Spurious correlation #5,176 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air quality in Greenwood, South Carolina and the second variable is Number of Botox Injections Administered to Women. The chart goes from 1997 to 2016, 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 quality in Greenwood, South Carolina and the second variable is Number of Botox Injections Administered to Women. The chart goes from 1997 to 2016, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the air quality improved, more oxygen was available, leading the women in Greenwood to have that healthy, glowing skin - the perfect canvas for Botox injections! Remember, when it comes to fresh faces, it's all about that fresh air.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image inspired by a Polaroid photo, showing a group of women under a clear blue sky in Greenwood, South Carolina. The women have radiant, glowing skin and bright smiles, reflecting the improved air quality. Their vibrant energy suggests the rising trend of Botox injections in the area, radiating confidence and freshness. The composition should capture the natural beauty of the scene, showcasing the women's vitality and the clear, blue sky as the backdrop. 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 quality in Greenwood, South Carolina and Number of Botox Injections Administered to Women. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: a Polaroid photo Surrealism Cyberpunk Vaporwave Low poly
ChatGPT: a Polaroid photo

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 quality in Greenwood, South Carolina and Number of Botox Injections Administered to Women, in the following situation: As the air quality improved, more oxygen was available, leading the women in Greenwood to have that healthy, glowing skin - the perfect canvas for Botox injections! Remember, when it comes to fresh faces, it's all about that fresh air.. Keep the description simple.
ChatGPT: In Greenwood, South Carolina, the clear blue sky served as the perfect backdrop for a group of women. Their radiant, glowing skin reflected the improved air quality, and their bright smiles showed the benefits of the fresh oxygen. As they chatted, it was evident that the number of Botox injections administered to women in the area was on the rise, as they all seemed to radiate confidence and freshness.

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 Polaroid photo ] and depicting this scene: In Greenwood, South Carolina, the clear blue sky served as the perfect backdrop for a group of women. Their radiant, glowing skin reflected the improved air quality, and their bright smiles showed the benefits of the fresh oxygen. As they chatted, it was evident that the number of Botox injections administered to women in the area was on the rise, as they all seemed to radiate confidence and freshness. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image inspired by a Polaroid photo, showing a group of women under a clear blue sky in Greenwood, South Carolina. The women have radiant, glowing skin and bright smiles, reflecting the improved air quality. Their vibrant energy suggests the rising trend of Botox injections in the area, radiating confidence and freshness. The composition should capture the natural beauty of the scene, showcasing the women's vitality and the clear, blue sky as the backdrop.

*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 an increase in Air quality in Greenwood, South Carolina caused Number of Botox Injections Administered to Women to increase. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

Injecting Some Fresh Air: The Correlation between Air Quality in Greenwood, South Carolina, and Botox Injections Administered to Women

The Journal of Cosmetic Epidemiology

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 quality in Greenwood, South Carolina and Number of Botox Injections Administered to Women. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then 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]]
"Injecting Some Fresh Air: The Correlation between Air Quality in Greenwood, South Carolina, and Botox Injections Administered to Women"

[[ABSTRACT]]
This study investigates the relationship between air quality in Greenwood, South Carolina, and the number of Botox injections administered to women over a 20-year period. Using data from the Environmental Protection Agency and the American Society for Aesthetic Plastic Surgery, a correlation coefficient of 0.8213269 and a statistically significant p-value of less than 0.01 were calculated for the years 1997 to 2016. The findings suggest a notable association between air quality and the demand for Botox treatments. While this connection may initially seem like a stretch, our data analysis raises intriguing questions about the potential influence of environmental factors on aesthetic procedures. Further research is warranted to explore the mechanisms underlying this peculiar correlation and its implications for public health and beauty standards.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The pursuit of beauty and the quest for clean air are not often mentioned in the same breath. However, this study delves into the unexpected intersection of these two seemingly disparate realms. We delve into the intriguing connection between air quality in Greenwood, South Carolina, and the number of Botox injections administered to women. Comparing environmental data and cosmetic procedures statistics, we unveil a correlation that may raise an eyebrow or, perhaps more aptly, smooth a forehead.
The relationship between pollution and personal appearance may appear as elusive as a piece of loose hair in a gust of wind. Nevertheless, our analysis reveals a striking correlation coefficient of 0.8213269, indicating a strong statistical relationship between these variables. The calculated p-value of less than 0.01 further suggests that this association is not purely coincidental. Indeed, the p-value is so low, it practically suggests that something's in the air – and no, we're not just talking about oxygen and nitrogen.
The notion that air quality could influence the demand for cosmetic treatments may raise a few eyebrows, or, quite possibly, smooth them out with the aid of a syringe. Nevertheless, our data analysis presents us with more than just a wrinkle in our understanding of environmental and aesthetic dynamics. It prompts us to expound on the link between atmospheric factors and the desire to maintain one's youthful appearance – a topic that is not just skin-deep, but potentially reaches into the very air we breathe.
While some may perceive this correlation as an odd pair, like mismatched socks or lopsided earrings, the implications of our findings extend beyond mere curiosity. This unexpected association raises intriguing questions about the potential influence of environmental factors on beauty standards and cosmetic preferences. By peeling back the layers of this unanticipated connection, we unveil a phenomenon that necessitates further investigation and discussion. It is clear that beneath the surface of this correlation lies a deeper understanding of societal norms, personal choices, and the influence of our surroundings on our pursuit of beauty.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

A substantial body of research has examined the impact of environmental factors on human health and well-being, with particular emphasis on respiratory and dermatological conditions. In "The Effects of Air Pollution on Health," Smith et al. provide a comprehensive overview of the detrimental effects of air pollution on various physiological systems, highlighting the potential consequences for dermatological health. Similarly, Doe's "Air Quality and Public Health" underscores the pervasive influence of air quality on respiratory ailments but also touches upon the broader implications for general health, including cosmetic concerns. Additionally, Jones' "Environmental Factors and Aesthetic Practices" delves into the intricate relationship between environmental elements and personal grooming habits, laying the groundwork for understanding the subtle dynamics at play in our investigation.

Expanding beyond the confines of strictly scientific literature, non-fiction works such as "The Air We Breathe: A Cultural History of Clean Air" and "Beauty and the Environment: Exploring the Aesthetic Implications of Ecology" offer interdisciplinary perspectives that tease at the broader implications of our research. Moving into the realm of fiction, the inclusion of novels with titles like "A Wrinkle in Time" and "The Picture of Dorian Gray" may initially appear whimsical, but their thematic resonance with our investigation is not to be overlooked. These literary works underscore the symbiotic relationship between environmental conditions and aesthetic pursuits, resonating with the multifaceted nature of our inquiry.

Taking a more unconventional approach, the analysis extends to popular culture, where children's cartoons and shows provide valuable insights into societal perceptions of beauty and the environment. The prevalence of episodes exploring air pollution and beauty standards in animated series such as "Captain Planet and the Planeteers" and "The Powerpuff Girls" reflects a broader cultural consciousness of these seemingly incongruous themes. While seemingly divergent from academic research, these cultural artifacts serve to enrich our understanding of the undercurrents that shape perceptions of beauty and environmental consciousness in contemporary society.

Steering into uncharted territory, the inclusion of an eclectic array of sources enriches the breadth and depth of our survey of literature, providing a holistic perspective that transcends traditional disciplinary boundaries. While the significance of these sources may not be immediately evident, their contribution to the contextual fabric of our investigation should not be underestimated.

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 quality in Greenwood, South Carolina and Number of Botox Injections Administered to Women. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and American Society for Aesthetic Plastic Surgery . You used data from 1997 to 2016

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]]
"Injecting Some Fresh Air: The Correlation between Air Quality in Greenwood, South Carolina, and Botox Injections Administered to Women"

[[ABSTRACT]]
This study investigates the relationship between air quality in Greenwood, South Carolina, and the number of Botox injections administered to women over a 20-year period. Using data from the Environmental Protection Agency and the American Society for Aesthetic Plastic Surgery, a correlation coefficient of 0.8213269 and a statistically significant p-value of less than 0.01 were calculated for the years 1997 to 2016. The findings suggest a notable association between air quality and the demand for Botox treatments. While this connection may initially seem like a stretch, our data analysis raises intriguing questions about the potential influence of environmental factors on aesthetic procedures. Further research is warranted to explore the mechanisms underlying this peculiar correlation and its implications for public health and beauty standards.

[[INTRODUCTION]]
The pursuit of beauty and the quest for clean air are not often mentioned in the same breath. However, this study delves into the unexpected intersection of these two seemingly disparate realms. We delve into the intriguing connection between air quality in Greenwood, South Carolina, and the number of Botox injections administered to women. Comparing environmental data and cosmetic procedures statistics, we unveil a correlation that may raise an eyebrow or, perhaps more aptly, smooth a forehead.
The relationship between pollution and personal appearance may appear as elusive as a piece of loose hair in a gust of wind. Nevertheless, our analysis reveals a striking correlation coefficient of 0.8213269, indicating a strong statistical relationship between these variables. The calculated p-value of less than 0.01 further suggests that this association is not purely coincidental. Indeed, the p-value is so low, it practically suggests that something's in the air – and no, we're not just talking about oxygen and nitrogen.
The notion that air quality could influence the demand for cosmetic treatments may raise a few eyebrows, or, quite possibly, smooth them out with the aid of a syringe. Nevertheless, our data analysis presents us with more than just a wrinkle in our understanding of environmental and aesthetic dynamics. It prompts us to expound on the link between atmospheric factors and the desire to maintain one's youthful appearance – a topic that is not just skin-deep, but potentially reaches into the very air we breathe.
While some may perceive this correlation as an odd pair, like mismatched socks or lopsided earrings, the implications of our findings extend beyond mere curiosity. This unexpected association raises intriguing questions about the potential influence of environmental factors on beauty standards and cosmetic preferences. By peeling back the layers of this unanticipated connection, we unveil a phenomenon that necessitates further investigation and discussion. It is clear that beneath the surface of this correlation lies a deeper understanding of societal norms, personal choices, and the influence of our surroundings on our pursuit of beauty.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The data for air quality in Greenwood, South Carolina, was sourced from the Environmental Protection Agency (EPA). The EPA provides comprehensive air quality monitoring data through its Air Quality System, which was utilized to capture levels of particulate matter, ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide. These pollutants were selected to ensure a holistic assessment of atmospheric conditions, as well as to keep things interesting for the researchers, as too much focus on a single pollutant can be a real breath of fresh air.

As for the number of Botox injections administered to women, data was obtained from the American Society for Aesthetic Plastic Surgery (ASAPS). The ASAPS compiles detailed statistics on cosmetic procedures, including Botox injections, and provides a reliable source for tracking trends in aesthetic treatments. The decision to focus on Botox injections was made as it is a widely sought-after non-invasive cosmetic procedure, and because the researchers found it to be a particularly injective topic.

Data Analysis:
The air quality data and Botox injection statistics were subjected to rigorous statistical analysis. To investigate the potential relationship between air quality and cosmetic procedures, a correlation analysis was conducted. It should be noted that this analysis involved complex mathematical formulas and models, which were so intricate that they could make the common calculator break into a sweat.

In addition to correlation analysis, a time-series analysis was also performed to scrutinize the trends in air quality and Botox injections over the 20-year period. This analysis involved methods that were as time-consuming as they were time-revealing, and provided insight into the temporal dynamics of air quality and cosmetic treatment trends. The time-series analysis was not just an exercise in time travel, but also a means to identify any temporal patterns in the data that could account for the observed correlation.

Furthermore, to ensure the robustness of the findings, various statistical tests were conducted to assess the significance of the observed correlation. These tests were not just statistically significant, but also served as a delightful opportunity for the researchers to exercise their statistical prowess and impress their peers.

Ethical Considerations:
In conducting this research, ethical considerations were upheld, including the protection of individual privacy and the responsible use of data. The researchers maintained the confidentiality of personal information related to cosmetic procedures and adhered to principles of data ethics, thereby ensuring that neither personal nor environmental "beauty secrets" were compromised in the pursuit of knowledge.

Limitations:
While every effort was made to ensure the reliability and validity of the findings, it is imperative to acknowledge the limitations of this study. As with all research, the complexity and multifaceted nature of environmental and cosmetic factors present challenges in establishing causality. Additionally, the specific contextual nuances of Greenwood, South Carolina, and the potential influence of other variables were difficult to control for, making the interpretation of the findings a bit like reading between the smog-filled lines.

Nevertheless, despite these limitations, the methodology employed in this study provided a robust framework for investigating the correlation between air quality and Botox injections. The quirky mix of data analysis, ethical considerations, and a healthy dose of humor set the stage for unraveling the mysterious connection between environmental ambience and aesthetic enhancements.

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 quality in Greenwood, South Carolina and Number of Botox Injections Administered to Women. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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]]
"Injecting Some Fresh Air: The Correlation between Air Quality in Greenwood, South Carolina, and Botox Injections Administered to Women"

[[ABSTRACT]]
This study investigates the relationship between air quality in Greenwood, South Carolina, and the number of Botox injections administered to women over a 20-year period. Using data from the Environmental Protection Agency and the American Society for Aesthetic Plastic Surgery, a correlation coefficient of 0.8213269 and a statistically significant p-value of less than 0.01 were calculated for the years 1997 to 2016. The findings suggest a notable association between air quality and the demand for Botox treatments. While this connection may initially seem like a stretch, our data analysis raises intriguing questions about the potential influence of environmental factors on aesthetic procedures. Further research is warranted to explore the mechanisms underlying this peculiar correlation and its implications for public health and beauty standards.

[[LITERATURE REVIEW]]
A substantial body of research has examined the impact of environmental factors on human health and well-being, with particular emphasis on respiratory and dermatological conditions. In "The Effects of Air Pollution on Health," Smith et al. provide a comprehensive overview of the detrimental effects of air pollution on various physiological systems, highlighting the potential consequences for dermatological health. Similarly, Doe's "Air Quality and Public Health" underscores the pervasive influence of air quality on respiratory ailments but also touches upon the broader implications for general health, including cosmetic concerns. Additionally, Jones' "Environmental Factors and Aesthetic Practices" delves into the intricate relationship between environmental elements and personal grooming habits, laying the groundwork for understanding the subtle dynamics at play in our investigation.
Expanding beyond the confines of strictly scientific literature, non-fiction works such as "The Air We Breathe: A Cultural History of Clean Air" and "Beauty and the Environment: Exploring the Aesthetic Implications of Ecology" offer interdisciplinary perspectives that tease at the broader implications of our research. Moving into the realm of fiction, the inclusion of novels with titles like "A Wrinkle in Time" and "The Picture of Dorian Gray" may initially appear whimsical, but their thematic resonance with our investigation is not to be overlooked. These literary works underscore the symbiotic relationship between environmental conditions and aesthetic pursuits, resonating with the multifaceted nature of our inquiry.
Taking a more unconventional approach, the analysis extends to popular culture, where children's cartoons and shows provide valuable insights into societal perceptions of beauty and the environment. The prevalence of episodes exploring air pollution and beauty standards in animated series such as "Captain Planet and the Planeteers" and "The Powerpuff Girls" reflects a broader cultural consciousness of these seemingly incongruous themes. While seemingly divergent from academic research, these cultural artifacts serve to enrich our understanding of the undercurrents that shape perceptions of beauty and environmental consciousness in contemporary society.
Steering into uncharted territory, the inclusion of an eclectic array of sources enriches the breadth and depth of our survey of literature, providing a holistic perspective that transcends traditional disciplinary boundaries. While the significance of these sources may not be immediately evident, their contribution to the contextual fabric of our investigation should not be underestimated.

[[RESULTS]]
The examination of the connection between air quality in Greenwood, South Carolina, and the number of Botox injections administered to women yielded some captivating findings. Over the 20-year period from 1997 to 2016, a correlation coefficient of 0.8213269 was uncovered, indicating a robust relationship between these seemingly unrelated variables. The r-squared value of 0.6745778 further demonstrated that approximately 67.5% of the variability in Botox injections administered to women could be explained by the fluctuations in air quality. Implying that, roughly two-thirds of the changes in Botox treatments can be attributed to changes in air quality, quite an eye-opener indeed.
Additionally, the calculated p-value of less than 0.01 provided compelling evidence that the observed correlation is not a mere stroke of luck. Or should we say, not just a lucky strike of a syringe? This statistical significance indicates that the likelihood of this association occurring by chance is so low, it's almost as remarkable as finding a wrinkle-free face in a wind tunnel.
Fig. 1 depicts the scatterplot illustrating the striking correlation between air quality and the number of Botox injections administered to women. The substantial clustering of data points along a positively sloped trend line paints a clear picture of the relationship between these variables, serving as a visual testament to the surprising interplay between environmental quality and aesthetic treatments.
In summary, the analysis of the data collected from the Environmental Protection Agency and the American Society for Aesthetic Plastic Surgery has brought to light a compelling correlation between air quality in Greenwood, South Carolina, and the demand for Botox injections among women. This discovery has peeled back the layers of conventional wisdom, revealing a connection that extends beyond the surface – a notion that may cause one to raise an eyebrow, only to consider a Botox injection afterward.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The present study has shed light on a most unexpected relationship between air quality in Greenwood, South Carolina, and the frequency of Botox injections among women. The robust correlation coefficient of 0.8213269 and the substantial r-squared value of 0.6745778 indicate a surprisingly strong association, suggesting that fluctuations in air quality account for approximately two-thirds of the variability in Botox treatments. This finding not only underscores the magnitude of the relationship between environmental conditions and cosmetic procedures but also prompts a reconsideration of the interconnectedness of seemingly disparate aspects of human experience - a connection more intricate than the creases on a furrowed brow.

The results support and extend prior research that has explored the influence of environmental factors on personal grooming habits and general health. The established detrimental effects of air pollution on dermatological health, as elaborated by Smith et al., find resonance in our findings, as do the broader implications for general health emphasized by Doe. Jones' investigation into the interplay between environmental elements and personal grooming practices provides a conceptual framework for understanding the unforeseen dynamics at play in our study. The thematic connection to inter-disciplinary perspectives from non-fiction works such as "The Air We Breathe: A Cultural History of Clean Air" and "Beauty and the Environment: Exploring the Aesthetic Implications of Ecology" becomes strikingly relevant, underscoring the nuanced relationship between environmental conditions and aesthetic pursuits.

The unexpected yet substantial nature of the correlation unveiled in this study aligns with the unconventional inclusion of novels like "A Wrinkle in Time" and "The Picture of Dorian Gray" in our literature review. These seemingly whimsical selections now serve as unassuming but pertinent reflections of the nuanced connection between environmental conditions and aesthetic perceptions, extending far beyond the boundaries of conventional research. Similarly, the insightful exploration of societal perceptions of beauty and environment in children's animated series anticipates and supports the unearthing of this remarkable relationship, highlighting the cultural consciousness that permeates our understanding of seemingly incongruous themes.

In sum, the analysis presented here has illustrated a robust and statistically significant correlation between air quality in Greenwood, South Carolina, and the demand for Botox injections among women. This unexpected association challenges preconceived notions and calls attention to the intricate interplay between environmental quality and aesthetic practices, perpetuating a compelling narrative of unexpected connections in our dynamic world. Further research is warranted to probe the mechanisms underlying this correlation and to elucidate its broader implications for public health and beauty standards. The implications of this peculiar correlation provoke not only further scientific inquiry but also, quite possibly, a raised eyebrow or two.

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 quality in Greenwood, South Carolina and Number of Botox Injections Administered to Women. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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]]
"Injecting Some Fresh Air: The Correlation between Air Quality in Greenwood, South Carolina, and Botox Injections Administered to Women"

[[ABSTRACT]]
This study investigates the relationship between air quality in Greenwood, South Carolina, and the number of Botox injections administered to women over a 20-year period. Using data from the Environmental Protection Agency and the American Society for Aesthetic Plastic Surgery, a correlation coefficient of 0.8213269 and a statistically significant p-value of less than 0.01 were calculated for the years 1997 to 2016. The findings suggest a notable association between air quality and the demand for Botox treatments. While this connection may initially seem like a stretch, our data analysis raises intriguing questions about the potential influence of environmental factors on aesthetic procedures. Further research is warranted to explore the mechanisms underlying this peculiar correlation and its implications for public health and beauty standards.

[[INTRDUCTION]]
The pursuit of beauty and the quest for clean air are not often mentioned in the same breath. However, this study delves into the unexpected intersection of these two seemingly disparate realms. We delve into the intriguing connection between air quality in Greenwood, South Carolina, and the number of Botox injections administered to women. Comparing environmental data and cosmetic procedures statistics, we unveil a correlation that may raise an eyebrow or, perhaps more aptly, smooth a forehead.
The relationship between pollution and personal appearance may appear as elusive as a piece of loose hair in a gust of wind. Nevertheless, our analysis reveals a striking correlation coefficient of 0.8213269, indicating a strong statistical relationship between these variables. The calculated p-value of less than 0.01 further suggests that this association is not purely coincidental. Indeed, the p-value is so low, it practically suggests that something's in the air – and no, we're not just talking about oxygen and nitrogen.
The notion that air quality could influence the demand for cosmetic treatments may raise a few eyebrows, or, quite possibly, smooth them out with the aid of a syringe. Nevertheless, our data analysis presents us with more than just a wrinkle in our understanding of environmental and aesthetic dynamics. It prompts us to expound on the link between atmospheric factors and the desire to maintain one's youthful appearance – a topic that is not just skin-deep, but potentially reaches into the very air we breathe.
While some may perceive this correlation as an odd pair, like mismatched socks or lopsided earrings, the implications of our findings extend beyond mere curiosity. This unexpected association raises intriguing questions about the potential influence of environmental factors on beauty standards and cosmetic preferences. By peeling back the layers of this unanticipated connection, we unveil a phenomenon that necessitates further investigation and discussion. It is clear that beneath the surface of this correlation lies a deeper understanding of societal norms, personal choices, and the influence of our surroundings on our pursuit of beauty.

[[RESULTS]]
The examination of the connection between air quality in Greenwood, South Carolina, and the number of Botox injections administered to women yielded some captivating findings. Over the 20-year period from 1997 to 2016, a correlation coefficient of 0.8213269 was uncovered, indicating a robust relationship between these seemingly unrelated variables. The r-squared value of 0.6745778 further demonstrated that approximately 67.5% of the variability in Botox injections administered to women could be explained by the fluctuations in air quality. Implying that, roughly two-thirds of the changes in Botox treatments can be attributed to changes in air quality, quite an eye-opener indeed.
Additionally, the calculated p-value of less than 0.01 provided compelling evidence that the observed correlation is not a mere stroke of luck. Or should we say, not just a lucky strike of a syringe? This statistical significance indicates that the likelihood of this association occurring by chance is so low, it's almost as remarkable as finding a wrinkle-free face in a wind tunnel.
Fig. 1 depicts the scatterplot illustrating the striking correlation between air quality and the number of Botox injections administered to women. The substantial clustering of data points along a positively sloped trend line paints a clear picture of the relationship between these variables, serving as a visual testament to the surprising interplay between environmental quality and aesthetic treatments.
In summary, the analysis of the data collected from the Environmental Protection Agency and the American Society for Aesthetic Plastic Surgery has brought to light a compelling correlation between air quality in Greenwood, South Carolina, and the demand for Botox injections among women. This discovery has peeled back the layers of conventional wisdom, revealing a connection that extends beyond the surface – a notion that may cause one to raise an eyebrow, only to consider a Botox injection afterward.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

The examination of the correlation between air quality in Greenwood, South Carolina, and the number of Botox injections administered to women over a 20-year period has brought to light an unexpected and, dare we say, breath-taking connection. The statistically significant correlation coefficient of 0.8213269 and a p-value of less than 0.01 suggest a relationship that is not just skin-deep, but resonates with the very air we breathe. This finding, while surprising at first glance, underscores the potential influence of environmental factors on aesthetic preferences – a realization that may cause one to furrow their smooth, Botox-treated brow in contemplation.

The striking correlation uncovered in this study, with an r-squared value implying that around two-thirds of the variability in Botox treatments can be attributed to changes in air quality, prompts us to consider the unexpected ways in which our surroundings may shape our beauty standards and cosmetic decisions. The scatterplot depicting the clustering of data points along a positively sloped trend line provides a visual testament to the intriguing interplay between atmospheric quality and the pursuit of youthfulness.

With the evidence presented, it is clear that this phenomenon merits further investigation, although one might say we have already injected a substantial amount of insight into this area of research. The implications of this correlation extend beyond the scope of conventional understanding, hinting at the profound influence of our environment on our desire to defy the signs of aging. However, despite the potential for further exploration, it is safe to assert, with a wrinkle-free certainty, that no more research is needed in this area.

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited Injecting Some Fresh Air: The Correlation between Air Quality in Greenwood, South Carolina, and Botox Injections Administered to Women. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Cosmetic Epidemiology

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 Injecting Some Fresh Air: The Correlation between Air Quality in Greenwood, South Carolina, and Botox Injections Administered to Women. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Atmospheric Cosmetic Research

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 Injecting Some Fresh Air: The Correlation between Air Quality in Greenwood, South Carolina, and Botox Injections Administered to Women with an abstract of This study investigates the relationship between air quality in Greenwood, South Carolina, and the number of Botox injections administered to women over a 20-year period. Using data from the Environmental Protection Agency and the American Society for Aesthetic Plastic Surgery, a correlation coefficient of 0.8213269 and a statistically significant p-value of less than 0.01 were calculated for the years 1997 to 2016. The findings suggest a notable association between air quality and the demand for Botox treatments. While this connection may initially seem like a stretch, our data analysis raises intriguing questions about the potential influence of environmental factors on aesthetic procedures. Further research is warranted to explore the mechanisms underlying this peculiar correlation and its implications for public health and beauty standards.

ChatGPT:

greenwood south carolina, air quality, botox injections, women, correlation, environmental factors, aesthetic procedures, public health, beauty standards

*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 quality in Greenwood, South Carolina
Detailed data title: Percentage of days with good or moderate air quality in Greenwood, SC
Source: Environmental Protection Agency
See what else correlates with Air quality in Greenwood, South Carolina

Number of Botox Injections Administered to Women
Source: American Society for Aesthetic Plastic Surgery
See what else correlates with Number of Botox Injections Administered to Women

Correlation r = 0.8213269 (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 = 0.6745778 (Coefficient of determination)
This means 67.5% of the change in the one variable (i.e., Number of Botox Injections Administered to Women) is predictable based on the change in the other (i.e., Air quality in Greenwood, South Carolina) over the 20 years from 1997 through 2016.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 9.03E-6.^Show 0.0000090328028335848070000000
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 0.82 in 0.000903% of random cases. Said differently, if you correlated 110,708 random variables^Note You don't actually need 110 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.

[ 0.6, 0.93 ] 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.

	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016
Air quality in Greenwood, South Carolina (Good air quality)	0.895833	0.842324	0.835878	0.878788	0.886447	0.860806	0.959259	0.969466	0.939394	0.94382	0.916084	0.973684	1	1	1	0.995261	1	1	1	1
Number of Botox Injections Administered to Women (Injections)	60000	148000	455000	971000	1380000	1424000	1963000	2525000	2990000	2881000	2446000	2239000	2299000	2212000	2355000	2916000	3381000	3174000	3828000	4144000

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([0.895833,0.842324,0.835878,0.878788,0.886447,0.860806,0.959259,0.969466,0.939394,0.94382,0.916084,0.973684,1,1,1,0.995261,1,1,1,1,])
array_2 = np.array([60000,148000,455000,971000,1380000,1424000,1963000,2525000,2990000,2881000,2446000,2239000,2299000,2212000,2355000,2916000,3381000,3174000,3828000,4144000,])
array_1_name = "Air quality in Greenwood, South Carolina"
array_2_name = "Number of Botox Injections Administered to Women"

# 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 quality in Greenwood, South Carolina
Line chart for only Number of Botox Injections Administered to Women
AI-generated correlation image
The spurious research paper: Injecting Some Fresh Air: The Correlation between Air Quality in Greenwood, South Carolina, and Botox Injections Administered to Women

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Correlation ID: 5176 · Black Variable ID: 22313 · Red Variable ID: 9


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