Spurious correlation #1,980 · 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 Savannah, Georgia and the second variable is Kerosene used in South Korea. The chart goes from 1983 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 Savannah, Georgia and the second variable is Kerosene used in South Korea. The chart goes from 1983 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The smog from Savannah somehow contained inspirational messages in code, triggering a sudden surge in kerosene usage in South Korea as people tried to decipher the secrets of the skies. As ridiculous as it sounds, there may be actual statistical support for this pattern!

^{Show image generation prompt} Model: dalle-3
Prompt: Create a black-and-white newspaper illustration depicting the contrast in air quality between Savannah, Georgia and South Korea. In one section, show a dense, hazy atmosphere with a red warning sign flashing in Savannah, Georgia. In the other section, depict families gathering around kerosene lamps in South Korea, with the lamps providing warmth and light in the night. The illustration should capture the atmospheric contrast and the reliance on alternative light sources in the two locations. 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: Unhealthy air quality in Savannah, Georgia and Kerosene used in South Korea. 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: a Polaroid photo Magic realism High-tech A black-and-white newspaper illustration Concept art
ChatGPT: A black-and-white newspaper illustration

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 Unhealthy air quality in Savannah, Georgia and Kerosene used in South Korea together. Keep the description simple.
ChatGPT: In Savannah, Georgia, the air is thick and hazy, with a red warning sign flashing due to unhealthy air quality. Meanwhile, in South Korea, kerosene lamps illuminate the night as families gather around them for warmth and light.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A black-and-white newspaper illustration ] and depicting this scene: In Savannah, Georgia, the air is thick and hazy, with a red warning sign flashing due to unhealthy air quality. Meanwhile, in South Korea, kerosene lamps illuminate the night as families gather around them for warmth and light. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create a black-and-white newspaper illustration depicting the contrast in air quality between Savannah, Georgia and South Korea. In one section, show a dense, hazy atmosphere with a red warning sign flashing in Savannah, Georgia. In the other section, depict families gathering around kerosene lamps in South Korea, with the lamps providing warmth and light in the night. The illustration should capture the atmospheric contrast and the reliance on alternative light sources in the two locations.

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

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

AI academic paper

(Because p < 0.01)

Kerosene in Seoul Sends Savannah's Air South: A Correlative Analysis of Air Pollution in Georgia and Kerosene Consumption in South Korea

The Journal of Transcontinental Atmospheric Studies

Jan 2024

Reminder: This paper is AI-generated. Not real!

^{Show prompt used to generate this paper} Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between Air pollution in Savannah, Georgia and Kerosene used in South Korea. Make lots of 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. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
"Kerosene in Seoul Sends Savannah's Air South: A Correlative Analysis of Air Pollution in Georgia and Kerosene Consumption in South Korea"

[[ABSTRACT]]
This research study aimed to investigate the curious relationship between air pollution in Savannah, Georgia, and the consumption of kerosene in South Korea. Utilizing data from the Environmental Protection Agency and the Energy Information Administration spanning the years 1983 to 2022, a correlation coefficient of 0.6839014 and p < 0.01 was established, indicating a significant association between the two variables. The findings suggest a previously unrecognized intercontinental link between Savannah's air quality and South Korea's kerosene use. This connection, while unexpected, underscores the global impact of seemingly disparate phenomena and invites further exploration into the unconventional relationships within the complex web of environmental factors.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
The juxtaposition of kerosene consumption in South Korea and air pollution in Savannah, Georgia is a conundrum that has puzzled researchers and laymen alike. The correlation between these seemingly unrelated variables has raised eyebrows, piqued curiosity, and sparked lively debate in academic circles. The notion that air quality in the charming southern city of Savannah could be inextricably linked to the kerosene practices across the Pacific is indeed a fascinating enigma worthy of investigation.
The complexities of the global ecosystem have long confounded scientists, prompting many to ponder at the intricate dance of environmental variables. The Earth, it seems, is a stage for an elaborate performance, complete with surprising plot twists and unexpected connections. As researchers, we seek to uncover the hidden narrative, to unravel the mysteries that tie together disparate elements of our world.
This study delves into the intersection of air quality and kerosene consumption, casting a net across continents to capture the nuances of this peculiar relationship. The statistical analysis of the data reveals a correlation coefficient that not only raises eyebrows but also raises the question: could there be a causal link, or is this just a fluke of statistical fate?
The aim of this paper is not only to present the findings of this investigation but also to infuse a sense of wonder and amazement at the intricate tapestry of environmental interplay. Our goal is to highlight the interconnectedness of seemingly unrelated phenomena and to encourage a broader perspective in understanding the world around us. So, embark on this intellectual journey with us, as we unravel the tale of kerosene in Seoul sending Savannah's air south.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The literature surrounding the connection between air pollution in Savannah, Georgia, and kerosene consumption in South Korea is a fairly sparse landscape, yet there are some intriguing findings to consider. Smith's study in "The Journal of Environmental Science" lays the groundwork for understanding the dynamics of air pollution and its far-reaching impact. Meanwhile, Doe's analysis in "Energy Economics" provides a comprehensive overview of kerosene usage patterns in East Asia, albeit with a focus on broader economic implications rather than air quality concerns. Jones' work in "Atmospheric Environment" offers an intricate examination of the atmospheric composition in coastal regions, offering a potential window into the complexities of Savannah's air pollution.

Turning to non-fiction books relevant to the subject matter, "The Air Pollution of Cities," by John Smith, takes a deep dive into the historical and contemporary factors contributing to urban air pollution, although the book conspicuously neglects to discuss intercontinental kerosene relationships. On the other hand, "The Global Reach of Kerosene," by Jane Doe, presents an in-depth analysis of the multifaceted usage of kerosene and its impact on international energy dynamics, with nary a mention of its potential influence on the coastal breeze in Savannah.

In the realm of fiction, "The Kerosene Diaries" by J.K. Rowling may sound like a spellbinding account of a magical fuel's clandestine adventures, but it actually pertains to a hard-boiled detective's investigations in post-war America. Similarly, "The Air Affair" by Jasper Fforde seems to promise a whimsical tale of airborne shenanigans, but disappointingly, it revolves around literary crime-solving escapades rather than atmospheric kerosene escapades.

Board games such as "Polluted Skies," which simulates the effects of air pollution on global climate, and "Kerosene Quest," a virtual journey through the intricacies of kerosene supply chains, offer a tangential yet lighthearted approach to understanding the relationships between air pollution and kerosene usage.

In the intersection of scholarly works, non-fiction literature, fiction novels, and even board games, it becomes evident that the connection between air pollution in Savannah and kerosene consumption in South Korea is a subject not only of scientific inquiry but also of curious fascination and unexpected tangents.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between Air pollution in Savannah, Georgia and Kerosene used in South Korea. Make lots of 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 1983 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]]
"Kerosene in Seoul Sends Savannah's Air South: A Correlative Analysis of Air Pollution in Georgia and Kerosene Consumption in South Korea"

[[ABSTRACT]]
This research study aimed to investigate the curious relationship between air pollution in Savannah, Georgia, and the consumption of kerosene in South Korea. Utilizing data from the Environmental Protection Agency and the Energy Information Administration spanning the years 1983 to 2022, a correlation coefficient of 0.6839014 and p < 0.01 was established, indicating a significant association between the two variables. The findings suggest a previously unrecognized intercontinental link between Savannah's air quality and South Korea's kerosene use. This connection, while unexpected, underscores the global impact of seemingly disparate phenomena and invites further exploration into the unconventional relationships within the complex web of environmental factors.

[[INTRODUCTION]]
INTRODUCTION
The juxtaposition of kerosene consumption in South Korea and air pollution in Savannah, Georgia is a conundrum that has puzzled researchers and laymen alike. The correlation between these seemingly unrelated variables has raised eyebrows, piqued curiosity, and sparked lively debate in academic circles. The notion that air quality in the charming southern city of Savannah could be inextricably linked to the kerosene practices across the Pacific is indeed a fascinating enigma worthy of investigation.
The complexities of the global ecosystem have long confounded scientists, prompting many to ponder at the intricate dance of environmental variables. The Earth, it seems, is a stage for an elaborate performance, complete with surprising plot twists and unexpected connections. As researchers, we seek to uncover the hidden narrative, to unravel the mysteries that tie together disparate elements of our world.
This study delves into the intersection of air quality and kerosene consumption, casting a net across continents to capture the nuances of this peculiar relationship. The statistical analysis of the data reveals a correlation coefficient that not only raises eyebrows but also raises the question: could there be a causal link, or is this just a fluke of statistical fate?
The aim of this paper is not only to present the findings of this investigation but also to infuse a sense of wonder and amazement at the intricate tapestry of environmental interplay. Our goal is to highlight the interconnectedness of seemingly unrelated phenomena and to encourage a broader perspective in understanding the world around us. So, embark on this intellectual journey with us, as we unravel the tale of kerosene in Seoul sending Savannah's air south.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this research involved an intricate dance of data collection, statistical analysis, and a sprinkle of whimsy. Data on air pollution levels in Savannah, Georgia, were meticulously extracted from the treasure trove of information hosted by the Environmental Protection Agency, spanning the years 1983 to 2022. Meanwhile, the consumption of kerosene in South Korea was gleaned from the depths of the Energy Information Administration's databases. The chivalrous knights of our research team braved the digital terrain, armed with spreadsheets and calculators, to gather these invaluable datasets.

With our data in hand, we engaged in a rigorous tango of statistical analysis. The relationship between air pollution in Savannah and kerosene consumption in South Korea was explored using Pearson's correlation coefficient, a stalwart companion in the realm of quantitative inquiry. The statistical software at our disposal dutifully crunched the numbers, revealing a correlation coefficient of 0.6839014 and a p-value less than 0.01. This momentous revelation was met with an appreciative nod from the researchers and a fervent round of applause from the statistical software.

To ensure the robustness of our findings, sensitivity analyses were performed, subjecting the data to various tests and examinations in a manner akin to putting a thoroughbred through its paces. The results, much like a well-bred racehorse, remained steadfast in their affirmation of the connection between Savannah's air quality and South Korea's kerosene consumption.

In addition, cross-validation techniques were employed to validate the stability of our findings, prompting a nod of approval from the seasoned veterans of statistical analysis. The data, like a well-rehearsed troupe of performers, maintained their coherence and consistency across multiple validation exercises, lending further credence to the association uncovered in our study.

Finally, to add a touch of whimsy to our methodology, a ceremonial bonfire was lit, symbolizing the burning passion with which this research endeavor was pursued. In the glow of the flames, the researchers pondered the interconnectedness of the world's environmental tapestry, weaving together the disparate threads of air quality in Savannah and kerosene consumption in Seoul.

In essence, the methodology encapsulated a journey of data collection, statistical analysis, and introspective contemplation, culminating in the elucidation of the curious bond between seemingly distant environmental phenomena.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between Air pollution in Savannah, Georgia and Kerosene used in South Korea. Make lots of 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]]
"Kerosene in Seoul Sends Savannah's Air South: A Correlative Analysis of Air Pollution in Georgia and Kerosene Consumption in South Korea"

[[ABSTRACT]]
This research study aimed to investigate the curious relationship between air pollution in Savannah, Georgia, and the consumption of kerosene in South Korea. Utilizing data from the Environmental Protection Agency and the Energy Information Administration spanning the years 1983 to 2022, a correlation coefficient of 0.6839014 and p < 0.01 was established, indicating a significant association between the two variables. The findings suggest a previously unrecognized intercontinental link between Savannah's air quality and South Korea's kerosene use. This connection, while unexpected, underscores the global impact of seemingly disparate phenomena and invites further exploration into the unconventional relationships within the complex web of environmental factors.

[[LITERATURE REVIEW]]
The literature surrounding the connection between air pollution in Savannah, Georgia, and kerosene consumption in South Korea is a fairly sparse landscape, yet there are some intriguing findings to consider. Smith's study in "The Journal of Environmental Science" lays the groundwork for understanding the dynamics of air pollution and its far-reaching impact. Meanwhile, Doe's analysis in "Energy Economics" provides a comprehensive overview of kerosene usage patterns in East Asia, albeit with a focus on broader economic implications rather than air quality concerns. Jones' work in "Atmospheric Environment" offers an intricate examination of the atmospheric composition in coastal regions, offering a potential window into the complexities of Savannah's air pollution.
Turning to non-fiction books relevant to the subject matter, "The Air Pollution of Cities," by John Smith, takes a deep dive into the historical and contemporary factors contributing to urban air pollution, although the book conspicuously neglects to discuss intercontinental kerosene relationships. On the other hand, "The Global Reach of Kerosene," by Jane Doe, presents an in-depth analysis of the multifaceted usage of kerosene and its impact on international energy dynamics, with nary a mention of its potential influence on the coastal breeze in Savannah.
In the realm of fiction, "The Kerosene Diaries" by J.K. Rowling may sound like a spellbinding account of a magical fuel's clandestine adventures, but it actually pertains to a hard-boiled detective's investigations in post-war America. Similarly, "The Air Affair" by Jasper Fforde seems to promise a whimsical tale of airborne shenanigans, but disappointingly, it revolves around literary crime-solving escapades rather than atmospheric kerosene escapades.
Board games such as "Polluted Skies," which simulates the effects of air pollution on global climate, and "Kerosene Quest," a virtual journey through the intricacies of kerosene supply chains, offer a tangential yet lighthearted approach to understanding the relationships between air pollution and kerosene usage.
In the intersection of scholarly works, non-fiction literature, fiction novels, and even board games, it becomes evident that the connection between air pollution in Savannah and kerosene consumption in South Korea is a subject not only of scientific inquiry but also of curious fascination and unexpected tangents.

[[RESULTS]]
The statistical analysis of the data collected from the Environmental Protection Agency and the Energy Information Administration revealed a remarkable correlation between air pollution in Savannah, Georgia and kerosene consumption in South Korea. The correlation coefficient of 0.6839014 indicates a strong positive relationship between these two variables, suggesting that as kerosene consumption in South Korea increased, air pollution in Savannah also exhibited a concurrent rise.
Furthermore, the r-squared value of 0.4677212 signifies that approximately 46.77% of the variability in air pollution in Savannah can be explained by the variability in kerosene consumption in South Korea. This substantial proportion of variance explained underscores the importance of the relationship between these two seemingly distant phenomena.
The p-value of less than 0.01 provides compelling evidence to reject the null hypothesis and accept the alternative hypothesis that there is, indeed, a significant association between air pollution in Savannah and kerosene consumption in South Korea. This finding reinforces the strength of the observed correlation and its statistical significance.
Fig. 1 presents a scatterplot illustrating the robust correlation between the two variables, visually capturing the substantial relationship observed in the statistical analysis. The scatterplot depicts a clear positive trend, emphasizing the coherence between air pollution in Savannah and kerosene consumption in South Korea.
In conclusion, the results of this study unveil an unexpected and intriguing link between two geographically disparate phenomena, adding an unexpected twist to the narrative of environmental interconnectedness. The findings serve as a reminder of the intricate and often surprising connections within the global environmental web, inviting further exploration and underscoring the complexity of our planet's environmental dynamics.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]
The results of the present study are in line with prior research, confirming the peculiar connection between air pollution in Savannah, Georgia, and kerosene consumption in South Korea. The substantial correlation coefficient, robust R-squared value, and compelling p-value all point to a significant and noteworthy association, echoing the findings of previous investigations.

The literature review in this study took an eclectic approach, incorporating both non-fiction and fiction works relevant to the subject matter. In a surprising turn of events, it was discovered that "The Kerosene Diaries" by J.K. Rowling and "The Air Affair" by Jasper Fforde, while captivating in their own right, failed to shed light on the real-world intercontinental relationship under examination. Nevertheless, the scholarly articles and non-fiction literature provided valuable context for understanding the complex web of environmental factors.

In the realm of scientific inquiry, previous studies have often focused on regional or national air quality issues, neglecting the potential impact of international factors. The current findings bridge this gap and highlight the truly global nature of environmental phenomena. It appears that the atmospheric composition in coastal regions, as elucidated by Jones in "Atmospheric Environment," may indeed hold the key to understanding the nuances of Savannah's air pollution, despite initial skepticism about its relevance.

It is worth noting that "Polluted Skies" and "Kerosene Quest," the board games mentioned in the literature review, may have appeared tangential, but in retrospect, they offer a lighthearted lens through which to view the intricate relationships between air pollution and kerosene usage. Perhaps the inclusion of such unconventional sources ultimately enhanced the study's capacity to approach the research question from an unexpected angle.

Overall, the results of the study provide empirical support for the hypothesis that there is an unanticipated intercontinental connection between air pollution in Savannah and kerosene consumption in South Korea. This finding expands our understanding of the intricate tapestry of global environmental dynamics and underscores the need for further investigation into such unexpected relationships.

The unexpected twists and humorous tangents encountered in the literature review ultimately lent an air of intrigue to the study, proving that academic inquiry need not always unfold in a predictable fashion. This study demonstrates that even in the most unconventional of subjects, there is room for rigorous analysis, statistical evaluation, and the occasional chuckle at the quirky convergence of seemingly unrelated phenomena.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Air pollution in Savannah, Georgia and Kerosene used in South Korea. Make lots of 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]]
"Kerosene in Seoul Sends Savannah's Air South: A Correlative Analysis of Air Pollution in Georgia and Kerosene Consumption in South Korea"

[[ABSTRACT]]
This research study aimed to investigate the curious relationship between air pollution in Savannah, Georgia, and the consumption of kerosene in South Korea. Utilizing data from the Environmental Protection Agency and the Energy Information Administration spanning the years 1983 to 2022, a correlation coefficient of 0.6839014 and p < 0.01 was established, indicating a significant association between the two variables. The findings suggest a previously unrecognized intercontinental link between Savannah's air quality and South Korea's kerosene use. This connection, while unexpected, underscores the global impact of seemingly disparate phenomena and invites further exploration into the unconventional relationships within the complex web of environmental factors.

[[INTRDUCTION]]
INTRODUCTION
The juxtaposition of kerosene consumption in South Korea and air pollution in Savannah, Georgia is a conundrum that has puzzled researchers and laymen alike. The correlation between these seemingly unrelated variables has raised eyebrows, piqued curiosity, and sparked lively debate in academic circles. The notion that air quality in the charming southern city of Savannah could be inextricably linked to the kerosene practices across the Pacific is indeed a fascinating enigma worthy of investigation.
The complexities of the global ecosystem have long confounded scientists, prompting many to ponder at the intricate dance of environmental variables. The Earth, it seems, is a stage for an elaborate performance, complete with surprising plot twists and unexpected connections. As researchers, we seek to uncover the hidden narrative, to unravel the mysteries that tie together disparate elements of our world.
This study delves into the intersection of air quality and kerosene consumption, casting a net across continents to capture the nuances of this peculiar relationship. The statistical analysis of the data reveals a correlation coefficient that not only raises eyebrows but also raises the question: could there be a causal link, or is this just a fluke of statistical fate?
The aim of this paper is not only to present the findings of this investigation but also to infuse a sense of wonder and amazement at the intricate tapestry of environmental interplay. Our goal is to highlight the interconnectedness of seemingly unrelated phenomena and to encourage a broader perspective in understanding the world around us. So, embark on this intellectual journey with us, as we unravel the tale of kerosene in Seoul sending Savannah's air south.

[[RESULTS]]
The statistical analysis of the data collected from the Environmental Protection Agency and the Energy Information Administration revealed a remarkable correlation between air pollution in Savannah, Georgia and kerosene consumption in South Korea. The correlation coefficient of 0.6839014 indicates a strong positive relationship between these two variables, suggesting that as kerosene consumption in South Korea increased, air pollution in Savannah also exhibited a concurrent rise.
Furthermore, the r-squared value of 0.4677212 signifies that approximately 46.77% of the variability in air pollution in Savannah can be explained by the variability in kerosene consumption in South Korea. This substantial proportion of variance explained underscores the importance of the relationship between these two seemingly distant phenomena.
The p-value of less than 0.01 provides compelling evidence to reject the null hypothesis and accept the alternative hypothesis that there is, indeed, a significant association between air pollution in Savannah and kerosene consumption in South Korea. This finding reinforces the strength of the observed correlation and its statistical significance.
Fig. 1 presents a scatterplot illustrating the robust correlation between the two variables, visually capturing the substantial relationship observed in the statistical analysis. The scatterplot depicts a clear positive trend, emphasizing the coherence between air pollution in Savannah and kerosene consumption in South Korea.
In conclusion, the results of this study unveil an unexpected and intriguing link between two geographically disparate phenomena, adding an unexpected twist to the narrative of environmental interconnectedness. The findings serve as a reminder of the intricate and often surprising connections within the global environmental web, inviting further exploration and underscoring the complexity of our planet's environmental dynamics.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

The results of this study shed light on the remarkable association between air pollution in Savannah, Georgia and kerosene consumption in South Korea, proving that the world of environmental science is full of surprising twists and turns. As researchers, we never expected such a striking correlation, but as they say, "truth is stranger than fiction!"

The substantial correlation coefficient and r-squared value indicate that this is not just a statistical fluke - there's a real connection here, not just a case of random chance blowing in the wind. And with a p-value of less than 0.01, we can confidently say that this relationship is no statistical sleight of hand, but a genuine phenomenon worthy of further investigation.

While the connection may seem as unlikely as finding a needle in a haystack, the data speaks for itself. It's a reminder that in the world of research, even the most unexpected results can lead to important discoveries. As scientists, we must remain open to unconventional connections and unconventional wisdom.

This study's findings bring new meaning to the phrase "air mail" and remind us that in the global theater of air quality, every player - no matter how far-flung - has a part to play. Indeed, the impact of one country's kerosene use can send ripples across oceans and continents, affecting the air quality of cities half a world away.

In light of these findings, it is clear that the relationship between air pollution in Savannah and kerosene consumption in South Korea is both significant and worthy of further examination. It's time to take a deep breath of fresh air as we conclude that no further research is needed in this area. As they say, sometimes the most baffling connections are the ones that make the most sense in the end. And with that, let's bid adieu to this kerosene-fueled escapade and turn our attention to the next surprising correlation waiting to be discovered. Happy researching, my fellow scientists!

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 Kerosene in Seoul Sends Savannah's Air South: A Correlative Analysis of Air Pollution in Georgia and Kerosene Consumption in South Korea. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Transcontinental Atmospheric Studies

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited Kerosene in Seoul Sends Savannah's Air South: A Correlative Analysis of Air Pollution in Georgia and Kerosene Consumption in South Korea. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Global Environmental Analysis and Research (SGEAR)

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 Kerosene in Seoul Sends Savannah's Air South: A Correlative Analysis of Air Pollution in Georgia and Kerosene Consumption in South Korea with an abstract of This research study aimed to investigate the curious relationship between air pollution in Savannah, Georgia, and the consumption of kerosene in South Korea. Utilizing data from the Environmental Protection Agency and the Energy Information Administration spanning the years 1983 to 2022, a correlation coefficient of 0.6839014 and p < 0.01 was established, indicating a significant association between the two variables. The findings suggest a previously unrecognized intercontinental link between Savannah's air quality and South Korea's kerosene use. This connection, while unexpected, underscores the global impact of seemingly disparate phenomena and invites further exploration into the unconventional relationships within the complex web of environmental factors.

ChatGPT:

air pollution, Savannah Georgia, kerosene consumption, South Korea, correlation analysis, Environmental Protection Agency, Energy Information Administration, intercontinental link, global impact, unconventional relationships, environmental factors

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

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

Correlation r = 0.6839014 (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 = 0.4677212 (Coefficient of determination)
This means 46.8% of the change in the one variable (i.e., Kerosene used in South Korea) is predictable based on the change in the other (i.e., Air pollution in Savannah, Georgia) over the 40 years from 1983 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.1E-6.^Show 0.0000011461092062627836000000
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 0.68 in 0.00011% of random cases. Said differently, if you correlated 872,517 random variables^Note You don't actually need 872 thousand 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 39 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 39 because we have two variables measured over a period of 40 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.

[ 0.47, 0.82 ] 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.

	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 Savannah, Georgia (Bad air quality days)	0.00584795	0	0	0	0	0	0	0	0	0	0	0	0.0210084	0.0195312	0.0151515	0.0255682	0.0606061	0.0412088	0.0109589	0.00273973	0.010989	0.0163934	0.0136986	0.00274725	0.0191781	0.00819672	0.00273973	0	0.0274725	0.00273224	0	0	0	0.00821918	0	0	0	0.00273973	0.00275482	0
Kerosene used in South Korea (Million Barrels/Day)	22	21.9672	21.737	21.863	22.3479	27.2842	39.3973	68.3233	70.1479	93.582	118.521	131.071	171.688	201.279	198.162	146.211	211.249	191.429	171.685	164.57	150.345	122.317	112.008	90.1178	75.8712	78.4153	79.526	82.5616	72.6356	62.7213	54.5781	45.6575	48.6411	54.8388	51.3069	50.7589	45.7096	46.7623	45.7288	46.326

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. ^{Show the steps to do this.} Step 1: Download and install Python on your computer.

Step 2: Open a plaintext editor like Notepad and paste the code below into it.

Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

Step 7: Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."

# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([0.00584795,0,0,0,0,0,0,0,0,0,0,0,0.0210084,0.0195312,0.0151515,0.0255682,0.0606061,0.0412088,0.0109589,0.00273973,0.010989,0.0163934,0.0136986,0.00274725,0.0191781,0.00819672,0.00273973,0,0.0274725,0.00273224,0,0,0,0.00821918,0,0,0,0.00273973,0.00275482,0,])
array_2 = np.array([22,21.9672,21.737,21.863,22.3479,27.2842,39.3973,68.3233,70.1479,93.582,118.521,131.071,171.688,201.279,198.162,146.211,211.249,191.429,171.685,164.57,150.345,122.317,112.008,90.1178,75.8712,78.4153,79.526,82.5616,72.6356,62.7213,54.5781,45.6575,48.6411,54.8388,51.3069,50.7589,45.7096,46.7623,45.7288,46.326,])
array_1_name = "Air pollution in Savannah, Georgia"
array_2_name = "Kerosene used in South Korea"

# 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 Savannah, Georgia
Line chart for only Kerosene used in South Korea
AI-generated correlation image
The spurious research paper: Kerosene in Seoul Sends Savannah's Air South: A Correlative Analysis of Air Pollution in Georgia and Kerosene Consumption in South Korea

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Correlation ID: 1980 · Black Variable ID: 21691 · Red Variable ID: 24928


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