Download png, svg
AI explanation
The cleaner air in Lancaster somehow created a ripple effect, leading to a sudden surplus of fresh, pine-scented oxygen molecules. This inadvertently altered the chemical composition of gasoline in Norway, causing it to spontaneously convert into a more eco-friendly, electric car-charging elixir. It's like a breath of fresh air for the fuel industry!
Model: dalle-3
Prompt: ![A photo-realistic image] Generate an image of a serene street in Lancaster, Pennsylvania, capturing the clear skies with minimal smog. Show a peaceful gas station in Norway, with clean and unclogged gasoline pumps, hinting at reduced maintenance needs. Ensure the image encapsulates the contrast between the two locations, emphasizing the environmental improvements. 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 Lancaster, Pennsylvania and Gasoline pumped in Norway. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: A photo-realistic image Photorealism Concept art Parallax art Art nouveau
ChatGPT: A photo-realistic image
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 Lancaster, Pennsylvania and Gasoline pumped in Norway, in the following situation: Because fewer fumes from Lancaster's air pollution were wafting over the Atlantic, Norwegian gasoline pumps were no longer being clogged by American smog, leading to a decrease in the need for gasoline maintenance.. Keep the description simple.
ChatGPT: In Lancaster, Pennsylvania, we see less smog in the air as fewer fumes drift into the sky. Meanwhile, at a gas station in Norway, the gasoline pumps are clean and unclogged, indicating a decrease in the need for maintenance.
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 photo-realistic image ] and depicting this scene: In Lancaster, Pennsylvania, we see less smog in the air as fewer fumes drift into the sky. Meanwhile, at a gas station in Norway, the gasoline pumps are clean and unclogged, indicating a decrease in the need for maintenance. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: ![A photo-realistic image] Generate an image of a serene street in Lancaster, Pennsylvania, capturing the clear skies with minimal smog. Show a peaceful gas station in Norway, with clean and unclogged gasoline pumps, hinting at reduced maintenance needs. Ensure the image encapsulates the contrast between the two locations, emphasizing the environmental improvements.
*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.
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 Lancaster, Pennsylvania caused Gasoline pumped in Norway to decrease.
AI academic paper
 |
The Gasoline and the Gurgle: Unraveling the Curious Correlation between Lancaster's Air Pollution and Norway's Fuel The Journal of Eclectic Environmental Science Jan 2024 Reminder: This paper is AI-generated. Not real! |
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You write titles and abstracts of goofy academic research papers.
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, and add unexpected twists in your writing.
Please draft the title and abstract of an academic research paper presenting the findings of the connection between Air pollution in Lancaster, Pennsylvania and Gasoline pumped in Norway. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Include a pun in the title.
Your research team used data from Environmental Protection Agency and Energy Information Administration to assess this nagging question. You found a correlation coefficient of 0.7599349 and p < 0.01 for 1980 to 2022.
Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)
[[TITLE]]
Lorem ipsum dolor sit
[[ABSTRACT]]
Lorem ipsum dolor sit
ChatGPT:
[[TITLE]]
The Gasoline and the Gurgle: Unraveling the Curious Correlation between Lancaster's Air Pollution and Norway's Fuel
[[ABSTRACT]]
In this study, we investigated the surprising link between air pollution levels in Lancaster, Pennsylvania, and the quantity of gasoline pumped in Norway. Utilizing data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into the complex relationship between these seemingly disparate variables. Through rigorous statistical analysis, we identified a correlation coefficient of 0.7599349, with a remarkably significant p-value of less than 0.01 for the time frame spanning from 1980 to 2022.
While the association between these two elements initially seemed as incongruous as a penguin in a desert, our findings astoundingly revealed a compelling connection. This unexpected correlation could potentially have far-reaching implications for environmental policy, energy consumption patterns, and even cross-continental fuel dynamics. Our research serves as a whimsical reminder of the unpredictable and intertwined nature of global systems, where the seemingly unrelated can intertwine in the most peculiar ways.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the introduction 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 introduction section of an academic research paper presenting the findings of the connection between Air pollution in Lancaster, Pennsylvania and Gasoline pumped in Norway. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
Here is the title and abstract of the paper:
[[TITLE]]
The Gasoline and the Gurgle: Unraveling the Curious Correlation between Lancaster's Air Pollution and Norway's Fuel
[[ABSTRACT]]
In this study, we investigated the surprising link between air pollution levels in Lancaster, Pennsylvania, and the quantity of gasoline pumped in Norway. Utilizing data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into the complex relationship between these seemingly disparate variables. Through rigorous statistical analysis, we identified a correlation coefficient of 0.7599349, with a remarkably significant p-value of less than 0.01 for the time frame spanning from 1980 to 2022.
While the association between these two elements initially seemed as incongruous as a penguin in a desert, our findings astoundingly revealed a compelling connection. This unexpected correlation could potentially have far-reaching implications for environmental policy, energy consumption patterns, and even cross-continental fuel dynamics. Our research serves as a whimsical reminder of the unpredictable and intertwined nature of global systems, where the seemingly unrelated can intertwine in the most peculiar ways.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
[[INTRODUCTION]]
As the great detective Sherlock Holmes once said, “The game is afoot!” And indeed, as researchers delving into the enigmatic world of environmental science, we often find ourselves embarking on journeys as bewildering as trying to solve a riddle wrapped in a mystery inside an enigma. Our curiosity was piqued when we stumbled upon a most curious correlation between the air pollution levels in Lancaster, Pennsylvania, and the gasoline consumption in the picturesque land of fjords and Vikings, Norway.
Now, one might wonder what possible connection could exist between the smog of Lancaster and the liquid gold that fuels the chariots of the North. We shared in that wonder, our minds swirling with visions of statistical hypotheses and scatter plots. However, armed with our trusty data from the Environmental Protection Agency and the Energy Information Administration, we set off on the path less traveled, in pursuit of that elusive link.
It is said that correlation does not imply causation, but when confronted with a correlation coefficient of 0.7599349 and a p-value reminiscent of the Holy Grail (i.e., less than 0.01), it would seem that we are treading on some rather solid ground here. The journey to this revelation was no less arduous than a quest to find the mystical Unicorn, yet the thrill of discovery and the implications of our findings keep our spirits high.
As our analysis and charts unfolded before our eyes like a splendid magic trick, what appeared at first to be as mismatched as putting a square peg in a round hole began to make some sense – a sense as tantalizing as an all-you-can-eat buffet after a day of fasting. We found ourselves contemplating the ramifications of this unorthodox connection, which could potentially shake up the world of environmental policy and energy dynamics like a heavily caffeinated earthquake.
Through the lens of this study, we aim not only to shed light on this bewitching correlation but also to revel in the delightfully interconnected fabric of our world. After all, as the great physicist Niels Bohr once quipped, “Prediction is very difficult, especially if it's about the future.” And oh, how right he was. So, let us embark on this joyous and perplexing adventure, dear reader, as we unravel the entanglements of the gasoline and the gurgle in a tale fit for the most whimsical of scientific journals.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Lancaster, Pennsylvania and Gasoline pumped in Norway. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.
Make up the lorem and ipsum part, but make it sound related to the topic at hand.
Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading CVS receipts.
Here is the title and abstract of the paper:
[[TITLE]]
The Gasoline and the Gurgle: Unraveling the Curious Correlation between Lancaster's Air Pollution and Norway's Fuel
[[ABSTRACT]]
In this study, we investigated the surprising link between air pollution levels in Lancaster, Pennsylvania, and the quantity of gasoline pumped in Norway. Utilizing data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into the complex relationship between these seemingly disparate variables. Through rigorous statistical analysis, we identified a correlation coefficient of 0.7599349, with a remarkably significant p-value of less than 0.01 for the time frame spanning from 1980 to 2022.
While the association between these two elements initially seemed as incongruous as a penguin in a desert, our findings astoundingly revealed a compelling connection. This unexpected correlation could potentially have far-reaching implications for environmental policy, energy consumption patterns, and even cross-continental fuel dynamics. Our research serves as a whimsical reminder of the unpredictable and intertwined nature of global systems, where the seemingly unrelated can intertwine in the most peculiar ways.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
As the great detective Sherlock Holmes once said, “The game is afoot!” And indeed, as researchers delving into the enigmatic world of environmental science, we often find ourselves embarking on journeys as bewildering as trying to solve a riddle wrapped in a mystery inside an enigma. Our curiosity was piqued when we stumbled upon a most curious correlation between the air pollution levels in Lancaster, Pennsylvania, and the gasoline consumption in the picturesque land of fjords and Vikings, Norway.
Now, one might wonder what possible connection could exist between the smog of Lancaster and the liquid gold that fuels the chariots of the North. We shared in that wonder, our minds swirling with visions of statistical hypotheses and scatter plots. However, armed with our trusty data from the Environmental Protection Agency and the Energy Information Administration, we set off on the path less traveled, in pursuit of that elusive link.
It is said that correlation does not imply causation, but when confronted with a correlation coefficient of 0.7599349 and a p-value reminiscent of the Holy Grail (i.e., less than 0.01), it would seem that we are treading on some rather solid ground here. The journey to this revelation was no less arduous than a quest to find the mystical Unicorn, yet the thrill of discovery and the implications of our findings keep our spirits high.
As our analysis and charts unfolded before our eyes like a splendid magic trick, what appeared at first to be as mismatched as putting a square peg in a round hole began to make some sense – a sense as tantalizing as an all-you-can-eat buffet after a day of fasting. We found ourselves contemplating the ramifications of this unorthodox connection, which could potentially shake up the world of environmental policy and energy dynamics like a heavily caffeinated earthquake.
Through the lens of this study, we aim not only to shed light on this bewitching correlation but also to revel in the delightfully interconnected fabric of our world. After all, as the great physicist Niels Bohr once quipped, “Prediction is very difficult, especially if it's about the future.” And oh, how right he was. So, let us embark on this joyous and perplexing adventure, dear reader, as we unravel the entanglements of the gasoline and the gurgle in a tale fit for the most whimsical of scientific journals.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
The perplexing correlation between air pollution in Lancaster, Pennsylvania, and gasoline consumption in Norway has captivated the minds of researchers and environmental enthusiasts alike. In "Air Pollution in Urban Environments," Smith and Doe examine various factors contributing to urban air pollution and its impact on public health. Similarly, Jones et al., in "Fuel Consumption Patterns in European Countries," shed light on the complex dynamics of gasoline usage across European nations, delving into the socio-economic and environmental factors influencing fuel consumption trends.
Turning our attention to non-fiction literature, "The Big Necessity: The Unmentionable World of Human Waste and Why It Matters" by Rose George offers a fascinating exploration of environmental impacts and interconnected global systems, reminding us that even the most unlikely connections can paint a vivid picture. Additionally, "The Sixth Extinction: An Unnatural History" by Elizabeth Kolbert challenges our perceptions of biodiversity loss and its repercussions, urging us to consider the domino effect of human activities on the planet's delicate balance.
In the realm of fiction, the works of Jules Verne, particularly "Journey to the Center of the Earth" and "Twenty Thousand Leagues Under the Sea," evoke a sense of interconnectedness in the natural world, albeit in fantastical scenarios. The whimsical threads of connection between seemingly unrelated elements in these narratives prompt reflection on the discoveries that often emerge from the most unexpected sources.
As our pursuit of knowledge led us further down the rabbit hole, we stumbled upon a rather unconventional trove of insight – the enigmatic world of CVS receipts. Much like decoding an ancient hieroglyph, deciphering the cryptic messages of these lengthy scrolls revealed an unexpected goldmine of information. While the relevance of this discovery to our study may seem as puzzling as a cat wearing a monocle, it serves as a playful reminder of the serendipitous sources that can often illuminate the most peculiar connections.
In the spirit of embracing the unpredictable and embracing the delightfully unconventional, we gleefully invite readers to join us in unraveling the perplexing correlation between Lancaster's air pollution and Norway's gasoline consumption, as we navigate through the labyrinth of intertwined global systems and unearth the unexpected gems that illuminate the scientific landscape.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Lancaster, Pennsylvania and Gasoline pumped in Norway. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Energy Information Administration . You used data from 1980 to 2022
Make up the research methods you don't know. Make them a bit goofy and convoluted.
Here is the title, abstract, and introduction of the paper:
[[TITLE]]
The Gasoline and the Gurgle: Unraveling the Curious Correlation between Lancaster's Air Pollution and Norway's Fuel
[[ABSTRACT]]
In this study, we investigated the surprising link between air pollution levels in Lancaster, Pennsylvania, and the quantity of gasoline pumped in Norway. Utilizing data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into the complex relationship between these seemingly disparate variables. Through rigorous statistical analysis, we identified a correlation coefficient of 0.7599349, with a remarkably significant p-value of less than 0.01 for the time frame spanning from 1980 to 2022.
While the association between these two elements initially seemed as incongruous as a penguin in a desert, our findings astoundingly revealed a compelling connection. This unexpected correlation could potentially have far-reaching implications for environmental policy, energy consumption patterns, and even cross-continental fuel dynamics. Our research serves as a whimsical reminder of the unpredictable and intertwined nature of global systems, where the seemingly unrelated can intertwine in the most peculiar ways.
[[INTRODUCTION]]
As the great detective Sherlock Holmes once said, “The game is afoot!” And indeed, as researchers delving into the enigmatic world of environmental science, we often find ourselves embarking on journeys as bewildering as trying to solve a riddle wrapped in a mystery inside an enigma. Our curiosity was piqued when we stumbled upon a most curious correlation between the air pollution levels in Lancaster, Pennsylvania, and the gasoline consumption in the picturesque land of fjords and Vikings, Norway.
Now, one might wonder what possible connection could exist between the smog of Lancaster and the liquid gold that fuels the chariots of the North. We shared in that wonder, our minds swirling with visions of statistical hypotheses and scatter plots. However, armed with our trusty data from the Environmental Protection Agency and the Energy Information Administration, we set off on the path less traveled, in pursuit of that elusive link.
It is said that correlation does not imply causation, but when confronted with a correlation coefficient of 0.7599349 and a p-value reminiscent of the Holy Grail (i.e., less than 0.01), it would seem that we are treading on some rather solid ground here. The journey to this revelation was no less arduous than a quest to find the mystical Unicorn, yet the thrill of discovery and the implications of our findings keep our spirits high.
As our analysis and charts unfolded before our eyes like a splendid magic trick, what appeared at first to be as mismatched as putting a square peg in a round hole began to make some sense – a sense as tantalizing as an all-you-can-eat buffet after a day of fasting. We found ourselves contemplating the ramifications of this unorthodox connection, which could potentially shake up the world of environmental policy and energy dynamics like a heavily caffeinated earthquake.
Through the lens of this study, we aim not only to shed light on this bewitching correlation but also to revel in the delightfully interconnected fabric of our world. After all, as the great physicist Niels Bohr once quipped, “Prediction is very difficult, especially if it's about the future.” And oh, how right he was. So, let us embark on this joyous and perplexing adventure, dear reader, as we unravel the entanglements of the gasoline and the gurgle in a tale fit for the most whimsical of scientific journals.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
To untangle the enigmatic web of air pollution in Lancaster and gasoline consumption in Norway, we embarked on a methodological journey as labyrinthine as a puzzle designed by Daedalus himself. Our research approach combined quantitative analysis, statistical modeling, and a hint of whimsy to extract meaningful insights from the copious data at our disposal.
First, we teased apart the intricate skein of historical air pollution data in Lancaster, Pennsylvania, sourced primarily from the Environmental Protection Agency. This involved extracting hourly, daily, and annual pollutant measurements with the same diligence one might use to sift through a haystack in search of the proverbial needle. The variables considered included ambient air quality, particulate matter, sulfur dioxide, nitrogen dioxide, carbon monoxide levels, and the elusive scent of traffic fumes on a rainy day.
Simultaneously, we delved into the vast reservoir of gasoline consumption information in Norway, obtained from the sprawling archives of the Energy Information Administration. The data encompassed the volume of gasoline sales, fuel distribution patterns, vehicular usage, and the whispered secrets of petrol pumps scattered across the Norwegian landscape.
With these intricate datasets in hand, we performed a spirited pas de deux of statistical analysis. Employing techniques as diverse as linear regression, time series analysis, and autoregressive integrated moving average (ARIMA) modeling, we sought to uncover the hidden patterns and relationships lurking within the numbers. The meticulous dance of statistical rigor was accompanied by a symphony of hypotheses testing and diagnostic checks, ensuring that our findings were as robust as the proverbial oak tree.
Moreover, we ventured into the realm of spatial analysis to consider the geographic peculiarities and idiosyncrasies that might underlie the curious correlation. Mapping software and geographic information systems (GIS) were our trusty companions, guiding us through the topographical contours of Lancaster's smog and the fjord-kissed landscapes of Norway.
In addition to these analytical acrobatics, we also explored the temporal dimension, scrutinizing how the relationship between air pollution and gasoline consumption evolved over the years. This involved time series decomposition, seasonality analysis, and trend examination, akin to peering through the annals of history in search of patterns that whispered secrets of the past.
Finally, to bolster the validity of our findings, we carried out sensitivity analyses, cross-validation exercises, and robustness checks, akin to ensuring that our ship was seaworthy in the tempestuous sea of statistical inference.
Armed with an arsenal of statistical tools, a dollop of humor, and a pinch of scientific curiosity, we navigated the turbulent waters of data and methodology to unravel the serendipitous connection between Lancaster's air pollution and Norway's gasoline. The path was fraught with statistical peril, but as the old adage goes, “In data we trust, but a little validation doesn't hurt – unless performed on erroneous assumptions!” Thus, we endeavored to illuminate this unexpected correlation with the precision of a laser beam and the charm of a whimsical jest.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.You draft the results section of an academic research paper. You speak in the past tense (you found...).
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the results section of an academic research paper presenting the findings of the connection between Air pollution in Lancaster, Pennsylvania and Gasoline pumped in Norway. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Energy Information Administration .
For the time period 1980 to 2022, you found a correlation 0.7599349, r-squared of 0.5775010, and p < 0.01.
One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.
Here is the title and abstract of the paper:
[[TITLE]]
The Gasoline and the Gurgle: Unraveling the Curious Correlation between Lancaster's Air Pollution and Norway's Fuel
[[ABSTRACT]]
In this study, we investigated the surprising link between air pollution levels in Lancaster, Pennsylvania, and the quantity of gasoline pumped in Norway. Utilizing data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into the complex relationship between these seemingly disparate variables. Through rigorous statistical analysis, we identified a correlation coefficient of 0.7599349, with a remarkably significant p-value of less than 0.01 for the time frame spanning from 1980 to 2022.
While the association between these two elements initially seemed as incongruous as a penguin in a desert, our findings astoundingly revealed a compelling connection. This unexpected correlation could potentially have far-reaching implications for environmental policy, energy consumption patterns, and even cross-continental fuel dynamics. Our research serves as a whimsical reminder of the unpredictable and intertwined nature of global systems, where the seemingly unrelated can intertwine in the most peculiar ways.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
As the great detective Sherlock Holmes once said, “The game is afoot!” And indeed, as researchers delving into the enigmatic world of environmental science, we often find ourselves embarking on journeys as bewildering as trying to solve a riddle wrapped in a mystery inside an enigma. Our curiosity was piqued when we stumbled upon a most curious correlation between the air pollution levels in Lancaster, Pennsylvania, and the gasoline consumption in the picturesque land of fjords and Vikings, Norway.
Now, one might wonder what possible connection could exist between the smog of Lancaster and the liquid gold that fuels the chariots of the North. We shared in that wonder, our minds swirling with visions of statistical hypotheses and scatter plots. However, armed with our trusty data from the Environmental Protection Agency and the Energy Information Administration, we set off on the path less traveled, in pursuit of that elusive link.
It is said that correlation does not imply causation, but when confronted with a correlation coefficient of 0.7599349 and a p-value reminiscent of the Holy Grail (i.e., less than 0.01), it would seem that we are treading on some rather solid ground here. The journey to this revelation was no less arduous than a quest to find the mystical Unicorn, yet the thrill of discovery and the implications of our findings keep our spirits high.
As our analysis and charts unfolded before our eyes like a splendid magic trick, what appeared at first to be as mismatched as putting a square peg in a round hole began to make some sense – a sense as tantalizing as an all-you-can-eat buffet after a day of fasting. We found ourselves contemplating the ramifications of this unorthodox connection, which could potentially shake up the world of environmental policy and energy dynamics like a heavily caffeinated earthquake.
Through the lens of this study, we aim not only to shed light on this bewitching correlation but also to revel in the delightfully interconnected fabric of our world. After all, as the great physicist Niels Bohr once quipped, “Prediction is very difficult, especially if it's about the future.” And oh, how right he was. So, let us embark on this joyous and perplexing adventure, dear reader, as we unravel the entanglements of the gasoline and the gurgle in a tale fit for the most whimsical of scientific journals.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
The statistical analysis of the data unveiled a remarkably strong correlation between air pollution levels in Lancaster, Pennsylvania, and the quantity of gasoline pumped in Norway. The correlation coefficient of 0.7599349 suggested a robust relationship between these seemingly disparate variables. This finding was further corroborated by an r-squared value of 0.5775010, indicating that approximately 57.75% of the variability in gasoline consumption in Norway could be explained by the changes in air pollution levels in Lancaster.
The significance of this association was underscored by a p-value of less than 0.01, which implies that the likelihood of observing such a strong relationship by random chance alone is as rare as finding a four-leaf clover in a field of three-leaf clovers.
Figure 1 displays a scatterplot that vividly illustrates the strong correlation between the two variables. The data points are as tightly clustered as sardines in a can, leaving little room for doubt regarding the strength of the relationship. This visual representation serves as a compelling testament to the unexpected connection between the air pollution in Lancaster and the gasoline consumption in Norway.
The findings of this investigation add a touch of whimsy to the field of environmental research, reminding us that the interconnectedness of the world can sometimes be as surprising as stumbling upon a kangaroo in a snowstorm. The implications of this unexpected correlation extend beyond the realm of statistical analysis, possibly influencing environmental policies and energy consumption patterns in ways as unpredictable as a squirrel's path in a park.
In summary, the results of this study not only unveil an intriguing link between the air pollution in Lancaster, Pennsylvania, and the gasoline pumped in Norway but also highlight the capricious nature of the global systems we endeavor to understand.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Lancaster, Pennsylvania and Gasoline pumped in Norway. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
Limit your response to 500 tokens.
Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.
Do not write a conclusion. I will add a conclusion after this.
[[TITLE]]
The Gasoline and the Gurgle: Unraveling the Curious Correlation between Lancaster's Air Pollution and Norway's Fuel
[[ABSTRACT]]
In this study, we investigated the surprising link between air pollution levels in Lancaster, Pennsylvania, and the quantity of gasoline pumped in Norway. Utilizing data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into the complex relationship between these seemingly disparate variables. Through rigorous statistical analysis, we identified a correlation coefficient of 0.7599349, with a remarkably significant p-value of less than 0.01 for the time frame spanning from 1980 to 2022.
While the association between these two elements initially seemed as incongruous as a penguin in a desert, our findings astoundingly revealed a compelling connection. This unexpected correlation could potentially have far-reaching implications for environmental policy, energy consumption patterns, and even cross-continental fuel dynamics. Our research serves as a whimsical reminder of the unpredictable and intertwined nature of global systems, where the seemingly unrelated can intertwine in the most peculiar ways.
[[LITERATURE REVIEW]]
The perplexing correlation between air pollution in Lancaster, Pennsylvania, and gasoline consumption in Norway has captivated the minds of researchers and environmental enthusiasts alike. In "Air Pollution in Urban Environments," Smith and Doe examine various factors contributing to urban air pollution and its impact on public health. Similarly, Jones et al., in "Fuel Consumption Patterns in European Countries," shed light on the complex dynamics of gasoline usage across European nations, delving into the socio-economic and environmental factors influencing fuel consumption trends.
Turning our attention to non-fiction literature, "The Big Necessity: The Unmentionable World of Human Waste and Why It Matters" by Rose George offers a fascinating exploration of environmental impacts and interconnected global systems, reminding us that even the most unlikely connections can paint a vivid picture. Additionally, "The Sixth Extinction: An Unnatural History" by Elizabeth Kolbert challenges our perceptions of biodiversity loss and its repercussions, urging us to consider the domino effect of human activities on the planet's delicate balance.
In the realm of fiction, the works of Jules Verne, particularly "Journey to the Center of the Earth" and "Twenty Thousand Leagues Under the Sea," evoke a sense of interconnectedness in the natural world, albeit in fantastical scenarios. The whimsical threads of connection between seemingly unrelated elements in these narratives prompt reflection on the discoveries that often emerge from the most unexpected sources.
As our pursuit of knowledge led us further down the rabbit hole, we stumbled upon a rather unconventional trove of insight – the enigmatic world of CVS receipts. Much like decoding an ancient hieroglyph, deciphering the cryptic messages of these lengthy scrolls revealed an unexpected goldmine of information. While the relevance of this discovery to our study may seem as puzzling as a cat wearing a monocle, it serves as a playful reminder of the serendipitous sources that can often illuminate the most peculiar connections.
In the spirit of embracing the unpredictable and embracing the delightfully unconventional, we gleefully invite readers to join us in unraveling the perplexing correlation between Lancaster's air pollution and Norway's gasoline consumption, as we navigate through the labyrinth of intertwined global systems and unearth the unexpected gems that illuminate the scientific landscape.
[[RESULTS]]
The statistical analysis of the data unveiled a remarkably strong correlation between air pollution levels in Lancaster, Pennsylvania, and the quantity of gasoline pumped in Norway. The correlation coefficient of 0.7599349 suggested a robust relationship between these seemingly disparate variables. This finding was further corroborated by an r-squared value of 0.5775010, indicating that approximately 57.75% of the variability in gasoline consumption in Norway could be explained by the changes in air pollution levels in Lancaster.
The significance of this association was underscored by a p-value of less than 0.01, which implies that the likelihood of observing such a strong relationship by random chance alone is as rare as finding a four-leaf clover in a field of three-leaf clovers.
Figure 1 displays a scatterplot that vividly illustrates the strong correlation between the two variables. The data points are as tightly clustered as sardines in a can, leaving little room for doubt regarding the strength of the relationship. This visual representation serves as a compelling testament to the unexpected connection between the air pollution in Lancaster and the gasoline consumption in Norway.
The findings of this investigation add a touch of whimsy to the field of environmental research, reminding us that the interconnectedness of the world can sometimes be as surprising as stumbling upon a kangaroo in a snowstorm. The implications of this unexpected correlation extend beyond the realm of statistical analysis, possibly influencing environmental policies and energy consumption patterns in ways as unpredictable as a squirrel's path in a park.
In summary, the results of this study not only unveil an intriguing link between the air pollution in Lancaster, Pennsylvania, and the gasoline pumped in Norway but also highlight the capricious nature of the global systems we endeavor to understand.
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Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
Our results have brought to light a rather puzzling yet intriguing relationship between air pollution in Lancaster, Pennsylvania, and gasoline consumption in Norway. The robust correlation coefficient and impressively significant p-value underscore the strength of this association, leaving us as gobsmacked as a chemist discovering a new element. The unexpected nature of this correlation is as startling as a sudden erupting volcano in the midst of gentle rolling hills.
As advanced as our statistical analyses may be, they can only do so much to unravel the intricacies of such a perplexing connection. It’s akin to attempting to understand the behavior of subatomic particles while blindfolded – there's an element of unpredictability that keeps us on our toes.
Remarkably, these findings align with previous research, much like pieces of a jigsaw puzzle fitting snugly together. The work of Smith and Doe on urban air pollution and Jones et al.'s investigation into fuel consumption patterns in European nations laid the groundwork for our own exploration into this unexpected correlation. Their contributions are as essential as a catalyst in a chemical reaction, propelling our understanding forward.
Moreover, our results resonate with the whimsical notion often portrayed in the fiction works of Jules Verne, where seemingly unrelated elements are intricately intertwined. The sense of interconnectedness depicted in his narratives mirrors the surprising link we have uncovered between air pollution and gasoline consumption. One cannot help but ponder the colorful tapestry of the scientific world, where the most unexpected threads are woven into the fabric of knowledge.
Our findings also lend credence to the notion of leveraging unconventional sources of insight, much like the captivating world of CVS receipts. The relevance of delving into such intriguing finds may be as confounding as a physicist pondering the mysteries of dark matter, but sometimes, these offbeat avenues lead to unanticipated discoveries that enrich our understanding.
In essence, this study not only adds a delightful touch of whimsy to the scientific discourse, but it also serves as a gentle reminder of the interconnectedness and unpredictability that define the scientific landscape. The results corroborate the importance of embracing the unexpected, and spark enthusiasm for further quirky explorations into the enigmatic relationships that underpin our world.
Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 Lancaster, Pennsylvania and Gasoline pumped in Norway. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.
Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.
Here are the title, abstract, introduction, and results sections.
[[TITLE]]
The Gasoline and the Gurgle: Unraveling the Curious Correlation between Lancaster's Air Pollution and Norway's Fuel
[[ABSTRACT]]
In this study, we investigated the surprising link between air pollution levels in Lancaster, Pennsylvania, and the quantity of gasoline pumped in Norway. Utilizing data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into the complex relationship between these seemingly disparate variables. Through rigorous statistical analysis, we identified a correlation coefficient of 0.7599349, with a remarkably significant p-value of less than 0.01 for the time frame spanning from 1980 to 2022.
While the association between these two elements initially seemed as incongruous as a penguin in a desert, our findings astoundingly revealed a compelling connection. This unexpected correlation could potentially have far-reaching implications for environmental policy, energy consumption patterns, and even cross-continental fuel dynamics. Our research serves as a whimsical reminder of the unpredictable and intertwined nature of global systems, where the seemingly unrelated can intertwine in the most peculiar ways.
[[INTRDUCTION]]
As the great detective Sherlock Holmes once said, “The game is afoot!” And indeed, as researchers delving into the enigmatic world of environmental science, we often find ourselves embarking on journeys as bewildering as trying to solve a riddle wrapped in a mystery inside an enigma. Our curiosity was piqued when we stumbled upon a most curious correlation between the air pollution levels in Lancaster, Pennsylvania, and the gasoline consumption in the picturesque land of fjords and Vikings, Norway.
Now, one might wonder what possible connection could exist between the smog of Lancaster and the liquid gold that fuels the chariots of the North. We shared in that wonder, our minds swirling with visions of statistical hypotheses and scatter plots. However, armed with our trusty data from the Environmental Protection Agency and the Energy Information Administration, we set off on the path less traveled, in pursuit of that elusive link.
It is said that correlation does not imply causation, but when confronted with a correlation coefficient of 0.7599349 and a p-value reminiscent of the Holy Grail (i.e., less than 0.01), it would seem that we are treading on some rather solid ground here. The journey to this revelation was no less arduous than a quest to find the mystical Unicorn, yet the thrill of discovery and the implications of our findings keep our spirits high.
As our analysis and charts unfolded before our eyes like a splendid magic trick, what appeared at first to be as mismatched as putting a square peg in a round hole began to make some sense – a sense as tantalizing as an all-you-can-eat buffet after a day of fasting. We found ourselves contemplating the ramifications of this unorthodox connection, which could potentially shake up the world of environmental policy and energy dynamics like a heavily caffeinated earthquake.
Through the lens of this study, we aim not only to shed light on this bewitching correlation but also to revel in the delightfully interconnected fabric of our world. After all, as the great physicist Niels Bohr once quipped, “Prediction is very difficult, especially if it's about the future.” And oh, how right he was. So, let us embark on this joyous and perplexing adventure, dear reader, as we unravel the entanglements of the gasoline and the gurgle in a tale fit for the most whimsical of scientific journals.
[[RESULTS]]
The statistical analysis of the data unveiled a remarkably strong correlation between air pollution levels in Lancaster, Pennsylvania, and the quantity of gasoline pumped in Norway. The correlation coefficient of 0.7599349 suggested a robust relationship between these seemingly disparate variables. This finding was further corroborated by an r-squared value of 0.5775010, indicating that approximately 57.75% of the variability in gasoline consumption in Norway could be explained by the changes in air pollution levels in Lancaster.
The significance of this association was underscored by a p-value of less than 0.01, which implies that the likelihood of observing such a strong relationship by random chance alone is as rare as finding a four-leaf clover in a field of three-leaf clovers.
Figure 1 displays a scatterplot that vividly illustrates the strong correlation between the two variables. The data points are as tightly clustered as sardines in a can, leaving little room for doubt regarding the strength of the relationship. This visual representation serves as a compelling testament to the unexpected connection between the air pollution in Lancaster and the gasoline consumption in Norway.
The findings of this investigation add a touch of whimsy to the field of environmental research, reminding us that the interconnectedness of the world can sometimes be as surprising as stumbling upon a kangaroo in a snowstorm. The implications of this unexpected correlation extend beyond the realm of statistical analysis, possibly influencing environmental policies and energy consumption patterns in ways as unpredictable as a squirrel's path in a park.
In summary, the results of this study not only unveil an intriguing link between the air pollution in Lancaster, Pennsylvania, and the gasoline pumped in Norway but also highlight the capricious nature of the global systems we endeavor to understand.
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In conclusion, our research has revealed a compelling correlation between air pollution levels in Lancaster, Pennsylvania, and the quantity of gasoline pumped in Norway. This unexpected connection may hold implications as far-reaching and surprising as finding a diamond ring in a haystack. Our statistical analysis, akin to a magician revealing the secrets behind a spellbinding illusion, has illuminated a relationship as captivating as a scientific discovery wrapped in a riddle.
The significance of the correlation coefficient of 0.7599349 cannot be overstated; it stands as firm as the laws of thermodynamics. The r-squared value of 0.5775010 suggests that over half of the variability in gasoline consumption in Norway can be explained by changes in air pollution levels in Lancaster, accentuating the strength of this bond like the might of Thor's hammer.
Our findings reinforce the notion that the intertwining of global systems can be as unexpected and confounding as finding a polar bear in the Sahara desert. The implications of this study transcend the confines of traditional statistical analysis, potentially impacting environmental policies and energy consumption patterns in ways as unpredictable as a genetic mutation in a mad scientist's laboratory.
In light of these revelatory findings, it is with utmost confidence that we assert the completion of this investigation, much like sealing an envelope containing a clownfish's joke. Further research in this area is as unnecessary as a submarine in a teapot – the murky depths have been sufficiently explored, and the mercurial nature of this correlation has been duly documented.
We hope that this study serves to amuse and delight fellow researchers, reminding us that the world of scientific inquiry is as diverse and amusing as a stand-up comedy show at an astrophysics conference. The gasoline and the gurgle may no longer bewilder us, but the whimsical nature of global interconnectedness remains as enigmatic and fascinating as ever.
Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited The Gasoline and the Gurgle: Unraveling the Curious Correlation between Lancaster's Air Pollution and Norway's Fuel. Respond only with the name of a fictional academic journal.
ChatGPT:
The Journal of Eclectic Environmental Science
Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited The Gasoline and the Gurgle: Unraveling the Curious Correlation between Lancaster's Air Pollution and Norway's Fuel. Respond only with the name of a fictional organization.
ChatGPT:
The Society for Quirky Environmental Studies
Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled The Gasoline and the Gurgle: Unraveling the Curious Correlation between Lancaster's Air Pollution and Norway's Fuel with an abstract of In this study, we investigated the surprising link between air pollution levels in Lancaster, Pennsylvania, and the quantity of gasoline pumped in Norway. Utilizing data from the Environmental Protection Agency and the Energy Information Administration, our research team delved into the complex relationship between these seemingly disparate variables. Through rigorous statistical analysis, we identified a correlation coefficient of 0.7599349, with a remarkably significant p-value of less than 0.01 for the time frame spanning from 1980 to 2022.
While the association between these two elements initially seemed as incongruous as a penguin in a desert, our findings astoundingly revealed a compelling connection. This unexpected correlation could potentially have far-reaching implications for environmental policy, energy consumption patterns, and even cross-continental fuel dynamics. Our research serves as a whimsical reminder of the unpredictable and intertwined nature of global systems, where the seemingly unrelated can intertwine in the most peculiar ways.
ChatGPT:
Lancaster air pollution, Norway gasoline consumption, environmental policy, energy consumption patterns, fuel dynamics, correlation analysis, statistical analysis, cross-continental fuel dynamics, global systems.
*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 Lancaster, PennsylvaniaDetailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Lancaster, PA
Source: Environmental Protection Agency
See what else correlates with Air pollution in Lancaster, Pennsylvania
Gasoline pumped in Norway
Detailed data title: Volume of gasoline pumped consumed in Norway in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Gasoline pumped in Norway
Correlation 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 = 0.5775010 (Coefficient of determination)
This means 57.8% of the change in the one variable (i.e., Gasoline pumped in Norway) is predictable based on the change in the other (i.e., Air pollution in Lancaster, Pennsylvania) over the 43 years from 1980 through 2022.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.4E-9. 0.0000000034232509298263940000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. 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 0.76 in 3.4E-7% of random cases. Said differently, if you correlated 292,119,982 random variables You don't actually need 292 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.
p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.
In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.
Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 42 degrees of freedom, 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.
[ 0.6, 0.86 ] 95% correlation confidence interval (using the Fisher z-transformation)
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: 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 Lancaster, Pennsylvania (Bad air quality days) | 8.93372 | 8.52273 | 10.4225 | 11.2948 | 7.41758 | 7.14286 | 5.20548 | 11.8132 | 13.9726 | 4.10959 | 6.88705 | 12.9121 | 4.91803 | 11.5068 | 10.7438 | 11.1732 | 8.74317 | 10.411 | 14.5205 | 10.9589 | 6.55738 | 12.0548 | 12.3288 | 6.57534 | 4.37158 | 12.0548 | 7.39726 | 9.86301 | 4.64481 | 1.09589 | 7.67123 | 5.47945 | 5.46448 | 2.19178 | 4.10959 | 2.73973 | 1.63934 | 1.09589 | 1.36986 | 2.73973 | 1.36612 | 2.19178 | 1.91781 |
| Gasoline pumped in Norway (Million Barrels/Day) | 34 | 32 | 35 | 36 | 34.5137 | 36.8329 | 39.3589 | 40.6959 | 41.0492 | 41.2822 | 41.389 | 40.0521 | 39.1066 | 38.9178 | 39.0301 | 38.5918 | 39.041 | 38.5699 | 38.9644 | 38.6877 | 37.3962 | 38.9151 | 38.526 | 38.1534 | 37.8197 | 36.5781 | 35.189 | 33.3589 | 31.2131 | 29.4082 | 28.3863 | 26.0274 | 24.3552 | 22.9151 | 21.8521 | 20.1781 | 19.7568 | 19.3178 | 18.7425 | 17.7562 | 16.6858 | 16.5342 | 15.211 |
Why this works
- Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
- Lack of causal connection: There is probably
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
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. 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([8.93372,8.52273,10.4225,11.2948,7.41758,7.14286,5.20548,11.8132,13.9726,4.10959,6.88705,12.9121,4.91803,11.5068,10.7438,11.1732,8.74317,10.411,14.5205,10.9589,6.55738,12.0548,12.3288,6.57534,4.37158,12.0548,7.39726,9.86301,4.64481,1.09589,7.67123,5.47945,5.46448,2.19178,4.10959,2.73973,1.63934,1.09589,1.36986,2.73973,1.36612,2.19178,1.91781,])
array_2 = np.array([34,32,35,36,34.5137,36.8329,39.3589,40.6959,41.0492,41.2822,41.389,40.0521,39.1066,38.9178,39.0301,38.5918,39.041,38.5699,38.9644,38.6877,37.3962,38.9151,38.526,38.1534,37.8197,36.5781,35.189,33.3589,31.2131,29.4082,28.3863,26.0274,24.3552,22.9151,21.8521,20.1781,19.7568,19.3178,18.7425,17.7562,16.6858,16.5342,15.211,])
array_1_name = "Air pollution in Lancaster, Pennsylvania"
array_2_name = "Gasoline pumped in Norway"
# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)
# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)Reuseable content
You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. 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.
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Download images for these variables:
- High resolution line chart
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 Lancaster, Pennsylvania
- Line chart for only Gasoline pumped in Norway
- AI-generated correlation image
- The spurious research paper: The Gasoline and the Gurgle: Unraveling the Curious Correlation between Lancaster's Air Pollution and Norway's Fuel
You're the correlation whisperer we needed!
Correlation ID: 2772 · Black Variable ID: 20796 · Red Variable ID: 24779
