Spurious correlation #2,184 · 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 Iowa City and the second variable is The number of library technicians in Iowa. The chart goes from 2003 to 2022, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Iowa City and the second variable is The number of library technicians in Iowa. The chart goes from 2003 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The improved air quality led to happier, more productive residents who pursued higher education and careers, reducing the demand for library services in Iowa. It's a classic case of clean air leading to cleaner career paths!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a futuristic library scene in Iowa City. Show a group of library technicians wearing high-tech face masks as they diligently organize bookshelves and assist patrons. Capture the smoky haze from nearby wildfires affecting the air quality outside, creating a hazy, muted atmosphere. Highlight the technicians' dedication to providing a calm and resourceful environment within the library, despite the challenging conditions outside. 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: Less than ideal air quality in Iowa City and The number of library technicians in Iowa. 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: Glitch art Cubism inspired by bauhaus Futuristic Cute 2D animation
ChatGPT: Futuristic

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 Less than ideal air quality in Iowa City and The number of library technicians in Iowa together. Keep the description simple.
ChatGPT: In Iowa City, a group of library technicians wear face masks as they work to organize bookshelves and assist patrons, mindful of the less than ideal air quality outside. The smoky haze from nearby wildfires has affected the air, but the technicians are dedicated to providing a peaceful and resourceful environment within the library.

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: [ Futuristic ] and depicting this scene: In Iowa City, a group of library technicians wear face masks as they work to organize bookshelves and assist patrons, mindful of the less than ideal air quality outside. The smoky haze from nearby wildfires has affected the air, but the technicians are dedicated to providing a peaceful and resourceful environment within the library. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a futuristic library scene in Iowa City. Show a group of library technicians wearing high-tech face masks as they diligently organize bookshelves and assist patrons. Capture the smoky haze from nearby wildfires affecting the air quality outside, creating a hazy, muted atmosphere. Highlight the technicians' dedication to providing a calm and resourceful environment within the library, despite the challenging conditions outside.

*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 Iowa City caused The number of library technicians in Iowa to decrease. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

The Air We Breathe: A Study on the Correlation Between Air Pollution in Iowa City and the Employment of Library Technicians in Iowa

Journal of Environmental Studies and Occupational Trends

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 Iowa City and The number of library technicians in Iowa. 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. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
The Air We Breathe: A Study on the Correlation Between Air Pollution in Iowa City and the Employment of Library Technicians in Iowa

[[ABSTRACT]]
This research paper presents a comprehensive analysis of the relationship between air pollution in Iowa City and the employment of library technicians in the state of Iowa. The study utilized data from the Environmental Protection Agency and the Bureau of Labor Statistics to investigate this surprising connection. The findings revealed a remarkably strong correlation between levels of air pollution in Iowa City and the number of library technicians employed in Iowa, with a correlation coefficient of 0.8459054, and a p-value of less than 0.01 for the years 2003 to 2022. The paper discusses the plausible implications of this connection, and proposes potential avenues for further exploration and research in this uncharted territory at the intersection of environmental science and labor economics.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Air pollution is a pervasive and persistent environmental issue that affects communities worldwide. The adverse health effects of air pollution have long been a subject of concern for public health officials, medical researchers, and asthmatic individuals everywhere. However, the connection between air pollution and the employment of library technicians, an ostensibly unrelated field, has remained an enigma, waiting to be unraveled like a particularly stubborn knot in a shoelace.
The present study delves into this curious correlation, seeking to shed light on the obscure relationship between the quality of the air we breathe and the number of individuals adept at organizing books by the Dewey Decimal System. By examining data from the Environmental Protection Agency alongside employment statistics from the Bureau of Labor Statistics, we aim to offer empirical evidence of this unexpected connection, or as some may dub it, the "dusty trail" linking air pollution and library technical employment.
In shedding light on this unlikely association, we endeavor to expand the horizons of environmental science and labor economics, proving that even the most seemingly unrelated elements of our world may be intertwined in ways we have yet to comprehend, much like a complex literary plot waiting to be deciphered. This endeavor is not merely a quest for knowledge but also an adventure into the uncharted intersections of seemingly disparate fields—a journey akin to navigating the convoluted stacks of a library in pursuit of a hidden gem of information.
As we embark on this investigation, we invoke the spirit of curiosity and inquiry, much like a diligent librarian seeking to unveil the mysteries of an ancient tome hidden among the shelves. We invite the reader to join us in this exploration of the unexpected correlations that permeate the ever-complex tapestry of modern existence.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

A number of scholarly works have sought to investigate the relationship between environmental factors and labor market dynamics. Smith et al. (2015) found a strong association between air quality and workforce productivity in their seminal study, "Air Quality and Labor: An Empirical Analysis." Similarly, Doe's (2018) investigation, "The Impact of Pollution on Employment Patterns," revealed compelling evidence of the influence of pollution levels on occupational trends. Jones' research on "Environmental Quality and Labor Market Outcomes" further contributes to the body of literature exploring the intersection of environmental factors and employment.

Turning to more specialized sources, "The Economics of Dust and Dander" by Goldstein and Silverman (2016) delves into the economic impact of air pollutants and allergens, albeit in a context more closely related to household cleaning services. In a tangentially related vein, "The Politics of Fresh Air" by Green (2019) provides a nuanced exploration of the societal implications of air quality regulations, though its focus lies primarily in the realm of policy analysis.

Brushing on works with more anecdotal appeal, the fictional account "The Dusty Chronicles" by Wordsworth (1923) explores the lives of librarians in a whimsical tale that may offer insights into the human dimensions of library employment. "The Murky Mysteries of The Dewey Decimal Dilemma" by Agatha Christie (1968) is a work of fiction enveloped in a cloud of mystery, emblematic of the enigmatic correlation we seek to unravel.

Drawing inspiration from somewhat unexpected sources, a parallel can be drawn to the board game "Agricola: All Creatures Big and Small," where players must balance air quality and occupational achievements in a pastoral setting, though with markedly less focus on library technicians.

The implications of these disparate insights and associations are not readily apparent, but they offer a glimpse into the multifaceted nature of our interdisciplinary exploration. As we maneuver through this labyrinth of research, we remain ever cognizant of the myriad influences that may shape our understanding of the unexpected correlations between air pollution and the employment of library technicians in Iowa.

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 Iowa City and The number of library technicians in Iowa. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Bureau of Larbor Statistics . You used data from 2003 to 2022

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

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
The Air We Breathe: A Study on the Correlation Between Air Pollution in Iowa City and the Employment of Library Technicians in Iowa

[[ABSTRACT]]
This research paper presents a comprehensive analysis of the relationship between air pollution in Iowa City and the employment of library technicians in the state of Iowa. The study utilized data from the Environmental Protection Agency and the Bureau of Labor Statistics to investigate this surprising connection. The findings revealed a remarkably strong correlation between levels of air pollution in Iowa City and the number of library technicians employed in Iowa, with a correlation coefficient of 0.8459054, and a p-value of less than 0.01 for the years 2003 to 2022. The paper discusses the plausible implications of this connection, and proposes potential avenues for further exploration and research in this uncharted territory at the intersection of environmental science and labor economics.

[[INTRODUCTION]]
Air pollution is a pervasive and persistent environmental issue that affects communities worldwide. The adverse health effects of air pollution have long been a subject of concern for public health officials, medical researchers, and asthmatic individuals everywhere. However, the connection between air pollution and the employment of library technicians, an ostensibly unrelated field, has remained an enigma, waiting to be unraveled like a particularly stubborn knot in a shoelace.
The present study delves into this curious correlation, seeking to shed light on the obscure relationship between the quality of the air we breathe and the number of individuals adept at organizing books by the Dewey Decimal System. By examining data from the Environmental Protection Agency alongside employment statistics from the Bureau of Labor Statistics, we aim to offer empirical evidence of this unexpected connection, or as some may dub it, the "dusty trail" linking air pollution and library technical employment.
In shedding light on this unlikely association, we endeavor to expand the horizons of environmental science and labor economics, proving that even the most seemingly unrelated elements of our world may be intertwined in ways we have yet to comprehend, much like a complex literary plot waiting to be deciphered. This endeavor is not merely a quest for knowledge but also an adventure into the uncharted intersections of seemingly disparate fields—a journey akin to navigating the convoluted stacks of a library in pursuit of a hidden gem of information.
As we embark on this investigation, we invoke the spirit of curiosity and inquiry, much like a diligent librarian seeking to unveil the mysteries of an ancient tome hidden among the shelves. We invite the reader to join us in this exploration of the unexpected correlations that permeate the ever-complex tapestry of modern existence.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this study is akin to embarking on a quest for a rare literary artifact within the labyrinthine halls of a grand library, though thankfully with less likelihood of encountering a minotaur. The research team, composed of intrepid investigators eager to unravel the enigmatic correlation between air pollution in Iowa City and the employment of library technicians in Iowa, designed a multifaceted approach to data collection and analysis.

To begin, air pollution data for Iowa City was obtained from the Environmental Protection Agency (EPA). This involved navigating through the digital expanse of the EPA's databases, not unlike sailing through the turbulent seas of cyberspace in search of hidden treasures, though in this case, the treasures were numerical values representing various air pollutants such as particulate matter (PM2.5 and PM10), nitrogen dioxide, sulfur dioxide, carbon monoxide, and ozone. These data, spanning the years 2003 to 2022, were meticulously gathered, organized, and subjected to rigorous quality control procedures, akin to the careful restoration of ancient manuscripts in the dusty archives of a scholarly institution.

Simultaneously, employment figures for library technicians in the state of Iowa were obtained from the Bureau of Labor Statistics (BLS). As with the air pollution data, this process involved sifting through the extensive repositories of the BLS, not unlike an archeological expedition uncovering the buried remnants of bygone civilizations, albeit with a greater prevalence of spreadsheets and statistical tables. The employment data, covering the same time period as the air pollution data, were collated, cross-referenced, and verified to ensure accuracy and reliability, resembling the meticulous cataloging of literary works in a grand library for future scholars to peruse.

Once the data were amassed, a statistical analysis was conducted to explore the potential relationship between air pollution in Iowa City and the employment of library technicians in the state of Iowa. This involved employing correlation coefficients, regression models, and hypothesis testing, not unlike deciphering the intricate patterns of a cryptic text to unveil its underlying meaning, though with a greater reliance on mathematical formulas and less on ancient runes.

Furthermore, robustness checks and sensitivity analyses were performed to assess the stability of the findings and mitigate potential confounding factors, akin to fortifying the foundations of a grand library to withstand the test of time and changing scholarly paradigms.

In summary, the methodology utilized in this study encompassed a thorough and systematic investigation, akin to a scholarly expedition delving into the depths of knowledge to unearth hidden connections and unravel the mysteries that permeate our intricate world.

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 Iowa City and The number of library technicians in Iowa. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 500 tokens.

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

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

[[TITLE]]
The Air We Breathe: A Study on the Correlation Between Air Pollution in Iowa City and the Employment of Library Technicians in Iowa

[[ABSTRACT]]
This research paper presents a comprehensive analysis of the relationship between air pollution in Iowa City and the employment of library technicians in the state of Iowa. The study utilized data from the Environmental Protection Agency and the Bureau of Labor Statistics to investigate this surprising connection. The findings revealed a remarkably strong correlation between levels of air pollution in Iowa City and the number of library technicians employed in Iowa, with a correlation coefficient of 0.8459054, and a p-value of less than 0.01 for the years 2003 to 2022. The paper discusses the plausible implications of this connection, and proposes potential avenues for further exploration and research in this uncharted territory at the intersection of environmental science and labor economics.

[[LITERATURE REVIEW]]
A number of scholarly works have sought to investigate the relationship between environmental factors and labor market dynamics. Smith et al. (2015) found a strong association between air quality and workforce productivity in their seminal study, "Air Quality and Labor: An Empirical Analysis." Similarly, Doe's (2018) investigation, "The Impact of Pollution on Employment Patterns," revealed compelling evidence of the influence of pollution levels on occupational trends. Jones' research on "Environmental Quality and Labor Market Outcomes" further contributes to the body of literature exploring the intersection of environmental factors and employment.
Turning to more specialized sources, "The Economics of Dust and Dander" by Goldstein and Silverman (2016) delves into the economic impact of air pollutants and allergens, albeit in a context more closely related to household cleaning services. In a tangentially related vein, "The Politics of Fresh Air" by Green (2019) provides a nuanced exploration of the societal implications of air quality regulations, though its focus lies primarily in the realm of policy analysis.
Brushing on works with more anecdotal appeal, the fictional account "The Dusty Chronicles" by Wordsworth (1923) explores the lives of librarians in a whimsical tale that may offer insights into the human dimensions of library employment. "The Murky Mysteries of The Dewey Decimal Dilemma" by Agatha Christie (1968) is a work of fiction enveloped in a cloud of mystery, emblematic of the enigmatic correlation we seek to unravel.
Drawing inspiration from somewhat unexpected sources, a parallel can be drawn to the board game "Agricola: All Creatures Big and Small," where players must balance air quality and occupational achievements in a pastoral setting, though with markedly less focus on library technicians.
The implications of these disparate insights and associations are not readily apparent, but they offer a glimpse into the multifaceted nature of our interdisciplinary exploration. As we maneuver through this labyrinth of research, we remain ever cognizant of the myriad influences that may shape our understanding of the unexpected correlations between air pollution and the employment of library technicians in Iowa.

[[RESULTS]]
The analysis of the data revealed a strong positive correlation between air pollution in Iowa City and the employment of library technicians in the state of Iowa for the years 2003 to 2022. The correlation coefficient was calculated to be 0.8459054, indicating a robust relationship between these seemingly disparate variables. Furthermore, the coefficient of determination (r-squared) was found to be 0.7155559, suggesting that approximately 71.56% of the variability in the employment of library technicians in Iowa can be explained by the levels of air pollution in Iowa City. The p-value of less than 0.01 indicates that the correlation is statistically significant, bolstering the validity of this unexpected connection.
As demonstrated in the scatterplot (Fig. 1), the data points cluster around a clear upward trend, illustrating the substantial association between air pollution levels in Iowa City and the number of employed library technicians in Iowa. This figurative depiction visually underscores the striking co-variation between these factors, serving as a compelling visual representation of the analysis's findings.
The strength of the correlation suggests that fluctuations in air pollution levels may indeed influence the demand for library technicians in Iowa, providing an intriguing avenue for further inquiry and exploration. The substantive nature of this correlation elicits consideration of the potential mechanisms underlying this unexpected relationship, prompting contemplation of the impact of environmental factors on labor market dynamics.
This unexpected linkage between air pollution and library technician employment highlights the intricate interplay of environmental quality and labor market trends, emphasizing the nuanced nature of socioeconomic phenomena. The findings of this investigation beckon for additional scrutiny and contemplation, compelling researchers to delve deeper into the unanticipated connections that permeate the complex web of societal dynamics, much like an intellectual treasure hunt through the labyrinthine aisles of a scholarly library.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The robust positive correlation between air pollution in Iowa City and the employment of library technicians in the state of Iowa, as revealed by the analysis, aligns with previous research that has explored the influence of environmental factors on labor market dynamics. Smith et al. (2015), Doe (2018), and Jones' work on "Environmental Quality and Labor Market Outcomes" have contributed to the body of literature elucidating the impact of air quality on employment patterns. While the findings of our investigation may initially seem surprising, they are consistent with the broader theme of the literature, which suggests that environmental quality exerts a discernible influence on occupational trends.

Building on the unexpected associations drawn in the literature review, the allegorical explorations of library employment in "The Dusty Chronicles" and the enigmatic mysteries of "The Dewey Decimal Dilemma" by Agatha Christie take on renewed significance in light of the actual correlation discovered in this study. These whimsical tales may not be as far-fetched as they initially appeared, as they appear to offer subtle hints at the underlying realities of library employment and its curious linkage to air pollution.

The scatterplot depicting the relationship between air pollution levels in Iowa City and the number of employed library technicians not only serves as a visual confirmation of the strength of the correlation but also brings to mind the metaphorical "treasure hunt" through the aisles of a scholarly library. The figurative analogy amusingly captures the intellectual journey embarked upon in this investigation and serves as a lighthearted reminder of the unexpected connections that can be uncovered through rigorous inquiry.

The substantial correlation coefficient, high coefficient of determination, and statistically significant p-value underscore the solidity of the uncovered relationship, prompting considerations of the potential mechanisms at play. The unexpected nature of this connection compels contemplation of the multifaceted influences shaping labor market dynamics, akin to navigating the diverse elements in the board game "Agricola: All Creatures Big and Small," albeit with a distinct emphasis on library technicians.

Moreover, the unexpected linkage between air pollution and library technician employment prompts reflections on the intricate interplay of environmental quality and labor market trends, much like the tangled web of societal dynamics. It beckons for further exploration, akin to the adventuresome spirit of the aforementioned parallel drawn to the "intellectual treasure hunt," inviting researchers to delve deeper into the labyrinthine interconnections that define our socio-economic landscape.

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 Iowa City and The number of library technicians in Iowa. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
The Air We Breathe: A Study on the Correlation Between Air Pollution in Iowa City and the Employment of Library Technicians in Iowa

[[ABSTRACT]]
This research paper presents a comprehensive analysis of the relationship between air pollution in Iowa City and the employment of library technicians in the state of Iowa. The study utilized data from the Environmental Protection Agency and the Bureau of Labor Statistics to investigate this surprising connection. The findings revealed a remarkably strong correlation between levels of air pollution in Iowa City and the number of library technicians employed in Iowa, with a correlation coefficient of 0.8459054, and a p-value of less than 0.01 for the years 2003 to 2022. The paper discusses the plausible implications of this connection, and proposes potential avenues for further exploration and research in this uncharted territory at the intersection of environmental science and labor economics.

[[INTRDUCTION]]
Air pollution is a pervasive and persistent environmental issue that affects communities worldwide. The adverse health effects of air pollution have long been a subject of concern for public health officials, medical researchers, and asthmatic individuals everywhere. However, the connection between air pollution and the employment of library technicians, an ostensibly unrelated field, has remained an enigma, waiting to be unraveled like a particularly stubborn knot in a shoelace.
The present study delves into this curious correlation, seeking to shed light on the obscure relationship between the quality of the air we breathe and the number of individuals adept at organizing books by the Dewey Decimal System. By examining data from the Environmental Protection Agency alongside employment statistics from the Bureau of Labor Statistics, we aim to offer empirical evidence of this unexpected connection, or as some may dub it, the "dusty trail" linking air pollution and library technical employment.
In shedding light on this unlikely association, we endeavor to expand the horizons of environmental science and labor economics, proving that even the most seemingly unrelated elements of our world may be intertwined in ways we have yet to comprehend, much like a complex literary plot waiting to be deciphered. This endeavor is not merely a quest for knowledge but also an adventure into the uncharted intersections of seemingly disparate fields—a journey akin to navigating the convoluted stacks of a library in pursuit of a hidden gem of information.
As we embark on this investigation, we invoke the spirit of curiosity and inquiry, much like a diligent librarian seeking to unveil the mysteries of an ancient tome hidden among the shelves. We invite the reader to join us in this exploration of the unexpected correlations that permeate the ever-complex tapestry of modern existence.

[[RESULTS]]
The analysis of the data revealed a strong positive correlation between air pollution in Iowa City and the employment of library technicians in the state of Iowa for the years 2003 to 2022. The correlation coefficient was calculated to be 0.8459054, indicating a robust relationship between these seemingly disparate variables. Furthermore, the coefficient of determination (r-squared) was found to be 0.7155559, suggesting that approximately 71.56% of the variability in the employment of library technicians in Iowa can be explained by the levels of air pollution in Iowa City. The p-value of less than 0.01 indicates that the correlation is statistically significant, bolstering the validity of this unexpected connection.
As demonstrated in the scatterplot (Fig. 1), the data points cluster around a clear upward trend, illustrating the substantial association between air pollution levels in Iowa City and the number of employed library technicians in Iowa. This figurative depiction visually underscores the striking co-variation between these factors, serving as a compelling visual representation of the analysis's findings.
The strength of the correlation suggests that fluctuations in air pollution levels may indeed influence the demand for library technicians in Iowa, providing an intriguing avenue for further inquiry and exploration. The substantive nature of this correlation elicits consideration of the potential mechanisms underlying this unexpected relationship, prompting contemplation of the impact of environmental factors on labor market dynamics.
This unexpected linkage between air pollution and library technician employment highlights the intricate interplay of environmental quality and labor market trends, emphasizing the nuanced nature of socioeconomic phenomena. The findings of this investigation beckon for additional scrutiny and contemplation, compelling researchers to delve deeper into the unanticipated connections that permeate the complex web of societal dynamics, much like an intellectual treasure hunt through the labyrinthine aisles of a scholarly library.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, the findings of this study elucidate a compelling association between air pollution in Iowa City and the employment of library technicians in the state of Iowa. The remarkably strong correlation coefficient of 0.8459054 and the statistically significant p-value of less than 0.01 underscore the robustness of this unexpected relationship. The results of this investigation not only unveil the hitherto overlooked connection between environmental quality and the demand for library technicians, but also prompt consideration of the potential mechanisms underlying this peculiar correlation.

The implications of these findings extend beyond mere statistical correlations and delve into the intricate dynamics of labor market trends and environmental influences. It behooves us to ponder the implications of such unanticipated relationships, as they provide a glimpse into the interconnectedness of seemingly disparate facets of our societal fabric. While the precise mechanisms driving this correlation remain enigmatic, the results of this study open the door to further inquiry and exploration in this uncharted territory at the intersection of environmental science and labor economics.

The unexpected convergence of air pollution and library technician employment serves as a poignant reminder that the tapestry of societal phenomena is woven with unexpected threads, much like stumbling upon a genre-defying book in the labyrinthine shelves of a library. The implications of this study extend to not only the academic realm but also to policymakers and practitioners, urging a reevaluation of the factors influencing workforce dynamics.

While this study sheds light on an intriguing and unexpected correlation, it also prompts contemplation of the broader complexities that underlie labor market dynamics and the multifaceted impacts of environmental influences. As we gaze upon the figurative scatterplot of societal interconnections, let us not only marvel at the remarkable correlation uncovered but also be inspired to seek out the unanticipated connections that permeate our intricate societal tapestry.

In light of these findings, it is evident that no further research is needed in this area.

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited The Air We Breathe: A Study on the Correlation Between Air Pollution in Iowa City and the Employment of Library Technicians in Iowa. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Environmental Studies and Occupational Trends

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited The Air We Breathe: A Study on the Correlation Between Air Pollution in Iowa City and the Employment of Library Technicians in Iowa. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Environmental Quality and Occupational Trends (CEQOT)

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled The Air We Breathe: A Study on the Correlation Between Air Pollution in Iowa City and the Employment of Library Technicians in Iowa with an abstract of This research paper presents a comprehensive analysis of the relationship between air pollution in Iowa City and the employment of library technicians in the state of Iowa. The study utilized data from the Environmental Protection Agency and the Bureau of Labor Statistics to investigate this surprising connection. The findings revealed a remarkably strong correlation between levels of air pollution in Iowa City and the number of library technicians employed in Iowa, with a correlation coefficient of 0.8459054, and a p-value of less than 0.01 for the years 2003 to 2022. The paper discusses the plausible implications of this connection, and proposes potential avenues for further exploration and research in this uncharted territory at the intersection of environmental science and labor economics.

ChatGPT:

Air pollution Iowa City, library technicians employment Iowa, correlation air pollution library technicians, EPA air pollution data, Bureau of Labor Statistics employment data, environmental science labor economics intersection, air pollution impact employment, Iowa air quality employment correlation, library technician job market Iowa, air pollution effects labor market

*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 Iowa City
Detailed data title: Percentage of days with moderate or worse air quality in Iowa City, IA
Source: Environmental Protection Agency
See what else correlates with Air pollution in Iowa City

The number of library technicians in Iowa
Detailed data title: BLS estimate of library technicians in Iowa
Source: Bureau of Larbor Statistics
See what else correlates with The number of library technicians in Iowa

Correlation r = 0.8459054 (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 = 0.7155559 (Coefficient of determination)
This means 71.6% of the change in the one variable (i.e., The number of library technicians in Iowa) is predictable based on the change in the other (i.e., Air pollution in Iowa City) over the 20 years from 2003 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.6E-6.^Show 0.0000026220894513942490000000
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 0.85 in 0.00026% of random cases. Said differently, if you correlated 381,375 random variables^Note You don't actually need 381 thousand variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 19 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 19 because we have two variables measured over a period of 20 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.

[ 0.64, 0.94 ] 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.

	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Air pollution in Iowa City (Bad air quality days)	38.0165	30.3279	44.2623	31.6667	39.759	32.7586	36.7978	50.6849	48.0769	44.2623	33.4247	28.022	23.2877	16.6667	19.2308	20.7182	23.6264	25.4144	29.9451	16.9863
The number of library technicians in Iowa (Library Technicians)	1460	1680	2050	1700	1820	1820	2100	2050	2110	1910	1630	1320	1210	1300	1340	1200	1300	1230	1050	1200

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([38.0165,30.3279,44.2623,31.6667,39.759,32.7586,36.7978,50.6849,48.0769,44.2623,33.4247,28.022,23.2877,16.6667,19.2308,20.7182,23.6264,25.4144,29.9451,16.9863,])
array_2 = np.array([1460,1680,2050,1700,1820,1820,2100,2050,2110,1910,1630,1320,1210,1300,1340,1200,1300,1230,1050,1200,])
array_1_name = "Air pollution in Iowa City"
array_2_name = "The number of library technicians in Iowa"

# 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 Iowa City
Line chart for only The number of library technicians in Iowa
AI-generated correlation image
The spurious research paper: The Air We Breathe: A Study on the Correlation Between Air Pollution in Iowa City and the Employment of Library Technicians in Iowa

View another random correlation

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Correlation ID: 2184 · Black Variable ID: 20740 · Red Variable ID: 7930


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