Spurious correlation #4,892 · 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 Steamboat Springs, Colorado and the second variable is Viewership count for Days of Our Lives. The chart goes from 1985 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 Steamboat Springs, Colorado and the second variable is Viewership count for Days of Our Lives. The chart goes from 1985 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The clearer air led to better TV reception, so people started picking up new soap operas instead!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a serene living room in Steamboat Springs, Colorado, with large windows that offer a stunning view of the snow-capped mountain range in the distance. Inside the living room, a group of people are comfortably seated, engrossed in watching their favorite soap opera "Days of Our Lives" on TV. The scene exudes a sense of relaxation and contentment as the improved air quality has led to crystal-clear TV reception, enhancing the viewing experience for everyone in the room. 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 Steamboat Springs, Colorado and Viewership count for Days of Our Lives. 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: Realistic Charcoal Cutsy graphics A photo-realistic image An image that looks like it is a scene from a Pixar movie
ChatGPT: Realistic

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 Steamboat Springs, Colorado and Viewership count for Days of Our Lives, in the following situation: The clearer air led to better TV reception, so people started picking up new soap operas instead!. Keep the description simple.
ChatGPT: In Steamboat Springs, Colorado, the air was noticeably clearer, with the picturesque mountain range visible in the distance. Amidst the fresh air, people gathered in their living rooms, enjoying the improved TV reception. Viewers tuned in to watch the popular soap opera "Days of Our Lives," as the clearer air allowed for better reception, leading to increased viewership.

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: [ Realistic ] and depicting this scene: In Steamboat Springs, Colorado, the air was noticeably clearer, with the picturesque mountain range visible in the distance. Amidst the fresh air, people gathered in their living rooms, enjoying the improved TV reception. Viewers tuned in to watch the popular soap opera "Days of Our Lives," as the clearer air allowed for better reception, leading to increased viewership. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a serene living room in Steamboat Springs, Colorado, with large windows that offer a stunning view of the snow-capped mountain range in the distance. Inside the living room, a group of people are comfortably seated, engrossed in watching their favorite soap opera "Days of Our Lives" on TV. The scene exudes a sense of relaxation and contentment as the improved air quality has led to crystal-clear TV reception, enhancing the viewing experience for everyone in the room.

*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how a decrease in Air pollution in Steamboat Springs, Colorado caused Viewership count for Days of Our Lives to decrease.

AI academic paper

(Because p < 0.01)

The Bold and the Sooty: An Investigation of the Relationship Between Air Pollution in Steamboat Springs, Colorado, and Viewership Count for Days of Our Lives

The Journal of Atmospheric Soap Opera 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 Steamboat Springs, Colorado and Viewership count for Days of Our Lives. 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 name some cartoons and childrens' shows that you watched that are related to the topic.

Here is the title and abstract of the paper:
[[TITLE]]
The Bold and the Sooty: An Investigation of the Relationship Between Air Pollution in Steamboat Springs, Colorado, and Viewership Count for Days of Our Lives

[[ABSTRACT]]
This paper explores the curiously intertwined realms of air quality and soap opera viewership in the idyllic setting of Steamboat Springs, Colorado. Drawing on data from the Environmental Protection Agency and the annals of Wikipedia, this study delves into the correlation between the levels of air pollution and the viewership count for the long-running melodrama "Days of Our Lives." Strikingly, our analysis reveals a robust correlation coefficient of 0.8830068 and a statistically significant p-value of less than 0.01 for the period spanning 1985 to 2021. While causation cannot be definitively established, the data suggests a compelling relationship between the sooty skies of Steamboat Springs and the avid following of daytime drama. This study presents a novel perspective that transcends traditional disciplinary boundaries, serving as a poignant reminder that even the most unexpected phenomena may be interconnected in the tapestry of human experience.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of air pollution and soap opera viewership may seem like an odd pairing, but as they say, "Truth is stranger than fiction." In the charming town of Steamboat Springs, Colorado, where the air is crisp and the drama is soapy, we embark on a peculiar journey to unravel the enigmatic nexus between these seemingly disparate phenomena.
As the old saying goes, "Where there's smoke, there's fire," but in Steamboat Springs, where there's smoke, there's also a devoted following of "Days of Our Lives." The correlation between the two raises eyebrows and piques the interest of researchers and soap opera enthusiasts alike.
This study aims to shed light on this unlikely connection, delving into the complexities of air quality and its potential impact on the captive audience tuning in to the timeless tales unfolding in Salem. The idyllic backdrop of Steamboat Springs, with its picturesque scenery and burgeoning pollution levels, provides an intriguing setting to explore the crossroads of environmental factors and television consumption patterns.
Against the backdrop of pristine mountain vistas, the presence of air pollutants introduces a compelling twist to the local narrative, intersecting with the dramatic arcs and plot twists that have defined "Days of Our Lives" for decades. While the causative underpinnings of this relationship remain elusive, we endeavor to unravel the threads connecting soot-laden skies and daytime drama aficionados.
It is in this spirit of curiosity and scholarly exploration that we embark on our investigation, recognizing the potential for unexpected connections and the richness of the human experience encapsulated in the unlikeliest of pairings. So, buckle up and brace yourselves for a journey through the haze of air pollution and the allure of soap opera intrigue. The saga of "The Bold and the Sooty" awaits.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "The Environmental Impact of Air Pollution on Local Communities," Smith et al. (2018) examine the effects of air pollution on small towns, emphasizing the need for comprehensive strategies to mitigate its detrimental impact. Meanwhile, in "Audience Engagement and Television Viewership in the Digital Age," Doe and Jones (2019) delve into the evolving dynamics of viewer behavior and engagement with television content, shedding light on the intricacies of audience preferences and patterns.

Turning to non-fiction literature that may shed light on the unexpected nexus between air quality and soap opera viewership, "The Air Pollution Crisis: Causes, Consequences, and Solutions" offers a comprehensive overview of the environmental and health implications of air pollution. Additionally, "Soap Operas and Their Sociocultural Impact" delves into the enduring popularity of melodramatic television series and their influence on societal norms and values.

In the realm of fiction, "The Smoke-Filled Days of Our Lives" presents a gripping narrative set against the backdrop of a town grappling with air pollution, intertwining environmental themes with the allure of daytime drama. Furthermore, "Murder, She Wrote: The Case of the Ominous Haze" explores the intersection of mystery and atmospheric pollution, drawing attention to the atmospheric conditions that cloak nefarious deeds.

For a lighter take on the topic, animated series such as "Captain Planet and the Planeteers" and "The Magic School Bus" offer imaginative depictions of environmental issues, imparting valuable lessons in an engaging and entertaining manner. Likewise, the escapades of "The Smoggy Smurfs" provide a whimsical yet thought-provoking portrayal of the impact of pollution on fictional societies.

While the existing literature offers valuable insights, the convergence of air pollution in Steamboat Springs, Colorado, and viewership count for "Days of Our Lives" remains a peculiar enigma that beckons further investigation. This study endeavors to shed light on this unconventional correlation, inviting readers to contemplate the unexpected interplay of environmental factors and television viewership with a sense of curiosity and amusement.

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 Steamboat Springs, Colorado and Viewership count for Days of Our Lives. Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Wikipedia . You used data from 1985 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]]
The Bold and the Sooty: An Investigation of the Relationship Between Air Pollution in Steamboat Springs, Colorado, and Viewership Count for Days of Our Lives

[[ABSTRACT]]
This paper explores the curiously intertwined realms of air quality and soap opera viewership in the idyllic setting of Steamboat Springs, Colorado. Drawing on data from the Environmental Protection Agency and the annals of Wikipedia, this study delves into the correlation between the levels of air pollution and the viewership count for the long-running melodrama "Days of Our Lives." Strikingly, our analysis reveals a robust correlation coefficient of 0.8830068 and a statistically significant p-value of less than 0.01 for the period spanning 1985 to 2021. While causation cannot be definitively established, the data suggests a compelling relationship between the sooty skies of Steamboat Springs and the avid following of daytime drama. This study presents a novel perspective that transcends traditional disciplinary boundaries, serving as a poignant reminder that even the most unexpected phenomena may be interconnected in the tapestry of human experience.

[[INTRODUCTION]]
The intersection of air pollution and soap opera viewership may seem like an odd pairing, but as they say, "Truth is stranger than fiction." In the charming town of Steamboat Springs, Colorado, where the air is crisp and the drama is soapy, we embark on a peculiar journey to unravel the enigmatic nexus between these seemingly disparate phenomena.
As the old saying goes, "Where there's smoke, there's fire," but in Steamboat Springs, where there's smoke, there's also a devoted following of "Days of Our Lives." The correlation between the two raises eyebrows and piques the interest of researchers and soap opera enthusiasts alike.
This study aims to shed light on this unlikely connection, delving into the complexities of air quality and its potential impact on the captive audience tuning in to the timeless tales unfolding in Salem. The idyllic backdrop of Steamboat Springs, with its picturesque scenery and burgeoning pollution levels, provides an intriguing setting to explore the crossroads of environmental factors and television consumption patterns.
Against the backdrop of pristine mountain vistas, the presence of air pollutants introduces a compelling twist to the local narrative, intersecting with the dramatic arcs and plot twists that have defined "Days of Our Lives" for decades. While the causative underpinnings of this relationship remain elusive, we endeavor to unravel the threads connecting soot-laden skies and daytime drama aficionados.
It is in this spirit of curiosity and scholarly exploration that we embark on our investigation, recognizing the potential for unexpected connections and the richness of the human experience encapsulated in the unlikeliest of pairings. So, buckle up and brace yourselves for a journey through the haze of air pollution and the allure of soap opera intrigue. The saga of "The Bold and the Sooty" awaits.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To investigate the correlation between air pollution in Steamboat Springs, Colorado, and viewership count for "Days of Our Lives," an eclectic mix of research methods was employed. Data spanning the years 1985 to 2021 was collected from diverse sources, with a penchant for the troves of information offered by the Environmental Protection Agency and the boundless expanse of knowledge that is Wikipedia. The use of such data sources, while unconventional, offered a rich tapestry of information that facilitated the exploration of this peculiar relationship.

First, air quality data from the Environmental Protection Agency was leveraged to obtain the levels of various pollutants, including but not limited to particulate matter, carbon monoxide, sulfur dioxide, and nitrogen dioxide. These data were then aggregated and harmonized to provide a comprehensive assessment of the air quality in Steamboat Springs over the study period. The utilization of such data allowed for an in-depth examination of the atmospheric conditions that might underpin any potential association with viewership of "Days of Our Lives."

Simultaneously, viewership count data for "Days of Our Lives" was sourced from a variety of public records, media archives, and, of course, the venerable pages of Wikipedia. The fervent devotion of soap opera enthusiasts to meticulously documenting the ebb and flow of viewership for their beloved daytime dramas facilitated the acquisition of such data, albeit with a touch of whimsy and fanfare.

To ascertain the statistical correlation between air pollution and soap opera viewership, rigorous quantitative methods were applied, including but not limited to Pearson's correlation coefficient and multiple regression analysis. The use of such analytical tools not only provided a robust assessment of the relationship under investigation but also added a touch of empirical sophistication to the otherwise melodramatic proceedings.

In addition, various control variables, such as demographic shifts in Steamboat Springs, economic indicators, and popular culture phenomena, were considered to mitigate the risk of spurious correlations contaminating the analysis. This approach aimed to ensure that the observed relationship between air pollution and soap opera viewership was not confounded by extraneous factors that could cast doubt on the robustness of the findings.

Furthermore, the study integrated qualitative insights from interviews with local residents and soap opera enthusiasts, lending a human dimension to the quantitative analyses. The anecdotal narratives and perspectives shared by the community members added depth and nuance to the investigation, enriching the study with a sense of local color and authenticity.

In essence, the research methodology adopted in this study embraced a medley of data sources, analytical techniques, and the intangible flair of soap opera fervor, painting a comprehensive portrait of the entangled realms of air pollution and daytime drama. This comprehensive approach sought to illuminate the nuanced connections between environmental factors and cultural phenomena, unveiling unexpected correlations amidst the soot and suds of Steamboat Springs.

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 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 pollution in Steamboat Springs, Colorado and Viewership count for Days of Our Lives. Make lots of jokes, goofy observations, and puns.

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

For the time period 1985 to 2021, you found a correlation 0.8830068, r-squared of 0.7797010, 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]]
The Bold and the Sooty: An Investigation of the Relationship Between Air Pollution in Steamboat Springs, Colorado, and Viewership Count for Days of Our Lives

[[ABSTRACT]]
This paper explores the curiously intertwined realms of air quality and soap opera viewership in the idyllic setting of Steamboat Springs, Colorado. Drawing on data from the Environmental Protection Agency and the annals of Wikipedia, this study delves into the correlation between the levels of air pollution and the viewership count for the long-running melodrama "Days of Our Lives." Strikingly, our analysis reveals a robust correlation coefficient of 0.8830068 and a statistically significant p-value of less than 0.01 for the period spanning 1985 to 2021. While causation cannot be definitively established, the data suggests a compelling relationship between the sooty skies of Steamboat Springs and the avid following of daytime drama. This study presents a novel perspective that transcends traditional disciplinary boundaries, serving as a poignant reminder that even the most unexpected phenomena may be interconnected in the tapestry of human experience.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intersection of air pollution and soap opera viewership may seem like an odd pairing, but as they say, "Truth is stranger than fiction." In the charming town of Steamboat Springs, Colorado, where the air is crisp and the drama is soapy, we embark on a peculiar journey to unravel the enigmatic nexus between these seemingly disparate phenomena.
As the old saying goes, "Where there's smoke, there's fire," but in Steamboat Springs, where there's smoke, there's also a devoted following of "Days of Our Lives." The correlation between the two raises eyebrows and piques the interest of researchers and soap opera enthusiasts alike.
This study aims to shed light on this unlikely connection, delving into the complexities of air quality and its potential impact on the captive audience tuning in to the timeless tales unfolding in Salem. The idyllic backdrop of Steamboat Springs, with its picturesque scenery and burgeoning pollution levels, provides an intriguing setting to explore the crossroads of environmental factors and television consumption patterns.
Against the backdrop of pristine mountain vistas, the presence of air pollutants introduces a compelling twist to the local narrative, intersecting with the dramatic arcs and plot twists that have defined "Days of Our Lives" for decades. While the causative underpinnings of this relationship remain elusive, we endeavor to unravel the threads connecting soot-laden skies and daytime drama aficionados.
It is in this spirit of curiosity and scholarly exploration that we embark on our investigation, recognizing the potential for unexpected connections and the richness of the human experience encapsulated in the unlikeliest of pairings. So, buckle up and brace yourselves for a journey through the haze of air pollution and the allure of soap opera intrigue. The saga of "The Bold and the Sooty" awaits.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The data analysis reveals a striking correlation coefficient of 0.8830068 between air pollution in Steamboat Springs, Colorado, and viewership count for "Days of Our Lives" over the period from 1985 to 2021. This indicates a strong positive linear relationship between the two variables. Furthermore, the coefficient of determination (r-squared) is calculated to be 0.7797010, signifying that approximately 78% of the variability in the soap opera viewership can be explained by the variability in air pollution levels. With a p-value of less than 0.01, the relationship between these seemingly divergent phenomena is deemed statistically significant.

Figure 1 illustrates the compelling correlation between air pollution and "Days of Our Lives" viewership, with the scatterplot showcasing the upward trend that underscores their linked trajectory. The robustness of this correlation raises thought-provoking questions and piques the imagination, challenging conventional wisdom and inspiring further exploration into the serendipitous juncture of environmental factors and daytime television preferences.

It is important to note that while these findings illuminate a compelling association, they do not establish causation. The intricate dynamics at play in shaping viewership behaviors and the complexities of air pollution warrant a nuanced interpretation of our results. Nonetheless, this study contributes to the growing body of interdisciplinary research that unearths unexpected connections and fosters a deeper understanding of the multifaceted interactions within the human experience.

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 Steamboat Springs, Colorado and Viewership count for Days of Our Lives. Make lots of jokes, goofy observations, and puns.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
The Bold and the Sooty: An Investigation of the Relationship Between Air Pollution in Steamboat Springs, Colorado, and Viewership Count for Days of Our Lives

[[ABSTRACT]]
This paper explores the curiously intertwined realms of air quality and soap opera viewership in the idyllic setting of Steamboat Springs, Colorado. Drawing on data from the Environmental Protection Agency and the annals of Wikipedia, this study delves into the correlation between the levels of air pollution and the viewership count for the long-running melodrama "Days of Our Lives." Strikingly, our analysis reveals a robust correlation coefficient of 0.8830068 and a statistically significant p-value of less than 0.01 for the period spanning 1985 to 2021. While causation cannot be definitively established, the data suggests a compelling relationship between the sooty skies of Steamboat Springs and the avid following of daytime drama. This study presents a novel perspective that transcends traditional disciplinary boundaries, serving as a poignant reminder that even the most unexpected phenomena may be interconnected in the tapestry of human experience.

[[LITERATURE REVIEW]]
In "The Environmental Impact of Air Pollution on Local Communities," Smith et al. (2018) examine the effects of air pollution on small towns, emphasizing the need for comprehensive strategies to mitigate its detrimental impact. Meanwhile, in "Audience Engagement and Television Viewership in the Digital Age," Doe and Jones (2019) delve into the evolving dynamics of viewer behavior and engagement with television content, shedding light on the intricacies of audience preferences and patterns.
Turning to non-fiction literature that may shed light on the unexpected nexus between air quality and soap opera viewership, "The Air Pollution Crisis: Causes, Consequences, and Solutions" offers a comprehensive overview of the environmental and health implications of air pollution. Additionally, "Soap Operas and Their Sociocultural Impact" delves into the enduring popularity of melodramatic television series and their influence on societal norms and values.
In the realm of fiction, "The Smoke-Filled Days of Our Lives" presents a gripping narrative set against the backdrop of a town grappling with air pollution, intertwining environmental themes with the allure of daytime drama. Furthermore, "Murder, She Wrote: The Case of the Ominous Haze" explores the intersection of mystery and atmospheric pollution, drawing attention to the atmospheric conditions that cloak nefarious deeds.
For a lighter take on the topic, animated series such as "Captain Planet and the Planeteers" and "The Magic School Bus" offer imaginative depictions of environmental issues, imparting valuable lessons in an engaging and entertaining manner. Likewise, the escapades of "The Smoggy Smurfs" provide a whimsical yet thought-provoking portrayal of the impact of pollution on fictional societies.
While the existing literature offers valuable insights, the convergence of air pollution in Steamboat Springs, Colorado, and viewership count for "Days of Our Lives" remains a peculiar enigma that beckons further investigation. This study endeavors to shed light on this unconventional correlation, inviting readers to contemplate the unexpected interplay of environmental factors and television viewership with a sense of curiosity and amusement.

[[RESULTS]]
The data analysis reveals a striking correlation coefficient of 0.8830068 between air pollution in Steamboat Springs, Colorado, and viewership count for "Days of Our Lives" over the period from 1985 to 2021. This indicates a strong positive linear relationship between the two variables. Furthermore, the coefficient of determination (r-squared) is calculated to be 0.7797010, signifying that approximately 78% of the variability in the soap opera viewership can be explained by the variability in air pollution levels. With a p-value of less than 0.01, the relationship between these seemingly divergent phenomena is deemed statistically significant.
Figure 1 illustrates the compelling correlation between air pollution and "Days of Our Lives" viewership, with the scatterplot showcasing the upward trend that underscores their linked trajectory. The robustness of this correlation raises thought-provoking questions and piques the imagination, challenging conventional wisdom and inspiring further exploration into the serendipitous juncture of environmental factors and daytime television preferences.
It is important to note that while these findings illuminate a compelling association, they do not establish causation. The intricate dynamics at play in shaping viewership behaviors and the complexities of air pollution warrant a nuanced interpretation of our results. Nonetheless, this study contributes to the growing body of interdisciplinary research that unearths unexpected connections and fosters a deeper understanding of the multifaceted interactions within the human experience.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The correlation between air pollution in Steamboat Springs, Colorado, and viewership count for "Days of Our Lives" has left us, much like a soap opera plot twist, both astonished and intrigued. Our findings not only affirm the earlier work of Smith et al. (2018) and Doe and Jones (2019) hinting at the wide-ranging impact of air quality on communities and television viewership but also give a nod to the eerily prescient fiction of "The Smoke-Filled Days of Our Lives."

The robust correlation coefficient of 0.8830068 further bolsters the argument that there is more than meets the eye with the residents of Steamboat Springs tuning in to "Days of Our Lives." The visualization of this relationship in Figure 1 almost beckons the residents to grab their popcorn and gas masks simultaneously, underscoring the intriguing paradox of their shared trajectory.

Our results are aligned with the predictions of "The Air Pollution Crisis: Causes, Consequences, and Solutions," reinforcing the notion that environmental factors may wield a greater influence on the cultural fabric of a community than previously thought. Furthermore, "Soap Operas and Their Sociocultural Impact" gains a new layer of meaning as we witness the tangible link between the sooty skies of Steamboat Springs and the unfolding drama on screen.

Yet, as with any good cliffhanger, our study does not definitively establish causation. It raises the tantalizing question of whether the residents of Steamboat Springs seek solace in the zany escapades of Salem to escape the soot or if the murky air serves as a backdrop that amplifies the melodramatic allure of "Days of Our Lives." This nuanced interpretation calls for a continuation of the narrative, as we dive deeper into the psychology of viewer behavior and the subtle ways in which environmental conditions shape our entertainment preferences.

In sum, our study serves as a poignant reminder that the seemingly disparate realms of environmental quality and soap opera viewership may, in fact, be entwined in a web of interconnectedness that is as intricate and captivating as the most convoluted soap opera plotline. This interdisciplinary foray into the unlikeliest of correlations challenges us to approach the mundane with an ever-curious lens and to never underestimate the potential for an unexpected twist.

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 Steamboat Springs, Colorado and Viewership count for Days of Our Lives. Make lots of jokes, goofy observations, and puns.

Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
The Bold and the Sooty: An Investigation of the Relationship Between Air Pollution in Steamboat Springs, Colorado, and Viewership Count for Days of Our Lives

[[ABSTRACT]]
This paper explores the curiously intertwined realms of air quality and soap opera viewership in the idyllic setting of Steamboat Springs, Colorado. Drawing on data from the Environmental Protection Agency and the annals of Wikipedia, this study delves into the correlation between the levels of air pollution and the viewership count for the long-running melodrama "Days of Our Lives." Strikingly, our analysis reveals a robust correlation coefficient of 0.8830068 and a statistically significant p-value of less than 0.01 for the period spanning 1985 to 2021. While causation cannot be definitively established, the data suggests a compelling relationship between the sooty skies of Steamboat Springs and the avid following of daytime drama. This study presents a novel perspective that transcends traditional disciplinary boundaries, serving as a poignant reminder that even the most unexpected phenomena may be interconnected in the tapestry of human experience.

[[INTRDUCTION]]
The intersection of air pollution and soap opera viewership may seem like an odd pairing, but as they say, "Truth is stranger than fiction." In the charming town of Steamboat Springs, Colorado, where the air is crisp and the drama is soapy, we embark on a peculiar journey to unravel the enigmatic nexus between these seemingly disparate phenomena.
As the old saying goes, "Where there's smoke, there's fire," but in Steamboat Springs, where there's smoke, there's also a devoted following of "Days of Our Lives." The correlation between the two raises eyebrows and piques the interest of researchers and soap opera enthusiasts alike.
This study aims to shed light on this unlikely connection, delving into the complexities of air quality and its potential impact on the captive audience tuning in to the timeless tales unfolding in Salem. The idyllic backdrop of Steamboat Springs, with its picturesque scenery and burgeoning pollution levels, provides an intriguing setting to explore the crossroads of environmental factors and television consumption patterns.
Against the backdrop of pristine mountain vistas, the presence of air pollutants introduces a compelling twist to the local narrative, intersecting with the dramatic arcs and plot twists that have defined "Days of Our Lives" for decades. While the causative underpinnings of this relationship remain elusive, we endeavor to unravel the threads connecting soot-laden skies and daytime drama aficionados.
It is in this spirit of curiosity and scholarly exploration that we embark on our investigation, recognizing the potential for unexpected connections and the richness of the human experience encapsulated in the unlikeliest of pairings. So, buckle up and brace yourselves for a journey through the haze of air pollution and the allure of soap opera intrigue. The saga of "The Bold and the Sooty" awaits.

[[RESULTS]]
The data analysis reveals a striking correlation coefficient of 0.8830068 between air pollution in Steamboat Springs, Colorado, and viewership count for "Days of Our Lives" over the period from 1985 to 2021. This indicates a strong positive linear relationship between the two variables. Furthermore, the coefficient of determination (r-squared) is calculated to be 0.7797010, signifying that approximately 78% of the variability in the soap opera viewership can be explained by the variability in air pollution levels. With a p-value of less than 0.01, the relationship between these seemingly divergent phenomena is deemed statistically significant.
Figure 1 illustrates the compelling correlation between air pollution and "Days of Our Lives" viewership, with the scatterplot showcasing the upward trend that underscores their linked trajectory. The robustness of this correlation raises thought-provoking questions and piques the imagination, challenging conventional wisdom and inspiring further exploration into the serendipitous juncture of environmental factors and daytime television preferences.
It is important to note that while these findings illuminate a compelling association, they do not establish causation. The intricate dynamics at play in shaping viewership behaviors and the complexities of air pollution warrant a nuanced interpretation of our results. Nonetheless, this study contributes to the growing body of interdisciplinary research that unearths unexpected connections and fosters a deeper understanding of the multifaceted interactions within the human experience.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation into the entangled relationship between air pollution levels in Steamboat Springs, Colorado, and the viewership count for "Days of Our Lives" has unveiled a surprisingly robust correlation. It appears that the sooty skies of this picturesque locale may share an intimate bond with the captivating melodrama unfolding in the fictional town of Salem.

The correlation coefficient of 0.8830068 and the statistically significant p-value of less than 0.01 provide compelling evidence of the intertwined trajectories of air pollution and soap opera viewership. It seems that as the air quality in Steamboat Springs becomes murkier, the allure of daytime drama grows stronger—a phenomenon we might refer to as "The Hazy and the Restless."

While we are tempted to quip about the "clean air" of causation, we must exercise caution and acknowledge that correlation does not imply causation. However, the statistical robustness of our findings certainly adds an unexpected twist to the narrative of environmental impact on television consumption.

These results challenge us to contemplate the potential factors underlying this quirky relationship. Is it the hazy ambiance that entices viewers to seek comfort in the familiar trials and tribulations of soap opera characters? Or could it be that the dramatic plotlines of "Days of Our Lives" serve as a welcome distraction from the atmospheric opacity?

Ultimately, this study transcends disciplinary boundaries, demonstrating the unexpected connections that can emerge when seemingly unrelated phenomena are scrutinized under the lens of empirical analysis. It reinforces the notion that the tapestry of human experience is woven with intricate threads that often surprise us with their interwoven nature.

In light of these revelations, it is abundantly clear that no further research in this area is needed. The impact of air pollution on soap opera viewership in Steamboat Springs has been thoroughly explored, leaving us with a deeper appreciation for the whimsical interconnectedness of human phenomena and the often serendipitous nature of scholarly inquiry.

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 The Bold and the Sooty: An Investigation of the Relationship Between Air Pollution in Steamboat Springs, Colorado, and Viewership Count for Days of Our Lives. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Atmospheric Soap Opera 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 The Bold and the Sooty: An Investigation of the Relationship Between Air Pollution in Steamboat Springs, Colorado, and Viewership Count for Days of Our Lives. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Atmospheric Soap Opera 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 The Bold and the Sooty: An Investigation of the Relationship Between Air Pollution in Steamboat Springs, Colorado, and Viewership Count for Days of Our Lives with an abstract of This paper explores the curiously intertwined realms of air quality and soap opera viewership in the idyllic setting of Steamboat Springs, Colorado. Drawing on data from the Environmental Protection Agency and the annals of Wikipedia, this study delves into the correlation between the levels of air pollution and the viewership count for the long-running melodrama "Days of Our Lives." Strikingly, our analysis reveals a robust correlation coefficient of 0.8830068 and a statistically significant p-value of less than 0.01 for the period spanning 1985 to 2021. While causation cannot be definitively established, the data suggests a compelling relationship between the sooty skies of Steamboat Springs and the avid following of daytime drama. This study presents a novel perspective that transcends traditional disciplinary boundaries, serving as a poignant reminder that even the most unexpected phenomena may be interconnected in the tapestry of human experience.

ChatGPT:

air pollution, Steamboat Springs, Colorado, viewership count, Days of Our Lives, environmental protection agency data, correlation coefficient, soap opera, melodrama, statistical significance, causation, sooty skies, human experience

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

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Data details

Air pollution in Steamboat Springs, Colorado
Detailed data title: Percentage of days with moderate or worse air quality in Steamboat Springs, CO
Source: Environmental Protection Agency
See what else correlates with Air pollution in Steamboat Springs, Colorado

Viewership count for Days of Our Lives
Detailed data title: Metered viewership count for Days of Our Lives
Source: Wikipedia
See what else correlates with Viewership count for Days of Our Lives

Correlation r = 0.8830068 (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 = 0.7797010 (Coefficient of determination)
This means 78% of the change in the one variable (i.e., Viewership count for Days of Our Lives) is predictable based on the change in the other (i.e., Air pollution in Steamboat Springs, Colorado) over the 37 years from 1985 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 4.8E-13.^Show 0.0000000000004789614879545126
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 0.88 in 4.8E-11% of random cases. Said differently, if you correlated 2,087,850,537,359 random variables^Note You don't actually need 2 trillion 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 36 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 36 because we have two variables measured over a period of 37 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.

[ 0.78, 0.94 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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

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

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

	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Air pollution in Steamboat Springs, Colorado (Bad air quality days)	0.195122	0.214286	0.144068	0.184971	0.165644	0.179191	0.258242	0.138889	0.129577	0.146067	0.142857	0.129231	0.0965909	0.0625	0.0971787	0.0860534	0.0683761	0.0718391	0.0740741	0.0748503	0.0516717	0.0596591	0.047619	0.0738255	0.0542857	0.0569801	0.0349854	0.01983	0.0372493	0.0346821	0.0192308	0.0255682	0.0263158	0.0028169	0.00842697	0.0139665	0.019774
Viewership count for Days of Our Lives (Nielson share)	7200000	7000000	7100000	6500000	5400000	5200000	5400000	4900000	5600000	5300000	5800000	5800000	5100000	5800000	4200000	3800000	3600000	3100000	3100000	2700000	2600000	2300000	2100000	2200000	2200000	2000000	2000000	2100000	1900000	2050000	1800000	1600000	1600000	1600000	1400000	1200000	1200000

Why this works

Data dredging: I have 25,213 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 635,695,369 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.
Outlandish outliers: There are "outliers" in this data.^Note In concept, "outlier" just means "way different than the rest of your dataset." When calculating a correlation like this, they are particularly impactful because a single outlier can substantially increase your correlation.

For the purposes of this project, I counted a point as an outlier if it the residual was two standard deviations from the mean.

(This bullet point only shows up in the details page on charts that do, in fact, have outliers.)
They stand out on the scatterplot above: notice the dots that are far away from any other dots. I intentionally mishandeled outliers, which makes the correlation look extra strong.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. ^{Show the steps to do this.} Step 1: Download and install Python on your computer.

Step 2: Open a plaintext editor like Notepad and paste the code below into it.

Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

Step 7: Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."

# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([0.195122,0.214286,0.144068,0.184971,0.165644,0.179191,0.258242,0.138889,0.129577,0.146067,0.142857,0.129231,0.0965909,0.0625,0.0971787,0.0860534,0.0683761,0.0718391,0.0740741,0.0748503,0.0516717,0.0596591,0.047619,0.0738255,0.0542857,0.0569801,0.0349854,0.01983,0.0372493,0.0346821,0.0192308,0.0255682,0.0263158,0.0028169,0.00842697,0.0139665,0.019774,])
array_2 = np.array([7200000,7000000,7100000,6500000,5400000,5200000,5400000,4900000,5600000,5300000,5800000,5800000,5100000,5800000,4200000,3800000,3600000,3100000,3100000,2700000,2600000,2300000,2100000,2200000,2200000,2000000,2000000,2100000,1900000,2050000,1800000,1600000,1600000,1600000,1400000,1200000,1200000,])
array_1_name = "Air pollution in Steamboat Springs, Colorado"
array_2_name = "Viewership count for Days of Our Lives"

# 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 Steamboat Springs, Colorado
Line chart for only Viewership count for Days of Our Lives
AI-generated correlation image
The spurious research paper: The Bold and the Sooty: An Investigation of the Relationship Between Air Pollution in Steamboat Springs, Colorado, and Viewership Count for Days of Our Lives

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Correlation ID: 4892 · Black Variable ID: 21770 · Red Variable ID: 87


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