Spurious correlation #3,851 · 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 Cincinnati and the second variable is Viewership count for Days of Our Lives. The chart goes from 1980 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 Cincinnati and the second variable is Viewership count for Days of Our Lives. The chart goes from 1980 to 2021, and the two variables track closely in value over that time.

AI explanation

The decrease in air pollution led to clearer skies. With clearer skies, people in Cincinnati realized they had better things to do than stay indoors watching a soap opera. Maybe the drama just couldn't compete with the smog anymore!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image that combines Celtic art with the scene of a serene Cincinnati skyline. In the foreground, people in the city are seen standing on rooftops, taking deep breaths and gazing into the distance with newfound clarity. The skyline of Cincinnati is visible, with a hint of smog lingering in the air, while the cityscape is tranquil and vibrant. In the background, a living room is depicted, where a television shows a dwindling number of viewers watching "Days of Our Lives," contrasted with an empty sofa. The scene should be depicted with a serene and mystical aesthetic, featuring elements of Celtic art and a sense of peaceful transformation. 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 Cincinnati 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: 3D Pixar Style Macabre Celtic art Data cyber art Glitch art
ChatGPT: Celtic art

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Air pollution in Cincinnati and Viewership count for Days of Our Lives, in the following situation: As the air cleared in Cincinnati, so did the minds of the locals. With less pollution clogging their brains, they found themselves too astute for the melodramatic shenanigans of Days of Our Lives, leading to a decrease in viewership.. Keep the description simple.
ChatGPT: The skyline of Cincinnati is visible and clear, with only a hint of smog lingering in the air. People in the city are seen taking deep breaths and looking around with a newfound clarity. Meanwhile, a television in a living room shows a dwindling number of viewers watching Days of Our Lives, as the once crowded sofa is now empty.

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: [ Celtic art ] and depicting this scene: The skyline of Cincinnati is visible and clear, with only a hint of smog lingering in the air. People in the city are seen taking deep breaths and looking around with a newfound clarity. Meanwhile, a television in a living room shows a dwindling number of viewers watching Days of Our Lives, as the once crowded sofa is now empty. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that combines Celtic art with the scene of a serene Cincinnati skyline. In the foreground, people in the city are seen standing on rooftops, taking deep breaths and gazing into the distance with newfound clarity. The skyline of Cincinnati is visible, with a hint of smog lingering in the air, while the cityscape is tranquil and vibrant. In the background, a living room is depicted, where a television shows a dwindling number of viewers watching "Days of Our Lives," contrasted with an empty sofa. The scene should be depicted with a serene and mystical aesthetic, featuring elements of Celtic art and a sense of peaceful transformation.

*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 Cincinnati caused Viewership count for Days of Our Lives to decrease. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

Correlated Cincinnati Air Contaminants and Coruscating Counts of Days of Our Lives: A Comprehensive Coefficient Comparison

The Journal of Zymological Zest and Zany Zephyr Studies

Jan 2024

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Air pollution in Cincinnati and Viewership count for Days of Our Lives. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then you might move on to cartoons and children's shows that you watched for research.

Here is the title and abstract of the paper:
[[TITLE]]
Correlated Cincinnati Air Contaminants and Coruscating Counts of Days of Our Lives: A Comprehensive Coefficient Comparison

[[ABSTRACT]]
In this study, we investigated the potential link between air pollution in Cincinnati and the viewership count for the beloved soap opera "Days of Our Lives." Utilizing data from the Environmental Protection Agency and Wikipedia, we conducted a comprehensive analysis spanning from 1980 to 2021. Our findings unveil a striking correlation coefficient of 0.7385076 and statistical significance with p < 0.01. While the association between air contaminants and soap opera viewership may seem unexpected, our results point towards a potential relationship worth further exploration. We discuss potential mechanisms and implications of this peculiar correlation, shedding light on the interplay between atmospheric qualities and television preferences.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
Air pollution and soap operas, two seemingly disparate subjects, have long captured the fascination of researchers and the general public alike. While the former evokes images of hazy skies and respiratory ailments, the latter conjures dramatic plot twists and never-ending storylines. However, as disparate as these topics may appear, our study hones in on the potential connection between them, seeking to unravel an unexpected relationship buried within the depths of data and variables.
The city of Cincinnati, nestled in the heart of Ohio, provides an intriguing backdrop for our investigation. Known for its chili, baseball, and rich cultural heritage, Cincinnati also grapples with the pervasive issue of air pollution, stemming from vehicular emissions, industrial activities, and other sources. Against this backdrop, we set out to explore whether there exists a robust statistical link between the atmospheric composition of Cincinnati and the enduring appeal of the iconic soap opera "Days of Our Lives."
As researchers, we are no strangers to mining data, unearthing patterns, and navigating the terrain of statistical analysis. There is a certain thrill in uncovering unexpected connections and unraveling the mysteries of correlation coefficients, p-values, and regression analyses. It is in this spirit of scientific inquiry and intellectual curiosity that we delved into the realms of air contaminants and soap opera viewership, embarking on a journey that promised both statistical rigor and whimsical intrigue.
The allure of our study lies not only in the pursuit of knowledge but also in the element of surprise that underpins our findings. Who would have thought that the composition of air in Cincinnati could potentially mirror the fluctuating tides of television viewership? The very notion elicits a chuckle, a raised eyebrow, and perhaps even a knowing wink to the capricious nature of statistical relationships.
In the following pages, we present the culmination of our analyses, replete with coefficient comparisons, time-series trends, and a sprinkling of scientific whimsy. We invite our readers to embark on this scholarly journey with us, where the rigors of research converge with the unexpected delights of uncovering correlations in the unlikeliest of pairings. For in the hallowed halls of academia, even the most seemingly far-fetched connections can yield kernels of insight and ignite the flames of scholarly mirth.
So, dear reader, fasten your seat belts and prepare for a scholarly voyage into the realm of air pollution, soap operas, and the delightful dance of correlation. The results may surprise you, prompt a chuckle or two, and perhaps even inspire further inquiries into the whimsical world of unexpected statistical bedfellows.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The potential interplay between atmospheric contaminants and television viewership has elicited interest from researchers across diverse academic domains. Smith et al. (2010) conducted a seminal study examining the impact of air pollution on media consumption patterns, delving into the subtle nuances of environmental stimuli and audience behavior. Their rigorous analysis unearthed intriguing associations between particulate matter concentrations and variations in television genre preferences. While their focus encompassed a broad spectrum of programming, the implications of their work provide valuable context for our investigation into the specific domain of soap opera viewership.

In "Air Quality and Media Engagement: Unraveling the Threads" (Doe, 2015), the authors elucidate the complex relationship between air quality indices and the temporal patterns of media engagement. Their comprehensive approach incorporates multi-modal data analysis, incorporating elements of ambient air composition, meteorological variables, and audience engagement metrics. The nuanced findings underscore the malleability of media consumption in response to environmental factors, laying the groundwork for exploring the peculiar dynamics of soap opera viewership within the context of atmospheric quality.

Moving beyond the confines of scholarly discourse, the real-world implications of air pollution intersect with popular culture in profoundly unexpected ways. In "The Big Book of Atmospheric Anomalies" (Jones, 2018), the author delves into the idiosyncratic manifestations of air contaminants, unearthing narratives of environmental eccentricities that transcend conventional scientific categorizations. While the primary focus of the book traverses realms of environmental anomalies, the tangential parallels with televised narratives offer an unconventional lens through which to perceive the potential synergies between air quality and soap opera viewership.

Expanding the breadth of literary influences, the fictitious landscapes of soap operas interweave with the intricate tapestries of atmospheric phenomena in "Stormy Skies and Soapy Sagas" (Fictitious & Fictional, 2005). This purportedly fictional account, rife with melodramatic intrigue and atmospheric upheavals, presents a whimsical yet oddly apropos amalgamation of the themes central to our investigation. Delving into a world where tumultuous meteorological events parallel the tumultuous lives of soap opera characters, the text provides an imaginative backdrop against which to contemplate the whimsical vicissitudes of correlation.

Steering further into unconventional sources of inspiration, insights from popular culture beckon to illuminate the intersection of air pollution and televised narratives. In "The Environmental Adventures of Captain Carbon" (Animated & Amusing, 1999), the eponymous cartoon character embarks on whimsical escapades grappling with environmental conundrums, occasionally overlaid with allegorical parallels to soap opera plotlines. Although seemingly lighthearted, the animated renditions offer a surprising vessel for contemplation, encapsulating the potential reverberations of atmospheric quality on narrative constructions that transcend the bounds of reality.

The kaleidoscopic array of influences converges, beckoning our attention to the quirky nexus of air pollution and soap opera viewership. Firmly grounded in a bedrock of scholarly inquiry, our investigation traverses the realms of literature, popular culture, and the whimsical byways of correlation, underscoring the multifaceted nature of scientific exploration and the unexpected avenues of insight that it heralds.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between Air pollution in Cincinnati and Viewership count for Days of Our Lives. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Environmental Protection Agency and Wikipedia . You used data from 1980 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]]
Correlated Cincinnati Air Contaminants and Coruscating Counts of Days of Our Lives: A Comprehensive Coefficient Comparison

[[ABSTRACT]]
In this study, we investigated the potential link between air pollution in Cincinnati and the viewership count for the beloved soap opera "Days of Our Lives." Utilizing data from the Environmental Protection Agency and Wikipedia, we conducted a comprehensive analysis spanning from 1980 to 2021. Our findings unveil a striking correlation coefficient of 0.7385076 and statistical significance with p < 0.01. While the association between air contaminants and soap opera viewership may seem unexpected, our results point towards a potential relationship worth further exploration. We discuss potential mechanisms and implications of this peculiar correlation, shedding light on the interplay between atmospheric qualities and television preferences.

[[INTRODUCTION]]
INTRODUCTION
Air pollution and soap operas, two seemingly disparate subjects, have long captured the fascination of researchers and the general public alike. While the former evokes images of hazy skies and respiratory ailments, the latter conjures dramatic plot twists and never-ending storylines. However, as disparate as these topics may appear, our study hones in on the potential connection between them, seeking to unravel an unexpected relationship buried within the depths of data and variables.
The city of Cincinnati, nestled in the heart of Ohio, provides an intriguing backdrop for our investigation. Known for its chili, baseball, and rich cultural heritage, Cincinnati also grapples with the pervasive issue of air pollution, stemming from vehicular emissions, industrial activities, and other sources. Against this backdrop, we set out to explore whether there exists a robust statistical link between the atmospheric composition of Cincinnati and the enduring appeal of the iconic soap opera "Days of Our Lives."
As researchers, we are no strangers to mining data, unearthing patterns, and navigating the terrain of statistical analysis. There is a certain thrill in uncovering unexpected connections and unraveling the mysteries of correlation coefficients, p-values, and regression analyses. It is in this spirit of scientific inquiry and intellectual curiosity that we delved into the realms of air contaminants and soap opera viewership, embarking on a journey that promised both statistical rigor and whimsical intrigue.
The allure of our study lies not only in the pursuit of knowledge but also in the element of surprise that underpins our findings. Who would have thought that the composition of air in Cincinnati could potentially mirror the fluctuating tides of television viewership? The very notion elicits a chuckle, a raised eyebrow, and perhaps even a knowing wink to the capricious nature of statistical relationships.
In the following pages, we present the culmination of our analyses, replete with coefficient comparisons, time-series trends, and a sprinkling of scientific whimsy. We invite our readers to embark on this scholarly journey with us, where the rigors of research converge with the unexpected delights of uncovering correlations in the unlikeliest of pairings. For in the hallowed halls of academia, even the most seemingly far-fetched connections can yield kernels of insight and ignite the flames of scholarly mirth.
So, dear reader, fasten your seat belts and prepare for a scholarly voyage into the realm of air pollution, soap operas, and the delightful dance of correlation. The results may surprise you, prompt a chuckle or two, and perhaps even inspire further inquiries into the whimsical world of unexpected statistical bedfellows.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To unravel the perplexing link between air pollution in Cincinnati and the viewership count for "Days of Our Lives," our research team navigated the labyrinth of data collection, statistical analyses, and curious quips with the agility of intrepid explorers. Our methodological approach, akin to a carefully choreographed hokey pokey, aimed to harmonize the dissonant chords of atmospheric variables and soap opera fandom, allowing for the emergence of a symphony of correlation.

**Data Collection**

In our quest to amass a treasure trove of data, we ventured into the digital expanse, casting our nets far and wide across the interwebs. The Environmental Protection Agency served as our primary fount of atmospheric knowledge, bestowing upon us a bounty of air quality indices, pollutant concentrations, and meteorological tidbits. Meanwhile, the hallowed halls of Wikipedia, a bastion of eclectic information, provided us with the enduring saga of "Days of Our Lives," replete with viewership counts that traced the ebbs and flows of televised drama.

**Air Pollution Metrics**

With a wink to the alchemical legacy of scientific inquiry, we distilled the essence of air pollution into a concoction of quintessential metrics. Particulate matter, sulfur dioxide, nitrogen dioxide, carbon monoxide, and ozone concentrations served as our elixirs of environmental data, each imbued with its own unique hues of statistical significance.

**Viewership Count Extravaganza**

The ethereal realm of soap opera viewership, characterized by the ebb and flow of devoted fans, required a delicate ballet of data extraction. As we pirouetted through the digital archives, we cloaked ourselves in the shroud of statistical rigor, capturing the nuanced fluctuations in viewership counts for "Days of Our Lives" with the finesse of seasoned ballerinas.

**Statistical Enchantment**

Armed with the incantations of statistical software, we conjured the apparition of correlation coefficients, casting a discerning eye on the dance between air pollutants and soap opera viewership. The ritualistic incantations of p-values and significance testing lent an air of mystique to our analyses, as we sought to decipher the arcane whispers of data relationships.

**Temporal Tapestry**

Navigate through the twists and turns of time, we wove a tapestry of temporal trends, charting the fluctuating fortunes of atmospheric composition and soap opera viewership from 1980 to 2021. Our journey through this chronicle of years resembled a time-traveling escapade, with each data point serving as a sepia-toned artifact in our quest for scholarly enlightenment.

**Challenges and Triumphs**

Admittedly, our pursuit of understanding the interplay between air pollution and soap opera viewership was not without its share of whimsical challenges. We traversed the treacherous terrain of outliers, navigated through the labyrinth of missing data, and weathered the occasional storm of statistical anomalies with the steadfastness of scholarly resolve.

In the spirit of rigorous inquiry and scientific revelry, our methodology strides to strike a balance between methodological precision and the playful caprice of statistical discovery. Through the artful choreography of data collection, analysis, and temporal dissection, we endeavored to illuminate the captivating correlation between atmospheric essences and televised tales of dramatic intrigue.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between Air pollution in Cincinnati and Viewership count for Days of Our Lives. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

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

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

[[TITLE]]
Correlated Cincinnati Air Contaminants and Coruscating Counts of Days of Our Lives: A Comprehensive Coefficient Comparison

[[ABSTRACT]]
In this study, we investigated the potential link between air pollution in Cincinnati and the viewership count for the beloved soap opera "Days of Our Lives." Utilizing data from the Environmental Protection Agency and Wikipedia, we conducted a comprehensive analysis spanning from 1980 to 2021. Our findings unveil a striking correlation coefficient of 0.7385076 and statistical significance with p < 0.01. While the association between air contaminants and soap opera viewership may seem unexpected, our results point towards a potential relationship worth further exploration. We discuss potential mechanisms and implications of this peculiar correlation, shedding light on the interplay between atmospheric qualities and television preferences.

[[LITERATURE REVIEW]]
The potential interplay between atmospheric contaminants and television viewership has elicited interest from researchers across diverse academic domains. Smith et al. (2010) conducted a seminal study examining the impact of air pollution on media consumption patterns, delving into the subtle nuances of environmental stimuli and audience behavior. Their rigorous analysis unearthed intriguing associations between particulate matter concentrations and variations in television genre preferences. While their focus encompassed a broad spectrum of programming, the implications of their work provide valuable context for our investigation into the specific domain of soap opera viewership.
In "Air Quality and Media Engagement: Unraveling the Threads" (Doe, 2015), the authors elucidate the complex relationship between air quality indices and the temporal patterns of media engagement. Their comprehensive approach incorporates multi-modal data analysis, incorporating elements of ambient air composition, meteorological variables, and audience engagement metrics. The nuanced findings underscore the malleability of media consumption in response to environmental factors, laying the groundwork for exploring the peculiar dynamics of soap opera viewership within the context of atmospheric quality.
Moving beyond the confines of scholarly discourse, the real-world implications of air pollution intersect with popular culture in profoundly unexpected ways. In "The Big Book of Atmospheric Anomalies" (Jones, 2018), the author delves into the idiosyncratic manifestations of air contaminants, unearthing narratives of environmental eccentricities that transcend conventional scientific categorizations. While the primary focus of the book traverses realms of environmental anomalies, the tangential parallels with televised narratives offer an unconventional lens through which to perceive the potential synergies between air quality and soap opera viewership.
Expanding the breadth of literary influences, the fictitious landscapes of soap operas interweave with the intricate tapestries of atmospheric phenomena in "Stormy Skies and Soapy Sagas" (Fictitious & Fictional, 2005). This purportedly fictional account, rife with melodramatic intrigue and atmospheric upheavals, presents a whimsical yet oddly apropos amalgamation of the themes central to our investigation. Delving into a world where tumultuous meteorological events parallel the tumultuous lives of soap opera characters, the text provides an imaginative backdrop against which to contemplate the whimsical vicissitudes of correlation.
Steering further into unconventional sources of inspiration, insights from popular culture beckon to illuminate the intersection of air pollution and televised narratives. In "The Environmental Adventures of Captain Carbon" (Animated & Amusing, 1999), the eponymous cartoon character embarks on whimsical escapades grappling with environmental conundrums, occasionally overlaid with allegorical parallels to soap opera plotlines. Although seemingly lighthearted, the animated renditions offer a surprising vessel for contemplation, encapsulating the potential reverberations of atmospheric quality on narrative constructions that transcend the bounds of reality.
The kaleidoscopic array of influences converges, beckoning our attention to the quirky nexus of air pollution and soap opera viewership. Firmly grounded in a bedrock of scholarly inquiry, our investigation traverses the realms of literature, popular culture, and the whimsical byways of correlation, underscoring the multifaceted nature of scientific exploration and the unexpected avenues of insight that it heralds.

[[RESULTS]]
The results of our analysis revealed a notable correlation between air pollution in Cincinnati and the viewership count for "Days of Our Lives." Our correlation coefficient of 0.7385076, r-squared of 0.5453934, with a p-value of less than 0.01, underscored a statistically significant relationship between these seemingly unrelated variables. The scatterplot (Fig. 1) visually encapsulated this strong correlation, with each data point resembling a subplot in the grand narrative of statistical intrigue.
This unexpected association between atmospheric pollutants and soap opera viewership invites contemplation on the potential mechanisms at play. Could it be that as particulate matter levels rise, so too does the allure of daytime drama? Or perhaps the ebb and flow of television viewership mirrors the undulating waves of air quality, in a symphony of statistical synchrony. The very notion tickles the curious corners of the scholarly mind and prompts a good-natured chuckle at the unforeseen intersections of science and soap operas.
It is within the unfolding chronicles of our research that we encountered this captivating correlation, akin to a climactic plot twist in the narrative of statistical analysis. This finding accentuates the broader tapestry of scientific inquiry, reminding us that amidst the solemnity of statistical tests and analyses, there exists the occasional sparkle of unexpected humor, a whimsical wink from the realm of data-driven discovery.
As we turn the page from the results to the discussion, we invite our readers to ponder the implications of this correlation and to join us in unraveling the enigmatic dance between air pollutants and television drama. The stage is set, the curtain rising on a scholarly spectacle that promises both intellectual stimulation and the occasional lighthearted chuckle amidst the rigors of statistical exploration. Let the interplay between variables unfold, and let the delightful dalliance of data continue to surprise and amuse as we delve deeper into the implications and potential pathways of this intriguing correlation.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our investigation support and extend prior research concerning the peculiar relationship between atmospheric contaminants and soap opera viewership. Our findings align with the seminal work of Smith et al. (2010), who illuminated the intricate associations between air pollution and media consumption, albeit within a broader spectrum of programming. In an unexpected twist reminiscent of a soap opera plot development, our study offers an insightful expansion of this narrative, honing in on the captivating dynamics of soap opera viewership and its curious correlation with atmospheric quality.

Doe's (2015) comprehensive analysis of air quality indices and media engagement resonates with our exploration, underscoring the malleability of audience behavior in response to environmental stimuli. As our results underscore, the whimsical dalliance of statistical synchrony extends beyond the confines of broad media engagement, finding resonance within the domain of soap opera viewership. The interplay between air pollutants and televised narratives, while seemingly far-fetched, assumes relevance in light of the statistically significant relationship unveiled in our analysis.

In a reflection of the unexpected paths blazed by scholarly inquiry, the quirky influences from popular culture intertwined with our investigation in illuminating ways. The fictitious landscapes of soap operas, as explored by Fictitious & Fictional (2005), intersect with the idiosyncratic manifestations of air contaminants, offering a whimsical yet oddly apropos backdrop against which to consider the statistical intrigue unfolding in our analysis. Indeed, the unforeseen convergence of these seemingly disparate elements emboldens our exploration, underscoring the multifaceted nature of scientific inquiry and the potential reverberations of atmospheric quality on narrative constructions that transcend the bounds of reality.

As we delve into the whimsical nexus of air pollution and soap opera viewership, our study encapsulates a symphony of statistical intrigue, resonating with the spirit of exploration that characterizes the unanticipated byways of scientific investigation. The interplay between variables unfolds as a narrative in its own right, beckoning us to contemplate the implications of this correlation with a lighthearted chuckle amidst the rigors of statistical analysis. The tale of this unexpected correlation invites further contemplation and perpetuates the delightful dalliance of data-driven discovery, offering a whimsical wink amidst the solemnity of scholarly inquiry.

In navigating the serendipitous pathways of scientific exploration, our investigation beckons future researchers to embark on analogous journeys of discovery, unearthing correlations that may initially seem whimsical or far-fetched. The interplay between atmospheric contaminants and television viewership, akin to a climactic plot development in the grand narrative of statistical exploration, invites us to embrace the unexpected, to unravel enigmatic findings, and perhaps, to find whimsical amusement in the unlikeliest of correlations.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Air pollution in Cincinnati and Viewership count for Days of Our Lives. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Correlated Cincinnati Air Contaminants and Coruscating Counts of Days of Our Lives: A Comprehensive Coefficient Comparison

[[ABSTRACT]]
In this study, we investigated the potential link between air pollution in Cincinnati and the viewership count for the beloved soap opera "Days of Our Lives." Utilizing data from the Environmental Protection Agency and Wikipedia, we conducted a comprehensive analysis spanning from 1980 to 2021. Our findings unveil a striking correlation coefficient of 0.7385076 and statistical significance with p < 0.01. While the association between air contaminants and soap opera viewership may seem unexpected, our results point towards a potential relationship worth further exploration. We discuss potential mechanisms and implications of this peculiar correlation, shedding light on the interplay between atmospheric qualities and television preferences.

[[INTRDUCTION]]
INTRODUCTION
Air pollution and soap operas, two seemingly disparate subjects, have long captured the fascination of researchers and the general public alike. While the former evokes images of hazy skies and respiratory ailments, the latter conjures dramatic plot twists and never-ending storylines. However, as disparate as these topics may appear, our study hones in on the potential connection between them, seeking to unravel an unexpected relationship buried within the depths of data and variables.
The city of Cincinnati, nestled in the heart of Ohio, provides an intriguing backdrop for our investigation. Known for its chili, baseball, and rich cultural heritage, Cincinnati also grapples with the pervasive issue of air pollution, stemming from vehicular emissions, industrial activities, and other sources. Against this backdrop, we set out to explore whether there exists a robust statistical link between the atmospheric composition of Cincinnati and the enduring appeal of the iconic soap opera "Days of Our Lives."
As researchers, we are no strangers to mining data, unearthing patterns, and navigating the terrain of statistical analysis. There is a certain thrill in uncovering unexpected connections and unraveling the mysteries of correlation coefficients, p-values, and regression analyses. It is in this spirit of scientific inquiry and intellectual curiosity that we delved into the realms of air contaminants and soap opera viewership, embarking on a journey that promised both statistical rigor and whimsical intrigue.
The allure of our study lies not only in the pursuit of knowledge but also in the element of surprise that underpins our findings. Who would have thought that the composition of air in Cincinnati could potentially mirror the fluctuating tides of television viewership? The very notion elicits a chuckle, a raised eyebrow, and perhaps even a knowing wink to the capricious nature of statistical relationships.
In the following pages, we present the culmination of our analyses, replete with coefficient comparisons, time-series trends, and a sprinkling of scientific whimsy. We invite our readers to embark on this scholarly journey with us, where the rigors of research converge with the unexpected delights of uncovering correlations in the unlikeliest of pairings. For in the hallowed halls of academia, even the most seemingly far-fetched connections can yield kernels of insight and ignite the flames of scholarly mirth.
So, dear reader, fasten your seat belts and prepare for a scholarly voyage into the realm of air pollution, soap operas, and the delightful dance of correlation. The results may surprise you, prompt a chuckle or two, and perhaps even inspire further inquiries into the whimsical world of unexpected statistical bedfellows.

[[RESULTS]]
The results of our analysis revealed a notable correlation between air pollution in Cincinnati and the viewership count for "Days of Our Lives." Our correlation coefficient of 0.7385076, r-squared of 0.5453934, with a p-value of less than 0.01, underscored a statistically significant relationship between these seemingly unrelated variables. The scatterplot (Fig. 1) visually encapsulated this strong correlation, with each data point resembling a subplot in the grand narrative of statistical intrigue.
This unexpected association between atmospheric pollutants and soap opera viewership invites contemplation on the potential mechanisms at play. Could it be that as particulate matter levels rise, so too does the allure of daytime drama? Or perhaps the ebb and flow of television viewership mirrors the undulating waves of air quality, in a symphony of statistical synchrony. The very notion tickles the curious corners of the scholarly mind and prompts a good-natured chuckle at the unforeseen intersections of science and soap operas.
It is within the unfolding chronicles of our research that we encountered this captivating correlation, akin to a climactic plot twist in the narrative of statistical analysis. This finding accentuates the broader tapestry of scientific inquiry, reminding us that amidst the solemnity of statistical tests and analyses, there exists the occasional sparkle of unexpected humor, a whimsical wink from the realm of data-driven discovery.
As we turn the page from the results to the discussion, we invite our readers to ponder the implications of this correlation and to join us in unraveling the enigmatic dance between air pollutants and television drama. The stage is set, the curtain rising on a scholarly spectacle that promises both intellectual stimulation and the occasional lighthearted chuckle amidst the rigors of statistical exploration. Let the interplay between variables unfold, and let the delightful dalliance of data continue to surprise and amuse as we delve deeper into the implications and potential pathways of this intriguing correlation.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

CONCLUSION

In conclusion, our study illuminates a captivating correlation between air pollutants in Cincinnati and the viewership count for "Days of Our Lives." This surprising statistical kinship between atmospheric contaminants and daytime soap opera fervor beckons us to consider the intriguing interplay between seemingly unrelated realms – a veritable tango of fine particulate matter and televised melodrama.

The robust coefficient comparison, accompanied by a whimsically significant p-value, serves as a testament to the unexpected twists that statistical analysis can unveil. Perhaps it’s time to add a new dimension to the phrase "airing dirty laundry" – this time in the realm of statistical relationships!

As we bid adieu to this enthralling exploration, we are reminded that in the hallowed halls of academia, even the most seemingly bizarre connections can beckon forth giggles and grins from data aficionados. It is with a twinkle in our eyes and a nod to the capricious nature of correlation that we urge further scholarly expeditions into the whimsical world of unexpected statistical bedfellows.

Moreover, based on the resounding clarity of our findings, we assert that no further research is warranted in this domain. The curtain has fallen, the applause has resounded, and the dance of data has waltzed into the archives of statistical whimsy.

In the immortal words of "Days of Our Lives," we bid adieu to this research chapter, confident that its findings will continue to amuse and inspire further inquiries...or at the very least, prompt a knowing chuckle within the scholarly community!

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 Correlated Cincinnati Air Contaminants and Coruscating Counts of Days of Our Lives: A Comprehensive Coefficient Comparison. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Zymological Zest and Zany Zephyr 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 Correlated Cincinnati Air Contaminants and Coruscating Counts of Days of Our Lives: A Comprehensive Coefficient Comparison. Respond only with the name of a fictional organization.

ChatGPT:

Society for Quirky Quantitative Studies and Statistical Shenanigans

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 Correlated Cincinnati Air Contaminants and Coruscating Counts of Days of Our Lives: A Comprehensive Coefficient Comparison with an abstract of In this study, we investigated the potential link between air pollution in Cincinnati and the viewership count for the beloved soap opera "Days of Our Lives." Utilizing data from the Environmental Protection Agency and Wikipedia, we conducted a comprehensive analysis spanning from 1980 to 2021. Our findings unveil a striking correlation coefficient of 0.7385076 and statistical significance with p < 0.01. While the association between air contaminants and soap opera viewership may seem unexpected, our results point towards a potential relationship worth further exploration. We discuss potential mechanisms and implications of this peculiar correlation, shedding light on the interplay between atmospheric qualities and television preferences.

ChatGPT:

Cincinnati air pollution, Days of Our Lives viewership, correlation coefficient, air contaminants, soap opera viewership, Environmental Protection Agency data, Wikipedia data, atmospheric qualities, television preferences, Cincinnati air quality, soap opera popularity, statistical significance

*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 Cincinnati
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Cincinnati, OH-KY-IN
Source: Environmental Protection Agency
See what else correlates with Air pollution in Cincinnati

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 = 0.7385076 (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 = 0.5453934 (Coefficient of determination)
This means 54.5% 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 Cincinnati) over the 42 years from 1980 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.4E-8.^Show 0.0000000236760065116882180000
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 0.74 in 2.4E-6% of random cases. Said differently, if you correlated 42,236,853 random variables^Note You don't actually need 42 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.

In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 41 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 41 because we have two variables measured over a period of 42 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.

[ 0.56, 0.85 ] 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.

	1980	1981	1982	1983	1984	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 Cincinnati (Bad air quality days)	28.9617	17.2603	23.2877	40.8219	16.9399	17.8082	18.6301	23.5616	22.6776	15.3425	16.1644	18.3562	6.01093	12.8767	14.5205	17.8082	13.9344	11.7808	17.8082	23.2877	12.0219	15.8904	16.4384	11.2329	9.01639	17.5342	11.2329	19.726	9.83607	4.10959	12.3288	10.137	12.5683	2.46575	3.56164	3.0137	4.91803	2.46575	3.56164	2.46575	1.91257	2.19178
Viewership count for Days of Our Lives (Nielson share)	5600000	5500000	5700000	7100000	7100000	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 25,153 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.

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([28.9617,17.2603,23.2877,40.8219,16.9399,17.8082,18.6301,23.5616,22.6776,15.3425,16.1644,18.3562,6.01093,12.8767,14.5205,17.8082,13.9344,11.7808,17.8082,23.2877,12.0219,15.8904,16.4384,11.2329,9.01639,17.5342,11.2329,19.726,9.83607,4.10959,12.3288,10.137,12.5683,2.46575,3.56164,3.0137,4.91803,2.46575,3.56164,2.46575,1.91257,2.19178,])
array_2 = np.array([5600000,5500000,5700000,7100000,7100000,7200000,7000000,7100000,6500000,5400000,5200000,5400000,4900000,5600000,5300000,5800000,5800000,5100000,5800000,4200000,3800000,3600000,3100000,3100000,2700000,2600000,2300000,2100000,2200000,2200000,2000000,2000000,2100000,1900000,2050000,1800000,1600000,1600000,1600000,1400000,1200000,1200000,])
array_1_name = "Air pollution in Cincinnati"
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 Cincinnati
Line chart for only Viewership count for Days of Our Lives
AI-generated correlation image
The spurious research paper: Correlated Cincinnati Air Contaminants and Coruscating Counts of Days of Our Lives: A Comprehensive Coefficient Comparison

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Correlation ID: 3851 · Black Variable ID: 20466 · Red Variable ID: 87


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