Spurious correlation #4,655 · 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 Youngstown, Ohio and the second variable is Petroluem consumption in Denmark. 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 Youngstown, Ohio and the second variable is Petroluem consumption in Denmark. 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 Youngstown somehow created a global chain reaction, leading to more efficient fuel usage in Denmark. It's like the domino effect, but with air particles and Danish cars. Who knew that the smog in Ohio was inadvertently fueling the gas tanks in Denmark! It's a breath of fresh air for environmentalists and a wheely puzzling connection for everyone else.

^{Show image generation prompt} Model: dalle-3
Prompt: Generating prompt... Title: Clear Skies and Efficient Cars Description: In this realistic image, the skyline of Youngstown, Ohio is portrayed with gleaming, modern skyscrapers set against a backdrop of strikingly clear blue skies. The reduced smog provides a crisp and fresh atmosphere to the bustling cityscape. Meanwhile, in Denmark, a line of cars at a futuristic gas station is shown. Each car is refueling in an advanced and efficient manner, indicative of a significant reduction in petroleum consumption. The visual connection between the two scenes highlights the surprising global impact of Youngstown's cleaner air on the fuel efficiency of Danish cars. This prompt aims to create a compelling visual representation of the profound influence of environmental improvements in one location on the behaviors and technologies in another. The juxtaposition of these two seemingly unrelated scenes serves to communicate the interconnectedness of global environmental efforts. 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 Youngstown, Ohio and Petroluem consumption in Denmark. 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 Macabre Sci-fi fantacy a Polaroid photo Realistic
ChatGPT: Realistic

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Air pollution in Youngstown, Ohio and Petroluem consumption in Denmark, in the following situation: The cleaner air in Youngstown somehow created a global chain reaction, leading to more efficient fuel usage in Denmark. It's like the domino effect, but with air particles and Danish cars. Who knew that the smog in Ohio was inadvertently fueling the gas tanks in Denmark! It's a breath of fresh air for environmentalists and a wheely puzzling connection for everyone else.. Keep the description simple.
ChatGPT: In Youngstown, Ohio, the skyline is shown with significantly reduced smog and clear blue skies. Meanwhile, in Denmark, a line of cars at a gas station is seen, with each car refueling in a much more efficient manner, indicating a reduction in petroleum consumption. The visual connection between the two scenes symbolizes the surprising global impact of cleaner air in Youngstown on the fuel efficiency of Danish cars.

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: [ Realistic ] and depicting this scene: In Youngstown, Ohio, the skyline is shown with significantly reduced smog and clear blue skies. Meanwhile, in Denmark, a line of cars at a gas station is seen, with each car refueling in a much more efficient manner, indicating a reduction in petroleum consumption. The visual connection between the two scenes symbolizes the surprising global impact of cleaner air in Youngstown on the fuel efficiency of Danish cars. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generating prompt... Title: Clear Skies and Efficient Cars Description: In this realistic image, the skyline of Youngstown, Ohio is portrayed with gleaming, modern skyscrapers set against a backdrop of strikingly clear blue skies. The reduced smog provides a crisp and fresh atmosphere to the bustling cityscape. Meanwhile, in Denmark, a line of cars at a futuristic gas station is shown. Each car is refueling in an advanced and efficient manner, indicative of a significant reduction in petroleum consumption. The visual connection between the two scenes highlights the surprising global impact of Youngstown's cleaner air on the fuel efficiency of Danish cars. This prompt aims to create a compelling visual representation of the profound influence of environmental improvements in one location on the behaviors and technologies in another. The juxtaposition of these two seemingly unrelated scenes serves to communicate the interconnectedness of global environmental efforts.

*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 Youngstown, Ohio caused Petroluem consumption in Denmark to decrease.

AI academic paper

(Because p < 0.01)

Clearing the Air: Exploring the Link Between Air Pollution in Youngstown, Ohio, and Petroleum Consumption in Denmark

Journal of Ecological Connections

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 Youngstown, Ohio and Petroluem consumption in Denmark. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
"Clearing the Air: Exploring the Link Between Air Pollution in Youngstown, Ohio, and Petroleum Consumption in Denmark"

[[ABSTRACT]]
This study examines the relationship between air pollution levels in Youngstown, Ohio, and petroleum consumption patterns in Denmark, with a focus on the time period spanning from 1980 to 2022. Using comprehensive data from the Environmental Protection Agency and the Energy Information Administration, our research team employed rigorous statistical analysis to identify a robust correlation coefficient of 0.8706275, with a significance level of p < 0.01. The findings reveal an intriguing connection between these seemingly disparate locations, shedding light on the impacts of petroleum-related activities on air quality across distant regions. As we unravel this fascinating correlation, we hope to inspire further exploration of unconventional associations and unearth the unexpected interplay of factors in our ever-changing global environment.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Air pollution is a pervasive and pressing issue that affects communities around the world, with sources of pollution ranging from industrial activities to vehicular emissions. One might say that the topic is "air-resistible!" The city of Youngstown, Ohio, has grappled with air quality challenges, while Denmark, known for its windmills and fairy tales, has wrestled with petroleum consumption dynamics – a situation that is more complex than a Danish pastry recipe. These seemingly unrelated phenomena caught the attention of our research team, leading us to investigate whether there might be a connection between air pollution in Youngstown and petroleum consumption in Denmark. It's as though we embarked on a curious journey, armed with data and statistical tools, to explore the hidden links in the global atmospheric maze.
The research builds on prior studies that have examined various aspects of air pollution and petroleum consumption, but few have dared to take on the audacious task of linking such distinct geographical entities. The findings hold implications for environmental policies, energy strategizing, and, quite possibly, transcontinental conversations regarding air quality and energy use. As we delve into this intriguing correlation, we aim to provide a scholarly contribution that is both academically rigorous and, dare I say, a breath of fresh air in the field of environmental research.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The literature review begins with a thorough examination of prior studies that have investigated the intricate relationship between air pollution and petroleum consumption, setting the stage for our exploration of the intriguing link between Youngstown, Ohio, and Denmark. Smith and colleagues analyzed the impact of industrial emissions on local air quality in urban settings, elucidating the complex interplay of particulate matter, sulfur dioxide, and nitrogen oxides. Doe's comprehensive review of global petroleum consumption patterns unearthed the multifaceted dynamics of energy demand and supply, providing valuable insights into the intricate web of oil production and consumption.

Expanding beyond the conventional boundaries of environmental research, we draw inspiration from a multitude of sources that offer unique perspectives on air pollution and energy use. "The Sixth Extinction" by Elizabeth Kolbert presents a sobering analysis of humanity's impact on the planet, raising questions about the far-reaching consequences of resource extraction and industrial activities. In a more whimsical vein, "The Lorax" by Dr. Seuss conveys a poignant message about environmental stewardship, encouraging readers to reflect on the consequences of unchecked consumption and pollution. Furthermore, the works of fiction "Cloud Atlas" by David Mitchell and "Station Eleven" by Emily St. John Mandel traverse diverse landscapes, offering glimpses of environmental degradation and the resilience of human communities in the face of societal and ecological challenges.

Moreover, the insightful musings of social media users have surfaced in our investigation, with thought-provoking tweets and posts underscoring the global resonance of environmental concerns. One Twitter user pondered the parallels between air pollution levels and the characters' travails in "Great Expectations," while a Reddit thread delved into the enigmatic connection between petroleum consumption trends and the fantastical realms depicted in "Harry Potter." These online discussions have sparked intriguing speculations and prompted us to consider the unexpected intersections of pop culture and environmental phenomena.

As we navigate this diverse terrain of literature and discourse, we aim to illuminate the peculiar correlation between air pollution in Youngstown, Ohio, and petroleum consumption in Denmark, anchoring our analysis in a rich tapestry of scholarly inquiry and imaginative contemplation.

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 Youngstown, Ohio and Petroluem consumption in Denmark. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 500 tokens.

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

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

[[TITLE]]
"Clearing the Air: Exploring the Link Between Air Pollution in Youngstown, Ohio, and Petroleum Consumption in Denmark"

[[ABSTRACT]]
This study examines the relationship between air pollution levels in Youngstown, Ohio, and petroleum consumption patterns in Denmark, with a focus on the time period spanning from 1980 to 2022. Using comprehensive data from the Environmental Protection Agency and the Energy Information Administration, our research team employed rigorous statistical analysis to identify a robust correlation coefficient of 0.8706275, with a significance level of p < 0.01. The findings reveal an intriguing connection between these seemingly disparate locations, shedding light on the impacts of petroleum-related activities on air quality across distant regions. As we unravel this fascinating correlation, we hope to inspire further exploration of unconventional associations and unearth the unexpected interplay of factors in our ever-changing global environment.

[[LITERATURE REVIEW]]
The literature review begins with a thorough examination of prior studies that have investigated the intricate relationship between air pollution and petroleum consumption, setting the stage for our exploration of the intriguing link between Youngstown, Ohio, and Denmark. Smith and colleagues analyzed the impact of industrial emissions on local air quality in urban settings, elucidating the complex interplay of particulate matter, sulfur dioxide, and nitrogen oxides. Doe's comprehensive review of global petroleum consumption patterns unearthed the multifaceted dynamics of energy demand and supply, providing valuable insights into the intricate web of oil production and consumption.
Expanding beyond the conventional boundaries of environmental research, we draw inspiration from a multitude of sources that offer unique perspectives on air pollution and energy use. "The Sixth Extinction" by Elizabeth Kolbert presents a sobering analysis of humanity's impact on the planet, raising questions about the far-reaching consequences of resource extraction and industrial activities. In a more whimsical vein, "The Lorax" by Dr. Seuss conveys a poignant message about environmental stewardship, encouraging readers to reflect on the consequences of unchecked consumption and pollution. Furthermore, the works of fiction "Cloud Atlas" by David Mitchell and "Station Eleven" by Emily St. John Mandel traverse diverse landscapes, offering glimpses of environmental degradation and the resilience of human communities in the face of societal and ecological challenges.
Moreover, the insightful musings of social media users have surfaced in our investigation, with thought-provoking tweets and posts underscoring the global resonance of environmental concerns. One Twitter user pondered the parallels between air pollution levels and the characters' travails in "Great Expectations," while a Reddit thread delved into the enigmatic connection between petroleum consumption trends and the fantastical realms depicted in "Harry Potter." These online discussions have sparked intriguing speculations and prompted us to consider the unexpected intersections of pop culture and environmental phenomena.
As we navigate this diverse terrain of literature and discourse, we aim to illuminate the peculiar correlation between air pollution in Youngstown, Ohio, and petroleum consumption in Denmark, anchoring our analysis in a rich tapestry of scholarly inquiry and imaginative contemplation.

[[RESULTS]]
The analysis of the data collected from the Environmental Protection Agency and the Energy Information Administration unveiled a striking association between air pollution levels in Youngstown, Ohio, and petroleum consumption patterns in Denmark during the years 1980 to 2022. The correlation coefficient of 0.8706275 indicated a robust positive relationship between these seemingly disparate variables. This finding suggests a connection as strong as the bond between peanut butter and jelly, or perhaps even stronger – akin to the bond between Denmark and Lego!
The coefficient of determination (r-squared) of 0.7579923 further supported the strength of the relationship, indicating that approximately 75.8% of the variation in air pollution levels in Youngstown can be explained by the variation in petroleum consumption in Denmark. One might say that the connection between these two phenomena is as clear as the air on a breezy spring day – or as murky as the air on a particularly smoggy day in a bustling metropolis.
The significance level of p < 0.01 underscored the statistical strength of the relationship, indicating that the likelihood of observing such a strong association by chance alone is less than 1%. It seems this correlation is as unlikely as stumbling upon a four-leaf clover while strolling through a field of daisies – a rare and serendipitous discovery indeed!
To visually elucidate this captivating association, the scatterplot in Figure 1 illustrates the strong positive correlation between air pollution levels in Youngstown, Ohio, and petroleum consumption in Denmark. The data points appear to form a cohesive pattern, reminiscent of a carefully planned symphony – or perhaps a synchronized swan ballet in the realm of statistical visualization.
In summary, the findings of this study illuminate an unexpected and intriguing connection between air pollution in Youngstown, Ohio, and petroleum consumption in Denmark, providing valuable insights into the far-reaching impacts of petroleum-related activities on air quality. As we navigate the complex web of global environmental dynamics, these findings serve as a beacon, guiding us to explore and appreciate the interconnectedness of seemingly distant phenomena.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our investigation have unveiled a compelling relationship between air pollution levels in Youngstown, Ohio, and petroleum consumption patterns in Denmark. These findings build upon prior research that has delved into the intricate interplay of environmental factors and energy usage. Drawing from the literature review, we note the whimsical yet thought-provoking connections, such as the striking parallels between the bond between peanut butter and jelly and the robust positive association between air pollution in Youngstown and petroleum consumption in Denmark. Indeed, one cannot help but appreciate the unexpected similarities that emerge from seemingly disparate realms of inquiry.

Our results echo the insights gleaned from Smith and Doe's analyses, highlighting the profound impact of industrial emissions and global petroleum consumption on air quality. The robust correlation coefficient aligns with the multifaceted dynamics of energy demand and supply, as elucidated by previous studies. It appears that the connection between air pollution and petroleum consumption is as clear as day, or perhaps as clear as a pristine fjord in Denmark – a testament to the strength of this intriguing association.

Moreover, the coefficient of determination further underscores the significance of our findings, shedding light on the interconnectedness of air pollution in Youngstown and petroleum consumption in Denmark. This substantial explanatory power parallels the immersive storytelling in "Cloud Atlas" and "Station Eleven," where diverse landscapes intertwine with environmental challenges, reflecting the intricate web of variables shaping our global ecosystem.

The significance level of p < 0.01 emphasizes the statistical robustness of the observed relationship, mirroring the unlikely discoveries woven into the fabric of literary and online musings. This rarity of the correlation is akin to stumbling upon a four-leaf clover while exploring the interconnected fields of environmental inquiry and pop culture, underscoring the serendipitous nature of our discovery.

In summary, our findings offer a fresh perspective on the complex interplay of air pollution and petroleum consumption, echoing the steps of Dr. Seuss's Lorax as we navigate the far-reaching consequences of unchecked consumption and pollution. As we look to the future, this study serves as a testament to the captivating and unexpected connections that underscore our ever-changing global environment.

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 Youngstown, Ohio and Petroluem consumption in Denmark. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"Clearing the Air: Exploring the Link Between Air Pollution in Youngstown, Ohio, and Petroleum Consumption in Denmark"

[[ABSTRACT]]
This study examines the relationship between air pollution levels in Youngstown, Ohio, and petroleum consumption patterns in Denmark, with a focus on the time period spanning from 1980 to 2022. Using comprehensive data from the Environmental Protection Agency and the Energy Information Administration, our research team employed rigorous statistical analysis to identify a robust correlation coefficient of 0.8706275, with a significance level of p < 0.01. The findings reveal an intriguing connection between these seemingly disparate locations, shedding light on the impacts of petroleum-related activities on air quality across distant regions. As we unravel this fascinating correlation, we hope to inspire further exploration of unconventional associations and unearth the unexpected interplay of factors in our ever-changing global environment.

[[INTRDUCTION]]
Air pollution is a pervasive and pressing issue that affects communities around the world, with sources of pollution ranging from industrial activities to vehicular emissions. One might say that the topic is "air-resistible!" The city of Youngstown, Ohio, has grappled with air quality challenges, while Denmark, known for its windmills and fairy tales, has wrestled with petroleum consumption dynamics – a situation that is more complex than a Danish pastry recipe. These seemingly unrelated phenomena caught the attention of our research team, leading us to investigate whether there might be a connection between air pollution in Youngstown and petroleum consumption in Denmark. It's as though we embarked on a curious journey, armed with data and statistical tools, to explore the hidden links in the global atmospheric maze.
The research builds on prior studies that have examined various aspects of air pollution and petroleum consumption, but few have dared to take on the audacious task of linking such distinct geographical entities. The findings hold implications for environmental policies, energy strategizing, and, quite possibly, transcontinental conversations regarding air quality and energy use. As we delve into this intriguing correlation, we aim to provide a scholarly contribution that is both academically rigorous and, dare I say, a breath of fresh air in the field of environmental research.

[[RESULTS]]
The analysis of the data collected from the Environmental Protection Agency and the Energy Information Administration unveiled a striking association between air pollution levels in Youngstown, Ohio, and petroleum consumption patterns in Denmark during the years 1980 to 2022. The correlation coefficient of 0.8706275 indicated a robust positive relationship between these seemingly disparate variables. This finding suggests a connection as strong as the bond between peanut butter and jelly, or perhaps even stronger – akin to the bond between Denmark and Lego!
The coefficient of determination (r-squared) of 0.7579923 further supported the strength of the relationship, indicating that approximately 75.8% of the variation in air pollution levels in Youngstown can be explained by the variation in petroleum consumption in Denmark. One might say that the connection between these two phenomena is as clear as the air on a breezy spring day – or as murky as the air on a particularly smoggy day in a bustling metropolis.
The significance level of p < 0.01 underscored the statistical strength of the relationship, indicating that the likelihood of observing such a strong association by chance alone is less than 1%. It seems this correlation is as unlikely as stumbling upon a four-leaf clover while strolling through a field of daisies – a rare and serendipitous discovery indeed!
To visually elucidate this captivating association, the scatterplot in Figure 1 illustrates the strong positive correlation between air pollution levels in Youngstown, Ohio, and petroleum consumption in Denmark. The data points appear to form a cohesive pattern, reminiscent of a carefully planned symphony – or perhaps a synchronized swan ballet in the realm of statistical visualization.
In summary, the findings of this study illuminate an unexpected and intriguing connection between air pollution in Youngstown, Ohio, and petroleum consumption in Denmark, providing valuable insights into the far-reaching impacts of petroleum-related activities on air quality. As we navigate the complex web of global environmental dynamics, these findings serve as a beacon, guiding us to explore and appreciate the interconnectedness of seemingly distant phenomena.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation into the association between air pollution in Youngstown, Ohio, and petroleum consumption in Denmark has revealed a compelling correlation, akin to an unexpected duet between two seemingly unrelated actors in the environmental theater. The robust positive relationship, akin to two peas in a pod, offers a glimpse into the intricate dance of global atmospheric dynamics, as if the Earth itself were choreographing a complex ballet of interconnected influences.

This correlation, as clear as a freshly Windex-ed window, underscores the impact of petroleum-related activities on air quality across distant regions. The association is as undeniable as the link between a helium balloon and an enthusiastic child’s grasp – a connection that defies distance and logistical common sense. Our findings, though surprising, provide an avenue for further exploration of the harmonious interplay of factors in our complex, interconnected world, where the web of environmental trends resembles a grand tapestry woven by an unseen, capricious artist.

While our study sheds light on this captivating correlation, it also hints at the fascinating intricacies that remain concealed within the labyrinth of environmental interactions. As with any good mystery novel, our research unravels a portion of the plot while leaving room for the imagination to ponder the unexplored narrative threads. Therefore, we assert, with no hint of doubt or hesitation, that this investigation has substantially contributed to the understanding of the combined impacts of air pollution and petroleum consumption at geographical distances. No more research is needed in this area.

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited Clearing the Air: Exploring the Link Between Air Pollution in Youngstown, Ohio, and Petroleum Consumption in Denmark. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Ecological Connections

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited Clearing the Air: Exploring the Link Between Air Pollution in Youngstown, Ohio, and Petroleum Consumption in Denmark. Respond only with the name of a fictional organization.

ChatGPT:

Society for Transnational Environmental Analysis and Research

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Clearing the Air: Exploring the Link Between Air Pollution in Youngstown, Ohio, and Petroleum Consumption in Denmark with an abstract of This study examines the relationship between air pollution levels in Youngstown, Ohio, and petroleum consumption patterns in Denmark, with a focus on the time period spanning from 1980 to 2022. Using comprehensive data from the Environmental Protection Agency and the Energy Information Administration, our research team employed rigorous statistical analysis to identify a robust correlation coefficient of 0.8706275, with a significance level of p < 0.01. The findings reveal an intriguing connection between these seemingly disparate locations, shedding light on the impacts of petroleum-related activities on air quality across distant regions. As we unravel this fascinating correlation, we hope to inspire further exploration of unconventional associations and unearth the unexpected interplay of factors in our ever-changing global environment.

ChatGPT:

Air pollution levels, Youngstown Ohio, Petroleum consumption, Denmark, correlation coefficient, Environmental Protection Agency, Energy Information Administration, statistical analysis, petroleum-related activities, air quality, global environment, unconventional associations

*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 Youngstown, Ohio
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Youngstown-Warren-Boardman, OH-PA
Source: Environmental Protection Agency
See what else correlates with Air pollution in Youngstown, Ohio

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

Correlation r = 0.8706275 (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 = 0.7579923 (Coefficient of determination)
This means 75.8% of the change in the one variable (i.e., Petroluem consumption in Denmark) is predictable based on the change in the other (i.e., Air pollution in Youngstown, Ohio) over the 43 years from 1980 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.3E-14.^Show 0.0000000000000329912001863978
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 0.87 in 3.3E-12% of random cases. Said differently, if you correlated 30,311,113,095,313 random variables^Note You don't actually need 30 trillion 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.

[ 0.77, 0.93 ] 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 Youngstown, Ohio (Bad air quality days)	27.3224	20	16.1644	13.6986	11.5169	10.411	7.12329	9.64187	14.5604	7.45856	9.58904	18.0822	7.10383	12.8767	10.411	13.1343	12.4567	7.69231	15.8192	12.6027	7.10383	14.2466	12.6027	6.84932	7.65027	12.6027	5.20548	9.86301	5.19126	1.91781	4.38356	3.0137	6.8306	1.09589	1.64384	1.36986	1.63934	1.09589	1.36986	0	1.0929	0.821918	1.09589
Petroluem consumption in Denmark (Million Barrels/Day)	290.3	243	230	219	215.585	222.704	219.548	213.696	204.016	195.608	192.449	194.365	192.582	198.967	211.407	222.715	234.954	226.616	222.847	220.252	210	213.411	197.164	188.271	185.333	183.463	189.858	190.326	181.055	166.512	167.468	163.367	155.492	154.762	153.622	154.479	158.202	158.537	161.236	165.392	136.934	140.501	148.482

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([27.3224,20,16.1644,13.6986,11.5169,10.411,7.12329,9.64187,14.5604,7.45856,9.58904,18.0822,7.10383,12.8767,10.411,13.1343,12.4567,7.69231,15.8192,12.6027,7.10383,14.2466,12.6027,6.84932,7.65027,12.6027,5.20548,9.86301,5.19126,1.91781,4.38356,3.0137,6.8306,1.09589,1.64384,1.36986,1.63934,1.09589,1.36986,0,1.0929,0.821918,1.09589,])
array_2 = np.array([290.3,243,230,219,215.585,222.704,219.548,213.696,204.016,195.608,192.449,194.365,192.582,198.967,211.407,222.715,234.954,226.616,222.847,220.252,210,213.411,197.164,188.271,185.333,183.463,189.858,190.326,181.055,166.512,167.468,163.367,155.492,154.762,153.622,154.479,158.202,158.537,161.236,165.392,136.934,140.501,148.482,])
array_1_name = "Air pollution in Youngstown, Ohio"
array_2_name = "Petroluem consumption in Denmark"

# 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 Youngstown, Ohio
Line chart for only Petroluem consumption in Denmark
AI-generated correlation image
The spurious research paper: Clearing the Air: Exploring the Link Between Air Pollution in Youngstown, Ohio, and Petroleum Consumption in Denmark

View another random correlation

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Correlation ID: 4655 · Black Variable ID: 21491 · Red Variable ID: 24348


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