Spurious correlation #5,205 · 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 Salisbury, Maryland and the second variable is The number of statistical assistants in Maryland. The chart goes from 2003 to 2018, 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 Salisbury, Maryland and the second variable is The number of statistical assistants in Maryland. The chart goes from 2003 to 2018, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the air cleared up in Salisbury, the statistical assistants realized they were better suited for a different atmosphere. They all packed their bags and headed for greener pastures, leaving behind a data gap that no Excel sheet could fill. It seems like when it comes to their career, these assistants are always seeking the perfect 'stat'mosphere! Who knew that cleaning the air would lead to a case of missing data and missing assistants in Maryland - seems like a classic case of a statistical correlation!

^{Show image generation prompt} Model: dalle-3
Prompt: Create an image of a serene cityscape in Salisbury, Maryland, after the smog has cleared. Show the clear, blue sky and the city's skyline becoming visible in the distance. In the foreground, depict a group of statistical assistants packing their bags and bidding farewell to their office space. Convey a sense of a fresh start and change as they embark on a new journey. You may also consider adding symbolic elements such as clean air and a hopeful atmosphere to represent the positive change taking place. 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 Salisbury, Maryland and The number of statistical assistants in Maryland. 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: An image that looks like it is a scene from a Pixar movie A Discovery magazine cover image Glitch art An image that looks like it is a scene from 'Star Wars' Pop art
ChatGPT: A Discovery magazine cover image

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Air pollution in Salisbury, Maryland and The number of statistical assistants in Maryland, in the following situation: As the air cleared up in Salisbury, the statistical assistants realized they were better suited for a different atmosphere. They all packed their bags and headed for greener pastures, leaving behind a data gap that no Excel sheet could fill. It seems like when it comes to their career, these assistants are always seeking the perfect 'stat'mosphere! Who knew that cleaning the air would lead to a case of missing data and missing assistants in Maryland - seems like a classic case of a statistical correlation!. Keep the description simple.
ChatGPT: In Salisbury, Maryland, the smog gradually lifted as the air cleared up, revealing a beautiful, blue sky. As the cityscape became more visible, a group of statistical assistants were seen packing their bags, looking for a new opportunity. With a mix of excitement and reluctance, they bid farewell to their workspaces, leaving behind a noticeable gap in the bustling office. The once congested air made way for a fresh breeze, marking the beginning of a new chapter for both air quality and the statistical assistants in Maryland.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A Discovery magazine cover image ] and depicting this scene: In Salisbury, Maryland, the smog gradually lifted as the air cleared up, revealing a beautiful, blue sky. As the cityscape became more visible, a group of statistical assistants were seen packing their bags, looking for a new opportunity. With a mix of excitement and reluctance, they bid farewell to their workspaces, leaving behind a noticeable gap in the bustling office. The once congested air made way for a fresh breeze, marking the beginning of a new chapter for both air quality and the statistical assistants in Maryland. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create an image of a serene cityscape in Salisbury, Maryland, after the smog has cleared. Show the clear, blue sky and the city's skyline becoming visible in the distance. In the foreground, depict a group of statistical assistants packing their bags and bidding farewell to their office space. Convey a sense of a fresh start and change as they embark on a new journey. You may also consider adding symbolic elements such as clean air and a hopeful atmosphere to represent the positive change taking place.

*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 Salisbury, Maryland caused The number of statistical assistants in Maryland to decrease. Include one good pun.

AI academic paper

(Because p < 0.01)

Air We There Yet? The Atmos-fear of Air Pollution in Salisbury, Maryland: A Breath of Fresh Air for Statistical Assistants in Maryland

The Journal of Atmospheric Anxieties

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 Salisbury, Maryland and The number of statistical assistants in Maryland. 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]]
"Air We There Yet? The Atmos-fear of Air Pollution in Salisbury, Maryland: A Breath of Fresh Air for Statistical Assistants in Maryland"

[[ABSTRACT]]
The present study delves into the seemingly coincidental, yet intriguing, relationship between air pollution levels in Salisbury, Maryland, and the number of statistical assistants employed in the state of Maryland. Data sourced from the Environmental Protection Agency and the Bureau of Labor Statistics from 2003 to 2018 were meticulously analyzed to elucidate the correlation between these two seemingly disparate variables. Remarkably, our analysis revealed a robust correlation coefficient of 0.8961422 and a p-value of less than 0.01, indicating a statistically significant association. It appears that the air may be whispering secrets about the job market.

Indeed, the connection between air pollution and the employment of statistical assistants appears to be rather thrilling - a breath of fresh air in the world of statistical analysis, if you will. This unexpected association adds a new layer of intrigue to the field of environmental and labor economics, prompting further investigation into the underlying mechanisms at play. One might say this revelation has left us gasping for more.

Nevertheless, it is essential to approach these findings with caution and persistence, like navigating through a fog of uncertainties. While the correlation between air pollution levels and the number of statistical assistants in Maryland is indeed remarkable, it is vital to explore the causal pathways and potential confounding variables that may underlie this unexpected relationship. Perhaps this correlation is merely a smokescreen for other influential factors at play - a conundrum that continues to hover in the air like an unresolved punchline.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The relationship between environmental factors and labor market dynamics has long been a subject of scholarly interest. Environmental quality has often been linked to various aspects of human life, including health, well-being, and economic activities. However, the connection between air pollution and the employment of statistical assistants in Maryland may at first glance seem like a stretch - but rest assured, the numbers don't lie. It appears that there's more than just hot air to this relationship.
As the legendary statistical humorist once said, "If you laid all of the world's economists end to end, they still wouldn't reach a conclusion." Likewise, the connection between air pollution in Salisbury, Maryland, and the number of statistical assistants employed in the state may seem like a joke, but the statistical evidence suggests otherwise. This unexpected relationship has left many scratching their heads, much like a stubborn case of dandruff.
The city of Salisbury, Maryland, has been grappling with air pollution challenges, courtesy of various sources including industry emissions, vehicular exhaust, and other human activities. Ozone, fine particulate matter, sulfur dioxide, and nitrogen dioxide have been doing the rounds, uninvited guests in the local atmosphere. Meanwhile, statistical assistants in the state of Maryland have been diligently number-crunching, maneuvering through data sets with the grace of a ballet dancer – or perhaps more aptly, with the precision of a seasoned juggler.
It is a well-accepted fact that air pollution can have detrimental effects on human health and well-being. From respiratory issues to cardiovascular complications, the implications of poor air quality are nothing to sneeze at, pun fully intended. However, could it be that the influence of air pollution extends beyond human health and seeps into the realm of labor market dynamics? The data certainly suggests that the air pollution in Salisbury, Maryland, may have been sending some curious signals - a symphony of statistics carried on the wind, if you will. After all, isn't it a grand theory that air pollution could be an air-ressistible factor in the employment of statistical assistants?

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The scholarly inquiry into the relationship between air pollution and labor market phenomena is not novel. In "Airborne Economics: Understanding the Link Between Environmental Quality and Labor Market Dynamics" by Smith et al., the authors find a robust association between air pollution levels and various aspects of employment dynamics. Similarly, in "The Labor of Fresh Air: A Study of Air Quality and Labor Market Trends" by Doe and Jones, the authors highlight the potential influence of air pollution on job creation and retention. These studies provide a solid foundation for understanding the potential links between environmental factors and labor market dynamics, albeit without specific emphasis on the employment of statistical assistants.

Transitioning to a more whimsical lens, popular non-fiction works such as "The Air We Breathe" and "Breathless: A Journey Through Air Pollution" offer intriguing insights into the impact of air pollution on human health and environmental quality. These publications serve as valuable resources for contextualizing the broader implications of air pollution, shedding light on the multifaceted nature of this atmospheric conundrum. As for relevant fictional literature, the works of "Airbender: The Last Statistical Assistant" and "The Statistical Guide to the Galaxy" humorously engage with statistical analysis in surreal settings, providing an unexpected yet delightful departure from conventional academic literature.

Expanding the scope of inquiry, children's animated series such as "Captain Planet and the Planeteers" and the educational program "The Magic School Bus" serve as unconventional yet informative sources for understanding environmental issues, including air pollution. While these sources may not align with traditional academic literature, they play a valuable role in engaging younger audiences and fostering an early appreciation for environmental stewardship. In the spirit of scholarly curiosity, it is essential to draw inspiration from diverse sources, even if it means venturing into the realms of animated entertainment.

Now, returning to the peculiar correlation at hand, it is evident that the unexpected relationship between air pollution in Salisbury, Maryland, and the employment of statistical assistants in the state holds a narrative charm akin to a whimsical jest in a statistical folklore. This intriguing association beckons further exploration, blending the enigmatic allure of atmospheric phenomena with the pragmatic realities of labor market dynamics. As the investigation unfolds, it becomes apparent that the air may be whispering secrets about the job market, or perhaps it is simply indulging in a playfully cryptic tale of statistical serendipity.

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 Salisbury, Maryland and The number of statistical assistants in Maryland. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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 2018

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]]
"Air We There Yet? The Atmos-fear of Air Pollution in Salisbury, Maryland: A Breath of Fresh Air for Statistical Assistants in Maryland"

[[ABSTRACT]]
The present study delves into the seemingly coincidental, yet intriguing, relationship between air pollution levels in Salisbury, Maryland, and the number of statistical assistants employed in the state of Maryland. Data sourced from the Environmental Protection Agency and the Bureau of Labor Statistics from 2003 to 2018 were meticulously analyzed to elucidate the correlation between these two seemingly disparate variables. Remarkably, our analysis revealed a robust correlation coefficient of 0.8961422 and a p-value of less than 0.01, indicating a statistically significant association. It appears that the air may be whispering secrets about the job market.

Indeed, the connection between air pollution and the employment of statistical assistants appears to be rather thrilling - a breath of fresh air in the world of statistical analysis, if you will. This unexpected association adds a new layer of intrigue to the field of environmental and labor economics, prompting further investigation into the underlying mechanisms at play. One might say this revelation has left us gasping for more.

Nevertheless, it is essential to approach these findings with caution and persistence, like navigating through a fog of uncertainties. While the correlation between air pollution levels and the number of statistical assistants in Maryland is indeed remarkable, it is vital to explore the causal pathways and potential confounding variables that may underlie this unexpected relationship. Perhaps this correlation is merely a smokescreen for other influential factors at play - a conundrum that continues to hover in the air like an unresolved punchline.

[[INTRODUCTION]]
The relationship between environmental factors and labor market dynamics has long been a subject of scholarly interest. Environmental quality has often been linked to various aspects of human life, including health, well-being, and economic activities. However, the connection between air pollution and the employment of statistical assistants in Maryland may at first glance seem like a stretch - but rest assured, the numbers don't lie. It appears that there's more than just hot air to this relationship.
As the legendary statistical humorist once said, "If you laid all of the world's economists end to end, they still wouldn't reach a conclusion." Likewise, the connection between air pollution in Salisbury, Maryland, and the number of statistical assistants employed in the state may seem like a joke, but the statistical evidence suggests otherwise. This unexpected relationship has left many scratching their heads, much like a stubborn case of dandruff.
The city of Salisbury, Maryland, has been grappling with air pollution challenges, courtesy of various sources including industry emissions, vehicular exhaust, and other human activities. Ozone, fine particulate matter, sulfur dioxide, and nitrogen dioxide have been doing the rounds, uninvited guests in the local atmosphere. Meanwhile, statistical assistants in the state of Maryland have been diligently number-crunching, maneuvering through data sets with the grace of a ballet dancer – or perhaps more aptly, with the precision of a seasoned juggler.
It is a well-accepted fact that air pollution can have detrimental effects on human health and well-being. From respiratory issues to cardiovascular complications, the implications of poor air quality are nothing to sneeze at, pun fully intended. However, could it be that the influence of air pollution extends beyond human health and seeps into the realm of labor market dynamics? The data certainly suggests that the air pollution in Salisbury, Maryland, may have been sending some curious signals - a symphony of statistics carried on the wind, if you will. After all, isn't it a grand theory that air pollution could be an air-ressistible factor in the employment of statistical assistants?

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The research endeavor embarked upon a multifaceted methodological journey to scrutinize the connection between air pollution in Salisbury, Maryland, and the employment of statistical assistants in the state of Maryland. Data spanning the years 2003 to 2018, sourced primarily from the Environmental Protection Agency and the Bureau of Labor Statistics, served as the bedrock of this investigation. As we waded through pools of statistics and atmospheric intricacies, we were reminded of the wise words of a seasoned researcher: "Statistics are like a bikini - what they reveal is suggestive, but what they conceal is vital."

The initial step of the methodology involved the collection of comprehensive air quality data for Salisbury, Maryland, encompassing various pollutants such as ozone, fine particulate matter, sulfur dioxide, and nitrogen dioxide. These air pollutants were scrutinized with the meticulousness of a detective examining evidence at a crime scene, searching for clues in the midst of environmental complexities. As we delved into the atmospheric intricacies, we couldn't help but ponder, "Why did the statistician put a fence around his backyard? Because he wanted to come up with a 'well-defined' area."

Simultaneously, data on the employment of statistical assistants in Maryland were extracted from the Bureau of Labor Statistics, allowing for an in-depth analysis of the labor market dynamics within the state. The statistical assistants' employment figures were assessed with the rapt attention of an audience watching a suspenseful thriller, eagerly anticipating the revelation of unexpected plot twists. Just like a statistical outlier, the unexpected relationship between air pollution in Salisbury and the employment of statistical assistants seemed to be vying for attention, beckoning us to uncover its hidden narrative.

Having collected the pertinent data, the research team employed a robust statistical methodology, including correlation analysis and regression modeling, to unravel the intricate web of interactions between air pollution levels and the employment of statistical assistants. The correlation coefficient served as our guiding compass, navigating through the statistical terrain with the precision of a seasoned cartographer, unveiling the strength and direction of the relationship between these seemingly disparate variables. As we uncovered the statistically significant association, we couldn't help but quip, "Why did the statistician break up with the chemist? There was no 'chemistry' between them!"

Furthermore, regression models were harnessed to disentangle the potential causal pathways and shed light on the underlying mechanisms linking air pollution to the employment of statistical assistants. The models were intricately constructed, akin to a complex puzzle, striving to piece together the nuanced interplay of environmental factors and labor market dynamics. In the midst of these intricate analyses, we couldn't resist a chuckle and pondered, "Why do statisticians call Halloween 'Statistical Inference Day'? Because it's the one time they can make a BOO-tstrap estimate!"

In sum, the methodology of this research endeavor not only involved rigorous data collection and statistical analyses but also invoked a spirit of curiosity and humor, recognizing the unexpected twists and turns that often accompany scientific exploration. And though the journey was filled with statistical and atmospheric complexities, it certainly presented an opportunity to infuse scholarly pursuits with a breath of fresh statistical air.

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 Salisbury, Maryland and The number of statistical assistants in Maryland. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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]]
"Air We There Yet? The Atmos-fear of Air Pollution in Salisbury, Maryland: A Breath of Fresh Air for Statistical Assistants in Maryland"

[[ABSTRACT]]
The present study delves into the seemingly coincidental, yet intriguing, relationship between air pollution levels in Salisbury, Maryland, and the number of statistical assistants employed in the state of Maryland. Data sourced from the Environmental Protection Agency and the Bureau of Labor Statistics from 2003 to 2018 were meticulously analyzed to elucidate the correlation between these two seemingly disparate variables. Remarkably, our analysis revealed a robust correlation coefficient of 0.8961422 and a p-value of less than 0.01, indicating a statistically significant association. It appears that the air may be whispering secrets about the job market.

Indeed, the connection between air pollution and the employment of statistical assistants appears to be rather thrilling - a breath of fresh air in the world of statistical analysis, if you will. This unexpected association adds a new layer of intrigue to the field of environmental and labor economics, prompting further investigation into the underlying mechanisms at play. One might say this revelation has left us gasping for more.

Nevertheless, it is essential to approach these findings with caution and persistence, like navigating through a fog of uncertainties. While the correlation between air pollution levels and the number of statistical assistants in Maryland is indeed remarkable, it is vital to explore the causal pathways and potential confounding variables that may underlie this unexpected relationship. Perhaps this correlation is merely a smokescreen for other influential factors at play - a conundrum that continues to hover in the air like an unresolved punchline.

[[LITERATURE REVIEW]]
The scholarly inquiry into the relationship between air pollution and labor market phenomena is not novel. In "Airborne Economics: Understanding the Link Between Environmental Quality and Labor Market Dynamics" by Smith et al., the authors find a robust association between air pollution levels and various aspects of employment dynamics. Similarly, in "The Labor of Fresh Air: A Study of Air Quality and Labor Market Trends" by Doe and Jones, the authors highlight the potential influence of air pollution on job creation and retention. These studies provide a solid foundation for understanding the potential links between environmental factors and labor market dynamics, albeit without specific emphasis on the employment of statistical assistants.
Transitioning to a more whimsical lens, popular non-fiction works such as "The Air We Breathe" and "Breathless: A Journey Through Air Pollution" offer intriguing insights into the impact of air pollution on human health and environmental quality. These publications serve as valuable resources for contextualizing the broader implications of air pollution, shedding light on the multifaceted nature of this atmospheric conundrum. As for relevant fictional literature, the works of "Airbender: The Last Statistical Assistant" and "The Statistical Guide to the Galaxy" humorously engage with statistical analysis in surreal settings, providing an unexpected yet delightful departure from conventional academic literature.
Expanding the scope of inquiry, children's animated series such as "Captain Planet and the Planeteers" and the educational program "The Magic School Bus" serve as unconventional yet informative sources for understanding environmental issues, including air pollution. While these sources may not align with traditional academic literature, they play a valuable role in engaging younger audiences and fostering an early appreciation for environmental stewardship. In the spirit of scholarly curiosity, it is essential to draw inspiration from diverse sources, even if it means venturing into the realms of animated entertainment.
Now, returning to the peculiar correlation at hand, it is evident that the unexpected relationship between air pollution in Salisbury, Maryland, and the employment of statistical assistants in the state holds a narrative charm akin to a whimsical jest in a statistical folklore. This intriguing association beckons further exploration, blending the enigmatic allure of atmospheric phenomena with the pragmatic realities of labor market dynamics. As the investigation unfolds, it becomes apparent that the air may be whispering secrets about the job market, or perhaps it is simply indulging in a playfully cryptic tale of statistical serendipity.

[[RESULTS]]
The analysis of the relationship between air pollution levels in Salisbury, Maryland, and the employment of statistical assistants in the state of Maryland revealed a striking correlation coefficient of 0.8961422. This strong positive correlation indicates that as air pollution levels increase, so does the number of statistical assistants employed in Maryland. It's almost as if the statistical assistants have been following the scent of pollution like bloodhounds on a mission - or perhaps they just thrive in an environment where things are "up in the air."
Additionally, the r-squared value of 0.8030708 suggests that a substantial proportion of the variation in the employment of statistical assistants can be explained by the variation in air pollution levels. It's a bit like unraveling a complex mystery - the pieces of the puzzle are starting to fall into place, and the plot thickens as we delve deeper into the data.
The p-value of less than 0.01 further underscores the robustness of the association between air pollution and the employment of statistical assistants in Maryland. This statistical significance provides strong evidence to substantiate the claim that the two variables are indeed linked, like a pair of socks in the laundry - inseparable, yet always getting lost in the mix.
The scatterplot (Fig. 1) visually represents the compelling relationship between air pollution levels and the number of statistical assistants employed in Maryland. The data points form a clear upward trend, illustrating the positive correlation between these variables. It's almost as if the pollution levels are conducting a symphony, and the statistical assistants are dancing to the tune - a statistical waltz, if you will.
In conclusion, the empirical analysis conducted in this study has unveiled an unexpected and intriguing connection between air pollution in Salisbury, Maryland, and the employment of statistical assistants in the state of Maryland. This revelation emphasizes the need for further exploration into the mechanisms underpinning this curious relationship, as the air of mystery surrounding this association continues to linger.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of this study offer a tantalizing glimpse into the uncharted territory of environmental economics and labor market dynamics. Remarkably, the robust correlation coefficient and compelling statistical significance speak volumes about the interplay between air pollution in Salisbury, Maryland, and the employment of statistical assistants in the state of Maryland. It appears that the statistical assistants are not just crunching numbers, but also navigating through the atmospheric intricacies with unparalleled precision, much like a seasoned pilot skirting through turbulent weather.

Building on the existing literature, the present investigation echoes the sentiments of Smith et al. and Doe and Jones, cementing the notion that air pollution does exert a palpable influence on labor market phenomena. This unexpected connection between atmospheric conditions and statistical employment adds an element of whimsy to the empirical landscape, almost as if the statistical assistants were fated to become the air quality's unforeseen accomplice - a case of statistical fate, one might say. After all, who would have thought that air pollution levels could serve as a harbinger of job opportunities for statistical aficionados?

The scatterplot depicting the positive correlation between air pollution levels and the employment of statistical assistants is a stark visual testament to this phenomenon. It's as if the data points are engaged in a lively tango, perfectly synchronized in their dance of statistical significance. Indeed, one could almost envision the statistical assistants enthusiastically embracing the challenge presented by atmospheric complexities, much like intrepid explorers embarking on a novel statistical expedition. After all, who wouldn't want to be part of a statistical escapade where the stakes are sky-high?

The compelling nature of this correlation prompts further contemplation on the underlying mechanisms at play. What is it about heightened air pollution levels that seems to attract statistical assistants to the labor market? Is it the allure of unraveling the statistical mysteries shrouded in the haze, or perhaps a testament to their resilience in the face of environmental adversity? For now, the enigmatic link between air pollution and statistical employment looms like a playful riddle waiting to be deciphered, reminiscent of a statistical jigsaw puzzle with an atmospheric twist.

In unraveling this statistical enigma, it is crucial to acknowledge the potential implications of these findings. The unsuspected harmony between air pollution and statistical employment might warrant a reevaluation of the underlying factors shaping labor market dynamics in the presence of environmental challenges. This unforeseen correlation could birth a new era of interdisciplinary inquiry, where the realms of atmospheric science and statistical analysis converge in an intellectual duet - akin to a symphonic fusion of empirical rigor and environmental resonance.

In conclusion, the symbiotic liaison between air pollution in Salisbury, Maryland, and the employment of statistical assistants in the state of Maryland stands as a testament to the serendipitous interplay of seemingly unrelated phenomena. As the investigation progresses, the whimsical allure of this connection continues to beguile the empirical imagination, much like a statistical anecdote that defies conventional wisdom. A breath of fresh air for statistical employment, indeed.

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 Salisbury, Maryland and The number of statistical assistants in Maryland. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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]]
"Air We There Yet? The Atmos-fear of Air Pollution in Salisbury, Maryland: A Breath of Fresh Air for Statistical Assistants in Maryland"

[[ABSTRACT]]
The present study delves into the seemingly coincidental, yet intriguing, relationship between air pollution levels in Salisbury, Maryland, and the number of statistical assistants employed in the state of Maryland. Data sourced from the Environmental Protection Agency and the Bureau of Labor Statistics from 2003 to 2018 were meticulously analyzed to elucidate the correlation between these two seemingly disparate variables. Remarkably, our analysis revealed a robust correlation coefficient of 0.8961422 and a p-value of less than 0.01, indicating a statistically significant association. It appears that the air may be whispering secrets about the job market.

Indeed, the connection between air pollution and the employment of statistical assistants appears to be rather thrilling - a breath of fresh air in the world of statistical analysis, if you will. This unexpected association adds a new layer of intrigue to the field of environmental and labor economics, prompting further investigation into the underlying mechanisms at play. One might say this revelation has left us gasping for more.

Nevertheless, it is essential to approach these findings with caution and persistence, like navigating through a fog of uncertainties. While the correlation between air pollution levels and the number of statistical assistants in Maryland is indeed remarkable, it is vital to explore the causal pathways and potential confounding variables that may underlie this unexpected relationship. Perhaps this correlation is merely a smokescreen for other influential factors at play - a conundrum that continues to hover in the air like an unresolved punchline.

[[INTRDUCTION]]
The relationship between environmental factors and labor market dynamics has long been a subject of scholarly interest. Environmental quality has often been linked to various aspects of human life, including health, well-being, and economic activities. However, the connection between air pollution and the employment of statistical assistants in Maryland may at first glance seem like a stretch - but rest assured, the numbers don't lie. It appears that there's more than just hot air to this relationship.
As the legendary statistical humorist once said, "If you laid all of the world's economists end to end, they still wouldn't reach a conclusion." Likewise, the connection between air pollution in Salisbury, Maryland, and the number of statistical assistants employed in the state may seem like a joke, but the statistical evidence suggests otherwise. This unexpected relationship has left many scratching their heads, much like a stubborn case of dandruff.
The city of Salisbury, Maryland, has been grappling with air pollution challenges, courtesy of various sources including industry emissions, vehicular exhaust, and other human activities. Ozone, fine particulate matter, sulfur dioxide, and nitrogen dioxide have been doing the rounds, uninvited guests in the local atmosphere. Meanwhile, statistical assistants in the state of Maryland have been diligently number-crunching, maneuvering through data sets with the grace of a ballet dancer – or perhaps more aptly, with the precision of a seasoned juggler.
It is a well-accepted fact that air pollution can have detrimental effects on human health and well-being. From respiratory issues to cardiovascular complications, the implications of poor air quality are nothing to sneeze at, pun fully intended. However, could it be that the influence of air pollution extends beyond human health and seeps into the realm of labor market dynamics? The data certainly suggests that the air pollution in Salisbury, Maryland, may have been sending some curious signals - a symphony of statistics carried on the wind, if you will. After all, isn't it a grand theory that air pollution could be an air-ressistible factor in the employment of statistical assistants?

[[RESULTS]]
The analysis of the relationship between air pollution levels in Salisbury, Maryland, and the employment of statistical assistants in the state of Maryland revealed a striking correlation coefficient of 0.8961422. This strong positive correlation indicates that as air pollution levels increase, so does the number of statistical assistants employed in Maryland. It's almost as if the statistical assistants have been following the scent of pollution like bloodhounds on a mission - or perhaps they just thrive in an environment where things are "up in the air."
Additionally, the r-squared value of 0.8030708 suggests that a substantial proportion of the variation in the employment of statistical assistants can be explained by the variation in air pollution levels. It's a bit like unraveling a complex mystery - the pieces of the puzzle are starting to fall into place, and the plot thickens as we delve deeper into the data.
The p-value of less than 0.01 further underscores the robustness of the association between air pollution and the employment of statistical assistants in Maryland. This statistical significance provides strong evidence to substantiate the claim that the two variables are indeed linked, like a pair of socks in the laundry - inseparable, yet always getting lost in the mix.
The scatterplot (Fig. 1) visually represents the compelling relationship between air pollution levels and the number of statistical assistants employed in Maryland. The data points form a clear upward trend, illustrating the positive correlation between these variables. It's almost as if the pollution levels are conducting a symphony, and the statistical assistants are dancing to the tune - a statistical waltz, if you will.
In conclusion, the empirical analysis conducted in this study has unveiled an unexpected and intriguing connection between air pollution in Salisbury, Maryland, and the employment of statistical assistants in the state of Maryland. This revelation emphasizes the need for further exploration into the mechanisms underpinning this curious relationship, as the air of mystery surrounding this association continues to linger.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, the findings of this study have shed light on the peculiar relationship between air pollution levels in Salisbury, Maryland, and the employment of statistical assistants in the state of Maryland. The robust correlation coefficient and statistical significance of this association have left us breathless, much like a jog in a polluted city. It seems that the statistical assistants are not just number-crunchers, but also adept at sniffing out environmental cues, like a pack of data-driven bloodhounds.

The unexpected nature of this connection has certainly added a breath of fresh air to the realm of environmental and labor economics. It's as if the statistical assistants have embraced the challenge of working in an environment where the data is not the only thing that's "up in the air," if you catch my drift.

However, it is crucial to approach these findings with caution, as correlation does not imply causation, much like how counting the number of storks in a village does not explain the birth rate. Further research is essential to unravel the mechanisms and potential confounding variables that may underlie this surprising relationship. After all, we wouldn't want to be left with a statistical cliffhanger, would we?

As for now, it seems that no more research is needed in this area. After all, we wouldn't want to beat a dead horse, or in this case, a statistical assistant.

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 Air We There Yet? The Atmos-fear of Air Pollution in Salisbury, Maryland: A Breath of Fresh Air for Statistical Assistants in Maryland. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Atmospheric Anxieties

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 Air We There Yet? The Atmos-fear of Air Pollution in Salisbury, Maryland: A Breath of Fresh Air for Statistical Assistants in Maryland. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Air Quality Inquiry and Analysis

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 Air We There Yet? The Atmos-fear of Air Pollution in Salisbury, Maryland: A Breath of Fresh Air for Statistical Assistants in Maryland with an abstract of The present study delves into the seemingly coincidental, yet intriguing, relationship between air pollution levels in Salisbury, Maryland, and the number of statistical assistants employed in the state of Maryland. Data sourced from the Environmental Protection Agency and the Bureau of Labor Statistics from 2003 to 2018 were meticulously analyzed to elucidate the correlation between these two seemingly disparate variables. Remarkably, our analysis revealed a robust correlation coefficient of 0.8961422 and a p-value of less than 0.01, indicating a statistically significant association. It appears that the air may be whispering secrets about the job market.
Indeed, the connection between air pollution and the employment of statistical assistants appears to be rather thrilling - a breath of fresh air in the world of statistical analysis, if you will. This unexpected association adds a new layer of intrigue to the field of environmental and labor economics, prompting further investigation into the underlying mechanisms at play. One might say this revelation has left us gasping for more.
Nevertheless, it is essential to approach these findings with caution and persistence, like navigating through a fog of uncertainties. While the correlation between air pollution levels and the number of statistical assistants in Maryland is indeed remarkable, it is vital to explore the causal pathways and potential confounding variables that may underlie this unexpected relationship. Perhaps this correlation is merely a smokescreen for other influential factors at play - a conundrum that continues to hover in the air like an unresolved punchline.

ChatGPT:

Air pollution, statistical assistants, employment statistics, environmental impact, air pollution correlation, Maryland air quality, Bureau of Labor Statistics, Environmental Protection Agency, correlation analysis, air pollution levels, statistical analysis, labor market, environmental economics, air pollution statistics, atmospheric conditions, employment trends

*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 Salisbury, Maryland
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Salisbury, MD-DE
Source: Environmental Protection Agency
See what else correlates with Air pollution in Salisbury, Maryland

The number of statistical assistants in Maryland
Detailed data title: BLS estimate of statistical assistants in Maryland
Source: Bureau of Larbor Statistics
See what else correlates with The number of statistical assistants in Maryland

Correlation r = 0.8961422 (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 = 0.8030708 (Coefficient of determination)
This means 80.3% of the change in the one variable (i.e., The number of statistical assistants in Maryland) is predictable based on the change in the other (i.e., Air pollution in Salisbury, Maryland) over the 16 years from 2003 through 2018.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.6E-6.^Show 0.0000026452966186940300000000
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 0.00026% of random cases. Said differently, if you correlated 378,029 random variables^Note You don't actually need 378 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 15 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 15 because we have two variables measured over a period of 16 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.

[ 0.72, 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.

	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018
Air pollution in Salisbury, Maryland (Bad air quality days)	0.0888889	0.0684411	0.114068	0.0786517	0.0881226	0.0884615	0.00775194	0.0688525	0.0438356	0.0491803	0.0109589	0.00273973	0.00821918	0.00819672	0.00273973	0.0109589
The number of statistical assistants in Maryland (Statistical Assistants)	400	380	390	360	370	350	230	290	360	350	280	180	210	240	220	220

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([0.0888889,0.0684411,0.114068,0.0786517,0.0881226,0.0884615,0.00775194,0.0688525,0.0438356,0.0491803,0.0109589,0.00273973,0.00821918,0.00819672,0.00273973,0.0109589,])
array_2 = np.array([400,380,390,360,370,350,230,290,360,350,280,180,210,240,220,220,])
array_1_name = "Air pollution in Salisbury, Maryland"
array_2_name = "The number of statistical assistants in Maryland"

# 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 Salisbury, Maryland
Line chart for only The number of statistical assistants in Maryland
AI-generated correlation image
The spurious research paper: Air We There Yet? The Atmos-fear of Air Pollution in Salisbury, Maryland: A Breath of Fresh Air for Statistical Assistants in Maryland

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Correlation ID: 5205 · Black Variable ID: 21686 · Red Variable ID: 9247


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