Spurious correlation #5,419 · 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 Corpus Christi, Texas and the second variable is Votes for Democratic Senators in Texas. The chart goes from 1980 to 2020, 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 Corpus Christi, Texas and the second variable is Votes for Democratic Senators in Texas. The chart goes from 1980 to 2020, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The cleaner air led to clearer thinking, prompting some Democrats to reconsider their political views. Without all those pollutants fogging up their brains, they just couldn't resist the allure of the opposition's policies. As Corpus Christi cleared up, so did their party allegiance.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a vibrant Cyber folk gathering in a futuristic Corpus Christi, Texas. The democratic senators are depicted engaging in open discussions in a setting with visible air purification systems and clear, pollution-free skies. Show the senators in thoughtful conversation, with expressions that convey their receptiveness to considering alternative policies, as they are illuminated by the newfound clarity in the surrounding environment. The scene should evoke a sense of progress and optimism in the midst of technological and environmental advancements. 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 Corpus Christi, Texas and Votes for Democratic Senators in Texas. 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: Photorealism Cyber folk Scalar art High-tech Cyber folk
ChatGPT: Cyber folk

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 Corpus Christi, Texas and Votes for Democratic Senators in Texas, in the following situation: The cleaner air led to clearer thinking, prompting some Democrats to reconsider their political views. Without all those pollutants fogging up their brains, they just couldn't resist the allure of the opposition's policies. As Corpus Christi cleared up, so did their party allegiance.. Keep the description simple.
ChatGPT: In Corpus Christi, Texas, the air cleared as pollution levels dropped. Amidst the cleaner environment, a group of Democratic senators were seen engaging in open discussions and conversations, displaying an evident shift in their political views. They seemed more receptive to considering alternative policies, illuminated by the newfound clarity in the air around them.

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: [ Cyber folk ] and depicting this scene: In Corpus Christi, Texas, the air cleared as pollution levels dropped. Amidst the cleaner environment, a group of Democratic senators were seen engaging in open discussions and conversations, displaying an evident shift in their political views. They seemed more receptive to considering alternative policies, illuminated by the newfound clarity in the air around them. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a vibrant Cyber folk gathering in a futuristic Corpus Christi, Texas. The democratic senators are depicted engaging in open discussions in a setting with visible air purification systems and clear, pollution-free skies. Show the senators in thoughtful conversation, with expressions that convey their receptiveness to considering alternative policies, as they are illuminated by the newfound clarity in the surrounding environment. The scene should evoke a sense of progress and optimism in the midst of technological and environmental advancements.

*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 Corpus Christi, Texas caused Democrat votes for Senators in Texas to decrease.

AI academic paper

(Because p < 0.01)

Cloudy with a Chance of Blue States: The Particulate Matter between Air Pollution and Senatorial Preference in Corpus Christi, Texas

The Journal of Atmospheric Politics and Social Science

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Air pollution in Corpus Christi, Texas and Democrat votes for Senators in Texas. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name a few real TV shows that sound like they might be relevant to the topic that you watched as research.

Here is the title and abstract of the paper:
[[TITLE]]
"Cloudy with a Chance of Blue States: The Particulate Matter between Air Pollution and Senatorial Preference in Corpus Christi, Texas"

[[ABSTRACT]]
This paper presents an analysis of the relationship between air pollution in Corpus Christi, Texas, and Democratic votes for Senators in Texas. Using data from the Environmental Protection Agency and MIT Election Data and Science Lab, Harvard Dataverse, our research team investigated the purported connection between the inhalation of fine particulate matter and the political leanings of the electorate. Our findings reveal a remarkably strong correlation coefficient of 0.9172435 and p < 0.01 for the time span from 1980 to 2020. While we take the significance of these results seriously, we cannot help but ponder whether the citizens of Corpus Christi are literally "voting with their lungs" or if there is merely a whiff of political influence in the air. This study opens up avenues for further investigation into the intersection of environmental quality and political decision-making, and perhaps underscores the notion that where there's smog, there's a political fog.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The interaction between environmental factors and political dynamics has long fascinated scholars and armchair pundits alike. Whether it's the impact of climate change on electoral outcomes or the influence of clean water regulations on voting behavior, the nexus of ecology and politics continues to stir curiosity and debate. In this study, we turn our attention to the atmospheric arena, specifically examining the intriguing relationship between air pollution in Corpus Christi, Texas, and the voting preferences of residents for Senators in the Lone Star State.
Corpus Christi, known for its vibrant coastal culture and lively fiestas, also grapples with air quality issues due to industrial activities and transportation emissions. Meanwhile, Texas, as the saying goes, is a whole other country when it comes to its political landscape, with a long-standing reputation for being staunchly red. However, as we delve into the data, we set out to unravel whether there might be a blue-tinted haze hovering over Corpus Christi that mirrors the political inclinations of its residents.
The correlation between air pollution, particularly the inhalation of fine particulate matter, and political ideology is not merely an exercise in academic curiosity. It touches upon broader implications for public health, environmental justice, and democratic representation. As we embark on our empirical exploration, we are mindful of the serious implications of our findings, while allowing ourselves the occasional wry observation, akin to catching a scent of political intrigue blowing in with the prevailing winds.
This paper aims to contribute to the burgeoning literature on the intersection of environmental quality and electoral decisions, adding a dash of empirical evidence to the discourse on "voting with one's lungs." Our study, while rooted in rigorous statistical analysis, also beckons us to consider the metaphorical miasma of political influence that may permeate the very air constituents breathe. In the following sections, we present our methodology, illuminate our findings, and tentatively tease out the implications of our research, much like a breeze gently dispersing an enigmatic cloud of data. Join us as we venture into the atmospheric realm where environmental particles and political particles collide in a thought-provoking dance, with a side of whimsy and wonder.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The proposed association between environmental factors and political inclinations has been the subject of notable scholarly inquiry. Smith et al. (2010) presented a comprehensive analysis of the impact of air pollution on public perceptions of governmental policies, shedding light on the potential influence of environmental quality on political attitudes. Similarly, Doe and Jones (2015) examined the correlation between respiratory health and voting behavior, emphasizing the interconnectedness of physical well-being and civic engagement. These studies offer valuable insights into the relationship between environmental factors and political dynamics, setting the stage for our investigation into the specific case of air pollution in Corpus Christi, Texas, and its potential link to Senatorial preference in the state of Texas.

Turning to the broader context of environmental politics and public sentiment, "The Clean Air Act at 50: Making a Positive Difference" (Environmental Defense Fund, 2020) provides a comprehensive overview of policy interventions aimed at addressing air quality challenges in the United States. The book not only offers a historical perspective on legislative efforts but also underscores the societal impact of clean air regulations, laying the groundwork for our exploration of the ramifications of air pollution on electoral behavior.

On a more speculative note, fiction literature has occasionally delved into the imagined ramifications of environmental degradation on political landscapes. In "The Air We Breathe" (Smith, 2008), the protagonist navigates a dystopian world where air pollution has become a central tool for political manipulation, serving as a metaphor for the insidious nature of power dynamics. While not a work of empirical research, the novel sparks contemplation on the potential intersection of environmental hazards and political decision-making, offering a creative lens through which to ponder the dynamics suggested by our own study.

In the realm of popular culture, the television series "Breaking Smog" provides a dramatized portrayal of environmental activists navigating the complex terrain of air quality advocacy. Although not directly related to our research focus, the show's nuanced portrayal of environmental challenges invites reflection on the public consciousness surrounding pollution and its potential reverberations in the political sphere. Meanwhile, "The Office: Air Quality Edition" humorously parodies workplace dynamics against the backdrop of an ailing ventilation system, prompting viewers to consider the everyday implications of air quality in a light-hearted manner. While not a scholarly source, the show's comedic take on air-related hazards offers a touch of levity to the otherwise serious discourse on environmental factors and societal dynamics.

In synthesizing these diverse perspectives, our investigation into the correlation between air pollution in Corpus Christi, Texas, and Senatorial preference in Texas aims to contribute to the multi-faceted dialogue surrounding environmental quality and political decision-making. As we proceed to delve into our findings, we are mindful of these varied influences that shape our understanding of the intricate interplay between ecological conditions and electoral patterns.

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 Corpus Christi, Texas and Democrat votes for Senators in Texas. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Limit your response to 500 tokens.

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

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

[[TITLE]]
"Cloudy with a Chance of Blue States: The Particulate Matter between Air Pollution and Senatorial Preference in Corpus Christi, Texas"

[[ABSTRACT]]
This paper presents an analysis of the relationship between air pollution in Corpus Christi, Texas, and Democratic votes for Senators in Texas. Using data from the Environmental Protection Agency and MIT Election Data and Science Lab, Harvard Dataverse, our research team investigated the purported connection between the inhalation of fine particulate matter and the political leanings of the electorate. Our findings reveal a remarkably strong correlation coefficient of 0.9172435 and p < 0.01 for the time span from 1980 to 2020. While we take the significance of these results seriously, we cannot help but ponder whether the citizens of Corpus Christi are literally "voting with their lungs" or if there is merely a whiff of political influence in the air. This study opens up avenues for further investigation into the intersection of environmental quality and political decision-making, and perhaps underscores the notion that where there's smog, there's a political fog.

[[LITERATURE REVIEW]]
The proposed association between environmental factors and political inclinations has been the subject of notable scholarly inquiry. Smith et al. (2010) presented a comprehensive analysis of the impact of air pollution on public perceptions of governmental policies, shedding light on the potential influence of environmental quality on political attitudes. Similarly, Doe and Jones (2015) examined the correlation between respiratory health and voting behavior, emphasizing the interconnectedness of physical well-being and civic engagement. These studies offer valuable insights into the relationship between environmental factors and political dynamics, setting the stage for our investigation into the specific case of air pollution in Corpus Christi, Texas, and its potential link to Senatorial preference in the state of Texas.
Turning to the broader context of environmental politics and public sentiment, "The Clean Air Act at 50: Making a Positive Difference" (Environmental Defense Fund, 2020) provides a comprehensive overview of policy interventions aimed at addressing air quality challenges in the United States. The book not only offers a historical perspective on legislative efforts but also underscores the societal impact of clean air regulations, laying the groundwork for our exploration of the ramifications of air pollution on electoral behavior.
On a more speculative note, fiction literature has occasionally delved into the imagined ramifications of environmental degradation on political landscapes. In "The Air We Breathe" (Smith, 2008), the protagonist navigates a dystopian world where air pollution has become a central tool for political manipulation, serving as a metaphor for the insidious nature of power dynamics. While not a work of empirical research, the novel sparks contemplation on the potential intersection of environmental hazards and political decision-making, offering a creative lens through which to ponder the dynamics suggested by our own study.
In the realm of popular culture, the television series "Breaking Smog" provides a dramatized portrayal of environmental activists navigating the complex terrain of air quality advocacy. Although not directly related to our research focus, the show's nuanced portrayal of environmental challenges invites reflection on the public consciousness surrounding pollution and its potential reverberations in the political sphere. Meanwhile, "The Office: Air Quality Edition" humorously parodies workplace dynamics against the backdrop of an ailing ventilation system, prompting viewers to consider the everyday implications of air quality in a light-hearted manner. While not a scholarly source, the show's comedic take on air-related hazards offers a touch of levity to the otherwise serious discourse on environmental factors and societal dynamics.
In synthesizing these diverse perspectives, our investigation into the correlation between air pollution in Corpus Christi, Texas, and Senatorial preference in Texas aims to contribute to the multi-faceted dialogue surrounding environmental quality and political decision-making. As we proceed to delve into our findings, we are mindful of these varied influences that shape our understanding of the intricate interplay between ecological conditions and electoral patterns.

[[RESULTS]]
Our investigation into the relationship between air pollution in Corpus Christi, Texas, and Democratic votes for Senators in Texas yielded a correlation coefficient of 0.9172435, with an r-squared value of 0.8413356 and a p-value of less than 0.01. This statistical analysis suggests a remarkably strong association between the inhalation of fine particulate matter and the political inclinations of the electorate in this region.
Figure 1 depicts the scatterplot illustrating this robust correlation, which unmistakably resembles a Rorschach inkblot test – perhaps a subconscious plea for cleaner air or a hidden message from the lungs of Corpus Christi residents about their voting preferences.
The data reveal a striking trend over the four-decade period from 1980 to 2020, indicating that as air pollution levels increased, so did the proportion of Democratic votes for Senators in Texas. While we recognize the gravity of these results, we also cannot help but muse over whether the citizens of Corpus Christi are not only exhaling but also "voting with their lungs." This correlation may allude to a deeper symbiotic relationship between the atmospheric and the political, challenging us to ponder whether these constituents are not just blowing smoke but actively shaping political outcomes.
Our findings not only prompt contemplation about the impact of environmental factors on electoral behavior but also conjure up visions of a political fog lurking amidst the particulate matter. This study underscores the intricate interplay between environmental quality and democratic decision-making, eliciting a sense of cynicism in the air while prompting cautious optimism for future avenues of investigation.
In summary, the link between air pollution and Senatorial preference in Corpus Christi, Texas, is undeniably compelling, casting a shadow of doubt over conventional wisdom and raising the question: are the winds of change also carriers of political persuasion? Our study lays bare the intriguing confluence of environmental conditions and electoral outcomes, with a hint of whimsy in the mix, challenging us to sift through the haze and discern the contours of this intriguing relationship.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our investigation into the connection between air pollution in Corpus Christi, Texas, and Democratic votes for Senators in Texas yielded compelling results that add both scientific and whimsical depth to the discourse on environmental quality and political decision-making. Our findings not only confirm and extend prior research but also waft a breath of fresh air into the field, reminding us that even academic discussions can benefit from a dash of levity.

The robust correlation coefficient of 0.9172435, with an r-squared value of 0.8413356 and a p-value of less than 0.01, reflects a remarkably strong association between inhalation of fine particulate matter and the electorate's political inclinations. These results fall in line with earlier studies by Smith et al. (2010), which emphasized the potential influence of environmental quality on political attitudes, and Doe and Jones (2015), who highlighted the interconnectedness of physical well-being and civic engagement. While these studies were as serious as a lab rat with tenure, our findings bring a lighthearted twist, suggesting that the citizens of Corpus Christi may be quite literally "voting with their lungs."

The striking visual representation of our results in Figure 1, resembling a Rorschach inkblot test, invites contemplation about whether the residents of Corpus Christi are subconsciously expressing their desires for cleaner air or if there is a hidden message in the lungs of the electorate. This element of the study takes a page from fiction literature, such as Smith's "The Air We Breathe" (2008), where air pollution becomes a tool for political manipulation, serving as a metaphor for the often opaque nature of power dynamics. Our study highlights the eccentric yet profound ways in which environmental conditions may intertwine with political decision-making, adding a pinch of dash and a sprinkle of political intrigue to the academic stew.

As we navigate through the political fog that our study has unearthed, we are reminded of the comedic portrayal of air quality challenges in "The Office: Air Quality Edition," prompting us to consider the everyday implications of air pollution in a lighthearted manner. So, while we take our findings seriously, it's also important to recognize the immense potential for further investigation into this field, perhaps with a touch of humor and whimsy to complement the weighty scientific discussions.

In summary, our study not only contributes to our understanding of the complex interplay between environmental quality and democratic decision-making but also infuses a hint of levity into a traditionally serious discourse. It is as if the citizens of Corpus Christi are telling us, "where there's smog, there's a political fog," challenging us to peer through the haze and discern the contours of this captivating relationship.

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 Corpus Christi, Texas and Democrat votes for Senators in Texas. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"Cloudy with a Chance of Blue States: The Particulate Matter between Air Pollution and Senatorial Preference in Corpus Christi, Texas"

[[ABSTRACT]]
This paper presents an analysis of the relationship between air pollution in Corpus Christi, Texas, and Democratic votes for Senators in Texas. Using data from the Environmental Protection Agency and MIT Election Data and Science Lab, Harvard Dataverse, our research team investigated the purported connection between the inhalation of fine particulate matter and the political leanings of the electorate. Our findings reveal a remarkably strong correlation coefficient of 0.9172435 and p < 0.01 for the time span from 1980 to 2020. While we take the significance of these results seriously, we cannot help but ponder whether the citizens of Corpus Christi are literally "voting with their lungs" or if there is merely a whiff of political influence in the air. This study opens up avenues for further investigation into the intersection of environmental quality and political decision-making, and perhaps underscores the notion that where there's smog, there's a political fog.

[[INTRDUCTION]]
The interaction between environmental factors and political dynamics has long fascinated scholars and armchair pundits alike. Whether it's the impact of climate change on electoral outcomes or the influence of clean water regulations on voting behavior, the nexus of ecology and politics continues to stir curiosity and debate. In this study, we turn our attention to the atmospheric arena, specifically examining the intriguing relationship between air pollution in Corpus Christi, Texas, and the voting preferences of residents for Senators in the Lone Star State.
Corpus Christi, known for its vibrant coastal culture and lively fiestas, also grapples with air quality issues due to industrial activities and transportation emissions. Meanwhile, Texas, as the saying goes, is a whole other country when it comes to its political landscape, with a long-standing reputation for being staunchly red. However, as we delve into the data, we set out to unravel whether there might be a blue-tinted haze hovering over Corpus Christi that mirrors the political inclinations of its residents.
The correlation between air pollution, particularly the inhalation of fine particulate matter, and political ideology is not merely an exercise in academic curiosity. It touches upon broader implications for public health, environmental justice, and democratic representation. As we embark on our empirical exploration, we are mindful of the serious implications of our findings, while allowing ourselves the occasional wry observation, akin to catching a scent of political intrigue blowing in with the prevailing winds.
This paper aims to contribute to the burgeoning literature on the intersection of environmental quality and electoral decisions, adding a dash of empirical evidence to the discourse on "voting with one's lungs." Our study, while rooted in rigorous statistical analysis, also beckons us to consider the metaphorical miasma of political influence that may permeate the very air constituents breathe. In the following sections, we present our methodology, illuminate our findings, and tentatively tease out the implications of our research, much like a breeze gently dispersing an enigmatic cloud of data. Join us as we venture into the atmospheric realm where environmental particles and political particles collide in a thought-provoking dance, with a side of whimsy and wonder.

[[RESULTS]]
Our investigation into the relationship between air pollution in Corpus Christi, Texas, and Democratic votes for Senators in Texas yielded a correlation coefficient of 0.9172435, with an r-squared value of 0.8413356 and a p-value of less than 0.01. This statistical analysis suggests a remarkably strong association between the inhalation of fine particulate matter and the political inclinations of the electorate in this region.
Figure 1 depicts the scatterplot illustrating this robust correlation, which unmistakably resembles a Rorschach inkblot test – perhaps a subconscious plea for cleaner air or a hidden message from the lungs of Corpus Christi residents about their voting preferences.
The data reveal a striking trend over the four-decade period from 1980 to 2020, indicating that as air pollution levels increased, so did the proportion of Democratic votes for Senators in Texas. While we recognize the gravity of these results, we also cannot help but muse over whether the citizens of Corpus Christi are not only exhaling but also "voting with their lungs." This correlation may allude to a deeper symbiotic relationship between the atmospheric and the political, challenging us to ponder whether these constituents are not just blowing smoke but actively shaping political outcomes.
Our findings not only prompt contemplation about the impact of environmental factors on electoral behavior but also conjure up visions of a political fog lurking amidst the particulate matter. This study underscores the intricate interplay between environmental quality and democratic decision-making, eliciting a sense of cynicism in the air while prompting cautious optimism for future avenues of investigation.
In summary, the link between air pollution and Senatorial preference in Corpus Christi, Texas, is undeniably compelling, casting a shadow of doubt over conventional wisdom and raising the question: are the winds of change also carriers of political persuasion? Our study lays bare the intriguing confluence of environmental conditions and electoral outcomes, with a hint of whimsy in the mix, challenging us to sift through the haze and discern the contours of this intriguing relationship.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has shed light on the undeniably strong correlation between air pollution in Corpus Christi, Texas, and Democratic votes for Senators in Texas. The statistically robust relationship we uncovered, with a correlation coefficient of 0.9172435 and a p-value of less than 0.01, presents a compelling case for the influence of environmental quality on political preferences. As we reflect on these findings, one cannot help but appreciate the subtle allegory of citizens "voting with their lungs," pointing to the palpable impact of atmospheric conditions on democratic outcomes. It seems that the political landscape is not the only thing getting muddled – there's a whiff of implication in the very air we breathe!

As we picture the scatterplot resembling a Rorschach inkblot, we are reminded that data can sometimes speak in mysterious, whimsical ways, almost as if urging us to unravel the hidden messages within. The intriguing dance between environmental particles and political particles leaves us pondering whether there is more than just literal inhalation at play – are the winds of change also carriers of political persuasion, or is this merely a case of correlation without causation? The metaphorical miasma of political influence that may permeate the air in Corpus Christi beckons us to delve deeper into the intricate interplay of environmental quality and democratic decision-making, while also leaving us with a sense of cautious optimism and a touch of cynicism in the atmosphere.

In light of these compelling findings, it seems that our study has certainly made its mark, much like a lingering scent in the air. Nevertheless, as we take a breath of fresh air and contemplate the implications of our research, it is with a measure of satisfaction, and perhaps a dash of relief, that we assert the need for no further investigation in this area. After all, how much clearer can it get than "voting with one's lungs"?

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 Cloudy with a Chance of Blue States: The Particulate Matter between Air Pollution and Senatorial Preference in Corpus Christi, Texas. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Atmospheric Politics and Social Science

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 Cloudy with a Chance of Blue States: The Particulate Matter between Air Pollution and Senatorial Preference in Corpus Christi, Texas. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Environmental Politics and Atmospheric Research

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 Cloudy with a Chance of Blue States: The Particulate Matter between Air Pollution and Senatorial Preference in Corpus Christi, Texas with an abstract of This paper presents an analysis of the relationship between air pollution in Corpus Christi, Texas, and Democratic votes for Senators in Texas. Using data from the Environmental Protection Agency and MIT Election Data and Science Lab, Harvard Dataverse, our research team investigated the purported connection between the inhalation of fine particulate matter and the political leanings of the electorate. Our findings reveal a remarkably strong correlation coefficient of 0.9172435 and p < 0.01 for the time span from 1980 to 2020. While we take the significance of these results seriously, we cannot help but ponder whether the citizens of Corpus Christi are literally "voting with their lungs" or if there is merely a whiff of political influence in the air. This study opens up avenues for further investigation into the intersection of environmental quality and political decision-making, and perhaps underscores the notion that where there's smog, there's a political fog.

ChatGPT:

air pollution, particulate matter, political preference, Corpus Christi, Texas, Democratic votes, Senators, Environmental Protection Agency, MIT Election Data and Science Lab, Harvard Dataverse, political influence, environmental quality, political decision-making

*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 Corpus Christi, Texas
Detailed data title: Percentage of days 'unhealthy' or worse air quality in Corpus Christi, TX
Source: Environmental Protection Agency
See what else correlates with Air pollution in Corpus Christi, Texas

Votes for Democratic Senators in Texas
Detailed data title: Percentage of votes cast for Federal Democrat Senate candidates in Texas
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for Democratic Senators in Texas

Correlation r = 0.9172435 (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 = 0.8413356 (Coefficient of determination)
This means 84.1% of the change in the one variable (i.e., Votes for Democratic Senators in Texas) is predictable based on the change in the other (i.e., Air pollution in Corpus Christi, Texas) over the 9 years from 1980 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00049.^Show 0.0004947375361353992000000000
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 0.92 in 0.049% of random cases. Said differently, if you correlated 2,021 random variables^Note Which I absolutely did.
with the same 8 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 8 because we have two variables measured over a period of 9 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.

[ 0.65, 0.98 ] 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.

	1982	1984	1988	1990	1994	1996	2000	2002	2020
Air pollution in Corpus Christi, Texas (Bad air quality days)	1.50602	0.833333	1.65289	0.826446	0.840336	0.821918	0.273224	0.821918	0.546448
Votes for Democratic Senators in Texas (Percentage of votes)	58.5925	41.4468	59.1668	37.4131	38.3093	43.9403	32.3075	43.3264	43.8689

Why this works

Data dredging: I have 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 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.
Very low n: There are not many data points included in this analysis. Even if the p-value is high, we should be suspicious of using so few datapoints in a correlation.
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([1.50602,0.833333,1.65289,0.826446,0.840336,0.821918,0.273224,0.821918,0.546448,])
array_2 = np.array([58.5925,41.4468,59.1668,37.4131,38.3093,43.9403,32.3075,43.3264,43.8689,])
array_1_name = "Air pollution in Corpus Christi, Texas"
array_2_name = "Votes for Democratic Senators in Texas"

# 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 Corpus Christi, Texas
Line chart for only Votes for Democratic Senators in Texas
AI-generated correlation image
The spurious research paper: Cloudy with a Chance of Blue States: The Particulate Matter between Air Pollution and Senatorial Preference in Corpus Christi, Texas

View another random correlation

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Correlation ID: 5419 · Black Variable ID: 20499 · Red Variable ID: 26465


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