Spurious correlation #4,666 · 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 Vineland, New Jersey and the second variable is The number of tailors, dressmakers, and custom sewers in New Jersey. 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 Vineland, New Jersey and the second variable is The number of tailors, dressmakers, and custom sewers in New Jersey. The chart goes from 2003 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The air was sew clean that they just couldn't measure up anymore. As the pollution cleared, so did the need for alterations. The sewers couldn't stitch around the fact that the demand for their services was dwindling faster than a spool of thread. It seems like the dress for success motto took a hem-larious turn in Vineland!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image inspired by bauhaus depicting a scene in Vineland, New Jersey, where the clear blue sky is marred by smog, indicating high levels of air pollution. In stark contrast, there is a quaint tailor shop with few customers, and empty spools of thread and neatly organized bolts of fabric revealing the decline in demand for custom tailoring services. Capture the juxtaposition of the pollution in the sky against the overlooked tailor shop in a minimalist, yet impactful way. 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 Vineland, New Jersey and The number of tailors, dressmakers, and custom sewers in New Jersey. 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 a Polaroid photo inspired by bauhaus Cyber folk a meme
ChatGPT: inspired by bauhaus

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 Vineland, New Jersey and The number of tailors, dressmakers, and custom sewers in New Jersey, in the following situation: The air was sew clean that they just couldn't measure up anymore. As the pollution cleared, so did the need for alterations. The sewers couldn't stitch around the fact that the demand for their services was dwindling faster than a spool of thread. It seems like the dress for success motto took a hem-larious turn in Vineland!. Keep the description simple.
ChatGPT: In Vineland, New Jersey, the clear blue sky was marred by smog, indicating high levels of air pollution. In stark contrast, a quaint tailor shop had few customers, the empty spools of thread and neatly organized bolts of fabric revealing the decline in demand for custom tailoring services.

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: [ inspired by bauhaus ] and depicting this scene: In Vineland, New Jersey, the clear blue sky was marred by smog, indicating high levels of air pollution. In stark contrast, a quaint tailor shop had few customers, the empty spools of thread and neatly organized bolts of fabric revealing the decline in demand for custom tailoring services. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image inspired by bauhaus depicting a scene in Vineland, New Jersey, where the clear blue sky is marred by smog, indicating high levels of air pollution. In stark contrast, there is a quaint tailor shop with few customers, and empty spools of thread and neatly organized bolts of fabric revealing the decline in demand for custom tailoring services. Capture the juxtaposition of the pollution in the sky against the overlooked tailor shop in a minimalist, yet impactful way.

*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 Vineland, New Jersey caused The number of tailors, dressmakers, and custom sewers in New Jersey to decrease. Include one good pun.

AI academic paper

(Because p < 0.01)

Sewing Through the Smog: The Stitching Connection Between Air Pollution in Vineland, New Jersey and the Number of Tailors, Dressmakers, and Custom Sewers in New Jersey

The Journal of Eclectic Environmental Economics and Sartorial Studies.

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 Vineland, New Jersey and The number of tailors, dressmakers, and custom sewers in New Jersey. 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 devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading the backs of shampoo bottles.

Here is the title and abstract of the paper:
[[TITLE]]
Sewing Through the Smog: The Stitching Connection Between Air Pollution in Vineland, New Jersey and the Number of Tailors, Dressmakers, and Custom Sewers in New Jersey

[[ABSTRACT]]
This study investigates the intriguing correlation between air pollution levels in Vineland, New Jersey, and the quantity of tailors, dressmakers, and custom sewers across the state. Utilizing data from the Environmental Protection Agency and the Bureau of Labor Statistics, our research team delved into this unlikely association. Our results revealed a striking correlation coefficient of 0.8436693 and p < 0.01 for the period spanning 2003 to 2022.

Despite the seemingly unrelated nature of air quality and fashion-related occupations, our findings suggest a strong relationship between the two. It appears that as air pollution levels rise, so does the number of tailors, dressmakers, and custom sewers in New Jersey. This peculiar correlation raises the question: are the emissions causing a surge in the demand for alterations, or are our seamstresses and tailors simply responding to the environmental fabric?

The implications of this unexpected link between environmental pollution and the sewing industry are far-reaching. They emphasize the interconnectedness of seemingly disparate facets of society and underscore the need for further interdisciplinary investigations. As we navigate this intricate web of relationships, it is crucial to sew-lect comprehensive and holistic approaches to understanding and addressing environmental and occupational trends.

In conclusion, our research unpacks the intriguing connection between air pollution in Vineland, New Jersey, and the number of tailors, dressmakers, and custom sewers in the state. Our findings may prompt policymakers and researchers to thread carefully in exploring unconventional associations and to tailor their strategies for addressing environmental and labor market dynamics.

-------

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 pursuit. However, our research delves into perhaps one of the most unexpected connections yet - the curious correlation between air pollution levels in Vineland, New Jersey, and the quantity of tailors, dressmakers, and custom sewers across the state. It seems that the air in Vineland may not only be filled with pollutants but also with potential garment alterations! One might say that the pollution is sew-pernatural in its effect on the stitching industry.
The global fashion industry is often associated with glamorous runways and stylish ensembles, but our study uncovers a more down-to-earth aspect of this world – the influence of air quality on the demand for clothing alterations. It appears that as the air becomes more contaminated, the demand for alterations grows stronger, leading us to wonder whether the pollutants themselves are altering more than just the environment. It seems the air pollution may be sewing the seeds of change in the fashion industry. It would appear that the expression "sew what?" takes on a whole new meaning in light of our findings.
Although one may initially be inclined to brush off this correlation as nothing more than a statistical anomaly, the strength of the relationship we discovered suggests otherwise. Our research team has found a correlation coefficient so strong that it could practically stitch together a whole ensemble, with a p-value so low it could be called tailor-made. We might even say that these findings are as clear as a well-pressed shirt.
This unexpected link between pollutants and the sewing occupation raises a myriad of questions and prompts us to reconsider conventional notions of cause and effect in unexpected ways. Is the increase in need for alterations due to a rise in air pollution, or are the tailors and custom sewers simply cutting through the smog to meet the demands of their clientele of cleaner clothing wearers? It seems that we have stumbled upon a tangled web of stitching and air pollution that demands further unraveling.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The relationship between environmental factors and labor market dynamics has been a subject of interest among scholars for many years. Smith (2010) examined the impact of air pollution on various sectors of the economy, but did not explore its potential influence on the sewing industry. Doe (2015) investigated trends in occupational demand in New Jersey, yet failed to mention any potential connection to air quality. Jones (2019) conducted a comprehensive study on the fashion industry but overlooked any association with environmental factors. However, a notable gap remains in the literature regarding the peculiar correlation between air pollution levels in Vineland, New Jersey, and the quantity of tailors, dressmakers, and custom sewers across the state.

In "Air Pollution and Its Economic Impacts" by Greene and Brown, the authors find that air pollution levels can have wide-ranging effects on various economic activities, but they did not consider its potential influence on the demand for clothing alterations. In "Labor Markets and Occupational Trends" by Black and White, the authors examine shifts in occupational demand but do not explore any connection to environmental pollution. In "Fashion Industry Dynamics" by Red and Blue, the authors provide a comprehensive overview of the fashion industry without delving into its relationship with environmental factors.

Turning our attention to non-fiction books related to the topic, "The Big Necessity: The Unmentionable World of Human Waste and Why It Matters" by Rose George explores the often overlooked aspects of environmental pollution, providing intriguing parallels to our own study. "Garbology: Our Dirty Love Affair with Trash" by Edward Humes offers valuable insights into the broader implications of environmental pollutants, drawing useful comparisons to the unexpected relationship between air pollution and the sewing industry.

In the realm of fiction, "The Poisoned Pilgrim" by Oliver Pötzsch weaves a mystery in which air pollution may hold the key to unraveling a series of unfortunate events. In "The Dressmaker" by Rosalie Ham, the narrative delves into the world of dressmaking, teasing a potential connection to environmental factors that goes beyond the fabric. These literary works provide interesting parallels to our own investigation, highlighting the interconnectedness of environmental factors and occupational trends in ways that may initially seem unexpected.

In addition to these more conventional sources, a rigorous review of literature was conducted, including but not limited to, the reading of cereal boxes, the back of shampoo bottles, and the labels of household cleaning products. While these sources are not typically cited in scholarly research, they provide a novel and surprisingly relevant perspective on the topic at hand. We found that the ingredients in certain cleaning products had an unexpected correlation with the rise in demand for sewing alterations, leading us to wonder whether the cleaning products are inadvertently causing fabric shrinkage, or if they are simply cutting into the demand for new clothing.

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 Vineland, New Jersey and The number of tailors, dressmakers, and custom sewers in New Jersey. 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 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]]
Sewing Through the Smog: The Stitching Connection Between Air Pollution in Vineland, New Jersey and the Number of Tailors, Dressmakers, and Custom Sewers in New Jersey

[[ABSTRACT]]
This study investigates the intriguing correlation between air pollution levels in Vineland, New Jersey, and the quantity of tailors, dressmakers, and custom sewers across the state. Utilizing data from the Environmental Protection Agency and the Bureau of Labor Statistics, our research team delved into this unlikely association. Our results revealed a striking correlation coefficient of 0.8436693 and p < 0.01 for the period spanning 2003 to 2022.

Despite the seemingly unrelated nature of air quality and fashion-related occupations, our findings suggest a strong relationship between the two. It appears that as air pollution levels rise, so does the number of tailors, dressmakers, and custom sewers in New Jersey. This peculiar correlation raises the question: are the emissions causing a surge in the demand for alterations, or are our seamstresses and tailors simply responding to the environmental fabric?

The implications of this unexpected link between environmental pollution and the sewing industry are far-reaching. They emphasize the interconnectedness of seemingly disparate facets of society and underscore the need for further interdisciplinary investigations. As we navigate this intricate web of relationships, it is crucial to sew-lect comprehensive and holistic approaches to understanding and addressing environmental and occupational trends.

In conclusion, our research unpacks the intriguing connection between air pollution in Vineland, New Jersey, and the number of tailors, dressmakers, and custom sewers in the state. Our findings may prompt policymakers and researchers to thread carefully in exploring unconventional associations and to tailor their strategies for addressing environmental and labor market dynamics.

[[INTRODUCTION]]
The relationship between environmental factors and labor market dynamics has long been a subject of scholarly pursuit. However, our research delves into perhaps one of the most unexpected connections yet - the curious correlation between air pollution levels in Vineland, New Jersey, and the quantity of tailors, dressmakers, and custom sewers across the state. It seems that the air in Vineland may not only be filled with pollutants but also with potential garment alterations! One might say that the pollution is sew-pernatural in its effect on the stitching industry.
The global fashion industry is often associated with glamorous runways and stylish ensembles, but our study uncovers a more down-to-earth aspect of this world – the influence of air quality on the demand for clothing alterations. It appears that as the air becomes more contaminated, the demand for alterations grows stronger, leading us to wonder whether the pollutants themselves are altering more than just the environment. It seems the air pollution may be sewing the seeds of change in the fashion industry. It would appear that the expression "sew what?" takes on a whole new meaning in light of our findings.
Although one may initially be inclined to brush off this correlation as nothing more than a statistical anomaly, the strength of the relationship we discovered suggests otherwise. Our research team has found a correlation coefficient so strong that it could practically stitch together a whole ensemble, with a p-value so low it could be called tailor-made. We might even say that these findings are as clear as a well-pressed shirt.
This unexpected link between pollutants and the sewing occupation raises a myriad of questions and prompts us to reconsider conventional notions of cause and effect in unexpected ways. Is the increase in need for alterations due to a rise in air pollution, or are the tailors and custom sewers simply cutting through the smog to meet the demands of their clientele of cleaner clothing wearers? It seems that we have stumbled upon a tangled web of stitching and air pollution that demands further unraveling.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The data utilized in this research was gathered from the Environmental Protection Agency and the Bureau of Labor Statistics. Our research team conducted a thorough examination of the air quality measurements in Vineland, New Jersey, and the employment numbers of tailors, dressmakers, and custom sewers in New Jersey. The period of analysis spanned from 2003 to 2022, providing a comprehensive overview of the trends in air pollution and the sewing industry's workforce during this time. We consulted a variety of sources to ensure a well-rounded analysis, but let's be real, we mostly just spent a lot of time on the EPA and BLS websites.

Normalization and Cleaning:
To ensure the coherence and accuracy of the data, stringent normalization and cleaning procedures were employed. We scrubbed through the datasets like a tailor meticulously inspecting fabric for imperfections, and removed any outliers and inconsistencies that could have skewed our results. We ironed out the wrinkles in the data, leaving no thread loose in our pursuit of validity.

Correlation Analysis:
The next step involved quantifying the relationship between air pollution levels and the number of tailors, dressmakers, and custom sewers in New Jersey. We calculated the correlation coefficient using sophisticated statistical methods, designed to measure the strength and direction of the association between these seemingly unrelated variables. Our statistical analysis was as thorough as a seamstress analyzing the drape of a fabric, ensuring that no subtlety in the relationship was overlooked.

Time Series Analysis:
In addition, a time series analysis was conducted to capture any temporal patterns and trends in air pollution levels and the sewing industry workforce over the 20-year period. This allowed us to unravel the fabric of the data and discern how the relationship evolved over time, providing a dynamic perspective on the connection between environmental factors and labor market dynamics. We dived into the depths of time series analysis with the fervor of a tailor unraveling a new bolt of cloth, eager to reveal its hidden patterns and designs.

Modeling:
Furthermore, we utilized predictive modeling techniques to forecast the potential impact of air pollution on the future demand for tailors, dressmakers, and custom sewers in New Jersey. This involved fitting intricate models to the data, akin to crafting a bespoke garment that snugly conforms to the unique contours of the dataset. We tailored our models with precision, ensuring that they encapsulated the complexity of the relationship and provided valuable insights into potential future scenarios.

Ethical Considerations:
It is important to note that all data utilized in this research was obtained from publicly available sources, and stringent ethical considerations were adhered to throughout the study. Our team operated with the utmost integrity, as we believe that ethical conduct is the fabric that sews together the trust between researchers and society. We also refrained from using puns in the data analysis, as they were deemed to be statistically non-significant.

In conclusion, the methodology employed in this research harnessed a diverse range of techniques to comprehensively investigate the correlation between air pollution in Vineland, New Jersey, and the number of tailors, dressmakers, and custom sewers in the state. The rigorous application of these methods allowed us to weave together a robust analysis of this intriguing relationship, uncovering threads of connection that may have otherwise gone unnoticed.

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 Vineland, New Jersey and The number of tailors, dressmakers, and custom sewers in New Jersey. 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]]
Sewing Through the Smog: The Stitching Connection Between Air Pollution in Vineland, New Jersey and the Number of Tailors, Dressmakers, and Custom Sewers in New Jersey

[[ABSTRACT]]
This study investigates the intriguing correlation between air pollution levels in Vineland, New Jersey, and the quantity of tailors, dressmakers, and custom sewers across the state. Utilizing data from the Environmental Protection Agency and the Bureau of Labor Statistics, our research team delved into this unlikely association. Our results revealed a striking correlation coefficient of 0.8436693 and p < 0.01 for the period spanning 2003 to 2022.

Despite the seemingly unrelated nature of air quality and fashion-related occupations, our findings suggest a strong relationship between the two. It appears that as air pollution levels rise, so does the number of tailors, dressmakers, and custom sewers in New Jersey. This peculiar correlation raises the question: are the emissions causing a surge in the demand for alterations, or are our seamstresses and tailors simply responding to the environmental fabric?

The implications of this unexpected link between environmental pollution and the sewing industry are far-reaching. They emphasize the interconnectedness of seemingly disparate facets of society and underscore the need for further interdisciplinary investigations. As we navigate this intricate web of relationships, it is crucial to sew-lect comprehensive and holistic approaches to understanding and addressing environmental and occupational trends.

In conclusion, our research unpacks the intriguing connection between air pollution in Vineland, New Jersey, and the number of tailors, dressmakers, and custom sewers in the state. Our findings may prompt policymakers and researchers to thread carefully in exploring unconventional associations and to tailor their strategies for addressing environmental and labor market dynamics.

[[LITERATURE REVIEW]]
The relationship between environmental factors and labor market dynamics has been a subject of interest among scholars for many years. Smith (2010) examined the impact of air pollution on various sectors of the economy, but did not explore its potential influence on the sewing industry. Doe (2015) investigated trends in occupational demand in New Jersey, yet failed to mention any potential connection to air quality. Jones (2019) conducted a comprehensive study on the fashion industry but overlooked any association with environmental factors. However, a notable gap remains in the literature regarding the peculiar correlation between air pollution levels in Vineland, New Jersey, and the quantity of tailors, dressmakers, and custom sewers across the state.
In "Air Pollution and Its Economic Impacts" by Greene and Brown, the authors find that air pollution levels can have wide-ranging effects on various economic activities, but they did not consider its potential influence on the demand for clothing alterations. In "Labor Markets and Occupational Trends" by Black and White, the authors examine shifts in occupational demand but do not explore any connection to environmental pollution. In "Fashion Industry Dynamics" by Red and Blue, the authors provide a comprehensive overview of the fashion industry without delving into its relationship with environmental factors.
Turning our attention to non-fiction books related to the topic, "The Big Necessity: The Unmentionable World of Human Waste and Why It Matters" by Rose George explores the often overlooked aspects of environmental pollution, providing intriguing parallels to our own study. "Garbology: Our Dirty Love Affair with Trash" by Edward Humes offers valuable insights into the broader implications of environmental pollutants, drawing useful comparisons to the unexpected relationship between air pollution and the sewing industry.
In the realm of fiction, "The Poisoned Pilgrim" by Oliver Pötzsch weaves a mystery in which air pollution may hold the key to unraveling a series of unfortunate events. In "The Dressmaker" by Rosalie Ham, the narrative delves into the world of dressmaking, teasing a potential connection to environmental factors that goes beyond the fabric. These literary works provide interesting parallels to our own investigation, highlighting the interconnectedness of environmental factors and occupational trends in ways that may initially seem unexpected.
In addition to these more conventional sources, a rigorous review of literature was conducted, including but not limited to, the reading of cereal boxes, the back of shampoo bottles, and the labels of household cleaning products. While these sources are not typically cited in scholarly research, they provide a novel and surprisingly relevant perspective on the topic at hand. We found that the ingredients in certain cleaning products had an unexpected correlation with the rise in demand for sewing alterations, leading us to wonder whether the cleaning products are inadvertently causing fabric shrinkage, or if they are simply cutting into the demand for new clothing.

[[RESULTS]]
The analysis of the data collected from the Environmental Protection Agency and the Bureau of Labor Statistics revealed a significant correlation between air pollution levels in Vineland, New Jersey, and the number of tailors, dressmakers, and custom sewers in the state. From 2003 to 2022, a correlation coefficient of 0.8436693 with an r-squared of 0.7117778 was observed, indicating a strong relationship between these seemingly disparate variables. One might say that the pollutants in the air were weaving a tale of stitching demand across New Jersey.
The p-value of less than 0.01 further emphasizes the robustness of this correlation, suggesting that the likelihood of it occurring by mere coincidence is highly improbable. It seems that the association between air pollution and the sewing industry is as strong as a well-sewn seam.
(Fig. 1) demonstrates the strong positive correlation between air pollution levels in Vineland, New Jersey, and the number of tailors, dressmakers, and custom sewers in the state. The scatterplot showcases a clear pattern, indicating that as air pollution levels increase, so does the number of fashion-related artisans, making it clear that there is indeed a fabric-tic relationship between these two variables.
Our findings shed light on the intriguing connection between environmental pollution and the demand for sewing-related occupations. This unexpected association may seem as unlikely as finding a needle in a haystack, but the evidence speaks for itself. It appears that the air pollution in Vineland may not only be altering the environment but also the fabric of the labor market in New Jersey. These findings are indeed nothing to hem and haw about.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our study provide compelling evidence to support the unexpected association between air pollution levels in Vineland, New Jersey, and the number of tailors, dressmakers, and custom sewers in the state. This analysis has woven a thread of understanding, shedding light on the intricate relationship between environmental quality and occupational demand. Our findings confirm the earlier comic relief that emphasized the interconnectedness of seemingly disparate facets of society. It is clear from our results that the demand for clothing alterations appears to be intricately interwoven with the fabric of environmental pollution.

The correlation coefficient of 0.8436693 and the striking p-value of less than 0.01 emphasize the robustness of the relationship between air pollution and the sewing industry, highlighting an unparalleled connection that may initially seem sew unlikely. These findings echo the sentiments of previous literature, some of which were subtly inflected with wordplay and humor, unintentionally setting the tone for our own findings. The evidence suggests a need for tailored policies and responses to address the impact of air pollution on labor market trends. Our research has managed to thread the needle, revealing a strong fabric of evidence to support the unexpected relationship between air pollution and the demand for sewing-related occupations.

The implications of this study extend beyond the realms of occupational trends and environmental quality. This unlikely relationship elevates the need for interdisciplinary research approaches and broader policy considerations, including potentially sew-cial and environmental interventions in response to labor market shifts. Our findings suggest that policymakers and researchers need to sew together comprehensive strategies that account for the interconnected dynamics between environmental factors and occupational trends. It is imperative to thread carefully through the complexities of these relationships to ensure a well-fitted approach to addressing the multifaceted impacts of air pollution on the labor market.

In sum, our study establishes a sew-lid foundation for future research and policy initiatives aimed at understanding and responding to the intertwined dynamics of environmental pollution and occupational trends. As we continue to unravel the intricate web of relationships, it is essential to recognize the interconnectedness of seemingly disparate variables, and as our data illustrates, to have a stitch in time could significantly help in understanding and addressing the complex influence of air pollution on the sewing industry. Our findings unravel this peculiar correlation, prompting a call for further research to stitch together a comprehensive understanding of the intricate connections between seemingly unrelated phenomena.

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 Vineland, New Jersey and The number of tailors, dressmakers, and custom sewers in New Jersey. 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]]
Sewing Through the Smog: The Stitching Connection Between Air Pollution in Vineland, New Jersey and the Number of Tailors, Dressmakers, and Custom Sewers in New Jersey

[[ABSTRACT]]
This study investigates the intriguing correlation between air pollution levels in Vineland, New Jersey, and the quantity of tailors, dressmakers, and custom sewers across the state. Utilizing data from the Environmental Protection Agency and the Bureau of Labor Statistics, our research team delved into this unlikely association. Our results revealed a striking correlation coefficient of 0.8436693 and p < 0.01 for the period spanning 2003 to 2022.

Despite the seemingly unrelated nature of air quality and fashion-related occupations, our findings suggest a strong relationship between the two. It appears that as air pollution levels rise, so does the number of tailors, dressmakers, and custom sewers in New Jersey. This peculiar correlation raises the question: are the emissions causing a surge in the demand for alterations, or are our seamstresses and tailors simply responding to the environmental fabric?

The implications of this unexpected link between environmental pollution and the sewing industry are far-reaching. They emphasize the interconnectedness of seemingly disparate facets of society and underscore the need for further interdisciplinary investigations. As we navigate this intricate web of relationships, it is crucial to sew-lect comprehensive and holistic approaches to understanding and addressing environmental and occupational trends.

In conclusion, our research unpacks the intriguing connection between air pollution in Vineland, New Jersey, and the number of tailors, dressmakers, and custom sewers in the state. Our findings may prompt policymakers and researchers to thread carefully in exploring unconventional associations and to tailor their strategies for addressing environmental and labor market dynamics.

[[INTRDUCTION]]
The relationship between environmental factors and labor market dynamics has long been a subject of scholarly pursuit. However, our research delves into perhaps one of the most unexpected connections yet - the curious correlation between air pollution levels in Vineland, New Jersey, and the quantity of tailors, dressmakers, and custom sewers across the state. It seems that the air in Vineland may not only be filled with pollutants but also with potential garment alterations! One might say that the pollution is sew-pernatural in its effect on the stitching industry.
The global fashion industry is often associated with glamorous runways and stylish ensembles, but our study uncovers a more down-to-earth aspect of this world – the influence of air quality on the demand for clothing alterations. It appears that as the air becomes more contaminated, the demand for alterations grows stronger, leading us to wonder whether the pollutants themselves are altering more than just the environment. It seems the air pollution may be sewing the seeds of change in the fashion industry. It would appear that the expression "sew what?" takes on a whole new meaning in light of our findings.
Although one may initially be inclined to brush off this correlation as nothing more than a statistical anomaly, the strength of the relationship we discovered suggests otherwise. Our research team has found a correlation coefficient so strong that it could practically stitch together a whole ensemble, with a p-value so low it could be called tailor-made. We might even say that these findings are as clear as a well-pressed shirt.
This unexpected link between pollutants and the sewing occupation raises a myriad of questions and prompts us to reconsider conventional notions of cause and effect in unexpected ways. Is the increase in need for alterations due to a rise in air pollution, or are the tailors and custom sewers simply cutting through the smog to meet the demands of their clientele of cleaner clothing wearers? It seems that we have stumbled upon a tangled web of stitching and air pollution that demands further unraveling.

[[RESULTS]]
The analysis of the data collected from the Environmental Protection Agency and the Bureau of Labor Statistics revealed a significant correlation between air pollution levels in Vineland, New Jersey, and the number of tailors, dressmakers, and custom sewers in the state. From 2003 to 2022, a correlation coefficient of 0.8436693 with an r-squared of 0.7117778 was observed, indicating a strong relationship between these seemingly disparate variables. One might say that the pollutants in the air were weaving a tale of stitching demand across New Jersey.
The p-value of less than 0.01 further emphasizes the robustness of this correlation, suggesting that the likelihood of it occurring by mere coincidence is highly improbable. It seems that the association between air pollution and the sewing industry is as strong as a well-sewn seam.
(Fig. 1) demonstrates the strong positive correlation between air pollution levels in Vineland, New Jersey, and the number of tailors, dressmakers, and custom sewers in the state. The scatterplot showcases a clear pattern, indicating that as air pollution levels increase, so does the number of fashion-related artisans, making it clear that there is indeed a fabric-tic relationship between these two variables.
Our findings shed light on the intriguing connection between environmental pollution and the demand for sewing-related occupations. This unexpected association may seem as unlikely as finding a needle in a haystack, but the evidence speaks for itself. It appears that the air pollution in Vineland may not only be altering the environment but also the fabric of the labor market in New Jersey. These findings are indeed nothing to hem and haw about.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has unraveled the peculiar correlation between air pollution levels in Vineland, New Jersey, and the quantity of tailors, dressmakers, and custom sewers across the state. Our study has illuminated an unexpected connection, demonstrating that the increase in air pollution levels is, somewhat surprisingly, paralleled by the rise in demand for alterations and custom sewing services. It would seem that polluted air not only affects our breathing but also our pant hems!

This unlikely relationship raises the question of whether the surge in pollution irritates more than just our lungs – is it also a thorn in our side seams? Our findings suggest that there is indeed a visible link between these two variables, leaving us to wonder if the emissions are causing a surge in the demand for alterations or if the seamstresses and tailors are simply cutting through the smog to meet the needs of their clientele. It appears that the pollutants themselves may be shaping the fabric of the labor market in New Jersey, re-weaving the very threads of our societal tapestry.

One might say that this correlation is as strong as a reinforced seam, and the likelihood of it occurring by mere coincidence is as probable as finding a matching sock in the laundry. Our analysis uncovered a relationship so robust that we could practically sew a whole wardrobe from it – a true gem in the rough.

Therefore, in light of our findings, it is apparent that no more research is needed in this particular area. We have sufficiently unpicked the threads of this curious connection, leaving us with a clear understanding of the fabric-tic relationship between air pollution levels and the sewing industry in New Jersey. It seems that the air in Vineland might just be stitching its story into the hemlines of our state's fashion industry.

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 Sewing Through the Smog: The Stitching Connection Between Air Pollution in Vineland, New Jersey and the Number of Tailors, Dressmakers, and Custom Sewers in New Jersey. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Eclectic Environmental Economics and Sartorial Studies.

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 Sewing Through the Smog: The Stitching Connection Between Air Pollution in Vineland, New Jersey and the Number of Tailors, Dressmakers, and Custom Sewers in New Jersey. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Eco-Fashion and Atmospheric Studies (SEFAS)

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 Sewing Through the Smog: The Stitching Connection Between Air Pollution in Vineland, New Jersey and the Number of Tailors, Dressmakers, and Custom Sewers in New Jersey with an abstract of This study investigates the intriguing correlation between air pollution levels in Vineland, New Jersey, and the quantity of tailors, dressmakers, and custom sewers across the state. Utilizing data from the Environmental Protection Agency and the Bureau of Labor Statistics, our research team delved into this unlikely association. Our results revealed a striking correlation coefficient of 0.8436693 and p < 0.01 for the period spanning 2003 to 2022.
Despite the seemingly unrelated nature of air quality and fashion-related occupations, our findings suggest a strong relationship between the two. It appears that as air pollution levels rise, so does the number of tailors, dressmakers, and custom sewers in New Jersey. This peculiar correlation raises the question: are the emissions causing a surge in the demand for alterations, or are our seamstresses and tailors simply responding to the environmental fabric?
The implications of this unexpected link between environmental pollution and the sewing industry are far-reaching. They emphasize the interconnectedness of seemingly disparate facets of society and underscore the need for further interdisciplinary investigations. As we navigate this intricate web of relationships, it is crucial to sew-lect comprehensive and holistic approaches to understanding and addressing environmental and occupational trends.
In conclusion, our research unpacks the intriguing connection between air pollution in Vineland, New Jersey, and the number of tailors, dressmakers, and custom sewers in the state. Our findings may prompt policymakers and researchers to thread carefully in exploring unconventional associations and to tailor their strategies for addressing environmental and labor market dynamics.

ChatGPT:

air pollution, Vineland New Jersey, tailors, dressmakers, custom sewers, correlation, Bureau of Labor Statistics, Environmental Protection Agency, pollution levels, New Jersey, occupational trends, dressmaking industry, environmental fabric

*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 Vineland, New Jersey
Detailed data title: Percentage of days with moderate or worse air quality in Vineland-Bridgeton, NJ
Source: Environmental Protection Agency
See what else correlates with Air pollution in Vineland, New Jersey

The number of tailors, dressmakers, and custom sewers in New Jersey
Detailed data title: BLS estimate of tailors, dressmakers, and custom sewers in New Jersey
Source: Bureau of Larbor Statistics
See what else correlates with The number of tailors, dressmakers, and custom sewers in New Jersey

Correlation r = 0.8436693 (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 = 0.7117778 (Coefficient of determination)
This means 71.2% of the change in the one variable (i.e., The number of tailors, dressmakers, and custom sewers in New Jersey) is predictable based on the change in the other (i.e., Air pollution in Vineland, New Jersey) over the 20 years from 2003 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.0E-6.^Show 0.0000029595350725374606000000
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 0.84 in 0.0003% of random cases. Said differently, if you correlated 337,891 random variables^Note You don't actually need 337 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 Vineland, New Jersey (Bad air quality days)	22.067	21.1538	18.5393	44.6328	37.2222	38.7363	35.6164	29.4766	22.8412	23.9645	5.94059	6.04396	12.4309	17.7596	13.1868	16.4384	17.7285	12.6027	10.1648	7.73481
The number of tailors, dressmakers, and custom sewers in New Jersey (Laborers)	1350	1070	1110	1610	1550	1670	1050	850	920	800	610	430	370	350	310	500	670	630	290	530

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([22.067,21.1538,18.5393,44.6328,37.2222,38.7363,35.6164,29.4766,22.8412,23.9645,5.94059,6.04396,12.4309,17.7596,13.1868,16.4384,17.7285,12.6027,10.1648,7.73481,])
array_2 = np.array([1350,1070,1110,1610,1550,1670,1050,850,920,800,610,430,370,350,310,500,670,630,290,530,])
array_1_name = "Air pollution in Vineland, New Jersey"
array_2_name = "The number of tailors, dressmakers, and custom sewers in New Jersey"

# 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 Vineland, New Jersey
Line chart for only The number of tailors, dressmakers, and custom sewers in New Jersey
AI-generated correlation image
The spurious research paper: Sewing Through the Smog: The Stitching Connection Between Air Pollution in Vineland, New Jersey and the Number of Tailors, Dressmakers, and Custom Sewers in New Jersey

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Correlation ID: 4666 · Black Variable ID: 21395 · Red Variable ID: 11737


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