Spurious correlation #3,692 · 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 Greenville, 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 Greenville, 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 Greenville could finally go outside and spend more time with their spouses. With clearer skies and fresher air, couples were less irritable and more inclined to work things out instead of filing for divorce. It turns out, a breath of fresh air can do wonders for a marriage!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a serene scene set in Greenville, North Carolina, with a Brutalist style. The buildings are angular and imposing, contrasting against the bright blue sky and fluffy white clouds. People are leisurely strolling through the town, socializing and enjoying the fresh air. In the background, couples are holding hands and smiling, exuding a sense of peace and contentment. A prominent sign indicates a decreasing divorce rate, symbolizing the improved harmony and reduced conflict within the community. The overall scene should encapsulate the harmony and optimism of the town in a visually striking Brutalist interpretation. 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 Greenville, 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: Cutsy graphics Brutalism A scene from Skyrim A photo-realistic image Parallax art
ChatGPT: Brutalism

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 Greenville, North Carolina and The divorce rate in North Carolina, in the following situation: Because as the air quality improved in Greenville, North Carolina, people were no longer seeing red from the pollution, which led to fewer heated arguments and less hot air being blown around. With clearer skies, couples were finally able to see eye to eye and breathe easier, making it harder for them to part ways and causing the divorce rate to decrease.. Keep the description simple.
ChatGPT: In Greenville, North Carolina, the air is cleaner, with a bright blue sky and fluffy white clouds. People are seen happily strolling through the town, taking deep breaths of the fresh air. In the background, couples are walking hand in hand, smiling and chatting peacefully, seemingly more content. A prominent sign reveals a decreasing divorce rate, symbolizing the improved harmony and reduced conflict among the people in the community.

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: [ Brutalism ] and depicting this scene: In Greenville, North Carolina, the air is cleaner, with a bright blue sky and fluffy white clouds. People are seen happily strolling through the town, taking deep breaths of the fresh air. In the background, couples are walking hand in hand, smiling and chatting peacefully, seemingly more content. A prominent sign reveals a decreasing divorce rate, symbolizing the improved harmony and reduced conflict among the people in the community. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a serene scene set in Greenville, North Carolina, with a Brutalist style. The buildings are angular and imposing, contrasting against the bright blue sky and fluffy white clouds. People are leisurely strolling through the town, socializing and enjoying the fresh air. In the background, couples are holding hands and smiling, exuding a sense of peace and contentment. A prominent sign indicates a decreasing divorce rate, symbolizing the improved harmony and reduced conflict within the community. The overall scene should encapsulate the harmony and optimism of the town in a visually striking Brutalist interpretation.

*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 Greenville, North Carolina caused The divorce rate in North Carolina to decrease. Make it feel plausible.

AI academic paper

(Because p < 0.01)

Breathless in Greenville: The Correlation between Air Pollution and Divorce Rates in North Carolina

Journal of Ecological Sociology

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Air pollution in Greenville, North Carolina and The divorce rate in North Carolina. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then you might move on to cartoons and children's shows that you watched for research.

Here is the title and abstract of the paper:
[[TITLE]]
Breathless in Greenville: The Correlation between Air Pollution and Divorce Rates in North Carolina

[[ABSTRACT]]
The relationship between air pollution and divorce rates has long been a topic of debate, and our research team sought to untangle this knotty issue. By harnessing data from the Environmental Protection Agency and the CDC National Vital Statistics, we endeavored to shed light on whether a breath of fresh air in marital bliss may be affected by a whiff of nitrogen dioxide.

Our findings revealed a striking correlation coefficient of 0.9034507 and a jaw-dropping p-value of less than 0.01, indicating a robust relationship between air pollution in Greenville, North Carolina, and the divorce rate in the broader context of North Carolina. This statistical bond between air quality and marital dissolution begs the question: Is it time to clear the air in more ways than one?

In the spirit of full disclosure, we must *clear the air* and acknowledge the limitations of our research, recognizing that correlation does not imply causation. Our study merely suggests an intriguing association between air pollution and divorce rates, leaving ample room for future inquiry.

Nonetheless, our investigation urges policymakers and lovebirds alike to consider the potential impact of environmental factors on relationships. As the saying goes, "Love is in the air," but it seems that air pollution may also play a role in the game of love.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The proliferation of air pollution in modern society has become a matter of grave concern, particularly in its purported effects on public health and the environment. However, the implications of air pollution extend beyond pulmonary afflictions and ecological disruptions, delving into the delicate realm of human relationships. As we embark on this scholarly odyssey, we hope to breathe fresh perspective into the often-stifling discourse on the connection between air pollution and divorce rates.
As we venture into the murky haze of scientific inquiry, it is imperative to grasp the gravity of our undertaking. The correlation between air pollution and divorce rates is a thorny matter, not to be taken lightly. One might even say it's a marriage of convenience - the union of two seemingly disparate entities to explore their interconnectedness.
Our foray into this unconventional avenue of research was catalyzed by the realization that both air quality and marital stability are vital components of societal well-being. It is tempting to draw a parallel between a toxic relationship and toxic air, as both can leave one feeling breathless. However, we don't want to jump to conclusions, or as they say, "don't hold your breath until the statistical significance is confirmed."
In this study, we explore the specific case of Greenville, North Carolina, a city grappling with notable levels of air pollution, and its potential implications for the broader divorce rates in North Carolina. This geographical focus allows us to zoom in on the microcosm of our investigation while considering its repercussions on a macroscopic scale. It's akin to analyzing a marriage at the individual level and then assessing its impact on the institution of marriage as a whole.
So, as we plunge into the abyss of data analysis and statistical scrutiny, we invite you to join us on this illuminating expedition. One might say our approach is akin to a breathalyzer test for the relationship between air pollution and divorce rates - aiming to detect any intoxicating effects that could lead to a separation.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The relationship between environmental factors and human behavior has been a subject of interest for researchers in various disciplines. Smith, Jones, and Doe (2015) examined the impact of air pollution on societal well-being, focusing on its potential influence on mental health and cognitive function. Their study revealed a significant association between airborne pollutants and adverse effects on psychological well-being, shedding light on the broader ramifications of air quality on human experiences. In a similar vein, Brown and Green (2018) delved into the societal consequences of environmental degradation, emphasizing the interconnectedness between environmental factors and human relationships.

As we navigate the convoluted landscape of air pollution and its reverberations, it is crucial to consider the potential impact of *toxic* air on personal relationships. It's like the old joke about relationships - some are like nitrogen dioxide, sneaking up on you when you least expect it, and leaving you breathless.

Turning to non-fiction literature, "The Air We Breathe: A History of Air Pollution and Its Effects" by Guy H. Jones provides a comprehensive overview of air pollution's historical trajectory and its implications for human health. Additionally, "Marital Maladies: A Sociological Analysis" by Emily Smith offers valuable insights into the multifaceted dynamics of marital relationships, setting the stage for our investigation into the correlation between air pollution and divorce rates.

On a more lighthearted note, delving into fiction, "Love in the Time of Smog" by Gabriel Garcia Marquez evokes the intertwined complexities of love and environmental adversity, offering a whimsical perspective on the interplay between romance and air quality. Furthermore, "The Tainted Tango: A Suspenseful Saga of Love and Pollution" by Sarah Waters whimsically captures the entanglement of romance and environmental perils, albeit in an unconventional and fictional context.

Moreover, in the context of children's programming, "The Magic School Bus: Air Pollution Adventure" and "Captain Planet and the Planeteers" both provide unique, albeit elementary, explorations of environmental issues and their potential repercussions on human interactions. While perhaps not rigorous sources for academic exploration, these cultural touchstones offer a playful lens through which to contemplate the intersection of environmental factors and societal dynamics, much like a good dad joke at a stuffy academic conference.

In summary, the potential influence of air pollution on divorce rates merits careful consideration, extending beyond conventional boundaries of inquiry. As we progress in our investigation, it becomes increasingly evident that the air we breathe may indeed harbor unforeseen implications for the fabric of human relationships.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between Air pollution in Greenville, North Carolina and The divorce rate in North Carolina. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
Breathless in Greenville: The Correlation between Air Pollution and Divorce Rates in North Carolina

[[ABSTRACT]]
The relationship between air pollution and divorce rates has long been a topic of debate, and our research team sought to untangle this knotty issue. By harnessing data from the Environmental Protection Agency and the CDC National Vital Statistics, we endeavored to shed light on whether a breath of fresh air in marital bliss may be affected by a whiff of nitrogen dioxide.

Our findings revealed a striking correlation coefficient of 0.9034507 and a jaw-dropping p-value of less than 0.01, indicating a robust relationship between air pollution in Greenville, North Carolina, and the divorce rate in the broader context of North Carolina. This statistical bond between air quality and marital dissolution begs the question: Is it time to clear the air in more ways than one?

In the spirit of full disclosure, we must *clear the air* and acknowledge the limitations of our research, recognizing that correlation does not imply causation. Our study merely suggests an intriguing association between air pollution and divorce rates, leaving ample room for future inquiry.

Nonetheless, our investigation urges policymakers and lovebirds alike to consider the potential impact of environmental factors on relationships. As the saying goes, "Love is in the air," but it seems that air pollution may also play a role in the game of love.

[[INTRODUCTION]]
The proliferation of air pollution in modern society has become a matter of grave concern, particularly in its purported effects on public health and the environment. However, the implications of air pollution extend beyond pulmonary afflictions and ecological disruptions, delving into the delicate realm of human relationships. As we embark on this scholarly odyssey, we hope to breathe fresh perspective into the often-stifling discourse on the connection between air pollution and divorce rates.
As we venture into the murky haze of scientific inquiry, it is imperative to grasp the gravity of our undertaking. The correlation between air pollution and divorce rates is a thorny matter, not to be taken lightly. One might even say it's a marriage of convenience - the union of two seemingly disparate entities to explore their interconnectedness.
Our foray into this unconventional avenue of research was catalyzed by the realization that both air quality and marital stability are vital components of societal well-being. It is tempting to draw a parallel between a toxic relationship and toxic air, as both can leave one feeling breathless. However, we don't want to jump to conclusions, or as they say, "don't hold your breath until the statistical significance is confirmed."
In this study, we explore the specific case of Greenville, North Carolina, a city grappling with notable levels of air pollution, and its potential implications for the broader divorce rates in North Carolina. This geographical focus allows us to zoom in on the microcosm of our investigation while considering its repercussions on a macroscopic scale. It's akin to analyzing a marriage at the individual level and then assessing its impact on the institution of marriage as a whole.
So, as we plunge into the abyss of data analysis and statistical scrutiny, we invite you to join us on this illuminating expedition. One might say our approach is akin to a breathalyzer test for the relationship between air pollution and divorce rates - aiming to detect any intoxicating effects that could lead to a separation.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To probe the potential correlation between air pollution in Greenville, North Carolina, and the divorce rate in North Carolina, our research team employed a multi-faceted and rigorous approach. Our data collection process resembled a treasure hunt, scouring the depths of the internet and unearthing valuable data nuggets from the Environmental Protection Agency and CDC National Vital Statistics. It was a bit like panning for gold in the vast river of online information, but we struck statistical paydirt.

We first gathered data on air quality indicators such as levels of nitrogen dioxide, particulate matter, and ozone from the Environmental Protection Agency's comprehensive databases. This research phase involved navigating through a labyrinth of environmental metrics, not unlike embarking on a scientific scavenger hunt. We encountered our fair share of data outliers and statistical anomalies along the way, but perseverance prevailed.

Simultaneously, we diligently acquired divorce rates and demographic information from the CDC National Vital Statistics, spanning the years 1999 to 2021. This task resembled piecing together a jigsaw puzzle of marriage and dissolution, with each dataset serving as a crucial puzzle piece. It was a bit like playing Sherlock Holmes, except instead of solving crimes, we were deciphering the mysteries of matrimony and air pollution.

Subsequently, we harnessed the power of statistical software to conduct bivariate correlation analyses, revealing the extent of the relationship between air pollution in Greenville and the divorce rate in North Carolina. Picture this step as donning a pair of statistical spectacles to discern the hidden patterns within our data sets. We teased out the statistical nuances and unearthed a striking connection that left our team breathless – much like an unexpected plot twist in a scientific thriller.

Our study design adopted a quantitative approach, calculating correlation coefficients and p-values to unveil the strength and significance of the relationship between air pollution and divorce rates. It was akin to unraveling a scientific enigma, peeling back the layers of statistical inference to expose the underlying associations. Our statistical sleuthing yielded compelling evidence of a robust correlation, paving the way for a potential revision of the adage to “where there's air pollution, there's divorce.”

Moreover, we conducted spatial analysis to map the geographical distribution of air pollution and divorce rates in North Carolina, providing a visual representation of their interplay. This phase of our investigation resembled creating a cartographic masterpiece, painting the canvas of North Carolina with hues of air quality and marital discord. Our efforts culminated in a spatial visualization that served as a geographical testament to the intertwining of environmental and relationship dynamics.

Finally, we bolstered our findings with sensitivity analyses and robustness checks, akin to stress-testing the resilience of our conclusions. These analyses validated the stability of our results, fortifying the pillars of our research with statistical fortitude.

In summary, our methodological odyssey combined data excavation, statistical scrutiny, and geographical mapping to unravel the delicate dance between air pollution in Greenville and divorce rates in North Carolina. It was a scientific adventure brimming with twists and turns, much like a rollercoaster ride through the labyrinth of environmental and sociological intricacies.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between Air pollution in Greenville, North Carolina and The divorce rate in North Carolina. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
Breathless in Greenville: The Correlation between Air Pollution and Divorce Rates in North Carolina

[[ABSTRACT]]
The relationship between air pollution and divorce rates has long been a topic of debate, and our research team sought to untangle this knotty issue. By harnessing data from the Environmental Protection Agency and the CDC National Vital Statistics, we endeavored to shed light on whether a breath of fresh air in marital bliss may be affected by a whiff of nitrogen dioxide.

Our findings revealed a striking correlation coefficient of 0.9034507 and a jaw-dropping p-value of less than 0.01, indicating a robust relationship between air pollution in Greenville, North Carolina, and the divorce rate in the broader context of North Carolina. This statistical bond between air quality and marital dissolution begs the question: Is it time to clear the air in more ways than one?

In the spirit of full disclosure, we must *clear the air* and acknowledge the limitations of our research, recognizing that correlation does not imply causation. Our study merely suggests an intriguing association between air pollution and divorce rates, leaving ample room for future inquiry.

Nonetheless, our investigation urges policymakers and lovebirds alike to consider the potential impact of environmental factors on relationships. As the saying goes, "Love is in the air," but it seems that air pollution may also play a role in the game of love.

[[LITERATURE REVIEW]]
The relationship between environmental factors and human behavior has been a subject of interest for researchers in various disciplines. Smith, Jones, and Doe (2015) examined the impact of air pollution on societal well-being, focusing on its potential influence on mental health and cognitive function. Their study revealed a significant association between airborne pollutants and adverse effects on psychological well-being, shedding light on the broader ramifications of air quality on human experiences. In a similar vein, Brown and Green (2018) delved into the societal consequences of environmental degradation, emphasizing the interconnectedness between environmental factors and human relationships.
As we navigate the convoluted landscape of air pollution and its reverberations, it is crucial to consider the potential impact of *toxic* air on personal relationships. It's like the old joke about relationships - some are like nitrogen dioxide, sneaking up on you when you least expect it, and leaving you breathless.
Turning to non-fiction literature, "The Air We Breathe: A History of Air Pollution and Its Effects" by Guy H. Jones provides a comprehensive overview of air pollution's historical trajectory and its implications for human health. Additionally, "Marital Maladies: A Sociological Analysis" by Emily Smith offers valuable insights into the multifaceted dynamics of marital relationships, setting the stage for our investigation into the correlation between air pollution and divorce rates.
On a more lighthearted note, delving into fiction, "Love in the Time of Smog" by Gabriel Garcia Marquez evokes the intertwined complexities of love and environmental adversity, offering a whimsical perspective on the interplay between romance and air quality. Furthermore, "The Tainted Tango: A Suspenseful Saga of Love and Pollution" by Sarah Waters whimsically captures the entanglement of romance and environmental perils, albeit in an unconventional and fictional context.
Moreover, in the context of children's programming, "The Magic School Bus: Air Pollution Adventure" and "Captain Planet and the Planeteers" both provide unique, albeit elementary, explorations of environmental issues and their potential repercussions on human interactions. While perhaps not rigorous sources for academic exploration, these cultural touchstones offer a playful lens through which to contemplate the intersection of environmental factors and societal dynamics, much like a good dad joke at a stuffy academic conference.
In summary, the potential influence of air pollution on divorce rates merits careful consideration, extending beyond conventional boundaries of inquiry. As we progress in our investigation, it becomes increasingly evident that the air we breathe may indeed harbor unforeseen implications for the fabric of human relationships.

[[RESULTS]]
The statistical analysis of our data revealed a significant correlation between air pollution in Greenville, North Carolina, and the divorce rate in North Carolina for the period of 1999 to 2021. The correlation coefficient of 0.9034507 suggests a strong positive relationship between these two variables, indicating that as air pollution levels increased, so did the divorce rate. It appears that the quality of the air may indeed have an influence on the quality of relationships. One might say it's a case of "irreconcilable carbon emissions."
Furthermore, the r-squared value of 0.8162232 confirms that approximately 81.6% of the variability in the divorce rate can be explained by the variability in air pollution levels. This finding underscores the substantial influence of air quality on marital stability, and it seems that clean air is not the only thing that promotes harmony - clear statistical associations do too. It's as if the data is telling us, "breathe easy when the air is clean, and so will your marriage."
Moreover, the p-value of less than 0.01 provides compelling evidence to reject the null hypothesis and accept the alternative hypothesis that there is a significant relationship between air pollution and divorce rates. This statistical crispness in our findings would surely make any bag of potato chips jealous.
The scatterplot (Fig. 1) visually depicts the strong correlation between air pollution and divorce rates. The points on the plot align themselves almost as perfectly as a couple in a dance competition, illustrating the rhythm and harmony between these two variables. It's like they say, "You can't have a breath of fresh air without a partner to share it with."
In spite of these compelling results, it is important to note that correlation does not imply causation. While our study illuminates the statistical coupling of air pollution and divorce rates, it does not establish a causal relationship. So, although it seems that love may be like the air we breathe, influenced by the quality of the environment, we must tread carefully and not jump to hasty conclusions. After all, a correlation is not a marriage proposal; it's just a statistical fling.
Overall, our findings unveil a thought-provoking association between air pollution in Greenville, North Carolina, and the divorce rate in North Carolina, and they beckon further investigation into the complex interplay of environmental factors and human relationships. It appears that when it comes to matters of the heart, even the air we breathe may have a say.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of this study offer compelling evidence supporting the notion that air pollution in Greenville, North Carolina is positively correlated with the divorce rate in North Carolina. These results align with prior research by Smith, Jones, and Doe (2015) and Brown and Green (2018), who highlighted the interconnectedness between environmental factors and human relationships. The robust correlation coefficient and the p-value of less than 0.01 in our study further substantiate the significance of this relationship.

Our results underscore the importance of considering environmental factors in the study of human behavior and societal dynamics. As our data suggest, the quality of the air we breathe may indeed have implications for the quality of our relationships. It seems that when it comes to love, nitrogen dioxide may not be the most welcome third wheel!

The substantial r-squared value further accentuates the substantial influence of air quality on marital stability – it's like a statistical wizard casting a spell on the data, magically revealing the impact of air pollution on divorce rates. However, as we navigate this statistical terrain, we must resist the temptation to jump to causational conclusions. As any good statistician would caution, correlation does not imply causation – much like a correlation between two variables does not imply a romantic relationship between them.

The compelling visual representation of our findings in the form of the scatterplot vividly portrays the alignment between air pollution and divorce rates. The dots on the plot seem to waltz together with the elegance of a seasoned ballroom duo, as if performing a statistical tango illuminating the interconnectedness of these variables. It's a dance of data that elegantly proves the harmonious relationship between air pollution and divorce rates.

In light of these results, policymakers and individuals navigating the complexities of relationships may benefit from considering the potential impact of environmental factors on marital outcomes. Perhaps it's time to reassess the old adage "love is in the air" to include an acknowledgment of the potential role of air pollution in influencing the course of romantic entanglements. After all, when it comes to relationships, it seems that some couples may not only need a breath of fresh air but a reduction in airborne pollutants to clear the *atmosphere*.

The limitations of our study, particularly the absence of causational evidence, must be acknowledged. Nonetheless, the strength of the statistical relationship uncovered in this investigation encourages further exploration of the multifaceted interplay between environmental factors and human relationships. As we peel back the layers of this onion, we may find that the air we breathe holds more significance for our relationships than previously imagined – a revelation that may rival the plot twists of a good Agatha Christie novel.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Air pollution in Greenville, North Carolina and The divorce rate in North Carolina. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
Breathless in Greenville: The Correlation between Air Pollution and Divorce Rates in North Carolina

[[ABSTRACT]]
The relationship between air pollution and divorce rates has long been a topic of debate, and our research team sought to untangle this knotty issue. By harnessing data from the Environmental Protection Agency and the CDC National Vital Statistics, we endeavored to shed light on whether a breath of fresh air in marital bliss may be affected by a whiff of nitrogen dioxide.

Our findings revealed a striking correlation coefficient of 0.9034507 and a jaw-dropping p-value of less than 0.01, indicating a robust relationship between air pollution in Greenville, North Carolina, and the divorce rate in the broader context of North Carolina. This statistical bond between air quality and marital dissolution begs the question: Is it time to clear the air in more ways than one?

In the spirit of full disclosure, we must *clear the air* and acknowledge the limitations of our research, recognizing that correlation does not imply causation. Our study merely suggests an intriguing association between air pollution and divorce rates, leaving ample room for future inquiry.

Nonetheless, our investigation urges policymakers and lovebirds alike to consider the potential impact of environmental factors on relationships. As the saying goes, "Love is in the air," but it seems that air pollution may also play a role in the game of love.

[[INTRDUCTION]]
The proliferation of air pollution in modern society has become a matter of grave concern, particularly in its purported effects on public health and the environment. However, the implications of air pollution extend beyond pulmonary afflictions and ecological disruptions, delving into the delicate realm of human relationships. As we embark on this scholarly odyssey, we hope to breathe fresh perspective into the often-stifling discourse on the connection between air pollution and divorce rates.
As we venture into the murky haze of scientific inquiry, it is imperative to grasp the gravity of our undertaking. The correlation between air pollution and divorce rates is a thorny matter, not to be taken lightly. One might even say it's a marriage of convenience - the union of two seemingly disparate entities to explore their interconnectedness.
Our foray into this unconventional avenue of research was catalyzed by the realization that both air quality and marital stability are vital components of societal well-being. It is tempting to draw a parallel between a toxic relationship and toxic air, as both can leave one feeling breathless. However, we don't want to jump to conclusions, or as they say, "don't hold your breath until the statistical significance is confirmed."
In this study, we explore the specific case of Greenville, North Carolina, a city grappling with notable levels of air pollution, and its potential implications for the broader divorce rates in North Carolina. This geographical focus allows us to zoom in on the microcosm of our investigation while considering its repercussions on a macroscopic scale. It's akin to analyzing a marriage at the individual level and then assessing its impact on the institution of marriage as a whole.
So, as we plunge into the abyss of data analysis and statistical scrutiny, we invite you to join us on this illuminating expedition. One might say our approach is akin to a breathalyzer test for the relationship between air pollution and divorce rates - aiming to detect any intoxicating effects that could lead to a separation.

[[RESULTS]]
The statistical analysis of our data revealed a significant correlation between air pollution in Greenville, North Carolina, and the divorce rate in North Carolina for the period of 1999 to 2021. The correlation coefficient of 0.9034507 suggests a strong positive relationship between these two variables, indicating that as air pollution levels increased, so did the divorce rate. It appears that the quality of the air may indeed have an influence on the quality of relationships. One might say it's a case of "irreconcilable carbon emissions."
Furthermore, the r-squared value of 0.8162232 confirms that approximately 81.6% of the variability in the divorce rate can be explained by the variability in air pollution levels. This finding underscores the substantial influence of air quality on marital stability, and it seems that clean air is not the only thing that promotes harmony - clear statistical associations do too. It's as if the data is telling us, "breathe easy when the air is clean, and so will your marriage."
Moreover, the p-value of less than 0.01 provides compelling evidence to reject the null hypothesis and accept the alternative hypothesis that there is a significant relationship between air pollution and divorce rates. This statistical crispness in our findings would surely make any bag of potato chips jealous.
The scatterplot (Fig. 1) visually depicts the strong correlation between air pollution and divorce rates. The points on the plot align themselves almost as perfectly as a couple in a dance competition, illustrating the rhythm and harmony between these two variables. It's like they say, "You can't have a breath of fresh air without a partner to share it with."
In spite of these compelling results, it is important to note that correlation does not imply causation. While our study illuminates the statistical coupling of air pollution and divorce rates, it does not establish a causal relationship. So, although it seems that love may be like the air we breathe, influenced by the quality of the environment, we must tread carefully and not jump to hasty conclusions. After all, a correlation is not a marriage proposal; it's just a statistical fling.
Overall, our findings unveil a thought-provoking association between air pollution in Greenville, North Carolina, and the divorce rate in North Carolina, and they beckon further investigation into the complex interplay of environmental factors and human relationships. It appears that when it comes to matters of the heart, even the air we breathe may have a say.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has revealed a remarkable correlation between air pollution in Greenville, North Carolina, and the divorce rate in North Carolina. It seems that when the air gets dirty, so do the marital disputes, leading to the dissolution of relationships. One might say it's as if the carbon emissions are adding an unwanted third party to these marriages!

The compelling statistical evidence we have unearthed underscores the potential impact of environmental factors on the delicate fabric of human relationships. It appears that as the air quality deteriorates, so does the quality of matrimonial bliss. One might even say it's a case of "particulate matters of the heart."

Our findings also highlight the need for further exploration into the nuanced interplay between air pollution and divorce rates. Despite the robust correlation we have identified, we must resist the temptation to hastily jump to causation. As they say, "don't get carried away until the confounding variables are controlled."

In light of these revelations, it seems that love may indeed be in the air, but so is the influence of nitrogen dioxide and other airborne pollutants. This calls for a reevaluation of the popular saying, "Love is in the air," to perhaps "Love is in the air, but so is pollution - proceed with caution."

Therefore, it seems prudent to consider the potential implications of air quality on matters of the heart and to take a deep breath - of fresh air, of course - before leaping to conclusions about the impact of pollution on relationships.

In conclusion, it appears that our research has truly opened up a can of statistical worms in the field of environmental and marital dynamics. Given the robustness of our findings, it is safe to say that no more research is needed in this area. It's as clear as the blue skies on a pollution-free day!

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 Breathless in Greenville: The Correlation between Air Pollution and Divorce Rates in North Carolina. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Ecological Sociology

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 Breathless in Greenville: The Correlation between Air Pollution and Divorce Rates in North Carolina. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Ecological and Societal Analysis (SESA)

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 Breathless in Greenville: The Correlation between Air Pollution and Divorce Rates in North Carolina with an abstract of The relationship between air pollution and divorce rates has long been a topic of debate, and our research team sought to untangle this knotty issue. By harnessing data from the Environmental Protection Agency and the CDC National Vital Statistics, we endeavored to shed light on whether a breath of fresh air in marital bliss may be affected by a whiff of nitrogen dioxide.
Our findings revealed a striking correlation coefficient of 0.9034507 and a jaw-dropping p-value of less than 0.01, indicating a robust relationship between air pollution in Greenville, North Carolina, and the divorce rate in the broader context of North Carolina. This statistical bond between air quality and marital dissolution begs the question: Is it time to clear the air in more ways than one?
In the spirit of full disclosure, we must *clear the air* and acknowledge the limitations of our research, recognizing that correlation does not imply causation. Our study merely suggests an intriguing association between air pollution and divorce rates, leaving ample room for future inquiry.
Nonetheless, our investigation urges policymakers and lovebirds alike to consider the potential impact of environmental factors on relationships. As the saying goes, "Love is in the air," but it seems that air pollution may also play a role in the game of love.

ChatGPT:

Air pollution, divorce rates, North Carolina, correlation, Environmental Protection Agency, CDC National Vital Statistics, nitrogen dioxide, air quality, Greenville, marital dissolution, policymakers, environmental factors, relationship impact

*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 Greenville, North Carolina
Detailed data title: Percentage of days with moderate or worse air quality in Greenville, NC
Source: Environmental Protection Agency
See what else correlates with Air pollution in Greenville, 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 = 0.9034507 (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 = 0.8162232 (Coefficient of determination)
This means 81.6% 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 Greenville, 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 3.6E-9.^Show 0.0000000035534409645065210000
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 0.9 in 3.6E-7% of random cases. Said differently, if you correlated 281,417,367 random variables^Note You don't actually need 281 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 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.

[ 0.78, 0.96 ] 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 Greenville, North Carolina (Bad air quality days)	0.442149	0.540541	0.423077	0.448669	0.343874	0.330769	0.389961	0.358779	0.45	0.403846	0.144981	0.319066	0.328185	0.181102	0.10687	0.136719	0.127907	0.159722	0.142466	0.113043	0.125	0.0470914	0.0972222
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.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([0.442149,0.540541,0.423077,0.448669,0.343874,0.330769,0.389961,0.358779,0.45,0.403846,0.144981,0.319066,0.328185,0.181102,0.10687,0.136719,0.127907,0.159722,0.142466,0.113043,0.125,0.0470914,0.0972222,])
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 Greenville, 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 Greenville, North Carolina
Line chart for only The divorce rate in North Carolina
AI-generated correlation image
The spurious research paper: Breathless in Greenville: The Correlation between Air Pollution and Divorce Rates in North Carolina

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Correlation ID: 3692 · Black Variable ID: 21610 · Red Variable ID: 19814


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