Spurious correlation #3,467 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of obstetricians and gynecologists in Oregon and the second variable is Asthma prevalence in American children. The chart goes from 2003 to 2019, 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 The number of obstetricians and gynecologists in Oregon and the second variable is Asthma prevalence in American children. The chart goes from 2003 to 2019, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the number of obstetricians and gynecologists in Oregon decreased, there was a corresponding decrease in the number of baby gender reveal parties, leading to fewer airborne particulates, ultimately reducing the prevalence of childhood asthma in America. One-liner: "Who knew that not knowing the gender could lead to better air quality? It's a wheeze not to have as many sneeze-inducing parties!"

^{Show image generation prompt} Model: dalle-3
Prompt: Scene: In a cozy nursery in Oregon, a thoughtful obstetrician gently sprinkles fragrant baby powder on a smiling infant's bottom, ensuring a comfortable and dry feeling. Nearby, a group of happy children plays in a green park under blue skies. However, a cloud of concern hovers above them, representing the increased prevalence of asthma in American children due to the shortage of baby powder enthusiasts, leading to suboptimal powdering and heightened susceptibility to asthma triggers. DALLE-3 Prompt: Generate an image with a serene nursery scene showing a peaceful, smiling infant being gently powdered by an obstetrician. Outside, depict a joyful group of children playing in a lush green park under a beautiful blue sky. Include a subtle, symbolic cloud representing the concern of increased asthma prevalence due to the shortage of baby powder enthusiasts. The focus should be on the tender care of the nursery and the children's playful innocence, with the cloud serving as a subtle, thought-provoking element. Enhanced DALLE-3 Prompt: Create an image capturing the tranquility of a nursery in Oregon with a soft, pastel-colored nursery room filled with gentle light. Show an attentive obstetrician sprinkling fragrant baby powder on a content, smiling infant's bottom, surrounded by warm, welcoming details. Outside, depict a vibrant green park with happy children playing under a clear, sunny blue sky, evoking a sense of carefree joy and vitality. Incorporate a cloud hovering in the background with a symbolic representation of concern, subtly hinting at the increased asthma prevalence due to the shortage of baby powder enthusiasts, adding depth to the serene and idyllic scene. 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: The number of obstetricians and gynecologists in Oregon and Asthma prevalence in American children. 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: Macabre A scene from Skyrim Vaporwave Steampunk Data cyber art
ChatGPT: Data cyber art

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 The number of obstetricians and gynecologists in Oregon and Asthma prevalence in American children, in the following situation: Asthma prevalence in American children increased because the decrease in obstetricians and gynecologists in Oregon led to a shortage of baby powder enthusiasts, resulting in suboptimal powdering of infants' bottoms, causing heightened susceptibility to asthma triggers.. Keep the description simple.
ChatGPT: In a cozy nursery in Oregon, a thoughtful obstetrician gently sprinkles fragrant baby powder on a smiling infant's bottom, ensuring a comfortable and dry feeling. Nearby, a group of happy children plays in a green park under blue skies. However, a cloud of concern hovers above them, representing the increased prevalence of asthma in American children due to the shortage of baby powder enthusiasts, leading to suboptimal powdering and heightened susceptibility to asthma triggers.

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: [ Data cyber art ] and depicting this scene: In a cozy nursery in Oregon, a thoughtful obstetrician gently sprinkles fragrant baby powder on a smiling infant's bottom, ensuring a comfortable and dry feeling. Nearby, a group of happy children plays in a green park under blue skies. However, a cloud of concern hovers above them, representing the increased prevalence of asthma in American children due to the shortage of baby powder enthusiasts, leading to suboptimal powdering and heightened susceptibility to asthma triggers. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Scene: In a cozy nursery in Oregon, a thoughtful obstetrician gently sprinkles fragrant baby powder on a smiling infant's bottom, ensuring a comfortable and dry feeling. Nearby, a group of happy children plays in a green park under blue skies. However, a cloud of concern hovers above them, representing the increased prevalence of asthma in American children due to the shortage of baby powder enthusiasts, leading to suboptimal powdering and heightened susceptibility to asthma triggers. DALLE-3 Prompt: Generate an image with a serene nursery scene showing a peaceful, smiling infant being gently powdered by an obstetrician. Outside, depict a joyful group of children playing in a lush green park under a beautiful blue sky. Include a subtle, symbolic cloud representing the concern of increased asthma prevalence due to the shortage of baby powder enthusiasts. The focus should be on the tender care of the nursery and the children's playful innocence, with the cloud serving as a subtle, thought-provoking element. Enhanced DALLE-3 Prompt: Create an image capturing the tranquility of a nursery in Oregon with a soft, pastel-colored nursery room filled with gentle light. Show an attentive obstetrician sprinkling fragrant baby powder on a content, smiling infant's bottom, surrounded by warm, welcoming details. Outside, depict a vibrant green park with happy children playing under a clear, sunny blue sky, evoking a sense of carefree joy and vitality. Incorporate a cloud hovering in the background with a symbolic representation of concern, subtly hinting at the increased asthma prevalence due to the shortage of baby powder enthusiasts, adding depth to the serene and idyllic scene.

*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 The number of obstetricians and gynecologists in Oregon caused Asthma prevalence in American children to decrease. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

Ob-Gyn Density and Kids' Wheeze Tensity: A Rhyme Time Analysis

International Journal of Pediatric Parodies

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 The number of obstetricians and gynecologists in Oregon and Asthma prevalence in American children. 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]]
"Ob-Gyn Density and Kids' Wheeze Tensity: A Rhyme Time Analysis"

[[ABSTRACT]]
This study delves into the relationship between the number of obstetricians and gynecologists in Oregon and asthma prevalence in American children. Our research team utilized data from the Bureau of Labor Statistics and the National Center for Health Statistics to address this wheezy riddle. After crunching the numbers, we uncovered a striking correlation coefficient of 0.8656855 and a p-value less than 0.01 for the years 2003 to 2019.

It became clear that a higher density of ob-gyns in Oregon was associated with a higher prevalence of asthma among American children across the country. This unexpected connection had our research team pondering the phrase "when there's a wheeze, there's a way" with a mix of intrigue and amusement. Our findings suggest that there may be an intriguing link between the presence of ob-gyns and the respiratory challenges faced by the younger generation. The implications of this association could lead to further studies exploring the potential mechanisms at play, providing a breath of fresh air for future research in both medical and economic circles.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Asthma has long been a thorny respiratory issue for many children, causing wheezing, coughing, and a few sleepless nights for both kids and their parents. The prevalence of asthma among children has been a topic of interest for medical researchers and has sparked numerous studies in pursuit of understanding its underlying causes. However, the potential influence of obstetricians and gynecologists (ob-gyns) on asthma prevalence in children has largely gone unexplored, until now.
As we delve into this unexpected connection between ob-gyn density and kids' wheeze tensity, it's worth noting that the pun game here is strong – or should we say, wheezy. Our research seeks to untangle this seemingly obscure relationship, shedding light on the potential impact of healthcare provider density on the respiratory well-being of the younger population.
Findings in this area could have far-reaching implications, and dare we say, may even knock the wind out of established assumptions about the determinants of childhood asthma. With that in mind, let's dive into this curious coupling of medical and demographic factors and see if it wheezes – er, we mean, if it sneezes, it leads.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

Smith et al. (2015) in "Health Trends and Medical Practices" found that the density of healthcare providers in a region can have significant implications for the health outcomes of the population. Similarly, Doe and Jones (2018) in "The Impact of Medical Professionals on Community Health" highlighted the potential influence of obstetricians and gynecologists on various health indicators, albeit with minimal focus on pediatric respiratory conditions.

Now, onto the more rascally part of the literature review. It's time to take a break from the serious scholarly sources and delve into the intriguing world of non-fiction books related to our topic. "Breath: The New Science of a Lost Art" by James Nestor offers fascinating insights into the mechanics of respiration and its impact on overall health. In a similar vein, "Asthma for Dummies" by William E. Berger provides a comprehensive yet accessible guide to understanding the complexities of this respiratory condition.

Turning to the world of fiction, we encounter "The Asthma and Allergy Action Plan for Kids" by Mimi Guarneri, which, despite its seemingly practical title, may not actually delve much into action-packed adventure scenarios involving asthma and allergens. Likewise, "The Wheezing Dragon Chronicles" by Rebecca Rupp surprisingly does not feature fire-breathing dragons with respiratory issues, much to the disappointment of many fantasy enthusiasts.

In a striking departure from conventional academic research practices, our team also expanded our literature search to less traditional sources. This included a thorough analysis of the backs of shampoo bottles, where we surprisingly encountered a notable lack of insights into the potential relationship between ob-gyn density and childhood asthma prevalence. It appears that the elusive answers to this wheezy riddle do not lie in the world of personal care product descriptions, leaving us pondering the untapped potential of shampoo-based medical research.

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 The number of obstetricians and gynecologists in Oregon and Asthma prevalence in American children. 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 Bureau of Larbor Statistics and National Center for Health Statistics . You used data from 2003 to 2019

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]]
"Ob-Gyn Density and Kids' Wheeze Tensity: A Rhyme Time Analysis"

[[ABSTRACT]]
This study delves into the relationship between the number of obstetricians and gynecologists in Oregon and asthma prevalence in American children. Our research team utilized data from the Bureau of Labor Statistics and the National Center for Health Statistics to address this wheezy riddle. After crunching the numbers, we uncovered a striking correlation coefficient of 0.8656855 and a p-value less than 0.01 for the years 2003 to 2019.

It became clear that a higher density of ob-gyns in Oregon was associated with a higher prevalence of asthma among American children across the country. This unexpected connection had our research team pondering the phrase "when there's a wheeze, there's a way" with a mix of intrigue and amusement. Our findings suggest that there may be an intriguing link between the presence of ob-gyns and the respiratory challenges faced by the younger generation. The implications of this association could lead to further studies exploring the potential mechanisms at play, providing a breath of fresh air for future research in both medical and economic circles.

[[INTRODUCTION]]
Asthma has long been a thorny respiratory issue for many children, causing wheezing, coughing, and a few sleepless nights for both kids and their parents. The prevalence of asthma among children has been a topic of interest for medical researchers and has sparked numerous studies in pursuit of understanding its underlying causes. However, the potential influence of obstetricians and gynecologists (ob-gyns) on asthma prevalence in children has largely gone unexplored, until now.
As we delve into this unexpected connection between ob-gyn density and kids' wheeze tensity, it's worth noting that the pun game here is strong – or should we say, wheezy. Our research seeks to untangle this seemingly obscure relationship, shedding light on the potential impact of healthcare provider density on the respiratory well-being of the younger population.
Findings in this area could have far-reaching implications, and dare we say, may even knock the wind out of established assumptions about the determinants of childhood asthma. With that in mind, let's dive into this curious coupling of medical and demographic factors and see if it wheezes – er, we mean, if it sneezes, it leads.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To embark on our wheezy adventure, we first obtained data on the number of obstetricians and gynecologists in Oregon from the Bureau of Labor Statistics. We then scampered over to the National Center for Health Statistics to gather information on asthma prevalence among American children from 2003 to 2019. These datasets were our compass, guiding us through the labyrinth of statistical analysis as we sought to uncover the hidden connections between ob-gyn density and kids' wheeze tensity.

Our approach involved a dose of statistical alchemy, blending the art of regression analysis with the science of spatial econometrics. We tinkered with various models, including ordinary least squares and spatial lag models, to capture the spatial dependence inherent in healthcare provider distribution and asthma prevalence across different geographical regions. Just like navigating through a maze of medical data, our methods aimed to unravel the intertwined relationship between the presence of ob-gyns and the respiratory challenges faced by young ones.

We further spiced up our analysis by incorporating control variables such as socioeconomic status, air pollution levels, and access to healthcare services. Adjusting for these factors allowed us to sift through the confounding variables and zero in on the peculiar association between ob-gyn density in Oregon and the prevalence of asthma among American children. It was akin to performing a complex medical diagnosis on the intertwined factors at play, teasing out the nuances of this unexpected relationship.

Now, let's take a brief intermission for a dad joke:
Why did the doctor carry a red pen?
In case they needed to draw blood!

Back to our methodology—our data wrangling involved meticulously cleaning, harmonizing, and organizing the disparate datasets to ensure their compatibility. This step was crucial to prevent the spread of misinformation within our analysis – or as we'd like to say, to uphold the "health" of our statistical inferences.

To address the temporal dimension of our study, we conducted a series of robustness checks, including time-series analyses and dynamic panel data models. By delving into the ebb and flow of these data over a 16-year period, we aimed to capture the evolving dynamics of ob-gyn density and asthma prevalence, akin to monitoring the pulse of these interconnected variables.

An amusing interlude is in tow:
Why don't skeletons fight each other?
They don't have the guts!

Resuming our research methodology, we also ventured into sensitivity analyses, probing the robustness of our findings to different model specifications and assumptions. This scrutiny allowed us to gauge the stability of our results and assure ourselves that our statistical inferences were not just a "fluke" but stood the test of scientific rigor.

Finally, we implemented spatial mapping techniques to visually depict the geographic distribution of obstetricians and gynecologists in Oregon and the hotspots of asthma prevalence among American children. This visual narrative added a layer of depth to our analysis, painting a vivid picture of the spatial interplay between healthcare provider density and respiratory health outcomes.

In wrapping up our methodology, we aim to deliver a fitting punchline:
Why don't seagulls fly over the bay?
Because then they'd be bagels! I'm here all week, folks.

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 The number of obstetricians and gynecologists in Oregon and Asthma prevalence in American children. 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]]
"Ob-Gyn Density and Kids' Wheeze Tensity: A Rhyme Time Analysis"

[[ABSTRACT]]
This study delves into the relationship between the number of obstetricians and gynecologists in Oregon and asthma prevalence in American children. Our research team utilized data from the Bureau of Labor Statistics and the National Center for Health Statistics to address this wheezy riddle. After crunching the numbers, we uncovered a striking correlation coefficient of 0.8656855 and a p-value less than 0.01 for the years 2003 to 2019.

It became clear that a higher density of ob-gyns in Oregon was associated with a higher prevalence of asthma among American children across the country. This unexpected connection had our research team pondering the phrase "when there's a wheeze, there's a way" with a mix of intrigue and amusement. Our findings suggest that there may be an intriguing link between the presence of ob-gyns and the respiratory challenges faced by the younger generation. The implications of this association could lead to further studies exploring the potential mechanisms at play, providing a breath of fresh air for future research in both medical and economic circles.

[[LITERATURE REVIEW]]
Smith et al. (2015) in "Health Trends and Medical Practices" found that the density of healthcare providers in a region can have significant implications for the health outcomes of the population. Similarly, Doe and Jones (2018) in "The Impact of Medical Professionals on Community Health" highlighted the potential influence of obstetricians and gynecologists on various health indicators, albeit with minimal focus on pediatric respiratory conditions.
Now, onto the more rascally part of the literature review. It's time to take a break from the serious scholarly sources and delve into the intriguing world of non-fiction books related to our topic. "Breath: The New Science of a Lost Art" by James Nestor offers fascinating insights into the mechanics of respiration and its impact on overall health. In a similar vein, "Asthma for Dummies" by William E. Berger provides a comprehensive yet accessible guide to understanding the complexities of this respiratory condition.
Turning to the world of fiction, we encounter "The Asthma and Allergy Action Plan for Kids" by Mimi Guarneri, which, despite its seemingly practical title, may not actually delve much into action-packed adventure scenarios involving asthma and allergens. Likewise, "The Wheezing Dragon Chronicles" by Rebecca Rupp surprisingly does not feature fire-breathing dragons with respiratory issues, much to the disappointment of many fantasy enthusiasts.
In a striking departure from conventional academic research practices, our team also expanded our literature search to less traditional sources. This included a thorough analysis of the backs of shampoo bottles, where we surprisingly encountered a notable lack of insights into the potential relationship between ob-gyn density and childhood asthma prevalence. It appears that the elusive answers to this wheezy riddle do not lie in the world of personal care product descriptions, leaving us pondering the untapped potential of shampoo-based medical research.

[[RESULTS]]
The analysis revealed a significant positive correlation between the number of obstetricians and gynecologists in Oregon and the prevalence of asthma among American children. The correlation coefficient of 0.8656855 indicated a strong linear relationship between these variables, suggesting that as the density of ob-gyns increased, so did the prevalence of asthma among children nationwide. This unexpected association left our research team feeling a bit breathless – not from asthma, but from the surprising nature of our findings.
Fig. 1 illustrates the striking correlation between ob-gyn density and asthma prevalence, resembling two dancers in perfect sync. It's as if they were performing a wheezy waltz, showcasing the close connection between these seemingly unrelated factors. As the number of ob-gyns in Oregon increased, the prevalence of asthma in American children followed suit, leading to a captivating duet of data points pirouetting across the scatterplot.
The r-squared value of 0.7494113 highlighted that approximately 74.94% of the variance in asthma prevalence could be explained by the density of ob-gyns in Oregon. This statistical tango between ob-gyn density and asthma prevalence had us feeling like we were caught in a fascinating and unexpected dance routine, with each step revealing more about the intricate relationship between these factors.
Furthermore, the p-value of less than 0.01 provided strong evidence against the null hypothesis, indicating that the observed correlation was highly unlikely to have occurred by chance alone. This statistical punchline delivered a resounding "p-value-ous" blow to any doubts about the genuine connection between ob-gyn density and asthma prevalence, leaving us no choice but to take this intriguing relationship seriously.
In conclusion, our research uncovered a substantial and consistent correlation between the number of obstetricians and gynecologists in Oregon and the prevalence of asthma among American children. This unexpected finding nudged our research team to reconsider the phrase "delivering breaths of fresh air," as it now holds new significance in understanding the potential impact of healthcare provider density on children's respiratory health. Our findings open the door to further investigations into the mechanisms underlying this relationship, offering a breath-taking opportunity for future research and policy considerations.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our study uncovered a remarkable and previously unexplored link between the density of obstetricians and gynecologists in Oregon and the prevalence of asthma among American children. The results of our analysis reinforce the findings of Smith et al. (2015) and Doe and Jones (2018), highlighting the significant influence of healthcare provider density on population health outcomes. This unexpected connection emphasizes the need to consider the broader impact of medical practice distribution on pediatric respiratory conditions.

While the non-fiction books in our literature review may have been originally presented in a lighthearted manner, the insights they offered into respiratory mechanics and asthma management have unexpectedly resonated with our findings. Despite their seemingly unconventional connection to scholarly research, these sources provided valuable perspectives that complemented the more traditional literature. It just goes to show that sometimes, truth can be stranger than fiction, especially when it comes to statistical analysis.

Regarding the surprising inclusion of shampoo bottles in our literature review, it may have seemed tongue-in-cheek at the time, but the absence of relevant insights from these sources actually underscores the novelty of our findings. The potential relationship between ob-gyn density and childhood asthma prevalence remains largely unexplored, leaving us to ponder the untapped potential of unconventional sources in uncovering new research directions. Who knows, maybe the next groundbreaking study will emerge from the unlikeliest of places – perhaps the back of a conditioner bottle will contain the key to unlocking the next scientific mystery.

Our results not only validated the statistically significant correlation between ob-gyn density and childhood asthma prevalence but also extended the existing literature by demonstrating the robustness of this association over a 16-year period. The high correlation coefficient and low p-value left little room for skepticism and added weight to the evidence supporting a genuine relationship between these seemingly disparate variables. It seems that when it comes to healthcare provider density and pediatric respiratory health, the joke might just be on us for not uncovering this connection sooner.

In conducting this study, we ventured into uncharted territory, unearthing unexpected correlations and challenging conventional assumptions. Our findings have opened the door to a new avenue of research, shedding light on the intricate interplay between healthcare provider distribution and pediatric respiratory health. As we look to the future, it is clear that further investigations into the mechanisms underlying this relationship are warranted, offering a wheeze of opportunity for advancing our understanding of healthcare impacts on children's respiratory well-being.

Now, if only we could crack the mystery of why the asthma researcher broke up with her boyfriend... he didn't like her constant inhaler jokes.

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 The number of obstetricians and gynecologists in Oregon and Asthma prevalence in American children. 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]]
"Ob-Gyn Density and Kids' Wheeze Tensity: A Rhyme Time Analysis"

[[ABSTRACT]]
This study delves into the relationship between the number of obstetricians and gynecologists in Oregon and asthma prevalence in American children. Our research team utilized data from the Bureau of Labor Statistics and the National Center for Health Statistics to address this wheezy riddle. After crunching the numbers, we uncovered a striking correlation coefficient of 0.8656855 and a p-value less than 0.01 for the years 2003 to 2019.

It became clear that a higher density of ob-gyns in Oregon was associated with a higher prevalence of asthma among American children across the country. This unexpected connection had our research team pondering the phrase "when there's a wheeze, there's a way" with a mix of intrigue and amusement. Our findings suggest that there may be an intriguing link between the presence of ob-gyns and the respiratory challenges faced by the younger generation. The implications of this association could lead to further studies exploring the potential mechanisms at play, providing a breath of fresh air for future research in both medical and economic circles.

[[INTRDUCTION]]
Asthma has long been a thorny respiratory issue for many children, causing wheezing, coughing, and a few sleepless nights for both kids and their parents. The prevalence of asthma among children has been a topic of interest for medical researchers and has sparked numerous studies in pursuit of understanding its underlying causes. However, the potential influence of obstetricians and gynecologists (ob-gyns) on asthma prevalence in children has largely gone unexplored, until now.
As we delve into this unexpected connection between ob-gyn density and kids' wheeze tensity, it's worth noting that the pun game here is strong – or should we say, wheezy. Our research seeks to untangle this seemingly obscure relationship, shedding light on the potential impact of healthcare provider density on the respiratory well-being of the younger population.
Findings in this area could have far-reaching implications, and dare we say, may even knock the wind out of established assumptions about the determinants of childhood asthma. With that in mind, let's dive into this curious coupling of medical and demographic factors and see if it wheezes – er, we mean, if it sneezes, it leads.

[[RESULTS]]
The analysis revealed a significant positive correlation between the number of obstetricians and gynecologists in Oregon and the prevalence of asthma among American children. The correlation coefficient of 0.8656855 indicated a strong linear relationship between these variables, suggesting that as the density of ob-gyns increased, so did the prevalence of asthma among children nationwide. This unexpected association left our research team feeling a bit breathless – not from asthma, but from the surprising nature of our findings.
Fig. 1 illustrates the striking correlation between ob-gyn density and asthma prevalence, resembling two dancers in perfect sync. It's as if they were performing a wheezy waltz, showcasing the close connection between these seemingly unrelated factors. As the number of ob-gyns in Oregon increased, the prevalence of asthma in American children followed suit, leading to a captivating duet of data points pirouetting across the scatterplot.
The r-squared value of 0.7494113 highlighted that approximately 74.94% of the variance in asthma prevalence could be explained by the density of ob-gyns in Oregon. This statistical tango between ob-gyn density and asthma prevalence had us feeling like we were caught in a fascinating and unexpected dance routine, with each step revealing more about the intricate relationship between these factors.
Furthermore, the p-value of less than 0.01 provided strong evidence against the null hypothesis, indicating that the observed correlation was highly unlikely to have occurred by chance alone. This statistical punchline delivered a resounding "p-value-ous" blow to any doubts about the genuine connection between ob-gyn density and asthma prevalence, leaving us no choice but to take this intriguing relationship seriously.
In conclusion, our research uncovered a substantial and consistent correlation between the number of obstetricians and gynecologists in Oregon and the prevalence of asthma among American children. This unexpected finding nudged our research team to reconsider the phrase "delivering breaths of fresh air," as it now holds new significance in understanding the potential impact of healthcare provider density on children's respiratory health. Our findings open the door to further investigations into the mechanisms underlying this relationship, offering a breath-taking opportunity for future research and policy considerations.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In closing, the unexpected tango between ob-gyn density and asthma prevalence has left us reeling from the spin of unexpected correlation. It seems that when it comes to the density of obstetricians and gynecologists in Oregon and the prevalence of asthma among American children, the connection is as clear as a peak flow meter reading after a gust of wheeze-inducing wind. One might say that this correlation is nothing to "sneeze" at – a true "breath" of fresh air in the world of medical research.

This uncovering of a significant and consistent correlation has us contemplating whether obstetricians and gynecologists might unknowingly be spinning an intricate web of influence, intertwined with the respiratory well-being of the younger population. Perhaps we should consider renaming them "ob-gy-nemesis" for their unexpected impact on childhood respiratory health.

These findings raise tantalizing questions, offering a "lung-citing" opportunity for future investigations into the intricate dance between healthcare provider density and children's respiratory challenges. It's safe to say that this unexpected association has left us with a case of statistical "asthma-nishment."

In summary, our research has provided a lungful of evidence supporting the intriguing relationship between ob-gyn density in Oregon and asthma prevalence among American children, leaving no room for doubt about the genuine nature of this surprising connection. It's as clear as the air on a windy day – 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 Ob-Gyn Density and Kids' Wheeze Tensity: A Rhyme Time Analysis. Respond only with the name of a fictional academic journal.

ChatGPT:

International Journal of Pediatric Parodies

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 Ob-Gyn Density and Kids' Wheeze Tensity: A Rhyme Time Analysis. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Pediatric Rhyme 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 Ob-Gyn Density and Kids' Wheeze Tensity: A Rhyme Time Analysis with an abstract of This study delves into the relationship between the number of obstetricians and gynecologists in Oregon and asthma prevalence in American children. Our research team utilized data from the Bureau of Labor Statistics and the National Center for Health Statistics to address this wheezy riddle. After crunching the numbers, we uncovered a striking correlation coefficient of 0.8656855 and a p-value less than 0.01 for the years 2003 to 2019.
It became clear that a higher density of ob-gyns in Oregon was associated with a higher prevalence of asthma among American children across the country. This unexpected connection had our research team pondering the phrase "when there's a wheeze, there's a way" with a mix of intrigue and amusement. Our findings suggest that there may be an intriguing link between the presence of ob-gyns and the respiratory challenges faced by the younger generation. The implications of this association could lead to further studies exploring the potential mechanisms at play, providing a breath of fresh air for future research in both medical and economic circles.

ChatGPT:

ob-gyn density, pediatric asthma, respiratory health, medical specialist distribution, children's health, asthma prevalence, public health research, correlation analysis, Bureau of Labor Statistics, National Center for Health Statistics, healthcare workforce, children's respiratory challenges, medical specialists and child health, healthcare distribution impact on pediatric health

*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.

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Data details

The number of obstetricians and gynecologists in Oregon
Detailed data title: BLS estimate of obstetricians and gynecologists in Oregon
Source: Bureau of Larbor Statistics
See what else correlates with The number of obstetricians and gynecologists in Oregon

Asthma prevalence in American children
Detailed data title: Percent of children in the US with asthma
Source: National Center for Health Statistics
See what else correlates with Asthma prevalence in American children

Correlation r = 0.8656855 (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 = 0.7494113 (Coefficient of determination)
This means 74.9% of the change in the one variable (i.e., Asthma prevalence in American children) is predictable based on the change in the other (i.e., The number of obstetricians and gynecologists in Oregon) over the 17 years from 2003 through 2019.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 7.1E-6.^Show 0.0000071336379190062120000000
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 0.87 in 0.00071% of random cases. Said differently, if you correlated 140,181 random variables^Note You don't actually need 140 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 16 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 16 because we have two variables measured over a period of 17 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.

[ 0.66, 0.95 ] 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
The number of obstetricians and gynecologists in Oregon (Laborers)	280	320	310	510	420	440	460	560	600	450	310	230	140	230	180	210	100
Asthma prevalence in American children (Percent of children)	8.5	8.5	8.9	9.3	9.1	9.4	9.6	9.4	9.5	9.3	8.3	8.6	8.4	8.3	8.4	7.5	7

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([280,320,310,510,420,440,460,560,600,450,310,230,140,230,180,210,100,])
array_2 = np.array([8.5,8.5,8.9,9.3,9.1,9.4,9.6,9.4,9.5,9.3,8.3,8.6,8.4,8.3,8.4,7.5,7,])
array_1_name = "The number of obstetricians and gynecologists in Oregon"
array_2_name = "Asthma prevalence in American children"

# 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 The number of obstetricians and gynecologists in Oregon
Line chart for only Asthma prevalence in American children
AI-generated correlation image
The spurious research paper: Ob-Gyn Density and Kids' Wheeze Tensity: A Rhyme Time Analysis

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Correlation ID: 3467 · Black Variable ID: 13372 · Red Variable ID: 1133


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