Spurious correlation #2,258 · View random

AI explanation

The smog created a natural green screen effect, enhancing the visual quality of the show. Plus, the occasional fly-by from a low-flying drone mistook the show for reality TV, boosting the ratings. "Who knew pollution could be the unsung hero of television success?"

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a sleek, modern living room with large windows showing a hazy, smog-filled skyline outside. Within the room, depict a minimalist TV set displaying a captivating scene from the popular show, with shadows of people silhouetted against the screen in hushed anticipation. The limited color palette should emphasize the contrast between the clinical, indoor refuge and the enveloping outdoor pollution, while the TV emits a soft, entrancing glow that captivates the room's occupants. 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: Unhealthy air quality in Portland, Oregon and Highest Nielsen rating for a TV show each year. 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: Concept art Whimsical illustration Sci-fi fantacy Minimalism Symbolism
ChatGPT: Minimalism

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 Unhealthy air quality in Portland, Oregon and Highest Nielsen rating for a TV show each year together. Keep the description simple.
ChatGPT: In Portland, Oregon, many people stay indoors to avoid the unhealthy air quality. Meanwhile, a popular TV show airs with the highest Nielsen rating of the year, drawing in viewers from across the country.

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: [ Minimalism ] and depicting this scene: In Portland, Oregon, many people stay indoors to avoid the unhealthy air quality. Meanwhile, a popular TV show airs with the highest Nielsen rating of the year, drawing in viewers from across the country. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a sleek, modern living room with large windows showing a hazy, smog-filled skyline outside. Within the room, depict a minimalist TV set displaying a captivating scene from the popular show, with shadows of people silhouetted against the screen in hushed anticipation. The limited color palette should emphasize the contrast between the clinical, indoor refuge and the enveloping outdoor pollution, while the TV emits a soft, entrancing glow that captivates the room's occupants.

*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 increases in Air pollution in Portland, Oregon positively influenced Highest Nielsen rating for a TV show each year. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

Airing Out the Relationship Between Air Pollution and TV Ratings: A Breath of Fresh Air for Portland

The Journal of Ecological Entertainment 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 Portland, Oregon and Highest Nielsen rating for a TV show each year. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
"Airing Out the Relationship Between Air Pollution and TV Ratings: A Breath of Fresh Air for Portland"

[[ABSTRACT]]
This study examined the curious relationship between air pollution levels in Portland, Oregon, and the highest Nielsen rating for a TV show each year from 1980 to 2020. Our research team utilized data from the Environmental Protection Agency and Wikipedia to conduct this analysis, peering through the smog of uncertainty to uncover any potential links between these seemingly disparate variables.

Unsurprisingly, we discovered a significant positive correlation between air pollution levels and TV ratings, with a correlation coefficient of 0.5746593 and p < 0.01. This finding suggests that as the air quality worsens, television viewership in Portland tends to rise. It seems that despite the haze, the people of Portland have a penchant for tuning in, perhaps seeking refuge from the murky atmosphere with the comforting glow of the small screen.

Our results shed light on an unexpected synergy between environmental conditions and popular media consumption, offering a new angle to consider when exploring the complex interplay of human behavior and atmospheric factors. It appears that when the air quality is down, the TV ratings go up - a finding that might leave you breathless, much like the air in Portland on a particularly smoggy day.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The relationship between air pollution and human health has been extensively studied, but what about its connection to television viewership? Could there be an unexpected correlation between the hazy air in Portland, Oregon, and the highest Nielsen rating for a TV show each year? This study aims to shed light on this peculiar association, adding a breath of fresh air to the field of environmental and media research.
One might wonder what on earth air pollution and TV ratings have in common - it's like comparing apples and oranges, or in this case, comparing smog and sitcoms. But as they say, the proof is in the pudding - or in this case, the data. By examining air quality index (AQI) measurements and Nielsen ratings over four decades, we can unravel the mystery behind this seemingly unusual relationship. It's like solving a whodunit, with air quality and TV ratings as the unlikely suspects.
The idea of examining air pollution and TV ratings together may seem like a flight of fancy, but as researchers, we're not afraid to explore uncharted territory, even if it means traversing through the fog of statistical analysis. It's a bit like finding your way through a dense fog - you can't see far ahead, but with the right tools and perseverance, you can reveal the hidden patterns lurking in the mist.
Now, one might ask, "What's the air quality got to do with TV ratings, anyway?" It's a valid question, but perhaps we've been looking at these variables through a narrow lens. As we'll reveal in our findings, the relationship between air pollution and television viewership in Portland is not just a fluke - it's a phenomenon worthy of attention. Just like discovering a rare species in the wild, our analysis uncovers an unexpected connection, offering a refreshing perspective in the realm of environmental and media studies.
So, buckle up and prepare for a wild ride through the world of air pollution and TV ratings. As they say, the truth is out there - and in this case, the truth might just be as surprising as finding a TV show with off-the-charts ratings during a pollution peak.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The authors find in Smith et al.'s study that air pollution can have detrimental effects on human health, including respiratory illnesses and cardiovascular diseases (Smith et al., 2014). However, the connection between air pollution and television viewership remains largely unexplored in scholarly literature, leaving a gap in understanding the potential influence of environmental factors on media consumption patterns.

Speaking of environmental factors, have you heard about the TV show about pollution? It’s rubbish!

Doe and Jones further delve into the impact of air pollution on cognitive function, emphasizing the negative consequences of prolonged exposure to particulate matter on mental acuity and decision-making (Doe & Jones, 2017). While their research sheds light on the multifaceted repercussions of air pollution, it fails to address the curious relationship between air quality and TV ratings.

People in Portland are really tuned into their favorite TV shows. Maybe because they can't see anything else through the smog!

In "Air Quality and You," the authors explore the various sources of air pollution in urban environments, highlighting the role of vehicular emissions, industrial activities, and residential heating in contributing to elevated levels of air contaminants (Air Quality and You, 2019). This comprehensive analysis, though informative, is silent on the potential connection between air pollution and television viewership - a gap that our study seeks to fill.

The other day, I saw a TV show about air pollution in Portland. It was breathtaking.

Turning to the literature on media consumption, "Watching Screen Time: The Psychology of Television Viewing" delineates the psychological motivations underlying individuals' choices in television programming, touching on aspects of escapism and entertainment preferences (Watching Screen Time, 2018). While this work provides valuable insights into viewer behavior, it does not account for the influence of environmental conditions, such as air pollution, on TV ratings.

What's a TV show character's favorite kind of pollution? Suspense!

To further explore the relationship between environmental conditions and media consumption, we draw from fictional works that capture the essence of atmospheric influences. In "Misty Musings: A Novelist's Ode to Portland," the protagonist finds solace in television programs during foggy weather, hinting at a potential link between obscured visibility and heightened TV viewership (Misty Musings, 2015). Though purely anecdotal, this narrative offers an intriguing notion of environmental ambiance shaping media preferences.

I found an old TV guide from the 80s in Portland - you could say it was a bit dusty.

In a recent social media post, a Portland resident humorously remarked, "The fog is so thick today, I can't see my hand in front of my face, but at least I've got a clear view of my favorite TV show!" (Social Media Post, @PortlandTVFan, 2020). Although not a scholarly source, this lighthearted comment underscores the potential appeal of television as a form of indoor entertainment during periods of reduced visibility, warranting further investigation into the correlation between air pollution and TV ratings.

How does the TV show about air pollution in Portland end? With a cliffhanger!

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 Portland, Oregon and Highest Nielsen rating for a TV show each year. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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 2020

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]]
"Airing Out the Relationship Between Air Pollution and TV Ratings: A Breath of Fresh Air for Portland"

[[ABSTRACT]]
This study examined the curious relationship between air pollution levels in Portland, Oregon, and the highest Nielsen rating for a TV show each year from 1980 to 2020. Our research team utilized data from the Environmental Protection Agency and Wikipedia to conduct this analysis, peering through the smog of uncertainty to uncover any potential links between these seemingly disparate variables.

Unsurprisingly, we discovered a significant positive correlation between air pollution levels and TV ratings, with a correlation coefficient of 0.5746593 and p < 0.01. This finding suggests that as the air quality worsens, television viewership in Portland tends to rise. It seems that despite the haze, the people of Portland have a penchant for tuning in, perhaps seeking refuge from the murky atmosphere with the comforting glow of the small screen.

Our results shed light on an unexpected synergy between environmental conditions and popular media consumption, offering a new angle to consider when exploring the complex interplay of human behavior and atmospheric factors. It appears that when the air quality is down, the TV ratings go up - a finding that might leave you breathless, much like the air in Portland on a particularly smoggy day.

[[INTRODUCTION]]
The relationship between air pollution and human health has been extensively studied, but what about its connection to television viewership? Could there be an unexpected correlation between the hazy air in Portland, Oregon, and the highest Nielsen rating for a TV show each year? This study aims to shed light on this peculiar association, adding a breath of fresh air to the field of environmental and media research.
One might wonder what on earth air pollution and TV ratings have in common - it's like comparing apples and oranges, or in this case, comparing smog and sitcoms. But as they say, the proof is in the pudding - or in this case, the data. By examining air quality index (AQI) measurements and Nielsen ratings over four decades, we can unravel the mystery behind this seemingly unusual relationship. It's like solving a whodunit, with air quality and TV ratings as the unlikely suspects.
The idea of examining air pollution and TV ratings together may seem like a flight of fancy, but as researchers, we're not afraid to explore uncharted territory, even if it means traversing through the fog of statistical analysis. It's a bit like finding your way through a dense fog - you can't see far ahead, but with the right tools and perseverance, you can reveal the hidden patterns lurking in the mist.
Now, one might ask, "What's the air quality got to do with TV ratings, anyway?" It's a valid question, but perhaps we've been looking at these variables through a narrow lens. As we'll reveal in our findings, the relationship between air pollution and television viewership in Portland is not just a fluke - it's a phenomenon worthy of attention. Just like discovering a rare species in the wild, our analysis uncovers an unexpected connection, offering a refreshing perspective in the realm of environmental and media studies.
So, buckle up and prepare for a wild ride through the world of air pollution and TV ratings. As they say, the truth is out there - and in this case, the truth might just be as surprising as finding a TV show with off-the-charts ratings during a pollution peak.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To investigate the relationship between air pollution levels and the highest Nielsen rating for a TV show each year in Portland, Oregon, we employed a data-driven approach that blended quantitative analysis with a touch of whimsy. Firstly, we obtained air quality index (AQI) data from the Environmental Protection Agency (EPA), capturing the levels of common pollutants such as particulate matter (PM2.5 and PM10), nitrogen dioxide, sulfur dioxide, carbon monoxide, and ozone. Concurrently, we gathered Nielsen rating data from the annals of Wikipedia, sifting through the virtual stacks of television history to identify the top-rated show for each year from 1980 to 2020.

In the spirit of scientific inquiry, we then unleashed the mighty forces of statistical analysis to discern any discernible patterns amidst the haze. Employing both descriptive and inferential statistics, we calculated the mean, median, standard deviation, and range of air pollution levels for each year, allowing us to pinpoint the peaks and valleys in the atmospheric tumult. Meanwhile, we utilized regression analysis to determine the magnitude and direction of the relationship between air pollution and TV ratings, akin to untangling a knot of data threads with the precision of a seasoned statistician.

"We took a deep dive into the data, navigating through the murky waters of statistical analysis just like a salmon swimming upstream," remarked our lead statistician, Dr. Pundit. "It was quite the 'pHishy' endeavor, but we managed to keep our heads above water and emerge with some 'streamlined' findings."

In addition, we harnessed the power of time series analysis to examine the temporal evolution of air pollution and TV ratings, uncovering any cyclical or trend-based fluctuations. This approach allowed us to unravel the ebb and flow of both air quality and audience preferences, akin to tracing the ripples in a metaphorical data pond.

"It was like decoding a cryptic crossword puzzle, but in this case, the clues were hidden in the annals of environmental and media data," quipped Dr. Lexicon, our resident time series expert. "We had to 'tune in' to the frequency of the data and 'air' on the side of caution to ensure our analysis was 'spot-on'."

Upon meticulously examining the data through the lens of correlation analysis, we divulged the strength and direction of the connection between air pollution levels and TV ratings. This process involved measuring the Pearson correlation coefficient and conducting hypothesis testing to ascertain the statistical significance of our findings. "It was a 'breath of fresh air' to uncover a robust correlation amidst the data fog," commented Dr. Wit, our correlation connoisseur. "The results really 'cleared the air' about the relationship between these seemingly unrelated variables."

In summary, our methodology combined rigorous statistical analysis with a sprinkle of levity, ensuring that our investigation of the link between air pollution and TV ratings in Portland, Oregon, was as robust as it was refreshing, much like a crisp breeze in a pollution-free setting.

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 Portland, Oregon and Highest Nielsen rating for a TV show each year. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
"Airing Out the Relationship Between Air Pollution and TV Ratings: A Breath of Fresh Air for Portland"

[[ABSTRACT]]
This study examined the curious relationship between air pollution levels in Portland, Oregon, and the highest Nielsen rating for a TV show each year from 1980 to 2020. Our research team utilized data from the Environmental Protection Agency and Wikipedia to conduct this analysis, peering through the smog of uncertainty to uncover any potential links between these seemingly disparate variables.

Unsurprisingly, we discovered a significant positive correlation between air pollution levels and TV ratings, with a correlation coefficient of 0.5746593 and p < 0.01. This finding suggests that as the air quality worsens, television viewership in Portland tends to rise. It seems that despite the haze, the people of Portland have a penchant for tuning in, perhaps seeking refuge from the murky atmosphere with the comforting glow of the small screen.

Our results shed light on an unexpected synergy between environmental conditions and popular media consumption, offering a new angle to consider when exploring the complex interplay of human behavior and atmospheric factors. It appears that when the air quality is down, the TV ratings go up - a finding that might leave you breathless, much like the air in Portland on a particularly smoggy day.

[[LITERATURE REVIEW]]
The authors find in Smith et al.'s study that air pollution can have detrimental effects on human health, including respiratory illnesses and cardiovascular diseases (Smith et al., 2014). However, the connection between air pollution and television viewership remains largely unexplored in scholarly literature, leaving a gap in understanding the potential influence of environmental factors on media consumption patterns.
Speaking of environmental factors, have you heard about the TV show about pollution? It’s rubbish!
Doe and Jones further delve into the impact of air pollution on cognitive function, emphasizing the negative consequences of prolonged exposure to particulate matter on mental acuity and decision-making (Doe & Jones, 2017). While their research sheds light on the multifaceted repercussions of air pollution, it fails to address the curious relationship between air quality and TV ratings.
People in Portland are really tuned into their favorite TV shows. Maybe because they can't see anything else through the smog!
In "Air Quality and You," the authors explore the various sources of air pollution in urban environments, highlighting the role of vehicular emissions, industrial activities, and residential heating in contributing to elevated levels of air contaminants (Air Quality and You, 2019). This comprehensive analysis, though informative, is silent on the potential connection between air pollution and television viewership - a gap that our study seeks to fill.
The other day, I saw a TV show about air pollution in Portland. It was breathtaking.
Turning to the literature on media consumption, "Watching Screen Time: The Psychology of Television Viewing" delineates the psychological motivations underlying individuals' choices in television programming, touching on aspects of escapism and entertainment preferences (Watching Screen Time, 2018). While this work provides valuable insights into viewer behavior, it does not account for the influence of environmental conditions, such as air pollution, on TV ratings.
What's a TV show character's favorite kind of pollution? Suspense!
To further explore the relationship between environmental conditions and media consumption, we draw from fictional works that capture the essence of atmospheric influences. In "Misty Musings: A Novelist's Ode to Portland," the protagonist finds solace in television programs during foggy weather, hinting at a potential link between obscured visibility and heightened TV viewership (Misty Musings, 2015). Though purely anecdotal, this narrative offers an intriguing notion of environmental ambiance shaping media preferences.
I found an old TV guide from the 80s in Portland - you could say it was a bit dusty.
In a recent social media post, a Portland resident humorously remarked, "The fog is so thick today, I can't see my hand in front of my face, but at least I've got a clear view of my favorite TV show!" (Social Media Post, @PortlandTVFan, 2020). Although not a scholarly source, this lighthearted comment underscores the potential appeal of television as a form of indoor entertainment during periods of reduced visibility, warranting further investigation into the correlation between air pollution and TV ratings.
How does the TV show about air pollution in Portland end? With a cliffhanger!

[[RESULTS]]
The data analysis yielded a significant positive correlation between air pollution levels and TV ratings in Portland, Oregon from 1980 to 2020, with a correlation coefficient of 0.5746593 and an r-squared value of 0.3302333. It seems that as the air quality worsened, the TV ratings soared, suggesting that perhaps the citizens of Portland sought solace in the glow of television screens amidst the smoggy air. One might say they were "airing out" their grievances with the environment through their television choices.
The p-value of less than 0.01 further solidifies the strength of this correlation, indicating that it is highly unlikely to have occurred by chance. In other words, this relationship is as clear as the fresh mountain air - or in this case, as murky as the pollution-filled city air.
Interestingly, during years with elevated air pollution levels, there was a discernible uptick in the highest Nielsen ratings for TV shows. It's as if the viewers were saying, "The air may be unbearable, but at least there's a good show on!" This finding demonstrates the resilience and adaptability of Portland's television audience in the face of environmental adversity.
Fig. 1 shows a striking scatterplot illustrating the strong correlation between air pollution levels and TV ratings. The points on the plot are as closely intertwined as the particles in the smog-filled air, painting a vivid picture of the relationship between these variables.
This study presents compelling evidence of the unexpected synergy between air pollution and TV ratings, adding a layer of intrigue to the intricate web of human behavior and environmental factors. It seems that when the air quality in Portland got hazy, the TV ratings cleared up - a curious phenomenon that may leave you gasping for breath, much like the inhabitants of Portland on a particularly polluted day.
The link between air pollution and TV ratings reveals a captivating narrative, shedding light on the interplay between environmental conditions and media consumption. It accentuates the notion that amidst the haze of uncertainty, there are patterns waiting to be unveiled, much like eking out statistically significant results from a sea of data.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The linkage between air pollution and TV ratings in Portland, Oregon has yielded some intriguing results. Our findings not only confirm but emphasize the previously unexplored association between these seemingly incongruous variables. The significant positive correlation we identified echoes the humorous musings about television viewers seeking shelter from the misty Portland air in the glowing light of their favorite shows. It seems the citizens of Portland have a knack for "clearing the air" by tuning into their top TV picks.

Our study complements prior research on the adverse effects of air pollution on human health and cognitive function. Smith et al.'s work, for instance, highlights the tangible impact of air pollution on respiratory ailments, effectively setting the stage for our examination of a more lighthearted consequence: increased television viewership. One might say our findings add a breath of fresh air to the discourse on air pollution, infusing a hint of levity into the conversation.

Likewise, the dearth of scholarly exploration into the interplay between air quality and media consumption is now offset by our analysis, providing empirical support for the notion that as the air quality declines, television ratings ascend. This serves as a witty reminder that even amidst the smog of academic research, there are patterns waiting to be aired, much like a sitcom revealing its punchline.

Our results suggest that amidst the haze of uncertainty, the relationship between environmental conditions and media consumption offers a compelling narrative, much like a suspenseful TV drama unfolding. As we "air out" these findings, it becomes clear that the citizens of Portland are not just passive recipients of polluted air; rather, they actively seek solace in the world of television, turning a potential downside into a viewing upside. This unexpected synergy undoubtedly leaves the audience breathless - a sentiment shared by many Portland residents on particularly smoggy days.

In essence, our study not only reinforces the significant positive correlation between air pollution levels and TV ratings, but also highlights the resilience and adaptability of Portland's television audience in the face of environmental adversity. It is as if the citizens are collectively saying, "The air may be polluted, but our TV choices are crystal clear." This unexpected linkage offers a refreshing perspective on the intersection of environmental influences and human behavior, revealing that amidst the atmospheric haze, there are statistical patterns waiting to be unveiled - a revelation as captivating as a well-crafted cliffhanger.

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 Portland, Oregon and Highest Nielsen rating for a TV show each year. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
"Airing Out the Relationship Between Air Pollution and TV Ratings: A Breath of Fresh Air for Portland"

[[ABSTRACT]]
This study examined the curious relationship between air pollution levels in Portland, Oregon, and the highest Nielsen rating for a TV show each year from 1980 to 2020. Our research team utilized data from the Environmental Protection Agency and Wikipedia to conduct this analysis, peering through the smog of uncertainty to uncover any potential links between these seemingly disparate variables.

Unsurprisingly, we discovered a significant positive correlation between air pollution levels and TV ratings, with a correlation coefficient of 0.5746593 and p < 0.01. This finding suggests that as the air quality worsens, television viewership in Portland tends to rise. It seems that despite the haze, the people of Portland have a penchant for tuning in, perhaps seeking refuge from the murky atmosphere with the comforting glow of the small screen.

Our results shed light on an unexpected synergy between environmental conditions and popular media consumption, offering a new angle to consider when exploring the complex interplay of human behavior and atmospheric factors. It appears that when the air quality is down, the TV ratings go up - a finding that might leave you breathless, much like the air in Portland on a particularly smoggy day.

[[INTRDUCTION]]
The relationship between air pollution and human health has been extensively studied, but what about its connection to television viewership? Could there be an unexpected correlation between the hazy air in Portland, Oregon, and the highest Nielsen rating for a TV show each year? This study aims to shed light on this peculiar association, adding a breath of fresh air to the field of environmental and media research.
One might wonder what on earth air pollution and TV ratings have in common - it's like comparing apples and oranges, or in this case, comparing smog and sitcoms. But as they say, the proof is in the pudding - or in this case, the data. By examining air quality index (AQI) measurements and Nielsen ratings over four decades, we can unravel the mystery behind this seemingly unusual relationship. It's like solving a whodunit, with air quality and TV ratings as the unlikely suspects.
The idea of examining air pollution and TV ratings together may seem like a flight of fancy, but as researchers, we're not afraid to explore uncharted territory, even if it means traversing through the fog of statistical analysis. It's a bit like finding your way through a dense fog - you can't see far ahead, but with the right tools and perseverance, you can reveal the hidden patterns lurking in the mist.
Now, one might ask, "What's the air quality got to do with TV ratings, anyway?" It's a valid question, but perhaps we've been looking at these variables through a narrow lens. As we'll reveal in our findings, the relationship between air pollution and television viewership in Portland is not just a fluke - it's a phenomenon worthy of attention. Just like discovering a rare species in the wild, our analysis uncovers an unexpected connection, offering a refreshing perspective in the realm of environmental and media studies.
So, buckle up and prepare for a wild ride through the world of air pollution and TV ratings. As they say, the truth is out there - and in this case, the truth might just be as surprising as finding a TV show with off-the-charts ratings during a pollution peak.

[[RESULTS]]
The data analysis yielded a significant positive correlation between air pollution levels and TV ratings in Portland, Oregon from 1980 to 2020, with a correlation coefficient of 0.5746593 and an r-squared value of 0.3302333. It seems that as the air quality worsened, the TV ratings soared, suggesting that perhaps the citizens of Portland sought solace in the glow of television screens amidst the smoggy air. One might say they were "airing out" their grievances with the environment through their television choices.
The p-value of less than 0.01 further solidifies the strength of this correlation, indicating that it is highly unlikely to have occurred by chance. In other words, this relationship is as clear as the fresh mountain air - or in this case, as murky as the pollution-filled city air.
Interestingly, during years with elevated air pollution levels, there was a discernible uptick in the highest Nielsen ratings for TV shows. It's as if the viewers were saying, "The air may be unbearable, but at least there's a good show on!" This finding demonstrates the resilience and adaptability of Portland's television audience in the face of environmental adversity.
Fig. 1 shows a striking scatterplot illustrating the strong correlation between air pollution levels and TV ratings. The points on the plot are as closely intertwined as the particles in the smog-filled air, painting a vivid picture of the relationship between these variables.
This study presents compelling evidence of the unexpected synergy between air pollution and TV ratings, adding a layer of intrigue to the intricate web of human behavior and environmental factors. It seems that when the air quality in Portland got hazy, the TV ratings cleared up - a curious phenomenon that may leave you gasping for breath, much like the inhabitants of Portland on a particularly polluted day.
The link between air pollution and TV ratings reveals a captivating narrative, shedding light on the interplay between environmental conditions and media consumption. It accentuates the notion that amidst the haze of uncertainty, there are patterns waiting to be unveiled, much like eking out statistically significant results from a sea of data.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has revealed a compelling association between air pollution levels and TV ratings in Portland, Oregon. It seems that as the air quality deteriorates, the TV ratings escalate, highlighting the intriguing interplay between environmental factors and viewer behavior. It's as if the viewers were saying, "The air may be unbearable, but at least there's a good show on!" This proverbial breath of fresh air in the realm of environmental and media research offers a refreshing perspective, much like a brisk breeze on a sweltering day.

Our findings not only provide insight into the quirky quagmire where air pollution and TV ratings intersect, but they also underscore the adaptability of Portland's television audience in the face of environmental challenges, akin to finding the silver lining in a particularly dense cloud. It's like discovering a hidden gem in a smog-filled mine - an unexpected delight amid the haze.

With a correlation coefficient of 0.5746593 and an r-squared value of 0.3302333, our results demonstrate a robust relationship between these seemingly incongruous variables. This correlation is about as clear as the need for more puns in scholarly writing – unequivocal and potentially groan-inducing.

Despite the novelty and humor inherent in this unexpected correlation, the evidence is as sturdy as a well-built statistical model - it's no mere fluke. The p-value of less than 0.01 further cements the significance of this connection, indicating that this relationship is as unlikely to have occurred by chance as a scientist voluntarily giving up their lab coat.

In light of these findings, it is evident that no further research in this area is needed. It is as clear as the potential for a dad joke in an academic conclusion - all signs point to a resounding "no." This conclusion is as firm as the resolve of a researcher determined to squeeze in one last pun.

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 Airing Out the Relationship Between Air Pollution and TV Ratings: A Breath of Fresh Air for Portland. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Ecological Entertainment 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 Airing Out the Relationship Between Air Pollution and TV Ratings: A Breath of Fresh Air for Portland. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Air Quality and Entertainment Research (SAQER)

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 Airing Out the Relationship Between Air Pollution and TV Ratings: A Breath of Fresh Air for Portland with an abstract of This study examined the curious relationship between air pollution levels in Portland, Oregon, and the highest Nielsen rating for a TV show each year from 1980 to 2020. Our research team utilized data from the Environmental Protection Agency and Wikipedia to conduct this analysis, peering through the smog of uncertainty to uncover any potential links between these seemingly disparate variables.
Unsurprisingly, we discovered a significant positive correlation between air pollution levels and TV ratings, with a correlation coefficient of 0.5746593 and p < 0.01. This finding suggests that as the air quality worsens, television viewership in Portland tends to rise. It seems that despite the haze, the people of Portland have a penchant for tuning in, perhaps seeking refuge from the murky atmosphere with the comforting glow of the small screen.
Our results shed light on an unexpected synergy between environmental conditions and popular media consumption, offering a new angle to consider when exploring the complex interplay of human behavior and atmospheric factors. It appears that when the air quality is down, the TV ratings go up - a finding that might leave you breathless, much like the air in Portland on a particularly smoggy day.

ChatGPT:

air pollution, TV ratings, Portland, Oregon, Nielsen rating, Environmental Protection Agency, correlation, atmospheric factors, media consumption, human behavior, television viewership

*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 Portland, Oregon
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Portland-Vancouver-Hillsboro, OR-WA
Source: Environmental Protection Agency
See what else correlates with Air pollution in Portland, Oregon

Highest Nielsen rating for a TV show each year
Detailed data title: Highest average household Nielsen rating for TV shows
Source: Wikipedia
See what else correlates with Highest Nielsen rating for a TV show each year

Correlation r = 0.5746593 (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 = 0.3302333 (Coefficient of determination)
This means 33% of the change in the one variable (i.e., Highest Nielsen rating for a TV show each year) is predictable based on the change in the other (i.e., Air pollution in Portland, Oregon) over the 41 years from 1980 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 8.5E-5.^Show 0.0000851765812659454100000000
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 0.57 in 0.0085% of random cases. Said differently, if you correlated 11,740 random variables^Note Which I absolutely did.
with the same 40 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 40 because we have two variables measured over a period of 41 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.

[ 0.32, 0.75 ] 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
Air pollution in Portland, Oregon (Bad air quality days)	5.7377	11.7808	5.20548	4.93151	2.45902	5.47945	6.57534	6.84932	5.46448	1.91781	4.93151	4.10959	3.55191	0.273973	1.64384	1.64384	4.64481	0.547945	2.46575	3.83562	3.27869	2.46575	2.73973	2.46575	3.82514	4.10959	3.83562	6.0274	2.45902	4.65753	0.821918	4.65753	0.819672	4.65753	2.19178	1.91781	0.546448	4.38356	3.0137	0.821918	3.82514
Highest Nielsen rating for a TV show each year (Nielson Rating)	28.4	34.5	28.4	25.5	25.7	25	33.7	34.9	27.8	25.6	23.1	21.3	21.9	21.9	20.9	20.6	22	21.2	21.7	17.8	18.6	17.4	15	16.3	14.7	16.5	17.6	17.3	16.1	15.1	13.7	23.95	12.9	12.8	21.42	20.69	21.3	19.63	17.58	18.8	19.96

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([5.7377,11.7808,5.20548,4.93151,2.45902,5.47945,6.57534,6.84932,5.46448,1.91781,4.93151,4.10959,3.55191,0.273973,1.64384,1.64384,4.64481,0.547945,2.46575,3.83562,3.27869,2.46575,2.73973,2.46575,3.82514,4.10959,3.83562,6.0274,2.45902,4.65753,0.821918,4.65753,0.819672,4.65753,2.19178,1.91781,0.546448,4.38356,3.0137,0.821918,3.82514,])
array_2 = np.array([28.4,34.5,28.4,25.5,25.7,25,33.7,34.9,27.8,25.6,23.1,21.3,21.9,21.9,20.9,20.6,22,21.2,21.7,17.8,18.6,17.4,15,16.3,14.7,16.5,17.6,17.3,16.1,15.1,13.7,23.95,12.9,12.8,21.42,20.69,21.3,19.63,17.58,18.8,19.96,])
array_1_name = "Air pollution in Portland, Oregon"
array_2_name = "Highest Nielsen rating for a TV show each year"

# 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 Portland, Oregon
Line chart for only Highest Nielsen rating for a TV show each year
AI-generated correlation image
The spurious research paper: Airing Out the Relationship Between Air Pollution and TV Ratings: A Breath of Fresh Air for Portland

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Correlation ID: 2258 · Black Variable ID: 21086 · Red Variable ID: 88


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