Spurious correlation #4,894 · 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 Winston, North Carolina and the second variable is The divorce rate in North Carolina. The chart goes from 1999 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Winston, North Carolina and the second variable is The divorce rate in North Carolina. The chart goes from 1999 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the air quality improved, people in Winston-Salem were finally able to take a deep breath and reevaluate their relationships. With clearer lungs, they found it easier to see eye to eye and work out their differences, leading to a harmonious uptick in marital satisfaction. Who knew that cleaner air could lead to cleaner slates in love?!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate a comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s, depicting a peaceful evening in Winston-Salem, North Carolina. The scene should showcase couples strolling hand in hand against a backdrop of a clear, smog-free city. The fresh, clean air should be visually represented with a serene sunset, and the couples' body language should convey harmony and understanding. This visual should capture the newfound sense of ease and increased marital satisfaction across the city, without using any words or speech bubbles. 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 Winston, North Carolina and The divorce rate in North Carolina. 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 Discovery magazine cover image Data cyber art Cyber folk Brutalism A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s
ChatGPT: A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s

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 Winston, North Carolina and The divorce rate in North Carolina, in the following situation: As the air quality improved, people in Winston-Salem were finally able to take a deep breath and reevaluate their relationships. With clearer lungs, they found it easier to see eye to eye and work out their differences, leading to a harmonious uptick in marital satisfaction. Who knew that cleaner air could lead to cleaner slates in love?!. Keep the description simple.
ChatGPT: In Winston-Salem, North Carolina, the air cleared as the sun set behind the smog-free city. Couples strolled hand in hand, taking in the fresh, clean air. As they walked, their conversations flowed more easily, and the tensions that had weighed down their relationships seemed to dissipate with the pollution. The sky was clear, and so were their hearts, allowing for a newfound sense of harmony and understanding, fostering an increase in marital satisfaction across the city.

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 comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s ] and depicting this scene: In Winston-Salem, North Carolina, the air cleared as the sun set behind the smog-free city. Couples strolled hand in hand, taking in the fresh, clean air. As they walked, their conversations flowed more easily, and the tensions that had weighed down their relationships seemed to dissipate with the pollution. The sky was clear, and so were their hearts, allowing for a newfound sense of harmony and understanding, fostering an increase in marital satisfaction across the city. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate a comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s, depicting a peaceful evening in Winston-Salem, North Carolina. The scene should showcase couples strolling hand in hand against a backdrop of a clear, smog-free city. The fresh, clean air should be visually represented with a serene sunset, and the couples' body language should convey harmony and understanding. This visual should capture the newfound sense of ease and increased marital satisfaction across the city, without using any words or speech bubbles.

*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 Winston, North Carolina caused The divorce rate in North Carolina to decrease.

AI academic paper

(Because p < 0.01)

Pollution and Acrimony: A Statistical Examination of How Air Quality in Winston Impacts the Divorce Rate in North Carolina

The Journal of Environmental Interactions and Social Dynamics

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Air pollution in Winston, North Carolina and The divorce rate in North Carolina. 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 name a couple popular internet memes that are related to one of the topics.

Here is the title and abstract of the paper:
[[TITLE]]
Pollution and Acrimony: A Statistical Examination of How Air Quality in Winston Impacts the Divorce Rate in North Carolina

[[ABSTRACT]]
Despite the stagnant air of academia, our research team took a breath of fresh air and delved into the seemingly unbreathable topic of the relationship between air pollution and divorce. Using data from the Environmental Protection Agency and CDC National Vital Statistics, we embarked on a quest to uncover the hidden link between the smog-filled skies of Winston and the equally murky waters of marital discord across North Carolina.

Through our rigorous statistical analysis, we unearthed a correlation coefficient of 0.9227758 and p < 0.01 during the period of 1999 to 2021. Our findings suggest that as air pollution levels rise in Winston, so too does the divorce rate in North Carolina. While we cannot definitively prove causation, the correlation between these trends is as clear as a breath of fresh air.

So come join us as we take a deep breath into the unknown and explore the unexpected connections between environmental factors and human relationships. After all, in the world of research, sometimes the most intriguing discoveries are found where the air is neither fresh nor fair.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Ah, the tantalizing tango of data analysis and statistical musings. As researchers, we often find ourselves wading through mounds of numbers, hoping to uncover the hidden gems of correlation and causation. In this paper, we embark on a journey into the uncharted territory of air pollution and its potential impact on the divorce rate in North Carolina. Yes, you read that correctly. We are about to delve into the curious world of how the quality of the air we breathe may just ruffle the feathers of marital bliss.
The idea that something as intangible as air quality could have an impact on the intricacies of human relationships may raise a few eyebrows, akin to encountering a rare statistical anomaly. However, with the entertainingly high correlation coefficient and p-value of our findings, it seems that this peculiar connection may not just be a whimsical flight of fancy.
As we inhale and exhale the aroma of data sets and regression analyses, we strive to maintain scientific rigor while allowing a playful whisper of curiosity to flavor our quest for knowledge. Our journey through the realm of environmental influences and human behavior is a reminder that sometimes, the most unexpected relationships emerge when we least expect them. So, let's strap on our statistical safety goggles and venture forth into this captivating tapestry of numbers and peculiar connections. After all, in the realm of research, as in life, the unexpected often holds the most enticing surprises.
As we tease apart the threads of polluted air and fractured relationships, we aim to shed light on a topic that has been hidden in the shadows of scientific inquiry. So, let's take a deep breath, metaphorically speaking, and allow ourselves to be carried away on the winds of statistical exploration and lighthearted academic banter. After all, in the world of research, a dash of humor can be the breath of fresh air that makes the journey all the more enjoyable.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The investigation into the potential link between air pollution and divorce rates has sparked both curiosity and skepticism in the academic community. A variety of studies have attempted to shed light on this seemingly unusual connection, providing a spectrum of perspectives that range from the conventional to the delightfully absurd.

Smith and Doe (2015) delve into the impact of environmental factors on marital satisfaction, touching lightly on the possibility of air quality playing a role in the dissolution of marriages. Their findings provide a nuanced glimpse into the complex interplay of external influences on personal relationships. Meanwhile, Jones (2018) offers a comprehensive overview of air pollution's effects on public health, inadvertently laying a foundation for our foray into the quirky realm of divorce statistics.

In "Air Quality and Its Impact on Modern Society," the authors expound upon the detrimental effects of air pollution on physical health and well-being, inadvertently paving the way for our investigation into its potential impact on romantic harmony. On the other hand, in "Divorce and Its Socioeconomic Implications," the authors explore the multifaceted repercussions of marital dissolution, painting a picture of the intricate web of factors that contribute to the ebb and flow of divorce rates.

Adding a literary twist to the discussion, Miller's "Love in the Time of Smog" beautifully intertwines the themes of environmental degradation and romantic turmoil, offering a fictionalized account that evokes both laughter and contemplation. Slightly more on the nose, Stancil's "The Haze of Heartbreak" draws a more direct connection between environmental pollution and emotional distress, presenting an imaginative narrative that mirrors our own statistical exploration.

Turning to the digital realm, the ubiquitous "Distracted Boyfriend Meme" has found itself entwined in the topic of divorce rates, drawing parallels to the wandering gaze of unsatisfied partners and the drifting attention of a populace affected by polluted air. Similarly, the "This is Fine Dog" meme has become an unwitting emblem of individuals navigating the tumultuous waters of marital discord amidst a haze of environmental uncertainty.

As we navigate through the labyrinth of literature and cultural references, it becomes evident that the intersection of air pollution and divorce rates is not merely a fanciful flight of statistical fancy but a topic that resonates with both scholarly inquiry and popular imagination. So, with an inquisitive spirit and a sprinkle of whimsy, let us plunge deeper into this enigmatic connection, prepared to unravel its mysteries and embrace the unexpected twists and turns that lie ahead.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between Air pollution in Winston, North Carolina and The divorce rate in North Carolina. 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 CDC National Vital Statistics . You used data from 1999 to 2021

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
Pollution and Acrimony: A Statistical Examination of How Air Quality in Winston Impacts the Divorce Rate in North Carolina

[[ABSTRACT]]
Despite the stagnant air of academia, our research team took a breath of fresh air and delved into the seemingly unbreathable topic of the relationship between air pollution and divorce. Using data from the Environmental Protection Agency and CDC National Vital Statistics, we embarked on a quest to uncover the hidden link between the smog-filled skies of Winston and the equally murky waters of marital discord across North Carolina.

Through our rigorous statistical analysis, we unearthed a correlation coefficient of 0.9227758 and p < 0.01 during the period of 1999 to 2021. Our findings suggest that as air pollution levels rise in Winston, so too does the divorce rate in North Carolina. While we cannot definitively prove causation, the correlation between these trends is as clear as a breath of fresh air.

So come join us as we take a deep breath into the unknown and explore the unexpected connections between environmental factors and human relationships. After all, in the world of research, sometimes the most intriguing discoveries are found where the air is neither fresh nor fair.

[[INTRODUCTION]]
Ah, the tantalizing tango of data analysis and statistical musings. As researchers, we often find ourselves wading through mounds of numbers, hoping to uncover the hidden gems of correlation and causation. In this paper, we embark on a journey into the uncharted territory of air pollution and its potential impact on the divorce rate in North Carolina. Yes, you read that correctly. We are about to delve into the curious world of how the quality of the air we breathe may just ruffle the feathers of marital bliss.
The idea that something as intangible as air quality could have an impact on the intricacies of human relationships may raise a few eyebrows, akin to encountering a rare statistical anomaly. However, with the entertainingly high correlation coefficient and p-value of our findings, it seems that this peculiar connection may not just be a whimsical flight of fancy.
As we inhale and exhale the aroma of data sets and regression analyses, we strive to maintain scientific rigor while allowing a playful whisper of curiosity to flavor our quest for knowledge. Our journey through the realm of environmental influences and human behavior is a reminder that sometimes, the most unexpected relationships emerge when we least expect them. So, let's strap on our statistical safety goggles and venture forth into this captivating tapestry of numbers and peculiar connections. After all, in the realm of research, as in life, the unexpected often holds the most enticing surprises.
As we tease apart the threads of polluted air and fractured relationships, we aim to shed light on a topic that has been hidden in the shadows of scientific inquiry. So, let's take a deep breath, metaphorically speaking, and allow ourselves to be carried away on the winds of statistical exploration and lighthearted academic banter. After all, in the world of research, a dash of humor can be the breath of fresh air that makes the journey all the more enjoyable.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

In order to untangle the convoluted web of air pollution and its potential impact on the divorce rate in North Carolina, our research team embarked on a rather eccentric journey through the annals of data collection and statistical analysis. As with any scientific endeavor, our methodology was designed to capture the essence of both rigorous investigation and a hint of zany curiosity.

Data Collection:
First and foremost, our intrepid team scoured the virtual realms of the Environmental Protection Agency and CDC National Vital Statistics, diving headfirst into the depths of air quality measurements and divorce rates spanning the years 1999 to 2021. Hours were spent wrangling with spreadsheets, navigating the labyrinthine corridors of government databases, and resisting the siren call of online cat videos – all in the noble pursuit of data collection.

However, it must be noted that our primary source of data is from these esteemed organizations, and while we did briefly entertain the idea of utilizing information obtained from fortune cookies and cryptic omens, we ultimately decided to stick to more conventional sources.

Variable Selection:
With our capricious data in hand, we carefully selected the variables that would form the cornerstone of our analysis. Air pollution levels in the scenic locale of Winston, North Carolina were measured using the Environmental Protection Agency's air quality monitoring data. To provide a symmetrical contrast, the divorce rate in North Carolina was extracted from the CDC National Vital Statistics database, offering a glimpse into the tumultuous tides of marital relations.

Statistical Analysis:
Armed with our treasure trove of data and a hearty dose of skepticism, we embarked on an odyssey through the meandering pathways of statistical analysis. Utilizing the illustrious tools of correlation and regression analysis, we sought to uncover any whispers of connection between air pollution levels and the ebb and flow of divorces in the state of North Carolina. Our statistical toolbox was as varied as a well-curated spice rack, with robust software such as R and SPSS lending their analytical prowess to our noble quest.

It is important to note that our methodology adhered to the time-tested principles of scientific inquiry, tempered with a light-hearted spirit that has been known to engender both amusement and raised eyebrows. Our statistical jargon mingled with the occasional jest, for as any seasoned researcher knows, a sprinkle of humor can often be the key ingredient in an otherwise dry dish of data analysis.

In summary, our methodology blended the rigidity of scientific inquiry with a dash of whimsy, ultimately enabling us to paint a captivating portrait of the interconnected dance between air pollution in Winston and the divorce rate in North Carolina. So, with earnest hearts and statistical fervor, we present our splendid odyssey through the stimulating world of curiosity and scientific inquiry.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the 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 Winston, North Carolina and The divorce rate in North Carolina. 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]]
Pollution and Acrimony: A Statistical Examination of How Air Quality in Winston Impacts the Divorce Rate in North Carolina

[[ABSTRACT]]
Despite the stagnant air of academia, our research team took a breath of fresh air and delved into the seemingly unbreathable topic of the relationship between air pollution and divorce. Using data from the Environmental Protection Agency and CDC National Vital Statistics, we embarked on a quest to uncover the hidden link between the smog-filled skies of Winston and the equally murky waters of marital discord across North Carolina.

Through our rigorous statistical analysis, we unearthed a correlation coefficient of 0.9227758 and p < 0.01 during the period of 1999 to 2021. Our findings suggest that as air pollution levels rise in Winston, so too does the divorce rate in North Carolina. While we cannot definitively prove causation, the correlation between these trends is as clear as a breath of fresh air.

So come join us as we take a deep breath into the unknown and explore the unexpected connections between environmental factors and human relationships. After all, in the world of research, sometimes the most intriguing discoveries are found where the air is neither fresh nor fair.

[[LITERATURE REVIEW]]
The investigation into the potential link between air pollution and divorce rates has sparked both curiosity and skepticism in the academic community. A variety of studies have attempted to shed light on this seemingly unusual connection, providing a spectrum of perspectives that range from the conventional to the delightfully absurd.
Smith and Doe (2015) delve into the impact of environmental factors on marital satisfaction, touching lightly on the possibility of air quality playing a role in the dissolution of marriages. Their findings provide a nuanced glimpse into the complex interplay of external influences on personal relationships. Meanwhile, Jones (2018) offers a comprehensive overview of air pollution's effects on public health, inadvertently laying a foundation for our foray into the quirky realm of divorce statistics.
In "Air Quality and Its Impact on Modern Society," the authors expound upon the detrimental effects of air pollution on physical health and well-being, inadvertently paving the way for our investigation into its potential impact on romantic harmony. On the other hand, in "Divorce and Its Socioeconomic Implications," the authors explore the multifaceted repercussions of marital dissolution, painting a picture of the intricate web of factors that contribute to the ebb and flow of divorce rates.
Adding a literary twist to the discussion, Miller's "Love in the Time of Smog" beautifully intertwines the themes of environmental degradation and romantic turmoil, offering a fictionalized account that evokes both laughter and contemplation. Slightly more on the nose, Stancil's "The Haze of Heartbreak" draws a more direct connection between environmental pollution and emotional distress, presenting an imaginative narrative that mirrors our own statistical exploration.
Turning to the digital realm, the ubiquitous "Distracted Boyfriend Meme" has found itself entwined in the topic of divorce rates, drawing parallels to the wandering gaze of unsatisfied partners and the drifting attention of a populace affected by polluted air. Similarly, the "This is Fine Dog" meme has become an unwitting emblem of individuals navigating the tumultuous waters of marital discord amidst a haze of environmental uncertainty.
As we navigate through the labyrinth of literature and cultural references, it becomes evident that the intersection of air pollution and divorce rates is not merely a fanciful flight of statistical fancy but a topic that resonates with both scholarly inquiry and popular imagination. So, with an inquisitive spirit and a sprinkle of whimsy, let us plunge deeper into this enigmatic connection, prepared to unravel its mysteries and embrace the unexpected twists and turns that lie ahead.

[[RESULTS]]
Our data analysis revealed a striking correlation between air pollution levels in Winston, North Carolina, and the divorce rate in North Carolina as a whole from 1999 to 2021. The correlation coefficient of 0.9227758 indicated a strong positive relationship between these seemingly unrelated variables. It was as if the toxic fumes from industrial activities were sneakily whispering, "I do" to the corrosion of marital bonds.
The r-squared value of 0.8515151 further fortified the notion that the fluctuations in air quality in Winston could explain a whopping 85% of the variance in the divorce rate across the state. Essentially, it was like saying, "Hey, here's a lungful of air pollution, now go ahead and take 85% of this relationship drama along with it!"
Not to mention, the p-value of less than 0.01 provided compelling evidence that the observed relationship between air pollution and divorce rate was not merely a statistical fluke. The probability of observing such a strong association by random chance was as slim as finding a needle in a smog-filled haystack.
Fig. 1 showcases a scatterplot that visually captures the tight grip of this relationship. The data points cling to the regression line like a pair of smog-covered lovebirds, demonstrating the compelling nature of the association. It's almost as if the air pollution and divorce rate were engaged in a pas de deux of statistical significance, waltzing across the plot with an undeniable synchronicity.
In summary, our findings testify to the surprising interconnectedness of environmental factors and human relationships. It appears that the air we breathe may not only influence our physical health but also cast its murky shadow over the harmony of our interpersonal unions. It's a reminder that in the realm of research, the most unusual connections can often be the most revealing.
Now, if only we could figure out how to bottle up some fresh air and sprinkle it over troubled marriages.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our results lend substantial support to the prior research that hinted at the potential entanglement of air pollution and divorce rates. The correlation coefficient of 0.9227758 we uncovered not only reaffirms the suggestive musings of Smith and Doe (2015) on the impact of environmental factors on marital satisfaction, but also nudges the findings of Miller's "Love in the Time of Smog" towards a more empirical foundation. Who would have thought that a whimsical literary work could foreshadow such a statistically robust relationship? It's almost as if the air pollution itself was whispering, "Watch out, lovebirds, here comes trouble!"

On the statistical stage, our r-squared value of 0.8515151 takes center spotlight, underscoring the significant role of air quality in explaining the variance in divorce rates. It's akin to discovering that behind the scenes of a Shakespearean tragedy, the smog was pulling the strings of discord with an 85% influence. Meanwhile, our sparkling p-value of less than 0.01 puts the final flourish on this dramatic act, confirming that the association we've unveiled is no statistical fluke – it's as real as a melodramatic soap opera playing out in a fog-filled town.

The scatterplot in Fig. 1 represents the pièce de résistance of our analysis, visually encapsulating the magnetic bond between air pollution and divorce rates. The dots cling to the regression line like two star-crossed lovers, underscoring the compelling nature of their statistical tango. It's almost as if the air pollution and divorce rate are engaged in a waltz of undeniable significance, with each step mirroring the other in a haze of correlation.

So, what does all this mean for the world of academia, apart from providing ample fodder for smoggy wordplay and puns? Our findings offer a timely reminder that beneath the seemingly dissonant domains of environmental science and sociology, there lies a hidden harmony waiting to be uncovered. It's as if statistical gravity pulls the fields closer, revealing that the air we breathe might not only influence our personal well-being but may also cast its shadow over the fabric of our social relationships.

As we ponder the implications of this research, perhaps we should consider introducing "couples' inhalation therapy" as an unconventional yet potentially effective intervention for troubled marriages. After all, in the realm of research, sometimes the most unexpected connections lead to the most refreshing insights.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Air pollution in Winston, North Carolina and The divorce rate in North Carolina. 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]]
Pollution and Acrimony: A Statistical Examination of How Air Quality in Winston Impacts the Divorce Rate in North Carolina

[[ABSTRACT]]
Despite the stagnant air of academia, our research team took a breath of fresh air and delved into the seemingly unbreathable topic of the relationship between air pollution and divorce. Using data from the Environmental Protection Agency and CDC National Vital Statistics, we embarked on a quest to uncover the hidden link between the smog-filled skies of Winston and the equally murky waters of marital discord across North Carolina.

Through our rigorous statistical analysis, we unearthed a correlation coefficient of 0.9227758 and p < 0.01 during the period of 1999 to 2021. Our findings suggest that as air pollution levels rise in Winston, so too does the divorce rate in North Carolina. While we cannot definitively prove causation, the correlation between these trends is as clear as a breath of fresh air.

So come join us as we take a deep breath into the unknown and explore the unexpected connections between environmental factors and human relationships. After all, in the world of research, sometimes the most intriguing discoveries are found where the air is neither fresh nor fair.

[[INTRDUCTION]]
Ah, the tantalizing tango of data analysis and statistical musings. As researchers, we often find ourselves wading through mounds of numbers, hoping to uncover the hidden gems of correlation and causation. In this paper, we embark on a journey into the uncharted territory of air pollution and its potential impact on the divorce rate in North Carolina. Yes, you read that correctly. We are about to delve into the curious world of how the quality of the air we breathe may just ruffle the feathers of marital bliss.
The idea that something as intangible as air quality could have an impact on the intricacies of human relationships may raise a few eyebrows, akin to encountering a rare statistical anomaly. However, with the entertainingly high correlation coefficient and p-value of our findings, it seems that this peculiar connection may not just be a whimsical flight of fancy.
As we inhale and exhale the aroma of data sets and regression analyses, we strive to maintain scientific rigor while allowing a playful whisper of curiosity to flavor our quest for knowledge. Our journey through the realm of environmental influences and human behavior is a reminder that sometimes, the most unexpected relationships emerge when we least expect them. So, let's strap on our statistical safety goggles and venture forth into this captivating tapestry of numbers and peculiar connections. After all, in the realm of research, as in life, the unexpected often holds the most enticing surprises.
As we tease apart the threads of polluted air and fractured relationships, we aim to shed light on a topic that has been hidden in the shadows of scientific inquiry. So, let's take a deep breath, metaphorically speaking, and allow ourselves to be carried away on the winds of statistical exploration and lighthearted academic banter. After all, in the world of research, a dash of humor can be the breath of fresh air that makes the journey all the more enjoyable.

[[RESULTS]]
Our data analysis revealed a striking correlation between air pollution levels in Winston, North Carolina, and the divorce rate in North Carolina as a whole from 1999 to 2021. The correlation coefficient of 0.9227758 indicated a strong positive relationship between these seemingly unrelated variables. It was as if the toxic fumes from industrial activities were sneakily whispering, "I do" to the corrosion of marital bonds.
The r-squared value of 0.8515151 further fortified the notion that the fluctuations in air quality in Winston could explain a whopping 85% of the variance in the divorce rate across the state. Essentially, it was like saying, "Hey, here's a lungful of air pollution, now go ahead and take 85% of this relationship drama along with it!"
Not to mention, the p-value of less than 0.01 provided compelling evidence that the observed relationship between air pollution and divorce rate was not merely a statistical fluke. The probability of observing such a strong association by random chance was as slim as finding a needle in a smog-filled haystack.
Fig. 1 showcases a scatterplot that visually captures the tight grip of this relationship. The data points cling to the regression line like a pair of smog-covered lovebirds, demonstrating the compelling nature of the association. It's almost as if the air pollution and divorce rate were engaged in a pas de deux of statistical significance, waltzing across the plot with an undeniable synchronicity.
In summary, our findings testify to the surprising interconnectedness of environmental factors and human relationships. It appears that the air we breathe may not only influence our physical health but also cast its murky shadow over the harmony of our interpersonal unions. It's a reminder that in the realm of research, the most unusual connections can often be the most revealing.
Now, if only we could figure out how to bottle up some fresh air and sprinkle it over troubled marriages.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has unveiled a compelling association between air pollution in Winston, North Carolina, and the divorce rate in the state. The robust correlation coefficient and the p-value of less than 0.01 serve as a beacon, guiding our understanding of the interplay between environmental factors and human relationships. It's almost as if the smog and marital discord danced a statistical tango, suggesting a connection that is as clear as the air on a hazy day in Winston.

Our findings beckon us to ponder the subtle ways in which the atmosphere we inhale may permeate the fabric of our personal lives. It's as if the breeze carried not only whispers of pollutants but also whispers of marital dissension, blending the skies with the woes of matrimony.

As we draw the curtains on this chapter of research, it is clear that no further investigation is needed in this area. After all, sometimes, in the vast landscape of scientific inquiry, the most curious discoveries emerge when we least expect them. And in this case, we've uncovered an unexpected tie between the air we breathe and the bonds we share.

In the grand symphony of scientific exploration, let us not forget to appreciate the unexpected connections that emerge, like finding love in a hopeless place or discovering correlations between air pollution and the dissolution of marriages.

Therefore, we assert with unwavering confidence that no further research is warranted in this delightful, albeit unconventional, realm of scientific inquiry. For now, let's bask in the unusual insights we've gained and acknowledge that in the world of research, sometimes the most astonishing discoveries are found in the unlikeliest of places.

And with that, we bid adieu to this peculiar tale of statistical courtship between air pollution and divorce rates, with a slight chuckle at the whims of scientific curiosity.

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 Pollution and Acrimony: A Statistical Examination of How Air Quality in Winston Impacts the Divorce Rate in North Carolina. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Environmental Interactions and Social Dynamics

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 Pollution and Acrimony: A Statistical Examination of How Air Quality in Winston Impacts the Divorce Rate in North Carolina. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Socio-Environmental Studies and Community Well-being

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 Pollution and Acrimony: A Statistical Examination of How Air Quality in Winston Impacts the Divorce Rate in North Carolina with an abstract of Despite the stagnant air of academia, our research team took a breath of fresh air and delved into the seemingly unbreathable topic of the relationship between air pollution and divorce. Using data from the Environmental Protection Agency and CDC National Vital Statistics, we embarked on a quest to uncover the hidden link between the smog-filled skies of Winston and the equally murky waters of marital discord across North Carolina.
Through our rigorous statistical analysis, we unearthed a correlation coefficient of 0.9227758 and p < 0.01 during the period of 1999 to 2021. Our findings suggest that as air pollution levels rise in Winston, so too does the divorce rate in North Carolina. While we cannot definitively prove causation, the correlation between these trends is as clear as a breath of fresh air.
So come join us as we take a deep breath into the unknown and explore the unexpected connections between environmental factors and human relationships. After all, in the world of research, sometimes the most intriguing discoveries are found where the air is neither fresh nor fair.

ChatGPT:

air pollution, divorce rate, North Carolina, Winston, environmental factors, correlation coefficient, CDC National Vital Statistics, Environmental Protection Agency, statistical analysis

*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.

Random correlation

Discover a new correlation

View all correlations

View all research papers

Report an error

Data details

Air pollution in Winston, North Carolina
Detailed data title: Percentage of days with moderate or worse air quality in Winston-Salem, NC
Source: Environmental Protection Agency
See what else correlates with Air pollution in Winston, North Carolina

The divorce rate in North Carolina
Source: CDC National Vital Statistics
See what else correlates with The divorce rate in North Carolina

Correlation r = 0.9227758 (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 = 0.8515151 (Coefficient of determination)
This means 85.2% of the change in the one variable (i.e., The divorce rate in North Carolina) is predictable based on the change in the other (i.e., Air pollution in Winston, North Carolina) over the 23 years from 1999 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.7E-10.^Show 0.0000000003715283524197856000
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 0.92 in 3.7E-8% of random cases. Said differently, if you correlated 2,691,584,622 random variables^Note You don't actually need 2 billion variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 22 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 22 because we have two variables measured over a period of 23 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.82, 0.97 ] 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.

	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Air pollution in Winston, North Carolina (Bad air quality days)	69.3151	72.9508	66.8493	62.7397	62.1918	64.7541	62.1918	67.9452	65.7534	63.388	47.6712	56.9863	50.9589	47.8142	35.0685	45.2055	46.5753	37.7049	29.863	35.8904	38.3562	25.6831	32.6027
The divorce rate in North Carolina (Divorce rate)	4.6	4.5	4.6	4.4	4.2	4.2	4.1	4	4	3.8	3.8	3.8	3.7	3.7	3.4292	3.40196	3.08231	3.24832	3.10997	3.07532	3.13327	2.82912	3.15984

Why this works

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

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([69.3151,72.9508,66.8493,62.7397,62.1918,64.7541,62.1918,67.9452,65.7534,63.388,47.6712,56.9863,50.9589,47.8142,35.0685,45.2055,46.5753,37.7049,29.863,35.8904,38.3562,25.6831,32.6027,])
array_2 = np.array([4.6,4.5,4.6,4.4,4.2,4.2,4.1,4,4,3.8,3.8,3.8,3.7,3.7,3.4292,3.40196,3.08231,3.24832,3.10997,3.07532,3.13327,2.82912,3.15984,])
array_1_name = "Air pollution in Winston, North Carolina"
array_2_name = "The divorce rate in North Carolina"

# 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 Winston, North Carolina
Line chart for only The divorce rate in North Carolina
AI-generated correlation image
The spurious research paper: Pollution and Acrimony: A Statistical Examination of How Air Quality in Winston Impacts the Divorce Rate in North Carolina

View another random correlation

How fun was this correlation?

Big thanks for reviewing!

Correlation ID: 4894 · Black Variable ID: 21465 · Red Variable ID: 19814


Problem variable:
Issue:
Additional details: Optional
Confirm you are a human: