Spurious correlation #3,164 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Phoenix and the second variable is Visitors to Disneyland. The chart goes from 2007 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Phoenix and the second variable is Visitors to Disneyland. The chart goes from 2007 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The smog created a real-life 'Honey, I Shrunk the Kids' situation, making it harder for families to find their children in the park.

^{Show image generation prompt} Model: dalle-3
Prompt: [ A photo-realistic image ] Generate an image of the Phoenix skyline, depicting a hazy atmosphere created by smog. The city should be seen from a distance, capturing the pollution emitted by cars and buildings. In stark contrast, depict Disneyland bustling with visitors in front of the iconic castle. Show families laughing and taking photos, with a clear and fresh atmosphere around the theme park. The scene should illustrate the phenomenon of "smog-induced wanderlust" as people seek refuge in cleaner air, highlighting the draw of popular destinations like Disneyland to escape pollution. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: Air pollution in Phoenix and Visitors to Disneyland. 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: Cutsy art illustration Futuristic a funny image macro A photo-realistic image An image that looks like it is a scene from a Pixar movie
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 pollution in Phoenix and Visitors to Disneyland, in the following situation: Because as air pollution in Phoenix increased, visitors to Disneyland also increased. This is likely due to the phenomenon known as "smog-induced wanderlust," where individuals subconsciously seek out cleaner, fresher air by escaping to popular tourist destinations. As the air quality in Phoenix deteriorates, people are driven to seek refuge in places with purer air, such as the beaches and resorts of Southern California. This has inadvertently led to a surge in attendance at Disneyland, as families and tourists instinctively gravitate towards cleaner air and a breath of fresh, Mickey Mouse-shaped oxygen.. Keep the description simple.
ChatGPT: In the scene, the skyline of Phoenix is hazy with smog as cars and buildings emit pollutants into the air. In contrast, Disneyland is bustling with visitors, with families laughing and taking photos in front of the iconic castle. The air around Disneyland is clear and fresh, providing a stark contrast to the pollution in Phoenix. The scene portrays the phenomenon of "smog-induced wanderlust" as people seek refuge in cleaner air and are drawn to popular destinations like Disneyland to escape the pollution.

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 the scene, the skyline of Phoenix is hazy with smog as cars and buildings emit pollutants into the air. In contrast, Disneyland is bustling with visitors, with families laughing and taking photos in front of the iconic castle. The air around Disneyland is clear and fresh, providing a stark contrast to the pollution in Phoenix. The scene portrays the phenomenon of "smog-induced wanderlust" as people seek refuge in cleaner air and are drawn to popular destinations like Disneyland to escape the pollution. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ A photo-realistic image ] Generate an image of the Phoenix skyline, depicting a hazy atmosphere created by smog. The city should be seen from a distance, capturing the pollution emitted by cars and buildings. In stark contrast, depict Disneyland bustling with visitors in front of the iconic castle. Show families laughing and taking photos, with a clear and fresh atmosphere around the theme park. The scene should illustrate the phenomenon of "smog-induced wanderlust" as people seek refuge in cleaner air, highlighting the draw of popular destinations like Disneyland to escape pollution.

*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 pollution in Phoenix caused Visitors to Disneyland to decrease.

AI academic paper

(Because p < 0.01)

Clear Skies, Mickey Ears: Investigating the Impact of Phoenix Air Pollution on Disneyland Visitors

Journal of Amusement Park Environmental Studies

Jan 2024

Reminder: This paper is AI-generated. Not real!

^{Show prompt used to generate this paper} Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between Air pollution in Phoenix and Visitors to Disneyland. Make lots of 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 devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading CVS receipts.

Here is the title and abstract of the paper:
[[TITLE]]
"Clear Skies, Mickey Ears: Investigating the Impact of Phoenix Air Pollution on Disneyland Visitors"

[[ABSTRACT]]
This whimsical research endeavor delves into the unexpected connection between air pollution in Phoenix, Arizona, and the number of visitors flocking to the magical realm of Disneyland in Anaheim, California. Utilizing data from the Environmental Protection Agency and The Entertainment Association (TEA), our research team employed rigorous statistical analysis to explore the relationship between air quality in Phoenix and the attendance at Disneyland from 2007 to 2021.

Surprisingly, our findings revealed a strong negative correlation, with a correlation coefficient of -0.8945498 and a statistically significant p-value of less than 0.01. In other words, as air pollution in Phoenix rises, the number of visitors to Disneyland tends to decrease. While the causality of this relationship remains elusive, it seems that the enchantment of the "Happiest Place on Earth" may be somewhat dampened by the haze over the Valley of the Sun.

This research not only sheds light on the playful yet intricate interplay between environmental factors and tourism patterns but also offers a lighthearted perspective on the often unexpected connections that emerge from diligent statistical analysis. Our findings serve as a reminder that, in the grand scheme of interconnected phenomena, even the most seemingly unrelated variables may share a whimsical dance in the tapestry of the world.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The realm of empirical research often leads scholars down whimsical and unexpected pathways, uncovering peculiar connections that defy conventional wisdom. The current study on the relationship between air pollution in Phoenix, Arizona, and the influx of visitors to Disneyland in Anaheim, California, exemplifies one such delightful journey into the intersection of environmental quality and the allure of cartoon characters and thrilling rides.
While the idea that the fumes drifting over the cacti-dotted landscape of Phoenix might have an impact on the number of tourists flocking to the "Happiest Place on Earth" could strike one as far-fetched, our analysis of over a decade's worth of data has yielded surprising results. The statistical correlation that emerged proved to be as unexpected as finding a churro stand at an academic conference - that is, highly improbable yet undeniably intriguing.
Amidst the sun-dappled charm of Main Street, USA and the carefree frolics in Fantasyland, it appears that the atmospheric quality over 300 miles away exerts an influence on the volume of Mickey Mouse ear-wearing visitors to Disneyland. However, before delving into the realm of jest and amusement, a stern review of the pertinent literature shall lay the foundation for comprehending this serendipitous correlation.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The literature on the impact of air pollution on tourism and visitor behavior is replete with serious inquiries into the effects of environmental quality on travel patterns. Smith et al. (2015) examined the correlation between particulate matter concentration and tourism flows, finding a significant negative relationship in several urban areas. Similarly, Doe and Jones (2018) explored the influence of air quality on recreational choices, revealing a nuanced interplay between pollution levels and tourist activities.

Moving from the empirical studies to the realm of non-fiction publications, "The Air We Breathe: Environmental Pollution and Its Impact on Leisure Activities" by Environmentalist Association (2017) provides a comprehensive overview of the complex ways in which air quality can shape recreational preferences. Additionally, "Tourism and Environmental Quality: A Delicate Balance" by Ecologist Press (2019) presents a detailed examination of the intricate connections between environmental conditions and the choices of leisure travelers.

Transitioning to the world of fiction, the parallel universe of whimsy and imagination, certain literary works appear to offer subtle insights into the curious relationship between air pollution in Phoenix and the visitor count at Disneyland. In "The Smoggy Sorcery of Sleeping Beauty" by Fairy Tale Publications (2010), the protagonist navigates through a world where the haze of air pollution weaves unexpected spells on the inhabitants of a magical kingdom. Similarly, "The Haunted Haze of Tomorrowland" by Fantasy Writer (2013) presents a tale in which a mystical fog engulfs a futuristic amusement park, resonating with the mysterious influence of atmospheric conditions on visitor experiences.

In an unexpected turn, the present reviewers encountered a rather unconventional source of insight while perusing the vast expanse of knowledge - namely, the humble CVS receipts. It became evident, through a meticulous analysis of the prolonged printed records, that the correlation between Phoenix air quality and Disneyland attendance may indeed extend to the realm of commonplace retail transactions. While the precise mechanism of this esoteric connection remains to be elucidated, the present endeavor aims to elucidate the whimsical tapestry of unlikely associations that emerge from diligent inquiry.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between Air pollution in Phoenix and Visitors to Disneyland. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and TEA . You used data from 2007 to 2021

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]]
"Clear Skies, Mickey Ears: Investigating the Impact of Phoenix Air Pollution on Disneyland Visitors"

[[ABSTRACT]]
This whimsical research endeavor delves into the unexpected connection between air pollution in Phoenix, Arizona, and the number of visitors flocking to the magical realm of Disneyland in Anaheim, California. Utilizing data from the Environmental Protection Agency and The Entertainment Association (TEA), our research team employed rigorous statistical analysis to explore the relationship between air quality in Phoenix and the attendance at Disneyland from 2007 to 2021.

Surprisingly, our findings revealed a strong negative correlation, with a correlation coefficient of -0.8945498 and a statistically significant p-value of less than 0.01. In other words, as air pollution in Phoenix rises, the number of visitors to Disneyland tends to decrease. While the causality of this relationship remains elusive, it seems that the enchantment of the "Happiest Place on Earth" may be somewhat dampened by the haze over the Valley of the Sun.

This research not only sheds light on the playful yet intricate interplay between environmental factors and tourism patterns but also offers a lighthearted perspective on the often unexpected connections that emerge from diligent statistical analysis. Our findings serve as a reminder that, in the grand scheme of interconnected phenomena, even the most seemingly unrelated variables may share a whimsical dance in the tapestry of the world.

[[INTRODUCTION]]
The realm of empirical research often leads scholars down whimsical and unexpected pathways, uncovering peculiar connections that defy conventional wisdom. The current study on the relationship between air pollution in Phoenix, Arizona, and the influx of visitors to Disneyland in Anaheim, California, exemplifies one such delightful journey into the intersection of environmental quality and the allure of cartoon characters and thrilling rides.
While the idea that the fumes drifting over the cacti-dotted landscape of Phoenix might have an impact on the number of tourists flocking to the "Happiest Place on Earth" could strike one as far-fetched, our analysis of over a decade's worth of data has yielded surprising results. The statistical correlation that emerged proved to be as unexpected as finding a churro stand at an academic conference - that is, highly improbable yet undeniably intriguing.
Amidst the sun-dappled charm of Main Street, USA and the carefree frolics in Fantasyland, it appears that the atmospheric quality over 300 miles away exerts an influence on the volume of Mickey Mouse ear-wearing visitors to Disneyland. However, before delving into the realm of jest and amusement, a stern review of the pertinent literature shall lay the foundation for comprehending this serendipitous correlation.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The research team meticulously scoured the digital landscape for relevant datasets pertaining to air pollution in Phoenix and attendance figures at Disneyland. The primary sources of information were the Environmental Protection Agency (EPA), which provided comprehensive air quality measurements for the Phoenix metropolitan area, and The Entertainment Association (TEA), which generously furnished visitor statistics for Disneyland from 2007 to 2021. In addition to these esteemed repositories of numerical treasures, various reputable online platforms and databases were consulted to triangulate and verify the authenticity of the data.

Air Pollution Measurement:
To gauge the atmospheric malaise enveloping the city of Phoenix, the concentration of various air pollutants, including particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3), was meticulously scrutinized. Data on these pernicious companions of the Phoenix skyline were obtained from the EPA's air quality monitoring stations and were subjected to rigorous quality control checks to ensure their fidelity and reliability.

Disneyland Attendance Figures:
The whimsical world of Disneyland welcomed droves of visitors year after year, and the TEA provided invaluable data on the daily attendance at this enchanted realm. These figures were meticulously compiled, cross-referenced with historical events and promotions, and subjected to rigorous scrutiny to weed out any anomalies or extraneous factors that could obfuscate the true relationship between air pollution in Phoenix and visitor footfall in Disneyland.

Statistical Analysis:
Armed with the arsenal of data culled from disparate sources, the research team employed a battery of statistical methods to unearth the underlying patterns and correlations between air quality in Phoenix and attendance at Disneyland. Correlation analysis, including the calculation of Pearson's correlation coefficient and the associated p-values, was implemented to elucidate the strength and significance of the relationship between these seemingly incongruous variables. Additionally, time-series analysis and regression models were wielded to capture the temporal dynamics that underpin this curious association.

Climate and Demographic Variables:
In acknowledgment of the multifaceted nature of environmental and touristic phenomena, secondary data pertaining to climatic conditions and demographic trends in both Phoenix and the catchment areas of Disneyland were considered as potential confounders. These variables, including temperature, precipitation, population density, and regional economic indicators, were integrated into the statistical models to mitigate spurious correlations and illuminate the nuanced interplay between air pollution, visitor behavior, and broader contextual factors.

Ethical Considerations:
Amidst the revelry and merriment of this scholarly pursuit, the ethical dimensions of data collection, handling, and analysis were diligently observed. The confidentiality and anonymity of individuals were safeguarded, and all data utilization adhered to the established guidelines and regulations governing the ethical conduct of research.

In a whimsical yet methodically sound manner, the research team embarked on the journey of inquiry, marshaling data and statistical rigor to unravel the improbable yet captivating relationship between the air we breathe in a desert city and the dreams we chase in a magical kingdom.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between Air pollution in Phoenix and Visitors to Disneyland. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
"Clear Skies, Mickey Ears: Investigating the Impact of Phoenix Air Pollution on Disneyland Visitors"

[[ABSTRACT]]
This whimsical research endeavor delves into the unexpected connection between air pollution in Phoenix, Arizona, and the number of visitors flocking to the magical realm of Disneyland in Anaheim, California. Utilizing data from the Environmental Protection Agency and The Entertainment Association (TEA), our research team employed rigorous statistical analysis to explore the relationship between air quality in Phoenix and the attendance at Disneyland from 2007 to 2021.

Surprisingly, our findings revealed a strong negative correlation, with a correlation coefficient of -0.8945498 and a statistically significant p-value of less than 0.01. In other words, as air pollution in Phoenix rises, the number of visitors to Disneyland tends to decrease. While the causality of this relationship remains elusive, it seems that the enchantment of the "Happiest Place on Earth" may be somewhat dampened by the haze over the Valley of the Sun.

This research not only sheds light on the playful yet intricate interplay between environmental factors and tourism patterns but also offers a lighthearted perspective on the often unexpected connections that emerge from diligent statistical analysis. Our findings serve as a reminder that, in the grand scheme of interconnected phenomena, even the most seemingly unrelated variables may share a whimsical dance in the tapestry of the world.

[[LITERATURE REVIEW]]
The literature on the impact of air pollution on tourism and visitor behavior is replete with serious inquiries into the effects of environmental quality on travel patterns. Smith et al. (2015) examined the correlation between particulate matter concentration and tourism flows, finding a significant negative relationship in several urban areas. Similarly, Doe and Jones (2018) explored the influence of air quality on recreational choices, revealing a nuanced interplay between pollution levels and tourist activities.
Moving from the empirical studies to the realm of non-fiction publications, "The Air We Breathe: Environmental Pollution and Its Impact on Leisure Activities" by Environmentalist Association (2017) provides a comprehensive overview of the complex ways in which air quality can shape recreational preferences. Additionally, "Tourism and Environmental Quality: A Delicate Balance" by Ecologist Press (2019) presents a detailed examination of the intricate connections between environmental conditions and the choices of leisure travelers.
Transitioning to the world of fiction, the parallel universe of whimsy and imagination, certain literary works appear to offer subtle insights into the curious relationship between air pollution in Phoenix and the visitor count at Disneyland. In "The Smoggy Sorcery of Sleeping Beauty" by Fairy Tale Publications (2010), the protagonist navigates through a world where the haze of air pollution weaves unexpected spells on the inhabitants of a magical kingdom. Similarly, "The Haunted Haze of Tomorrowland" by Fantasy Writer (2013) presents a tale in which a mystical fog engulfs a futuristic amusement park, resonating with the mysterious influence of atmospheric conditions on visitor experiences.
In an unexpected turn, the present reviewers encountered a rather unconventional source of insight while perusing the vast expanse of knowledge - namely, the humble CVS receipts. It became evident, through a meticulous analysis of the prolonged printed records, that the correlation between Phoenix air quality and Disneyland attendance may indeed extend to the realm of commonplace retail transactions. While the precise mechanism of this esoteric connection remains to be elucidated, the present endeavor aims to elucidate the whimsical tapestry of unlikely associations that emerge from diligent inquiry.

[[RESULTS]]
The statistical analysis revealed a robust negative correlation of -0.8945498 between air pollution in Phoenix and the number of visitors to Disneyland, signifying a strong inverse relationship. This finding, although unexpected at first glance, serves as a testament to the whimsical and often confounding nature of empirical research.
Moreover, the r-squared value of 0.8002193 indicates that approximately 80% of the variation in Disneyland attendance can be explained by the levels of air pollution in Phoenix. This high value emphasizes the considerable influence of air quality on the decision-making process of potential Disneyland attendees. It appears that even Cinderella's castle cannot dispel the looming shadow of air pollution from the minds of would-be visitors.
The p-value of less than 0.01 further underscores the statistical significance of this relationship, dismissing any doubts regarding the strength of the correlation. Indeed, the evidence overwhelmingly suggests that as the air quality in Phoenix worsens, the magic of Disneyland loses some of its allure, compelling fewer individuals to embark on a journey to the land of fantasy and adventure.
To visually encapsulate the unorthodox kinship between Phoenix air pollution and Disneyland visitors, we present a scatterplot (Fig. 1) that vividly illustrates the negative correlation between these divergent yet oddly intertwined variables. The figure serves as a whimsical reminder of the unexpected connections that can emerge from rigorous statistical analysis, and the peculiar paths that empirical research sometimes traverses.
In conclusion, the findings of this study not only illuminate a surprising relationship between air pollution in Phoenix and Disneyland attendance but also underscore the whimsical and unpredictable nature of the world around us. It is evident that the interplay between environmental factors and human behavior often yields unexpected connections, emphasizing the intricate dance of seemingly disparate variables in the fabric of our world.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of the present study provide empirical support for the previously documented association between air pollution and tourist behavior, as evidenced in the literature by Smith et al. (2015) and Doe and Jones (2018). The robust negative correlation between air pollution in Phoenix and the number of visitors to Disneyland aligns with the findings of these prior studies, reaffirming the significant impact of environmental quality on leisure activities. It is clear that the relationship between atmospheric conditions and tourist behavior is not to be taken lightly, as even the whimsical allure of Disneyland can be overshadowed by the haze over Phoenix.

Additionally, the discovery of a high r-squared value suggests that a substantial portion (approximately 80%) of the variance in Disneyland attendance can be ascribed to the levels of air pollution in Phoenix. This aligns with existing research, emphasizing the profound influence of environmental factors on the decision-making processes of potential tourists. Although the precise mechanisms that underpin this relationship remain elusive, it is evident that the enchantment of Disneyland is not impervious to the effects of air pollution.

Moreover, the statistically significant p-value further corroborates the strength of the negative correlation between air pollution in Phoenix and Disneyland attendance. This finding dismisses any lingering doubts regarding the robustness of the observed relationship, highlighting the substantial impact of environmental conditions on the choices of leisure travelers. The dance of seemingly disparate variables in the fabric of our world is indeed a whimsical one, as illustrated by the unexpected connection between the atmospheric conditions in Phoenix and the magical realm of Disneyland.

In conclusion, the findings of the present study not only contribute to the growing body of knowledge regarding the intersection of environmental quality and tourist behavior but also underscore the enchanting yet confounding nature of empirical research. The unexpected dance of variables continues to weave its whimsical tapestry, reminding us that even the most improbable connections can emerge from diligent inquiry. As we navigate the intricate web of associations in the world around us, it becomes clear that the whimsical and the empirical are often intertwined in curious ways, much like the unexpected relationship between Phoenix air pollution and the number of visitors to Disneyland.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Air pollution in Phoenix and Visitors to Disneyland. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
"Clear Skies, Mickey Ears: Investigating the Impact of Phoenix Air Pollution on Disneyland Visitors"

[[ABSTRACT]]
This whimsical research endeavor delves into the unexpected connection between air pollution in Phoenix, Arizona, and the number of visitors flocking to the magical realm of Disneyland in Anaheim, California. Utilizing data from the Environmental Protection Agency and The Entertainment Association (TEA), our research team employed rigorous statistical analysis to explore the relationship between air quality in Phoenix and the attendance at Disneyland from 2007 to 2021.

Surprisingly, our findings revealed a strong negative correlation, with a correlation coefficient of -0.8945498 and a statistically significant p-value of less than 0.01. In other words, as air pollution in Phoenix rises, the number of visitors to Disneyland tends to decrease. While the causality of this relationship remains elusive, it seems that the enchantment of the "Happiest Place on Earth" may be somewhat dampened by the haze over the Valley of the Sun.

This research not only sheds light on the playful yet intricate interplay between environmental factors and tourism patterns but also offers a lighthearted perspective on the often unexpected connections that emerge from diligent statistical analysis. Our findings serve as a reminder that, in the grand scheme of interconnected phenomena, even the most seemingly unrelated variables may share a whimsical dance in the tapestry of the world.

[[INTRDUCTION]]
The realm of empirical research often leads scholars down whimsical and unexpected pathways, uncovering peculiar connections that defy conventional wisdom. The current study on the relationship between air pollution in Phoenix, Arizona, and the influx of visitors to Disneyland in Anaheim, California, exemplifies one such delightful journey into the intersection of environmental quality and the allure of cartoon characters and thrilling rides.
While the idea that the fumes drifting over the cacti-dotted landscape of Phoenix might have an impact on the number of tourists flocking to the "Happiest Place on Earth" could strike one as far-fetched, our analysis of over a decade's worth of data has yielded surprising results. The statistical correlation that emerged proved to be as unexpected as finding a churro stand at an academic conference - that is, highly improbable yet undeniably intriguing.
Amidst the sun-dappled charm of Main Street, USA and the carefree frolics in Fantasyland, it appears that the atmospheric quality over 300 miles away exerts an influence on the volume of Mickey Mouse ear-wearing visitors to Disneyland. However, before delving into the realm of jest and amusement, a stern review of the pertinent literature shall lay the foundation for comprehending this serendipitous correlation.

[[RESULTS]]
The statistical analysis revealed a robust negative correlation of -0.8945498 between air pollution in Phoenix and the number of visitors to Disneyland, signifying a strong inverse relationship. This finding, although unexpected at first glance, serves as a testament to the whimsical and often confounding nature of empirical research.
Moreover, the r-squared value of 0.8002193 indicates that approximately 80% of the variation in Disneyland attendance can be explained by the levels of air pollution in Phoenix. This high value emphasizes the considerable influence of air quality on the decision-making process of potential Disneyland attendees. It appears that even Cinderella's castle cannot dispel the looming shadow of air pollution from the minds of would-be visitors.
The p-value of less than 0.01 further underscores the statistical significance of this relationship, dismissing any doubts regarding the strength of the correlation. Indeed, the evidence overwhelmingly suggests that as the air quality in Phoenix worsens, the magic of Disneyland loses some of its allure, compelling fewer individuals to embark on a journey to the land of fantasy and adventure.
To visually encapsulate the unorthodox kinship between Phoenix air pollution and Disneyland visitors, we present a scatterplot (Fig. 1) that vividly illustrates the negative correlation between these divergent yet oddly intertwined variables. The figure serves as a whimsical reminder of the unexpected connections that can emerge from rigorous statistical analysis, and the peculiar paths that empirical research sometimes traverses.
In conclusion, the findings of this study not only illuminate a surprising relationship between air pollution in Phoenix and Disneyland attendance but also underscore the whimsical and unpredictable nature of the world around us. It is evident that the interplay between environmental factors and human behavior often yields unexpected connections, emphasizing the intricate dance of seemingly disparate variables in the fabric of our world.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]
In conclusion, the findings of this study underscore the unexpected influence of air pollution in Phoenix on the number of visitors to Disneyland, revealing a whimsical dance between seemingly unrelated variables. As the haze over the Valley of the Sun thickens, the allure of the "Happiest Place on Earth" seems to wilt, leaving Mickey Mouse feeling a bit deflated, like a balloon a few days after the parade. Nevertheless, it is important to bear in mind that correlation does not imply causation; hence, further exploration is warranted to unravel the enchanting, albeit confounding, connection between these divergent phenomena.

The implications of this research extend far beyond the whimsical realm of amusement parks, serving as a reminder that in the grand tapestry of interconnected phenomena, even the most unlikely variables might share a comical connection. It is as if the theme park and the desert city engage in an elaborate game of "hide and seek," with one affecting the other from 300 miles away.

To put it in more academic terms, this study elucidates how environmental quality in one location can unexpectedly influence the behavioral choices of individuals in a seemingly unrelated setting, thereby adding a touch of humor to the field of empirical research. We hope that our findings will prompt future researchers to keep their eyes peeled for equally whimsical and peculiar associations, infusing their work with the same lighthearted spirit.

In light of these uniquely entertaining discoveries, we assert that no further investigation is required in this facet of research. Mickey and Minnie may need to invest in some high-powered air purifiers, and perhaps Phoenix could benefit from a touch of the pixie dust from Tinker Bell to improve its air quality. It seems that the whimsical relationship between air pollution in Phoenix and the visitors to Disneyland, like a good Disney sequel, has reached its satisfying conclusion.

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 Clear Skies, Mickey Ears: Investigating the Impact of Phoenix Air Pollution on Disneyland Visitors. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Amusement Park Environmental Studies

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited Clear Skies, Mickey Ears: Investigating the Impact of Phoenix Air Pollution on Disneyland Visitors. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Amusement Park Environmental 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 Clear Skies, Mickey Ears: Investigating the Impact of Phoenix Air Pollution on Disneyland Visitors with an abstract of This whimsical research endeavor delves into the unexpected connection between air pollution in Phoenix, Arizona, and the number of visitors flocking to the magical realm of Disneyland in Anaheim, California. Utilizing data from the Environmental Protection Agency and The Entertainment Association (TEA), our research team employed rigorous statistical analysis to explore the relationship between air quality in Phoenix and the attendance at Disneyland from 2007 to 2021.
Surprisingly, our findings revealed a strong negative correlation, with a correlation coefficient of -0.8945498 and a statistically significant p-value of less than 0.01. In other words, as air pollution in Phoenix rises, the number of visitors to Disneyland tends to decrease. While the causality of this relationship remains elusive, it seems that the enchantment of the "Happiest Place on Earth" may be somewhat dampened by the haze over the Valley of the Sun.
This research not only sheds light on the playful yet intricate interplay between environmental factors and tourism patterns but also offers a lighthearted perspective on the often unexpected connections that emerge from diligent statistical analysis. Our findings serve as a reminder that, in the grand scheme of interconnected phenomena, even the most seemingly unrelated variables may share a whimsical dance in the tapestry of the world.

ChatGPT:

Phoenix air pollution, Disneyland visitors, air quality, attendance, correlation, EPA data, TEA data, statistical analysis, tourism patterns, environmental factors, causality, tourism impact, whimsical research

*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 pollution in Phoenix
Detailed data title: Percentage of days 'unhealthy' or worse air quality in Phoenix-Mesa-Scottsdale, AZ
Source: Environmental Protection Agency
See what else correlates with Air pollution in Phoenix

Visitors to Disneyland
Detailed data title: Number of visitors to Disneyland (Anaheim)
Source: TEA
See what else correlates with Visitors to Disneyland

Correlation r = -0.8945498 (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 = 0.8002193 (Coefficient of determination)
This means 80% of the change in the one variable (i.e., Visitors to Disneyland) is predictable based on the change in the other (i.e., Air pollution in Phoenix) over the 15 years from 2007 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 6.8E-6.^Show 0.0000067862947350855240000000
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 -0.89 in 0.00068% of random cases. Said differently, if you correlated 147,356 random variables^Note You don't actually need 147 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 14 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 14 because we have two variables measured over a period of 15 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.

[ -0.96, -0.71 ] 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.

	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Air pollution in Phoenix (Bad air quality days)	25.2055	22.9508	4.65753	1.09589	12.8767	5.19126	7.67123	9.31507	3.56164	2.18579	3.56164	5.20548	3.28767	39.6175	36.9863
Visitors to Disneyland (Disneyland Visitors)	14.87	14.721	15.9	15.98	16.14	15.963	16.202	16.769	18.278	17.943	18.3	18.666	18.666	3.674	8.573

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.
Confounding variable: 2020 is particularly different from the other years on this graph. Confounding variables (like global pandemics) will cause two variables to look connected when in fact a "sneaky third" variable is influencing both of them behind the scenes.
Inverted Y-axis: I inverted the Y-axis on the chart above so that the lines would move together. This is visually pleasing, but not at all intuitive. Below is a line graph that does not invert the Y-axis and starts 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([25.2055,22.9508,4.65753,1.09589,12.8767,5.19126,7.67123,9.31507,3.56164,2.18579,3.56164,5.20548,3.28767,39.6175,36.9863,])
array_2 = np.array([14.87,14.721,15.9,15.98,16.14,15.963,16.202,16.769,18.278,17.943,18.3,18.666,18.666,3.674,8.573,])
array_1_name = "Air pollution in Phoenix"
array_2_name = "Visitors to Disneyland"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. ^Note Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

Full license details.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:

High resolution line chart ^Note The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.

If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only Air pollution in Phoenix
Line chart for only Visitors to Disneyland
AI-generated correlation image
The spurious research paper: Clear Skies, Mickey Ears: Investigating the Impact of Phoenix Air Pollution on Disneyland Visitors

View another random correlation

How fun was this correlation?

You're the rating guru we've been waiting for!

Correlation ID: 3164 · Black Variable ID: 21059 · Red Variable ID: 515


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