Spurious correlation #4,565 · 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 Manchester, New Hampshire and the second variable is Google searches for 'i have the flu'. The chart goes from 2004 to 2023, 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 Manchester, New Hampshire and the second variable is Google searches for 'i have the flu'. The chart goes from 2004 to 2023, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the air quality improved, more people in Manchester, New Hampshire decided to venture outside. However, this merely served to spread the flu virus further, leading to an increase in flu cases. One could say that the flu was really "air-itated" by the sudden influx of healthier air and decided to make a strong comeback, much to the dismay of the residents.

^{Show image generation prompt} Model: dalle-3
Prompt: [ A photo-realistic image ] of Manchester, New Hampshire, showing a serene cityscape with clear blue skies, families strolling along the streets while others relax at a quaint park. The improved air quality is evident in the vibrant colors and clarity of the surroundings. However, amidst the tranquil scene, subtle cues of illness and unease are woven into the image as more and more individuals are observed checking their health on smartphones or expressing concern on their faces. The juxtaposition of the lively city and the rising flu cases is captured in this mesmerizing visual. 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 Manchester, New Hampshire and Google searches for 'i have the flu'. 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 scene from Skyrim Steampunk a funny image macro Celtic art A photo-realistic image
ChatGPT: A photo-realistic image

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Air quality in Manchester, New Hampshire and Google searches for 'i have the flu', in the following situation: As the air quality improved, more people in Manchester, New Hampshire decided to venture outside. However, this merely served to spread the flu virus further, leading to an increase in flu cases. One could say that the flu was really "air-itated" by the sudden influx of healthier air and decided to make a strong comeback, much to the dismay of the residents.. Keep the description simple.
ChatGPT: In Manchester, New Hampshire, as the air quality improved, more people were seen stepping out of their homes and enjoying the fresh air in the city. However, amidst the increased activity, a surge of people began searching for "i have the flu" on Google, indicating a rise in flu cases. The flu seemed to be spreading, quite literally, through the air, as the improved air quality unintentionally facilitated its resurgence.

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 photo-realistic image ] and depicting this scene: In Manchester, New Hampshire, as the air quality improved, more people were seen stepping out of their homes and enjoying the fresh air in the city. However, amidst the increased activity, a surge of people began searching for "i have the flu" on Google, indicating a rise in flu cases. The flu seemed to be spreading, quite literally, through the air, as the improved air quality unintentionally facilitated its resurgence. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ A photo-realistic image ] of Manchester, New Hampshire, showing a serene cityscape with clear blue skies, families strolling along the streets while others relax at a quaint park. The improved air quality is evident in the vibrant colors and clarity of the surroundings. However, amidst the tranquil scene, subtle cues of illness and unease are woven into the image as more and more individuals are observed checking their health on smartphones or expressing concern on their faces. The juxtaposition of the lively city and the rising flu cases is captured in this mesmerizing visual.

*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 Manchester, New Hampshire caused i have the flu (Google searches for 'i have the flu') to increase. Include one good pun.

AI academic paper

(Because p < 0.01)

A Breath of Fresh Search: Correlating Air Quality in Manchester, New Hampshire with Google Searches for 'I Have the Flu'

The Journal of Environmental Health and Online Behavior Tracking

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 goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Manchester, New Hampshire and Google searches for 'i have the flu'. 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 name a couple popular internet memes that are related to one of the topics.

Here is the title and abstract of the paper:
[[TITLE]]
A Breath of Fresh Search: Correlating Air Quality in Manchester, New Hampshire with Google Searches for 'I Have the Flu'

[[ABSTRACT]]
This study examines the connection between air quality in Manchester, New Hampshire and the frequency of Google searches for 'I Have the Flu.' With an air of scientific rigor (pun intended) and a dash of playfulness, we analyzed data from the Environmental Protection Agency and Google Trends to investigate this intriguing relationship. Our results reveal a significant correlation coefficient of 0.9003823 and p < 0.01, spanning the years 2004 to 2023. While we take a deep dive into the statistical analysis, we can't help but marvel at the sneaky connections between air pollution and digital health inquiries. Join us on this flu-rry of a journey through the hidden influences on public health-seeking behavior.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The interplay between environmental factors and human health has long been a subject of intrigue and investigation. From the subtle dance of air particles to the dance of statistical analysis, the quest to uncover hidden connections and their implications for public health continues to fascinate researchers.
In this study, we turn our attention to the bustling metropolis of Manchester, New Hampshire, where the air quality is as variable as a scatter plot on a windy day. The city's air pollution levels, like a misbehaving independent variable, have drawn the attention of environmentalists, policymakers, and researchers alike. Simultaneously, the digital realm has become a haven for the inquisitive and the hypochondriacal alike. Enter Google searches—an online repository of human curiosity and perhaps more than a touch of self-diagnosis. We, therefore, embark on a journey to bridge the gap between the tangibility of air quality and the intangibility of online inquiries, in a fusion of scientific inquiry and digital whimsy.
We acknowledge the rather unexpected nature of our endeavor. After all, who would have thought that the odorous molecules wafting through the streets of Manchester could be linked to the virtual queries for flu-related symptoms? Nevertheless, in the spirit of scientific exploration, we dive into the rabbit hole of data analysis with both precision and a sense of wonder.
So, dear reader, fasten your seatbelts as we venture into the realm of statistical correlations and virtual sniffles. This paper aims to shed light on the curious relationship between air quality and online health-seeking behavior, steering clear of hay fever but not of the occasional pun or two.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The investigation of the relationship between environmental factors and public health has yielded a wealth of scholarly contributions. Smith et al. (2015) delved into the intricate interplay between air quality and respiratory illnesses, shedding light on the potential ramifications for public health interventions. Similarly, Doe and Jones (2018) explored the digital landscape of health-related internet searches, unraveling the nuances of online behavior in seeking health information.

Transitioning to the realm of published literature, works such as "The Air We Breathe: A Comprehensive Analysis of Urban Pollution" by Clean et al. (2020) and "Breathless: The Impact of Air Quality on Health" by Fresh et al. (2017) demonstrate the multidisciplinary efforts to understand the implications of air pollution on human health. These scholarly contributions provide a solid foundation for the exploration of the correlation between air quality in Manchester, New Hampshire and Google searches for 'I Have the Flu'.

A departure from non-fiction literature, the fictional realm also offers intriguing narratives that echo the themes of environmental impact and health concerns. Fictional works such as "The Polluted City Chronicles" by Smoggy Author and "Gasping for Breath: A Novel on Air Quality" by Wheezy Writer capture the imagination with their creative interpretations of air quality's effects on public health. While not rooted in empirical research, these imaginative narratives highlight the societal awareness of the connections between environmental conditions and well-being.

In the digital sphere, internet memes can serve as a reflection of popular culture and public discourse surrounding health-related topics. Memes such as the "Two Buttons Meme," featuring options like "Google Symptoms" and "See a Doctor," humorously depict the dilemma of self-diagnosis versus seeking professional medical advice. Likewise, the "Flu Season Starter Pack Meme" humorously encapsulates the common experiences and behaviors associated with the flu season, offering a lighthearted lens through which to view health-related issues. These cultural artifacts mirror the public's engagement with health concerns and the online avenues through which information is sought and shared.

Now that we've taken a thorough and rigorous look at the scholarly literature, let's dive into the data analysis with our statistical life vests securely fastened.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Manchester, New Hampshire and Google searches for 'i have the flu'. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Google Trends . You used data from 2004 to 2023

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]]
A Breath of Fresh Search: Correlating Air Quality in Manchester, New Hampshire with Google Searches for 'I Have the Flu'

[[ABSTRACT]]
This study examines the connection between air quality in Manchester, New Hampshire and the frequency of Google searches for 'I Have the Flu.' With an air of scientific rigor (pun intended) and a dash of playfulness, we analyzed data from the Environmental Protection Agency and Google Trends to investigate this intriguing relationship. Our results reveal a significant correlation coefficient of 0.9003823 and p < 0.01, spanning the years 2004 to 2023. While we take a deep dive into the statistical analysis, we can't help but marvel at the sneaky connections between air pollution and digital health inquiries. Join us on this flu-rry of a journey through the hidden influences on public health-seeking behavior.

[[INTRODUCTION]]
The interplay between environmental factors and human health has long been a subject of intrigue and investigation. From the subtle dance of air particles to the dance of statistical analysis, the quest to uncover hidden connections and their implications for public health continues to fascinate researchers.
In this study, we turn our attention to the bustling metropolis of Manchester, New Hampshire, where the air quality is as variable as a scatter plot on a windy day. The city's air pollution levels, like a misbehaving independent variable, have drawn the attention of environmentalists, policymakers, and researchers alike. Simultaneously, the digital realm has become a haven for the inquisitive and the hypochondriacal alike. Enter Google searches—an online repository of human curiosity and perhaps more than a touch of self-diagnosis. We, therefore, embark on a journey to bridge the gap between the tangibility of air quality and the intangibility of online inquiries, in a fusion of scientific inquiry and digital whimsy.
We acknowledge the rather unexpected nature of our endeavor. After all, who would have thought that the odorous molecules wafting through the streets of Manchester could be linked to the virtual queries for flu-related symptoms? Nevertheless, in the spirit of scientific exploration, we dive into the rabbit hole of data analysis with both precision and a sense of wonder.
So, dear reader, fasten your seatbelts as we venture into the realm of statistical correlations and virtual sniffles. This paper aims to shed light on the curious relationship between air quality and online health-seeking behavior, steering clear of hay fever but not of the occasional pun or two.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

In this section, we delineate the convoluted yet scientifically sound methods employed to unearth the elusive connection between air quality and Google searches for 'I Have the Flu' in Manchester, New Hampshire. Our approach, much like a lab rat in a maze, was guided by precision, persistence, and the occasional burst of silliness.

**Data Collection**
We commenced our escapade by gathering air quality data from the Environmental Protection Agency (EPA). These datasets provided a treasure trove of information on various air pollutants, including particulate matter, nitrogen dioxide, ozone, and sulfur dioxide. Channeling our inner data miners, we extracted hourly, daily, and yearly averages, accumulating an impressive array of pollution metrics.

For the virtual aspect of our inquiry, we engaged with the spick-and-span world of Google Trends. Here, the frequency of searches for 'I Have the Flu' emerged as our digital canary in the coal mine, mirroring the ebb and flow of public health inquiries. We meticulously recorded the search interest over time, noting the seasonal spikes and the sporadic surges that mirrored a game of health-related Whac-A-Mole.

**Data Preprocessing**
Like a chef preparing a complex molecular gastronomy dish, we scrupulously cleaned and preprocessed the data to ensure its palatability for statistical analysis. Outliers were gently nudged towards the exit door, missing values were coaxed back into existence, and the datasets were massaged into a state of pristine data hygiene.

**Statistical Analysis**
Our statistical odyssey, much like Odysseus' journey home, was fraught with twists and turns. We began by calculating descriptive statistics for the air quality variables, reveling in the mean, median, and standard deviation. Next, we peeked behind the curtain of correlation analysis, where the Pearson correlation coefficient emerged as our beacon of insight, guiding us towards the promised land of statistical significance.

To appease the gods of scientific rigor, we performed linear regression analysis, modeling the relationship between air pollutants and the frequency of flu-related searches. The resulting coefficients and p-values, akin to the treasures of a statistical pirate, bestowed upon us the evidence needed to support our claims.

**Time Series Analysis**
Venturing into the twilight zone of time series analysis, we crafted intricate plots that mirrored the undulating patterns of air pollution and search interest. The cyclical nature of seasonal trends danced before our eyes, resembling a statistical waltz that hinted at the underlying rhythms of public health inquiries.

**Machine Learning Interlude**
In a moment of daring experimentation, we delved into the realm of machine learning, fashioning a predictive model that sought to forecast the volume of 'I Have the Flu' searches based on air quality variables. Like mad scientists in a data-driven laboratory, we tinkered with algorithms, fine-tuning our models to capture the intricacies of human curiosity and the flu season.

**Temporal Shift Analysis**
Recognizing the fluidity of time and the dynamics of influence, we embarked on a temporal shift analysis to unravel the temporal relationship between air quality and flu-related searches. This endeavor, akin to hunting for a statistical needle in a haystack, revealed lagged effects and temporal nuances that added depth to our understanding of the intertwined variables.

**Sensitivity Analysis**
To fortify the fortress of our findings, we conducted a sensitivity analysis, testing the robustness of our results to varying assumptions and statistical methodologies. The aim was to ensure that our conclusions stood firm, unaffected by the occasional gusts of statistical uncertainty.

As the dust settled and the statistical artifacts revealed their secrets, we emerged victorious, armed with insights that bridged the chasm between air quality and virtual sniffles. With a pinch of rigor and a dash of whimsy, our methodology traversed the realms of data collection, statistical analysis, and scientific play.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the results section of an academic research paper. You speak in the past tense (you found...).
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 results section of an academic research paper presenting the findings of the connection between Air quality in Manchester, New Hampshire and Google searches for 'i have the flu'. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Google Trends .

For the time period 2004 to 2023, you found a correlation 0.9003823, r-squared of 0.8106884, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
A Breath of Fresh Search: Correlating Air Quality in Manchester, New Hampshire with Google Searches for 'I Have the Flu'

[[ABSTRACT]]
This study examines the connection between air quality in Manchester, New Hampshire and the frequency of Google searches for 'I Have the Flu.' With an air of scientific rigor (pun intended) and a dash of playfulness, we analyzed data from the Environmental Protection Agency and Google Trends to investigate this intriguing relationship. Our results reveal a significant correlation coefficient of 0.9003823 and p < 0.01, spanning the years 2004 to 2023. While we take a deep dive into the statistical analysis, we can't help but marvel at the sneaky connections between air pollution and digital health inquiries. Join us on this flu-rry of a journey through the hidden influences on public health-seeking behavior.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The interplay between environmental factors and human health has long been a subject of intrigue and investigation. From the subtle dance of air particles to the dance of statistical analysis, the quest to uncover hidden connections and their implications for public health continues to fascinate researchers.
In this study, we turn our attention to the bustling metropolis of Manchester, New Hampshire, where the air quality is as variable as a scatter plot on a windy day. The city's air pollution levels, like a misbehaving independent variable, have drawn the attention of environmentalists, policymakers, and researchers alike. Simultaneously, the digital realm has become a haven for the inquisitive and the hypochondriacal alike. Enter Google searches—an online repository of human curiosity and perhaps more than a touch of self-diagnosis. We, therefore, embark on a journey to bridge the gap between the tangibility of air quality and the intangibility of online inquiries, in a fusion of scientific inquiry and digital whimsy.
We acknowledge the rather unexpected nature of our endeavor. After all, who would have thought that the odorous molecules wafting through the streets of Manchester could be linked to the virtual queries for flu-related symptoms? Nevertheless, in the spirit of scientific exploration, we dive into the rabbit hole of data analysis with both precision and a sense of wonder.
So, dear reader, fasten your seatbelts as we venture into the realm of statistical correlations and virtual sniffles. This paper aims to shed light on the curious relationship between air quality and online health-seeking behavior, steering clear of hay fever but not of the occasional pun or two.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The results of our analysis unveil a noteworthy correlation between air quality in Manchester, New Hampshire and the frequency of Google searches for 'I Have the Flu.' With a correlation coefficient of 0.9003823, an r-squared of 0.8106884, and a p-value less than 0.01, the connection between these two seemingly disparate variables is as clear as day (or as hazy as smog, depending on how you look at it).

The scatterplot in Fig. 1 illustrates this relationship with all the subtlety of a stampede in a library. Each data point, like a tiny digital sneeze, falls neatly in line with the overall trend, creating a picture-perfect representation of the strong correlation we observed.

As we marveled at our statistical findings, we couldn't help but marvel at the intricate dance between air quality and virtual health queries. It's almost as if the air pollution in Manchester whispered to the residents, "flu's the word," prompting them to consult Dr. Google for remedies and reassurance. Ah, the mysteries of human behavior in the age of the internet—we can only hope that future research will continue to unearth such delightful quirks.

In the world of statistics, a correlation of this magnitude is about as rare as finding a unicorn in a statistical software manual. The strength of the relationship between air quality and flu-related Google searches is nothing to sneeze at (pun fully intended), and it highlights the potential impact of environmental factors on public health-seeking behavior.

So, there you have it, dear reader. Our results not only confirm the significant correlation between air quality and online flu inquiries but also remind us that sometimes, the most unexpected connections can emerge from the most unlikely places. After all, who would have thought that the flu bug and air pollution could be caught in the same statistical net? It just goes to show that in the world of research, every data point tells a story—and sometimes, that story comes with a touch of whimsy.

In conclusion, our findings provide valuable insights into the interplay between environmental factors and digital health inquiries, painting a picture of a world where the air we breathe can influence the words we type into search engines. We invite future researchers to delve deeper into this intriguing relationship, armed with a healthy dose of skepticism, a keen eye for patterns, and perhaps a face mask for protection against both airborne pollutants and unexpected statistical surprises.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Manchester, New Hampshire and Google searches for 'i have the flu'. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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]]
A Breath of Fresh Search: Correlating Air Quality in Manchester, New Hampshire with Google Searches for 'I Have the Flu'

[[ABSTRACT]]
This study examines the connection between air quality in Manchester, New Hampshire and the frequency of Google searches for 'I Have the Flu.' With an air of scientific rigor (pun intended) and a dash of playfulness, we analyzed data from the Environmental Protection Agency and Google Trends to investigate this intriguing relationship. Our results reveal a significant correlation coefficient of 0.9003823 and p < 0.01, spanning the years 2004 to 2023. While we take a deep dive into the statistical analysis, we can't help but marvel at the sneaky connections between air pollution and digital health inquiries. Join us on this flu-rry of a journey through the hidden influences on public health-seeking behavior.

[[LITERATURE REVIEW]]
The investigation of the relationship between environmental factors and public health has yielded a wealth of scholarly contributions. Smith et al. (2015) delved into the intricate interplay between air quality and respiratory illnesses, shedding light on the potential ramifications for public health interventions. Similarly, Doe and Jones (2018) explored the digital landscape of health-related internet searches, unraveling the nuances of online behavior in seeking health information.
Transitioning to the realm of published literature, works such as "The Air We Breathe: A Comprehensive Analysis of Urban Pollution" by Clean et al. (2020) and "Breathless: The Impact of Air Quality on Health" by Fresh et al. (2017) demonstrate the multidisciplinary efforts to understand the implications of air pollution on human health. These scholarly contributions provide a solid foundation for the exploration of the correlation between air quality in Manchester, New Hampshire and Google searches for 'I Have the Flu'.
A departure from non-fiction literature, the fictional realm also offers intriguing narratives that echo the themes of environmental impact and health concerns. Fictional works such as "The Polluted City Chronicles" by Smoggy Author and "Gasping for Breath: A Novel on Air Quality" by Wheezy Writer capture the imagination with their creative interpretations of air quality's effects on public health. While not rooted in empirical research, these imaginative narratives highlight the societal awareness of the connections between environmental conditions and well-being.
In the digital sphere, internet memes can serve as a reflection of popular culture and public discourse surrounding health-related topics. Memes such as the "Two Buttons Meme," featuring options like "Google Symptoms" and "See a Doctor," humorously depict the dilemma of self-diagnosis versus seeking professional medical advice. Likewise, the "Flu Season Starter Pack Meme" humorously encapsulates the common experiences and behaviors associated with the flu season, offering a lighthearted lens through which to view health-related issues. These cultural artifacts mirror the public's engagement with health concerns and the online avenues through which information is sought and shared.

Now that we've taken a thorough and rigorous look at the scholarly literature, let's dive into the data analysis with our statistical life vests securely fastened.

[[RESULTS]]
The results of our analysis unveil a noteworthy correlation between air quality in Manchester, New Hampshire and the frequency of Google searches for 'I Have the Flu.' With a correlation coefficient of 0.9003823, an r-squared of 0.8106884, and a p-value less than 0.01, the connection between these two seemingly disparate variables is as clear as day (or as hazy as smog, depending on how you look at it).
The scatterplot in Fig. 1 illustrates this relationship with all the subtlety of a stampede in a library. Each data point, like a tiny digital sneeze, falls neatly in line with the overall trend, creating a picture-perfect representation of the strong correlation we observed.
As we marveled at our statistical findings, we couldn't help but marvel at the intricate dance between air quality and virtual health queries. It's almost as if the air pollution in Manchester whispered to the residents, "flu's the word," prompting them to consult Dr. Google for remedies and reassurance. Ah, the mysteries of human behavior in the age of the internet—we can only hope that future research will continue to unearth such delightful quirks.
In the world of statistics, a correlation of this magnitude is about as rare as finding a unicorn in a statistical software manual. The strength of the relationship between air quality and flu-related Google searches is nothing to sneeze at (pun fully intended), and it highlights the potential impact of environmental factors on public health-seeking behavior.
So, there you have it, dear reader. Our results not only confirm the significant correlation between air quality and online flu inquiries but also remind us that sometimes, the most unexpected connections can emerge from the most unlikely places. After all, who would have thought that the flu bug and air pollution could be caught in the same statistical net? It just goes to show that in the world of research, every data point tells a story—and sometimes, that story comes with a touch of whimsy.
In conclusion, our findings provide valuable insights into the interplay between environmental factors and digital health inquiries, painting a picture of a world where the air we breathe can influence the words we type into search engines. We invite future researchers to delve deeper into this intriguing relationship, armed with a healthy dose of skepticism, a keen eye for patterns, and perhaps a face mask for protection against both airborne pollutants and unexpected statistical surprises.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Having uncovered a statistically significant correlation between air quality in Manchester, New Hampshire and Google searches for 'I Have the Flu,' we're left pondering the delightful complexities of human behavior in response to environmental cues. Our findings not only support prior research linking air pollution and health outcomes but also reveal the intriguing interplay between public health-seeking behavior and atmospheric conditions. It's as if the digital world and the physical world decided to do an unexpected tango, with each step revealing new insights into the influence of air quality on online health inquiries.

As we knowingly wink at the literature, our results echo the serious work of Smith et al. (2015) and Doe and Jones (2018), demonstrating that the relationship between environmental factors and public health doesn't shy away from a bit of scientific playfulness. The connection we've uncovered is not only statistically robust but also as captivating as a good mystery novel—only instead of a whodunit, we're unravelling the secrets of "whocoughedit."

With a correlation coefficient the size of a blue whale in statistical waters, our results emphasize the importance of considering environmental factors in the realm of digital health inquiries. The dots on our scatterplot practically dance a jig, each one conspiring to reveal the robustness of this intriguing association. It's almost as if the data points themselves are angling for a leading role in a statistical sitcom, complete with hidden relationships, unexpected twists, and a touch of statistical humor.

In the grand tapestry of research, our findings serve as a compelling reminder that beyond the dry statistics and formidable p-values, there's a world of discovery waiting to unfold. We hope that our study sparks further investigations into the whimsical relationships that science can unveil. After all, who knows what other surprising connections might be lurking in the maze of data, ready to leap out with a statistical wink and a nudge?

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.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 Manchester, New Hampshire and Google searches for 'i have the flu'. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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]]
A Breath of Fresh Search: Correlating Air Quality in Manchester, New Hampshire with Google Searches for 'I Have the Flu'

[[ABSTRACT]]
This study examines the connection between air quality in Manchester, New Hampshire and the frequency of Google searches for 'I Have the Flu.' With an air of scientific rigor (pun intended) and a dash of playfulness, we analyzed data from the Environmental Protection Agency and Google Trends to investigate this intriguing relationship. Our results reveal a significant correlation coefficient of 0.9003823 and p < 0.01, spanning the years 2004 to 2023. While we take a deep dive into the statistical analysis, we can't help but marvel at the sneaky connections between air pollution and digital health inquiries. Join us on this flu-rry of a journey through the hidden influences on public health-seeking behavior.

[[INTRDUCTION]]
The interplay between environmental factors and human health has long been a subject of intrigue and investigation. From the subtle dance of air particles to the dance of statistical analysis, the quest to uncover hidden connections and their implications for public health continues to fascinate researchers.
In this study, we turn our attention to the bustling metropolis of Manchester, New Hampshire, where the air quality is as variable as a scatter plot on a windy day. The city's air pollution levels, like a misbehaving independent variable, have drawn the attention of environmentalists, policymakers, and researchers alike. Simultaneously, the digital realm has become a haven for the inquisitive and the hypochondriacal alike. Enter Google searches—an online repository of human curiosity and perhaps more than a touch of self-diagnosis. We, therefore, embark on a journey to bridge the gap between the tangibility of air quality and the intangibility of online inquiries, in a fusion of scientific inquiry and digital whimsy.
We acknowledge the rather unexpected nature of our endeavor. After all, who would have thought that the odorous molecules wafting through the streets of Manchester could be linked to the virtual queries for flu-related symptoms? Nevertheless, in the spirit of scientific exploration, we dive into the rabbit hole of data analysis with both precision and a sense of wonder.
So, dear reader, fasten your seatbelts as we venture into the realm of statistical correlations and virtual sniffles. This paper aims to shed light on the curious relationship between air quality and online health-seeking behavior, steering clear of hay fever but not of the occasional pun or two.

[[RESULTS]]
The results of our analysis unveil a noteworthy correlation between air quality in Manchester, New Hampshire and the frequency of Google searches for 'I Have the Flu.' With a correlation coefficient of 0.9003823, an r-squared of 0.8106884, and a p-value less than 0.01, the connection between these two seemingly disparate variables is as clear as day (or as hazy as smog, depending on how you look at it).
The scatterplot in Fig. 1 illustrates this relationship with all the subtlety of a stampede in a library. Each data point, like a tiny digital sneeze, falls neatly in line with the overall trend, creating a picture-perfect representation of the strong correlation we observed.
As we marveled at our statistical findings, we couldn't help but marvel at the intricate dance between air quality and virtual health queries. It's almost as if the air pollution in Manchester whispered to the residents, "flu's the word," prompting them to consult Dr. Google for remedies and reassurance. Ah, the mysteries of human behavior in the age of the internet—we can only hope that future research will continue to unearth such delightful quirks.
In the world of statistics, a correlation of this magnitude is about as rare as finding a unicorn in a statistical software manual. The strength of the relationship between air quality and flu-related Google searches is nothing to sneeze at (pun fully intended), and it highlights the potential impact of environmental factors on public health-seeking behavior.
So, there you have it, dear reader. Our results not only confirm the significant correlation between air quality and online flu inquiries but also remind us that sometimes, the most unexpected connections can emerge from the most unlikely places. After all, who would have thought that the flu bug and air pollution could be caught in the same statistical net? It just goes to show that in the world of research, every data point tells a story—and sometimes, that story comes with a touch of whimsy.
In conclusion, our findings provide valuable insights into the interplay between environmental factors and digital health inquiries, painting a picture of a world where the air we breathe can influence the words we type into search engines. We invite future researchers to delve deeper into this intriguing relationship, armed with a healthy dose of skepticism, a keen eye for patterns, and perhaps a face mask for protection against both airborne pollutants and unexpected statistical surprises.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study unravels the invisible threads connecting air quality in Manchester, New Hampshire and the virtual dance of 'I Have the Flu' Google searches. The statistically significant correlation we uncovered highlights the nuanced relationship between environmental variables and online health inquiries, emphasizing the impact of air pollution on public digital sniffles.
While our results may have raised a few eyebrows (or at least prompted a few virtual "achoo"s), they underscore the importance of considering the air we breathe as more than just a silent companion in our daily lives. As we bid adieu to this flu-rry of research, it's clear that the whims of statistical analysis have led us to unexpected findings. Let's just say, in the world of data exploration, there's always room for a few surprise sneezes!
With that said, we assert that further research in this area might be about as fruitful as searching for a statistical needle in a haystack. Therefore, we kindly suggest that future researchers steer their academic curiosity in different, less sniffle-inducing directions. After all, the air of discovery is vast and, dare we say, rather a-breath-taking.

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 A Breath of Fresh Search: Correlating Air Quality in Manchester, New Hampshire with Google Searches for 'I Have the Flu'. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Environmental Health and Online Behavior Tracking

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 A Breath of Fresh Search: Correlating Air Quality in Manchester, New Hampshire with Google Searches for 'I Have the Flu'. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Environmental Health and Wellness Studies

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 A Breath of Fresh Search: Correlating Air Quality in Manchester, New Hampshire with Google Searches for 'I Have the Flu' with an abstract of This study examines the connection between air quality in Manchester, New Hampshire and the frequency of Google searches for 'I Have the Flu.' With an air of scientific rigor (pun intended) and a dash of playfulness, we analyzed data from the Environmental Protection Agency and Google Trends to investigate this intriguing relationship. Our results reveal a significant correlation coefficient of 0.9003823 and p < 0.01, spanning the years 2004 to 2023. While we take a deep dive into the statistical analysis, we can't help but marvel at the sneaky connections between air pollution and digital health inquiries. Join us on this flu-rry of a journey through the hidden influences on public health-seeking behavior.

ChatGPT:

air quality, Manchester New Hampshire, Google searches, 'I Have the Flu', correlation, statistical analysis, public health, health-seeking behavior, environmental protection agency, pollution, digital health inquiries, flu, air pollution

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

Random correlation

Discover a new correlation

View all correlations

View all research papers

Report an error

Data details

Air quality in Manchester, New Hampshire
Detailed data title: Percentage of days with good air quality in Manchester-Nashua, NH
Source: Environmental Protection Agency
See what else correlates with Air quality in Manchester, New Hampshire

Google searches for 'i have the flu'
Detailed data title: Relative volume of Google searches for 'i have the flu' (Worldwide, without quotes)
Source: Google Trends
Additional Info: ^{See full details} Relative search volume (not absolute numbers)

See what else correlates with Google searches for 'i have the flu'

Correlation r = 0.9003823 (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 = 0.8106884 (Coefficient of determination)
This means 81.1% of the change in the one variable (i.e., Google searches for 'i have the flu') is predictable based on the change in the other (i.e., Air quality in Manchester, New Hampshire) over the 20 years from 2004 through 2023.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 6.4E-8.^Show 0.0000000636061665013738900000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.9 in 6.4E-6% of random cases. Said differently, if you correlated 15,721,746 random variables^Note You don't actually need 15 million 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.

[ 0.76, 0.96 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: ^Note If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
Air quality in Manchester, New Hampshire (Good air quality)	78.1421	75.6164	79.726	78.0822	81.1475	86.8493	81.9178	86.8493	84.153	84.6575	89.3151	88.4931	90.9836	93.6986	92.6027	96.7123	97.8142	91.7808	93.4247	84.6154
Google searches for 'i have the flu' (Rel. search volume)	3.33333	4.58333	4.08333	3.91667	6.91667	28.5833	11.1667	15.1667	16.6667	23.5	21.0833	19.4167	18.8333	24.1667	34	30	33.25	21.3333	29.3333	17.6667

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([78.1421,75.6164,79.726,78.0822,81.1475,86.8493,81.9178,86.8493,84.153,84.6575,89.3151,88.4931,90.9836,93.6986,92.6027,96.7123,97.8142,91.7808,93.4247,84.6154,])
array_2 = np.array([3.33333,4.58333,4.08333,3.91667,6.91667,28.5833,11.1667,15.1667,16.6667,23.5,21.0833,19.4167,18.8333,24.1667,34,30,33.25,21.3333,29.3333,17.6667,])
array_1_name = "Air quality in Manchester, New Hampshire"
array_2_name = "Google searches for 'i have the flu'"

# 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 Manchester, New Hampshire
Line chart for only Google searches for 'i have the flu'
AI-generated correlation image
The spurious research paper: A Breath of Fresh Search: Correlating Air Quality in Manchester, New Hampshire with Google Searches for 'I Have the Flu'

View another random correlation

How fun was this correlation?

Your correlation rating is out of this world!

Correlation ID: 4565 · Black Variable ID: 20867 · Red Variable ID: 1528


Problem variable:
Issue:
Additional details: Optional
Confirm you are a human: