curiosity, though one might expect a connection as clear as smog. In this study, we venture into the intriguing world of environmental and energy data to unravel the mysterious entanglement between air pollution in Atlanta, Georgia, and the seemingly unrelated gasoline consumption trends in Norway. Utilizing extensive datasets from the Environmental Protection Agency and the Energy Information Administration, our research team sought to shed light on this enigmatic correlation.

Our findings reveal a surprisingly robust correlation coefficient of 0.8367424 and a statistically significant p-value of less than 0.01 spanning the years 1980 to 2022. The undeniable connection between air pollution in Atlanta and gasoline consumption in Norway prompts a reevaluation of the oft-overlooked global impact of seemingly local phenomena. As we delve deeper into the web of environmental and energy dynamics, it becomes abundantly clear that the air we breathe and the fuel we use are intertwined in ways we are only beginning to comprehend.

This study not only offers a compelling demonstration of the interplay between distant environmental factors, but also serves as a reminder that even the most unexpected connections can lurk beneath the surface, much like lurking air pollutants. Further exploration into this unexpected relationship may unearth insights that could fuel innovative strategies for addressing air quality and energy consumption challenges on a global scale.

[[INTRODUCTION]]
The intricate dance between air pollution and gasoline consumption has captivated the minds of scholars and researchers for decades. The notion of unraveling the enigmatic connection between these seemingly disparate variables is enough to make one ponder the very essence of scientific inquiry - much like pondering the essence of why we always seem to find something missing in a cluttered lab. In this paper, we present the results of an extensive investigation into the surprising correlation between the air pollution levels in Atlanta, Georgia, and the consumption of gasoline in the distant land of Norway – a connection as unlikely as a penguin in a tropical rainforest.
The dynamics of environmental and energy data often resemble a complex maze, with twists and turns that can confound even the most seasoned researchers, not unlike conducting a statistical analysis after a sleepless night. Our quest to disentangle the relationships between air quality and fuel consumption led us to wade through oceans of data from the Environmental Protection Agency and the Energy Information Administration. Delving into these datasets felt akin to searching for a needle in a haystack, only to realize that there were multiple needles, each with its own point – much like finding significant correlations between variables in a sea of noise.
As we sifted through the mountain of data, we observed a striking correlation coefficient of 0.8367424, which jumped out at us like a lab rat in a maze. The statistically significant p-value of less than 0.01 sent waves of excitement through our research team, akin to the thrill of discovering a particularly elusive Pokémon. With these compelling statistical indicators in hand, we embarked on the arduous task of interpreting the implications of these findings – much like deciphering an ancient manuscript written in a cryptic language.
Our investigation sheds light on the intricate interplay between the air we breathe and the fuel that propels our modern world. The discovery of a substantial correlation between air pollution in Atlanta and gasoline consumption in Norway serves as a reminder that in the realm of environmental and energy dynamics, the dots may be more interconnected than they initially appear, much like connecting the dots in a cosmic game of celestial pinball.
In the subsequent sections of this paper, we will delve further into the unexpected relationship between air pollution in Atlanta and gasoline consumption in Norway, examining potential mechanisms and implications that may lead to a greater understanding of these seemingly disparate variables and the interlaced tapestry of our global ecosystem.
So, dear reader, buckle up and prepare to embark on a scientific journey filled with surprises, much like finding a unicorn – or perhaps in our case, a unicycle – in the realm of data and statistical analyses.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

In our pursuit to unravel the intriguing correlation between air pollution in Atlanta and gasoline consumption in Norway, we employed a rigorous and multipronged methodology, which involved a combination of data collection, statistical analysis, and a touch of whimsy, akin to conducting a symphony while riding a unicycle.

Data Collection:
We meticulously gathered environmental and energy data spanning the years 1980 to 2022, utilizing sources such as the Environmental Protection Agency and the Energy Information Administration. We meticulously combed through these datasets, akin to searching for a needle in a haystack, though in this case, multiple needles were hiding in plain sight, each with its own point – much like finding significant correlations between variables in a sea of noise. We harnessed the power of modern technology and the internet to gather information from various reputable sources, though, one must admit, we also spent a fair amount of time reading amusing cat memes – a necessary mental break in the demanding world of data collection.

Cleaning and Preprocessing:
The collected data underwent meticulous cleaning and preprocessing, resembling the arduous task of tidying up a cluttered laboratory after an experiment gone awry. Outliers were identified and handled with the precision of a surgeon excising anomalies from a dataset, though we must confess these anomalies were about as uncommon as finding a penguin in a tropical rainforest.

Statistical Analysis:
We employed a wide array of statistical techniques, including correlation analysis, regression models, and time series analysis. We calculated correlation coefficients that practically jumped out at us like lab rats in a maze, and p-values that sent waves of excitement through the research team, akin to the thrill of discovering a particularly elusive Pokémon.

Interpretation:
Interpreting the implications of these statistical findings required a delicate balancing act, not unlike deciphering an ancient manuscript written in a cryptic language. We delved into potential mechanisms and implications, akin to untangling a complex web of interwoven threads, each leading to a greater understanding of the interconnected tapestry of our global ecosystem.

While our methods may sound as unlikely as a unicorn – or perhaps, in this case, a unicycle – in the realm of academic research, we assure the reader that every effort was made with the utmost rigor and dedication to uncovering the smoggy connections between air pollution in Atlanta and gasoline consumption in Norway. As we continue into the depths of our findings, we encourage readers to prepare for a scientific journey filled with surprises, much like finding a unicycle on a cosmic game of celestial pinball.

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 Atlanta and Gasoline pumped in Norway. 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 2022, you found a correlation 0.8367424, r-squared of 0.7001379, 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]]
"Smoggy Connections: Uncovering the Correlation Between Atlanta Air Pollution and Norwegian Gasoline Consumption"

[[ABSTRACT]]
The relationship between air pollution and gasoline consumption has long been a subject of scholarly curiosity, though one might expect a connection as clear as smog. In this study, we venture into the intriguing world of environmental and energy data to unravel the mysterious entanglement between air pollution in Atlanta, Georgia, and the seemingly unrelated gasoline consumption trends in Norway. Utilizing extensive datasets from the Environmental Protection Agency and the Energy Information Administration, our research team sought to shed light on this enigmatic correlation.

Our findings reveal a surprisingly robust correlation coefficient of 0.8367424 and a statistically significant p-value of less than 0.01 spanning the years 1980 to 2022. The undeniable connection between air pollution in Atlanta and gasoline consumption in Norway prompts a reevaluation of the oft-overlooked global impact of seemingly local phenomena. As we delve deeper into the web of environmental and energy dynamics, it becomes abundantly clear that the air we breathe and the fuel we use are intertwined in ways we are only beginning to comprehend.

This study not only offers a compelling demonstration of the interplay between distant environmental factors, but also serves as a reminder that even the most unexpected connections can lurk beneath the surface, much like lurking air pollutants. Further exploration into this unexpected relationship may unearth insights that could fuel innovative strategies for addressing air quality and energy consumption challenges on a global scale.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intricate dance between air pollution and gasoline consumption has captivated the minds of scholars and researchers for decades. The notion of unraveling the enigmatic connection between these seemingly disparate variables is enough to make one ponder the very essence of scientific inquiry - much like pondering the essence of why we always seem to find something missing in a cluttered lab. In this paper, we present the results of an extensive investigation into the surprising correlation between the air pollution levels in Atlanta, Georgia, and the consumption of gasoline in the distant land of Norway – a connection as unlikely as a penguin in a tropical rainforest.
The dynamics of environmental and energy data often resemble a complex maze, with twists and turns that can confound even the most seasoned researchers, not unlike conducting a statistical analysis after a sleepless night. Our quest to disentangle the relationships between air quality and fuel consumption led us to wade through oceans of data from the Environmental Protection Agency and the Energy Information Administration. Delving into these datasets felt akin to searching for a needle in a haystack, only to realize that there were multiple needles, each with its own point – much like finding significant correlations between variables in a sea of noise.
As we sifted through the mountain of data, we observed a striking correlation coefficient of 0.8367424, which jumped out at us like a lab rat in a maze. The statistically significant p-value of less than 0.01 sent waves of excitement through our research team, akin to the thrill of discovering a particularly elusive Pokémon. With these compelling statistical indicators in hand, we embarked on the arduous task of interpreting the implications of these findings – much like deciphering an ancient manuscript written in a cryptic language.
Our investigation sheds light on the intricate interplay between the air we breathe and the fuel that propels our modern world. The discovery of a substantial correlation between air pollution in Atlanta and gasoline consumption in Norway serves as a reminder that in the realm of environmental and energy dynamics, the dots may be more interconnected than they initially appear, much like connecting the dots in a cosmic game of celestial pinball.
In the subsequent sections of this paper, we will delve further into the unexpected relationship between air pollution in Atlanta and gasoline consumption in Norway, examining potential mechanisms and implications that may lead to a greater understanding of these seemingly disparate variables and the interlaced tapestry of our global ecosystem.
So, dear reader, buckle up and prepare to embark on a scientific journey filled with surprises, much like finding a unicorn – or perhaps in our case, a unicycle – in the realm of data and statistical analyses.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The statistical analysis of our extensive dataset revealed a striking correlation coefficient of 0.8367424, indicating a strong positive relationship between air pollution in Atlanta, Georgia, and gasoline consumption in Norway. This robust correlation coefficient jumped out at us like a particularly eager participant in a game of statistical hide-and-seek. The r-squared value of 0.7001379 further solidifies the strength of this remarkable connection, akin to a sturdy bridge spanning the gap between two seemingly unrelated phenomena. Additionally, the statistically significant p-value of less than 0.01 provides compelling evidence of the validity of this correlation, sending ripples of excitement through our research team akin to discovering buried treasure in a statistical minefield.

As depicted in Figure 1, our scatterplot visually captures the undeniable relationship between air pollution in Atlanta and gasoline consumption in Norway. The data points align with a precision that would make any statistician smile, forming a pattern as clear as day - or as clear as a pollution-free sky, if you will.

These findings not only challenge conventional wisdom but also underscore the interconnected nature of global environmental and energy dynamics. The surprising correlation between seemingly disparate variables leaves us pondering the intricate web of cause and effect, much like pondering the root cause of a sudden urge to conduct statistical analyses at odd hours.

The robustness of this correlation prompts a reexamination of the far-reaching impact of seemingly localized environmental factors, serving as a poignant reminder that the most unexpected connections can often hold the key to unraveling complex global phenomena. The revelation of such a substantial correlation between air pollution in Atlanta and gasoline consumption in Norway encourages further exploration into the underlying mechanisms and implications, offering a glimpse into the intertwined fabric of our global ecosystem.

In the subsequent sections of this paper, we will delve deeper into the unexpected relationship between air pollution in Atlanta and gasoline consumption in Norway, exploring potential mechanisms and implications that may provide a deeper understanding of this captivating correlation. So, dear reader, fasten your seatbelt and prepare for a scientific journey filled with twists and turns, much like navigating through a maze of statistical surprises.

And remember, in the grand scheme of research, sometimes the most unexpected connections can lead to profound insights, much like stumbling upon a eureka moment in a haystack of data.

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 Atlanta and Gasoline pumped in Norway. 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]]
"Smoggy Connections: Uncovering the Correlation Between Atlanta Air Pollution and Norwegian Gasoline Consumption"

[[ABSTRACT]]
The relationship between air pollution and gasoline consumption has long been a subject of scholarly curiosity, though one might expect a connection as clear as smog. In this study, we venture into the intriguing world of environmental and energy data to unravel the mysterious entanglement between air pollution in Atlanta, Georgia, and the seemingly unrelated gasoline consumption trends in Norway. Utilizing extensive datasets from the Environmental Protection Agency and the Energy Information Administration, our research team sought to shed light on this enigmatic correlation.

Our findings reveal a surprisingly robust correlation coefficient of 0.8367424 and a statistically significant p-value of less than 0.01 spanning the years 1980 to 2022. The undeniable connection between air pollution in Atlanta and gasoline consumption in Norway prompts a reevaluation of the oft-overlooked global impact of seemingly local phenomena. As we delve deeper into the web of environmental and energy dynamics, it becomes abundantly clear that the air we breathe and the fuel we use are intertwined in ways we are only beginning to comprehend.

This study not only offers a compelling demonstration of the interplay between distant environmental factors, but also serves as a reminder that even the most unexpected connections can lurk beneath the surface, much like lurking air pollutants. Further exploration into this unexpected relationship may unearth insights that could fuel innovative strategies for addressing air quality and energy consumption challenges on a global scale.

[[LITERATURE REVIEW]]
In their seminal work, Smith et al. (2010) explored the complex relationship between air pollution and gasoline consumption, shedding light on the intricate web of environmental and energy dynamics. Their findings hinted at the potential interconnectedness of seemingly disparate variables, much like finding a pair of mismatched socks in the laundry - seemingly unrelated, yet undeniably connected.
Furthermore, Doe and Jones (2015) delved into the global impact of air pollution, emphasizing its far-reaching implications on climate and public health. Their study illuminated the pervasive nature of air pollutants, much like a persistent earworm of a song that refuses to leave one's mind.
Turning to broader environmental and energy literature, "The Clean Air Act" by Smith and "Fueling Our Future" by Doe provide insightful analyses of air quality regulations and energy consumption patterns, offering valuable context for our exploration of the air pollution-gasoline consumption nexus. These works laid the foundation for our investigation, much like the sturdy groundwork beneath a towering skyscraper.
In a delightful departure from non-fiction, "Blowing in the Wind: A Tale of Airborne Adventures" by John Doe and "Fueling Fantasies: The Mystic Mysteries of Gasoline" by Jane Smith offer whimsical yet thought-provoking narratives that touch upon the seemingly whimsical yet inexplicably linked relationship between air pollution and gasoline consumption.
With a nod to the unexpected, the researchers also turned to more unconventional sources of inspiration, skimming through cartoons and children's shows to unearth metaphors and analogies that encapsulate the enigmatic connection between air pollution in Atlanta and gasoline consumption in Norway. The idiosyncratic adventures of "Captain Planet" and the playful antics of "The Magic School Bus" provided unexpected insights, akin to stumbling upon a hidden treasure map in the midst of a scholarly exploration.
As we journey through the scholarly landscape, it becomes apparent that the intersection of air pollution and gasoline consumption is not merely a dry subject of statistical analysis, but a rich tapestry woven with unexpected connections and delightful surprises. With these diverse voices and sources as our guides, we turn our attention to the empirical studies that paved the way for our own investigation, eager to uncover the secrets hidden within the seemingly disparate realms of pollution and petroleum.
Well, dear reader, much like a skilled chef adding a pinch of spice to an otherwise mundane dish, we aim to infuse this literature review with a dash of humor and a sprinkle of eccentricity, for no scholarly pursuit is devoid of a touch of whimsy and wonder. Onward we go, ready to uncover the smoggy connections that underlie the interplay between air pollution in Atlanta and Norwegian gasoline consumption.

[[RESULTS]]
The statistical analysis of our extensive dataset revealed a striking correlation coefficient of 0.8367424, indicating a strong positive relationship between air pollution in Atlanta, Georgia, and gasoline consumption in Norway. This robust correlation coefficient jumped out at us like a particularly eager participant in a game of statistical hide-and-seek. The r-squared value of 0.7001379 further solidifies the strength of this remarkable connection, akin to a sturdy bridge spanning the gap between two seemingly unrelated phenomena. Additionally, the statistically significant p-value of less than 0.01 provides compelling evidence of the validity of this correlation, sending ripples of excitement through our research team akin to discovering buried treasure in a statistical minefield.
As depicted in Figure 1, our scatterplot visually captures the undeniable relationship between air pollution in Atlanta and gasoline consumption in Norway. The data points align with a precision that would make any statistician smile, forming a pattern as clear as day - or as clear as a pollution-free sky, if you will.
These findings not only challenge conventional wisdom but also underscore the interconnected nature of global environmental and energy dynamics. The surprising correlation between seemingly disparate variables leaves us pondering the intricate web of cause and effect, much like pondering the root cause of a sudden urge to conduct statistical analyses at odd hours.
The robustness of this correlation prompts a reexamination of the far-reaching impact of seemingly localized environmental factors, serving as a poignant reminder that the most unexpected connections can often hold the key to unraveling complex global phenomena. The revelation of such a substantial correlation between air pollution in Atlanta and gasoline consumption in Norway encourages further exploration into the underlying mechanisms and implications, offering a glimpse into the intertwined fabric of our global ecosystem.
In the subsequent sections of this paper, we will delve deeper into the unexpected relationship between air pollution in Atlanta and gasoline consumption in Norway, exploring potential mechanisms and implications that may provide a deeper understanding of this captivating correlation. So, dear reader, fasten your seatbelt and prepare for a scientific journey filled with twists and turns, much like navigating through a maze of statistical surprises.
And remember, in the grand scheme of research, sometimes the most unexpected connections can lead to profound insights, much like stumbling upon a eureka moment in a haystack of data.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our findings present a compelling case for the unexpected yet undeniable correlation between air pollution in Atlanta and gasoline consumption in Norway, a connection as puzzling as finding a penguin in the desert - intriguingly out of place, yet captivating in its incongruity. Our results not only echo the previous research by Smith et al. (2010) and Doe and Jones (2015) but also bolster their whimsical yet enlightening insights into the intertwined nature of environmental and energy phenomena.

The robust correlation coefficient and the statistically significant p-value underscore the strength and validity of this unexpected relationship, much like spotting a unicorn among a herd of statistical results - a rare and magical discovery in the realm of data analysis. Our statistical analyses align with the previous literature as tightly as a perfectly fitted jigsaw puzzle, affirming the interconnectedness of air pollution and gasoline consumption that transcends geographical boundaries.

As we navigate through this unexpected yet fascinating linkage, we are reminded of the unpredictable twists and turns that characterize the pursuit of scientific understanding. The unexpected correlations that emerge from our data challenge traditional assumptions and beckon us to explore the underlying mechanisms with the same curiosity one might have when dissecting a particularly enigmatic magic trick.

It is clear that the entwined relationship between air pollution in Atlanta and gasoline consumption in Norway opens a gateway to uncharted territory in the realms of environmental and energy dynamics, much like unlocking a mysterious door leading to a treasure trove of insights. Our results serve as a testament to the serendipitous discoveries that await those who delve into the complexities of seemingly unrelated variables, akin to unearthing a hidden Easter egg in a labyrinth of empirical data.

With our findings as a compass, we are poised to embark on a deeper exploration of the mechanisms and implications underlying this unprecedented correlation, brimming with excitement and curiosity much like setting sail on a voyage of scientific discovery through uncharted statistical waters. The alluring connection between two distant phenomena beckons us to unravel the mysteries that lie beneath, promising to unveil insights akin to finding a pot of gold at the end of a statistical rainbow.

In the grand tapestry of scientific inquiry, it is the unexpected connections and serendipitous discoveries that often lead to breakthroughs, enriching our understanding of the intricacies woven into the fabric of our complex world. Our study stands as a testament to the profound insights that can emerge from investigating the seemingly whimsical and enigmatic connections lurking within the depths of data, much like discovering a secret treasure trove within a chest of statistical wonders.

As we set our sights on unraveling the underlying mechanisms of this captivating correlation, we embrace the uncharted territory with the fervor of intrepid explorers, prepared to decipher the unspoken language of environmental and energy dynamics. The smoggy connections between air pollution in Atlanta and gasoline consumption in Norway beckon us onward, promising a scientific voyage teeming with surprises and revelations, much like embarking on an exhilarating expedition through a forest of statistical curiosities.

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 Atlanta and Gasoline pumped in Norway. 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]]
"Smoggy Connections: Uncovering the Correlation Between Atlanta Air Pollution and Norwegian Gasoline Consumption"

[[ABSTRACT]]
The relationship between air pollution and gasoline consumption has long been a subject of scholarly curiosity, though one might expect a connection as clear as smog. In this study, we venture into the intriguing world of environmental and energy data to unravel the mysterious entanglement between air pollution in Atlanta, Georgia, and the seemingly unrelated gasoline consumption trends in Norway. Utilizing extensive datasets from the Environmental Protection Agency and the Energy Information Administration, our research team sought to shed light on this enigmatic correlation.

Our findings reveal a surprisingly robust correlation coefficient of 0.8367424 and a statistically significant p-value of less than 0.01 spanning the years 1980 to 2022. The undeniable connection between air pollution in Atlanta and gasoline consumption in Norway prompts a reevaluation of the oft-overlooked global impact of seemingly local phenomena. As we delve deeper into the web of environmental and energy dynamics, it becomes abundantly clear that the air we breathe and the fuel we use are intertwined in ways we are only beginning to comprehend.

This study not only offers a compelling demonstration of the interplay between distant environmental factors, but also serves as a reminder that even the most unexpected connections can lurk beneath the surface, much like lurking air pollutants. Further exploration into this unexpected relationship may unearth insights that could fuel innovative strategies for addressing air quality and energy consumption challenges on a global scale.

[[INTRDUCTION]]
The intricate dance between air pollution and gasoline consumption has captivated the minds of scholars and researchers for decades. The notion of unraveling the enigmatic connection between these seemingly disparate variables is enough to make one ponder the very essence of scientific inquiry - much like pondering the essence of why we always seem to find something missing in a cluttered lab. In this paper, we present the results of an extensive investigation into the surprising correlation between the air pollution levels in Atlanta, Georgia, and the consumption of gasoline in the distant land of Norway – a connection as unlikely as a penguin in a tropical rainforest.
The dynamics of environmental and energy data often resemble a complex maze, with twists and turns that can confound even the most seasoned researchers, not unlike conducting a statistical analysis after a sleepless night. Our quest to disentangle the relationships between air quality and fuel consumption led us to wade through oceans of data from the Environmental Protection Agency and the Energy Information Administration. Delving into these datasets felt akin to searching for a needle in a haystack, only to realize that there were multiple needles, each with its own point – much like finding significant correlations between variables in a sea of noise.
As we sifted through the mountain of data, we observed a striking correlation coefficient of 0.8367424, which jumped out at us like a lab rat in a maze. The statistically significant p-value of less than 0.01 sent waves of excitement through our research team, akin to the thrill of discovering a particularly elusive Pokémon. With these compelling statistical indicators in hand, we embarked on the arduous task of interpreting the implications of these findings – much like deciphering an ancient manuscript written in a cryptic language.
Our investigation sheds light on the intricate interplay between the air we breathe and the fuel that propels our modern world. The discovery of a substantial correlation between air pollution in Atlanta and gasoline consumption in Norway serves as a reminder that in the realm of environmental and energy dynamics, the dots may be more interconnected than they initially appear, much like connecting the dots in a cosmic game of celestial pinball.
In the subsequent sections of this paper, we will delve further into the unexpected relationship between air pollution in Atlanta and gasoline consumption in Norway, examining potential mechanisms and implications that may lead to a greater understanding of these seemingly disparate variables and the interlaced tapestry of our global ecosystem.
So, dear reader, buckle up and prepare to embark on a scientific journey filled with surprises, much like finding a unicorn – or perhaps in our case, a unicycle – in the realm of data and statistical analyses.

[[RESULTS]]
The statistical analysis of our extensive dataset revealed a striking correlation coefficient of 0.8367424, indicating a strong positive relationship between air pollution in Atlanta, Georgia, and gasoline consumption in Norway. This robust correlation coefficient jumped out at us like a particularly eager participant in a game of statistical hide-and-seek. The r-squared value of 0.7001379 further solidifies the strength of this remarkable connection, akin to a sturdy bridge spanning the gap between two seemingly unrelated phenomena. Additionally, the statistically significant p-value of less than 0.01 provides compelling evidence of the validity of this correlation, sending ripples of excitement through our research team akin to discovering buried treasure in a statistical minefield.
As depicted in Figure 1, our scatterplot visually captures the undeniable relationship between air pollution in Atlanta and gasoline consumption in Norway. The data points align with a precision that would make any statistician smile, forming a pattern as clear as day - or as clear as a pollution-free sky, if you will.
These findings not only challenge conventional wisdom but also underscore the interconnected nature of global environmental and energy dynamics. The surprising correlation between seemingly disparate variables leaves us pondering the intricate web of cause and effect, much like pondering the root cause of a sudden urge to conduct statistical analyses at odd hours.
The robustness of this correlation prompts a reexamination of the far-reaching impact of seemingly localized environmental factors, serving as a poignant reminder that the most unexpected connections can often hold the key to unraveling complex global phenomena. The revelation of such a substantial correlation between air pollution in Atlanta and gasoline consumption in Norway encourages further exploration into the underlying mechanisms and implications, offering a glimpse into the intertwined fabric of our global ecosystem.
In the subsequent sections of this paper, we will delve deeper into the unexpected relationship between air pollution in Atlanta and gasoline consumption in Norway, exploring potential mechanisms and implications that may provide a deeper understanding of this captivating correlation. So, dear reader, fasten your seatbelt and prepare for a scientific journey filled with twists and turns, much like navigating through a maze of statistical surprises.
And remember, in the grand scheme of research, sometimes the most unexpected connections can lead to profound insights, much like stumbling upon a eureka moment in a haystack of data.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has brought to light a surprising and robust correlation between the air pollution levels in Atlanta, Georgia, and the consumption of gasoline in Norway – a connection as unlikely as finding a polar bear in the desert. The statistically significant p-value of less than 0.01 sent waves of excitement through our research team, reminiscent of the thrill of discovering a particularly elusive Pokémon – only this time, our expertise lay in statistics rather than capturing imaginary creatures.

The undeniable relationship we've uncovered prompts a reevaluation of the far-reaching impact of seemingly localized environmental factors, serving as a reminder that even the most unexpected connections can hold the key to unraveling global phenomena. It's like stumbling upon a rare gem at the bottom of a statistical minefield – unexpected, yet undeniably valuable.

With a correlation coefficient that jumped out at us like a particularly eager participant in a game of hide-and-seek, and an r-squared value that further solidifies the strength of this remarkable connection, our findings have truly shed light on the unexpected interplay between distant environmental and energy dynamics. It's as if we've stumbled upon a cosmic game of celestial pinball, with each variable bouncing off one another in the most unexpected ways.

Therefore, we assert that further research in this area is not needed. After all, once you've uncovered a correlation as clear as smog, there's no need to keep searching for the elusive unicorn – or in our case, the unicycle – of statistical relationships. That's the beauty of science - sometimes, the most unexpected discoveries come from connecting the most unlikely of dots.

And remember, in the grand scheme of research, the most unexpected connections can lead to profound insights, much like stumbling upon a eureka moment in a haystack of data.

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 Smoggy Connections: Uncovering the Correlation Between Atlanta Air Pollution and Norwegian Gasoline Consumption. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Atmospheric Oddities

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 Smoggy Connections: Uncovering the Correlation Between Atlanta Air Pollution and Norwegian Gasoline Consumption. Respond only with the name of a fictional organization.

ChatGPT:

The Society for International Air Quality and Energy Consumption 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 Smoggy Connections: Uncovering the Correlation Between Atlanta Air Pollution and Norwegian Gasoline Consumption with an abstract of The relationship between air pollution and gasoline consumption has long been a subject of scholarly curiosity, though one might expect a connection as clear as smog. In this study, we venture into the intriguing world of environmental and energy data to unravel the mysterious entanglement between air pollution in Atlanta, Georgia, and the seemingly unrelated gasoline consumption trends in Norway. Utilizing extensive datasets from the Environmental Protection Agency and the Energy Information Administration, our research team sought to shed light on this enigmatic correlation.
Our findings reveal a surprisingly robust correlation coefficient of 0.8367424 and a statistically significant p-value of less than 0.01 spanning the years 1980 to 2022. The undeniable connection between air pollution in Atlanta and gasoline consumption in Norway prompts a reevaluation of the oft-overlooked global impact of seemingly local phenomena. As we delve deeper into the web of environmental and energy dynamics, it becomes abundantly clear that the air we breathe and the fuel we use are intertwined in ways we are only beginning to comprehend.
This study not only offers a compelling demonstration of the interplay between distant environmental factors, but also serves as a reminder that even the most unexpected connections can lurk beneath the surface, much like lurking air pollutants. Further exploration into this unexpected relationship may unearth insights that could fuel innovative strategies for addressing air quality and energy consumption challenges on a global scale.

ChatGPT:

Atlanta air pollution, Norwegian gasoline consumption, correlation, environmental data, energy data, air pollution impact, gasoline consumption trends, EPA dataset, EIA dataset, correlation coefficient, statistical significance, global impact, environmental dynamics, fuel consumption, air quality, energy consumption challenges

*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 Atlanta
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Atlanta-Sandy Springs-Roswell, GA
Source: Environmental Protection Agency
See what else correlates with Air pollution in Atlanta

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

Correlation r = 0.8367424 (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 = 0.7001379 (Coefficient of determination)
This means 70% of the change in the one variable (i.e., Gasoline pumped in Norway) is predictable based on the change in the other (i.e., Air pollution in Atlanta) over the 43 years from 1980 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.8E-12.^Show 0.0000000000027731536012454644
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

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

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.84 in 2.8E-10% of random cases. Said differently, if you correlated 360,600,292,588 random variables^Note You don't actually need 360 billion variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 42 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 42 because we have two variables measured over a period of 43 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.72, 0.91 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: ^Note If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	1980	1981	1982	1983	1984	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Air pollution in Atlanta (Bad air quality days)	23.224	13.4247	12.3288	19.1781	14.4809	10.6849	20.8219	26.8493	27.0492	19.726	29.863	19.4521	17.4863	26.8493	15.0685	23.2877	19.3989	19.1781	29.863	35.3425	31.4208	22.4658	19.4521	15.6164	14.2077	18.6301	23.5616	22.7397	14.4809	6.57534	12.8767	18.3562	7.65027	3.56164	4.93151	4.38356	8.74317	3.0137	2.73973	5.20548	0.819672	2.19178	2.46575
Gasoline pumped in Norway (Million Barrels/Day)	34	32	35	36	34.5137	36.8329	39.3589	40.6959	41.0492	41.2822	41.389	40.0521	39.1066	38.9178	39.0301	38.5918	39.041	38.5699	38.9644	38.6877	37.3962	38.9151	38.526	38.1534	37.8197	36.5781	35.189	33.3589	31.2131	29.4082	28.3863	26.0274	24.3552	22.9151	21.8521	20.1781	19.7568	19.3178	18.7425	17.7562	16.6858	16.5342	15.211

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.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

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([23.224,13.4247,12.3288,19.1781,14.4809,10.6849,20.8219,26.8493,27.0492,19.726,29.863,19.4521,17.4863,26.8493,15.0685,23.2877,19.3989,19.1781,29.863,35.3425,31.4208,22.4658,19.4521,15.6164,14.2077,18.6301,23.5616,22.7397,14.4809,6.57534,12.8767,18.3562,7.65027,3.56164,4.93151,4.38356,8.74317,3.0137,2.73973,5.20548,0.819672,2.19178,2.46575,])
array_2 = np.array([34,32,35,36,34.5137,36.8329,39.3589,40.6959,41.0492,41.2822,41.389,40.0521,39.1066,38.9178,39.0301,38.5918,39.041,38.5699,38.9644,38.6877,37.3962,38.9151,38.526,38.1534,37.8197,36.5781,35.189,33.3589,31.2131,29.4082,28.3863,26.0274,24.3552,22.9151,21.8521,20.1781,19.7568,19.3178,18.7425,17.7562,16.6858,16.5342,15.211,])
array_1_name = "Air pollution in Atlanta"
array_2_name = "Gasoline pumped in Norway"

# 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 Atlanta
Line chart for only Gasoline pumped in Norway
AI-generated correlation image
The spurious research paper: Smoggy Connections: Uncovering the Correlation Between Atlanta Air Pollution and Norwegian Gasoline Consumption

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Correlation ID: 2338 · Black Variable ID: 20291 · Red Variable ID: 24779


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