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Spurious correlation #4,466 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Votes for Democratic Senators in Ohio and the second variable is The number of insurance underwriters in Ohio.  The chart goes from 2003 to 2018, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

The Democrat senators were just too good at convincing people to "vote for coverage," and as a result, insurance underwriters in Ohio were left scrambling to recalculate their policies for success. The political wave swept through the state, leaving the underwriters feeling a bit "uninsured" about their future in the industry. It seems like in this election, the stakes were high, but the number of underwriters ended up taking a dip instead of a rise.

Model: dalle-3
Prompt: Create an image of a bustling Ohio city where a group of Democrat senators is standing on a podium, rallying a large crowd of supporters waving blue flags and "Vote for Coverage" signs. In the background, depict a row of towering office buildings housing insurance underwriters engaged in meetings, with furrowed brows and fervent discussions. The imagery should convey the palpable tension in the air as the political wave washes over the state, leaving the underwriters feeling uncertain and apprehensive about their future. Capture the contrasting atmospheres of political fervor and professional unease within the cityscape. 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: Democrat votes for Senators in Ohio and The number of insurance underwriters in Ohio. 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: Pop art A scene from Skyrim An image that looks like it is a scene from A Disney Movie Photorealism 3D Pixar Style
ChatGPT: A scene from Skyrim

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 Democrat votes for Senators in Ohio and The number of insurance underwriters in Ohio, in the following situation: The Democrat senators were just too good at convincing people to "vote for coverage," and as a result, insurance underwriters in Ohio were left scrambling to recalculate their policies for success. The political wave swept through the state, leaving the underwriters feeling a bit "uninsured" about their future in the industry. It seems like in this election, the stakes were high, but the number of underwriters ended up taking a dip instead of a rise.. Keep the description simple.
ChatGPT: In a bustling Ohio city, a group of Democrat senators stand on a podium, rallying a large crowd of supporters waving blue flags and signs that read "Vote for Coverage." In the background, a row of office buildings houses insurance underwriters huddled in meetings, furiously updating policies and frantically discussing the sudden shift in the political landscape. The tension in the air is palpable as the political wave washes over the state, leaving the underwriters feeling uncertain and apprehensive about their future.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A scene from Skyrim ] and depicting this scene: In a bustling Ohio city, a group of Democrat senators stand on a podium, rallying a large crowd of supporters waving blue flags and signs that read "Vote for Coverage." In the background, a row of office buildings houses insurance underwriters huddled in meetings, furiously updating policies and frantically discussing the sudden shift in the political landscape. The tension in the air is palpable as the political wave washes over the state, leaving the underwriters feeling uncertain and apprehensive about their future. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create an image of a bustling Ohio city where a group of Democrat senators is standing on a podium, rallying a large crowd of supporters waving blue flags and "Vote for Coverage" signs. In the background, depict a row of towering office buildings housing insurance underwriters engaged in meetings, with furrowed brows and fervent discussions. The imagery should convey the palpable tension in the air as the political wave washes over the state, leaving the underwriters feeling uncertain and apprehensive about their future. Capture the contrasting atmospheres of political fervor and professional unease within the cityscape.

*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.

Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in Democrat votes for Senators in Ohio caused The number of insurance underwriters in Ohio to decrease. Include one good pun.

AI academic paper

(Because p < 0.01)
The Ohio Show: Democrat Votes and Insurance Underwriters' Bloat

The Journal of Political Circus Studies

Jan 2024

Reminder: This paper is AI-generated. Not real!
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 write titles and abstracts of goofy academic research papers.
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, and add unexpected twists in your writing.

Please draft the title and abstract of an academic research paper presenting the findings of the connection between Democrat votes for Senators in Ohio and The number of insurance underwriters in Ohio. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Rhyme words in the title.

Your research team used data from MIT Election Data and Science Lab, Harvard Dataverse and Bureau of Larbor Statistics to assess this nagging question. You found a correlation coefficient of -0.9817736 and p < 0.01 for 2003 to 2018.

Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)

[[TITLE]]
Lorem ipsum dolor sit

[[ABSTRACT]]
Lorem ipsum dolor sit



ChatGPT:
[[TITLE]]
The Ohio Show: Democrat Votes and Insurance Underwriters' Bloat

[[ABSTRACT]]
This research paper examines the curious relationship between votes for Democratic Senators in Ohio and the abundance of insurance underwriters in the state. Using data from MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics spanning from 2003 to 2018, our research team unveils a glaring correlation coefficient of -0.9817736 and a significance level of p < 0.01. The findings of our study may just leave you feeling like you've entered into the realm of political punditry, where a twist is just a ballot away. Our analysis challenges conventional political measures and pokes at the underbelly of economic indicators, offering a lens through which to view Ohio's political landscape in an unexpected, albeit statistically significant, light.


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 introduction 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 introduction section of an academic research paper presenting the findings of the connection between Democrat votes for Senators in Ohio and The number of insurance underwriters in Ohio. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Here is the title and abstract of the paper:
[[TITLE]]
The Ohio Show: Democrat Votes and Insurance Underwriters' Bloat

[[ABSTRACT]]
This research paper examines the curious relationship between votes for Democratic Senators in Ohio and the abundance of insurance underwriters in the state. Using data from MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics spanning from 2003 to 2018, our research team unveils a glaring correlation coefficient of -0.9817736 and a significance level of p < 0.01. The findings of our study may just leave you feeling like you've entered into the realm of political punditry, where a twist is just a ballot away. Our analysis challenges conventional political measures and pokes at the underbelly of economic indicators, offering a lens through which to view Ohio's political landscape in an unexpected, albeit statistically significant, light.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

In the annals of political analysis, the examination of peculiar correlations often awakens latent skepticism and warrants further investigation. The juxtaposition of Democratic Senatorial votes in Ohio with the proliferation of insurance underwriters in the state, while initially presenting itself as a topic to be brushed off like so much statistical dandruff, has beguiled our research group and beckoned us down the rabbit hole of a novel and, dare we say, enthralling research inquiry.

The interplay between political inclinations and economic players has long been a subject of interest. However, the extent to which such matters intertwine often evokes the analogy of a Rubik's Cube—perplexing, with a multitude of permutations and combinations that confound even the most astute solver. Like a statistical sleuth, we have delved into this enigmatic relationship armed with an arsenal of data drawn from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics. Our exploration between 2003 and 2018 has yielded a correlation coefficient of -0.9817736, piquing our interest and steering our investigation into uncharted terrain.

As we embark on this investigation, we stand poised at the intersection of political machinations and economic undercurrents, armed with the tools of statistical inference and the unwavering pursuit of academic rigor. With a twinkle in our eyes and the satisfaction of confronting an unexpected anomaly in our hearts, we invite you, dear reader, to accompany us on this revelatory journey into the unexpected nexus of political proclivities and labor market dynamics.


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 Democrat votes for Senators in Ohio and The number of insurance underwriters in Ohio. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
The Ohio Show: Democrat Votes and Insurance Underwriters' Bloat

[[ABSTRACT]]
This research paper examines the curious relationship between votes for Democratic Senators in Ohio and the abundance of insurance underwriters in the state. Using data from MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics spanning from 2003 to 2018, our research team unveils a glaring correlation coefficient of -0.9817736 and a significance level of p < 0.01. The findings of our study may just leave you feeling like you've entered into the realm of political punditry, where a twist is just a ballot away. Our analysis challenges conventional political measures and pokes at the underbelly of economic indicators, offering a lens through which to view Ohio's political landscape in an unexpected, albeit statistically significant, light.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
In the annals of political analysis, the examination of peculiar correlations often awakens latent skepticism and warrants further investigation. The juxtaposition of Democratic Senatorial votes in Ohio with the proliferation of insurance underwriters in the state, while initially presenting itself as a topic to be brushed off like so much statistical dandruff, has beguiled our research group and beckoned us down the rabbit hole of a novel and, dare we say, enthralling research inquiry.
The interplay between political inclinations and economic players has long been a subject of interest. However, the extent to which such matters intertwine often evokes the analogy of a Rubik's Cube—perplexing, with a multitude of permutations and combinations that confound even the most astute solver. Like a statistical sleuth, we have delved into this enigmatic relationship armed with an arsenal of data drawn from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics. Our exploration between 2003 and 2018 has yielded a correlation coefficient of -0.9817736, piquing our interest and steering our investigation into uncharted terrain.
As we embark on this investigation, we stand poised at the intersection of political machinations and economic undercurrents, armed with the tools of statistical inference and the unwavering pursuit of academic rigor. With a twinkle in our eyes and the satisfaction of confronting an unexpected anomaly in our hearts, we invite you, dear reader, to accompany us on this revelatory journey into the unexpected nexus of political proclivities and labor market dynamics.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

In "Smith et al. (2005)," the authors find that the voting behavior of Ohio citizens has been a subject of perennial interest, particularly in relation to broader economic variables. Furthermore, Doe and Jones (2010) delve into the nuanced dynamics of the insurance industry in Ohio, shedding light on the myriad factors that contribute to its ebb and flow over time. However, as our research unearths an unsuspected correlation between Democrat votes for Senators in Ohio and the number of insurance underwriters in the state, we find ourselves contending with an anomaly that harkens to the realm of the unexpected.

In "Lorem (2012)," the authors explore the intricate dance between political proclivities and labor market dynamics, setting the stage for our investigation into this eclectic pairing. Moreover, "Ipsum (2016)" ventures into the uncharted territory of political punditry and its potential impact on economic undercurrents, laying the groundwork for our unorthodox exploration.

Expanding beyond the confines of scholarly works, the study draws inspiration from real-life accounts and fictional narratives that may offer an unconventional lens through which to view our findings. Works such as "The Audacity of Hope" by Barack Obama and "The Art of the Deal" by Donald Trump provide a backdrop against which to assess the political landscape that our research endeavors to unravel. In a more speculative vein, we draw parallels between our investigation and the dystopian visions of George Orwell's "1984" and the allegorical nuances of Franz Kafka's "The Trial," probing the unexpected ramifications of political and economic entanglements.

As we venture further down this rabbit hole of peculiar pairings, we draw upon seemingly disparate sources of insight, including the whimsical world of children's cartoons and the irresistible allure of nostalgic shows. The animated antics of "SpongeBob SquarePants" and the capricious capers of "The Magic School Bus" prompt us to reflect on the seemingly whimsical, yet profoundly consequential, interplay between political decisions and economic realities, coaxing a grin from even the most discerning observer.

In the wake of these varied influences, our study stands as a testament to the potential for serendipitous discoveries and unexpected revelations in the seemingly prosaic landscape of political and economic interactions. With our findings poised at the cusp of scholarly convention and whimsical exploration, we encourage readers to accompany us on this journey into the delightful, yet statistically significant, nexus of Democrat votes and insurance underwriters in Ohio.


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 Democrat votes for Senators in Ohio and The number of insurance underwriters in Ohio. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from MIT Election Data and Science Lab, Harvard Dataverse and Bureau of Larbor Statistics . You used data from 2003 to 2018

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
The Ohio Show: Democrat Votes and Insurance Underwriters' Bloat

[[ABSTRACT]]
This research paper examines the curious relationship between votes for Democratic Senators in Ohio and the abundance of insurance underwriters in the state. Using data from MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics spanning from 2003 to 2018, our research team unveils a glaring correlation coefficient of -0.9817736 and a significance level of p < 0.01. The findings of our study may just leave you feeling like you've entered into the realm of political punditry, where a twist is just a ballot away. Our analysis challenges conventional political measures and pokes at the underbelly of economic indicators, offering a lens through which to view Ohio's political landscape in an unexpected, albeit statistically significant, light.

[[INTRODUCTION]]
In the annals of political analysis, the examination of peculiar correlations often awakens latent skepticism and warrants further investigation. The juxtaposition of Democratic Senatorial votes in Ohio with the proliferation of insurance underwriters in the state, while initially presenting itself as a topic to be brushed off like so much statistical dandruff, has beguiled our research group and beckoned us down the rabbit hole of a novel and, dare we say, enthralling research inquiry.
The interplay between political inclinations and economic players has long been a subject of interest. However, the extent to which such matters intertwine often evokes the analogy of a Rubik's Cube—perplexing, with a multitude of permutations and combinations that confound even the most astute solver. Like a statistical sleuth, we have delved into this enigmatic relationship armed with an arsenal of data drawn from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics. Our exploration between 2003 and 2018 has yielded a correlation coefficient of -0.9817736, piquing our interest and steering our investigation into uncharted terrain.
As we embark on this investigation, we stand poised at the intersection of political machinations and economic undercurrents, armed with the tools of statistical inference and the unwavering pursuit of academic rigor. With a twinkle in our eyes and the satisfaction of confronting an unexpected anomaly in our hearts, we invite you, dear reader, to accompany us on this revelatory journey into the unexpected nexus of political proclivities and labor market dynamics.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

Data Collection:
The data for this research was acquired from reputable sources, primarily consisting of the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics. The period under consideration spans from 2003 to 2018, capturing a political landscape that rivals the drama of a daytime soap opera. To ensure the accuracy and reliability of the data, our research team employed a variety of statistical incantations, mixing in a pinch of double-blind analysis for good measure.

Variable Selection:
The primary variables of interest in this study were the number of Democrat votes for Senators in Ohio, a whimsical rollercoaster ride in its own right, and the count of insurance underwriters in the state, a group known for keeping their premiums high and their equations even higher. Other covariates, such as demographic factors and political fervor, were also taken into account, adding a flavorful bouquet to our statistical stew.

Data Analysis:
Employing methods that are as classic as a Shakespearean tragedy, we used a combination of regression analysis and correlation coefficients to unravel the interwoven tapestry of political allegiance and professional domains. The statistical software utilized for this analysis was as robust as the Ohio political landscape itself, ensuring that no data point was left feeling neglected or lost in the cacophony of numbers.

Model Assumptions:
It is pertinent to note that our analysis operates under the assumption of a linear relationship between Democrat votes for Senators and the number of insurance underwriters in Ohio. Additionally, we assumed that the data observed adhere to the laws of statistical probability, unlike the whims of political maneuvering.

Limitations:
While we wielded the tools of academic inquiry with the finesse of a skilled artisan, it is imperative to acknowledge the limitations of our study. The findings are inherently observational, and as such, causality cannot be definitively inferred. Furthermore, our analysis is confined to the temporal scope of 2003 to 2018, leaving the future as open to interpretation as a Rorschach inkblot.

In summary, our methodology harnessed the quirks and idiosyncrasies of the data with the precision of a skilled conductor orchestrating a symphony, uncovering a correlation coefficient that would make any statistician raise an eyebrow in quiet admiration.


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

Please make up the results section of an academic research paper presenting the findings of the connection between Democrat votes for Senators in Ohio and The number of insurance underwriters in Ohio. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from MIT Election Data and Science Lab, Harvard Dataverse and Bureau of Larbor Statistics .

For the time period 2003 to 2018, you found a correlation -0.9817736, r-squared of 0.9638793, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
The Ohio Show: Democrat Votes and Insurance Underwriters' Bloat

[[ABSTRACT]]
This research paper examines the curious relationship between votes for Democratic Senators in Ohio and the abundance of insurance underwriters in the state. Using data from MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics spanning from 2003 to 2018, our research team unveils a glaring correlation coefficient of -0.9817736 and a significance level of p < 0.01. The findings of our study may just leave you feeling like you've entered into the realm of political punditry, where a twist is just a ballot away. Our analysis challenges conventional political measures and pokes at the underbelly of economic indicators, offering a lens through which to view Ohio's political landscape in an unexpected, albeit statistically significant, light.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
In the annals of political analysis, the examination of peculiar correlations often awakens latent skepticism and warrants further investigation. The juxtaposition of Democratic Senatorial votes in Ohio with the proliferation of insurance underwriters in the state, while initially presenting itself as a topic to be brushed off like so much statistical dandruff, has beguiled our research group and beckoned us down the rabbit hole of a novel and, dare we say, enthralling research inquiry.
The interplay between political inclinations and economic players has long been a subject of interest. However, the extent to which such matters intertwine often evokes the analogy of a Rubik's Cube—perplexing, with a multitude of permutations and combinations that confound even the most astute solver. Like a statistical sleuth, we have delved into this enigmatic relationship armed with an arsenal of data drawn from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics. Our exploration between 2003 and 2018 has yielded a correlation coefficient of -0.9817736, piquing our interest and steering our investigation into uncharted terrain.
As we embark on this investigation, we stand poised at the intersection of political machinations and economic undercurrents, armed with the tools of statistical inference and the unwavering pursuit of academic rigor. With a twinkle in our eyes and the satisfaction of confronting an unexpected anomaly in our hearts, we invite you, dear reader, to accompany us on this revelatory journey into the unexpected nexus of political proclivities and labor market dynamics.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The primary goal of this study was to discern any discernible link between the number of insurance underwriters in Ohio and the votes cast for Democratic Senators in the state. Our analysis revealed a remarkably strong negative correlation between the two variables, with a correlation coefficient of -0.9817736. This relationship was further strengthened by an r-squared value of 0.9638793, indicating that approximately 96.4% of the variation in the number of insurance underwriters can be explained by the votes for Democratic Senators. The p-value of less than 0.01 provided compelling evidence against the null hypothesis of no correlation, leaving us with quite the conundrum to ponder.

The scatterplot (Fig. 1) visually encapsulates the robust inverse association we uncovered, resembling a pair of magnetized entities repelling one another with fervent insistence. However, behold the cautionary tale of correlation versus causation: while our statistical findings elucidate a striking pattern, we remain keenly aware that interpreting this association as a sign of direct influence would be akin to hailing a rooster as the dawn's architect.

This discovery prompts us to ponder the intricate interplay between political decisions and the labor market. It compels us to contemplate whether the very act of casting ballots may exert an unseen force on the landscape of employment. Alas, we find ourselves in the peculiar position of musing upon the potential political pulse that seems to reverberate throughout the realm of insurance underwriting. As in all scientific pursuits, every answer seems to unveil a multitude of further inquiries, expanding the horizon of our curiosity like an ever-expanding universe of riddles and revelations.


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 Democrat votes for Senators in Ohio and The number of insurance underwriters in Ohio. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

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

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

[[TITLE]]
The Ohio Show: Democrat Votes and Insurance Underwriters' Bloat

[[ABSTRACT]]
This research paper examines the curious relationship between votes for Democratic Senators in Ohio and the abundance of insurance underwriters in the state. Using data from MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics spanning from 2003 to 2018, our research team unveils a glaring correlation coefficient of -0.9817736 and a significance level of p < 0.01. The findings of our study may just leave you feeling like you've entered into the realm of political punditry, where a twist is just a ballot away. Our analysis challenges conventional political measures and pokes at the underbelly of economic indicators, offering a lens through which to view Ohio's political landscape in an unexpected, albeit statistically significant, light.

[[LITERATURE REVIEW]]
In "Smith et al. (2005)," the authors find that the voting behavior of Ohio citizens has been a subject of perennial interest, particularly in relation to broader economic variables. Furthermore, Doe and Jones (2010) delve into the nuanced dynamics of the insurance industry in Ohio, shedding light on the myriad factors that contribute to its ebb and flow over time. However, as our research unearths an unsuspected correlation between Democrat votes for Senators in Ohio and the number of insurance underwriters in the state, we find ourselves contending with an anomaly that harkens to the realm of the unexpected.
In "Lorem (2012)," the authors explore the intricate dance between political proclivities and labor market dynamics, setting the stage for our investigation into this eclectic pairing. Moreover, "Ipsum (2016)" ventures into the uncharted territory of political punditry and its potential impact on economic undercurrents, laying the groundwork for our unorthodox exploration.
Expanding beyond the confines of scholarly works, the study draws inspiration from real-life accounts and fictional narratives that may offer an unconventional lens through which to view our findings. Works such as "The Audacity of Hope" by Barack Obama and "The Art of the Deal" by Donald Trump provide a backdrop against which to assess the political landscape that our research endeavors to unravel. In a more speculative vein, we draw parallels between our investigation and the dystopian visions of George Orwell's "1984" and the allegorical nuances of Franz Kafka's "The Trial," probing the unexpected ramifications of political and economic entanglements.
As we venture further down this rabbit hole of peculiar pairings, we draw upon seemingly disparate sources of insight, including the whimsical world of children's cartoons and the irresistible allure of nostalgic shows. The animated antics of "SpongeBob SquarePants" and the capricious capers of "The Magic School Bus" prompt us to reflect on the seemingly whimsical, yet profoundly consequential, interplay between political decisions and economic realities, coaxing a grin from even the most discerning observer.
In the wake of these varied influences, our study stands as a testament to the potential for serendipitous discoveries and unexpected revelations in the seemingly prosaic landscape of political and economic interactions. With our findings poised at the cusp of scholarly convention and whimsical exploration, we encourage readers to accompany us on this journey into the delightful, yet statistically significant, nexus of Democrat votes and insurance underwriters in Ohio.

[[RESULTS]]
The primary goal of this study was to discern any discernible link between the number of insurance underwriters in Ohio and the votes cast for Democratic Senators in the state. Our analysis revealed a remarkably strong negative correlation between the two variables, with a correlation coefficient of -0.9817736. This relationship was further strengthened by an r-squared value of 0.9638793, indicating that approximately 96.4% of the variation in the number of insurance underwriters can be explained by the votes for Democratic Senators. The p-value of less than 0.01 provided compelling evidence against the null hypothesis of no correlation, leaving us with quite the conundrum to ponder.
The scatterplot (Fig. 1) visually encapsulates the robust inverse association we uncovered, resembling a pair of magnetized entities repelling one another with fervent insistence. However, behold the cautionary tale of correlation versus causation: while our statistical findings elucidate a striking pattern, we remain keenly aware that interpreting this association as a sign of direct influence would be akin to hailing a rooster as the dawn's architect.
This discovery prompts us to ponder the intricate interplay between political decisions and the labor market. It compels us to contemplate whether the very act of casting ballots may exert an unseen force on the landscape of employment. Alas, we find ourselves in the peculiar position of musing upon the potential political pulse that seems to reverberate throughout the realm of insurance underwriting. As in all scientific pursuits, every answer seems to unveil a multitude of further inquiries, expanding the horizon of our curiosity like an ever-expanding universe of riddles and revelations.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

The prodigious negative correlation revealed in our study between Democrat votes for Senators in Ohio and the number of insurance underwriters prompts us to navigate the seemingly interconnected realms of politics and economics with a sense of both wonder and skepticism. These findings appear to echo the inexplicable phenomena observed in previous studies, quelling doubts and breathing life into the peculiar pairings of variables that may appear unrelated at first glance.

Drawing from the work of Smith et al. (2005), which delves into the paradoxical dance between Ohio citizens' voting behavior and economic variables, one might have previously dismissed the notion of a substantial relationship between insurance underwriters and political proclivities as some form of statistical whimsy. Yet, lo and behold, our research has unearthed a striking association that aligns with their enigmatic discoveries.

Likewise, our exploration resonates with the magnanimous speculation of Lorem (2012), who illuminates the convoluted interplay between political leanings and labor market dynamics, providing a fertile ground for our unexpected revelations. By revealing the underlying connections between seemingly incongruous domains, our findings align themselves with the foresight and spirit of inquiry embodied in his work.

Furthermore, the unexpected connection between political decisions and the landscape of employment brings to mind the whimsical world of children's cartoons, where unforeseen alliances and peculiar partnerships reign supreme. Just as in "SpongeBob SquarePants" or "The Magic School Bus," where improbable associations lead to charming conundrums, we find ourselves amid a capricious and yet statistically robust tapestry of political praxis and market forces.

As we wade deeper into this web of seemingly disparate influences, we are reminded of the inherent unpredictability of the scientific endeavor, where serendipity and chance encounters often serve as the catalysts for groundbreaking discoveries. So, while our findings may appear unorthodox, they stand as a testament to the essence of research - to challenge convention, uncover hidden patterns, and tantalize the inquiring mind with paradoxical pairings that defy the mundane rhythms of academic inquiry.

In a world where the scientific pursuit serves as both the architect and the witness to the symphony of statistical anomalies, our study attempts to cast an unforgettable impression on the fabric of economic and political scholarship, revealing unexpected connections that often lie beneath the surface.


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 Democrat votes for Senators in Ohio and The number of insurance underwriters in Ohio. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
The Ohio Show: Democrat Votes and Insurance Underwriters' Bloat

[[ABSTRACT]]
This research paper examines the curious relationship between votes for Democratic Senators in Ohio and the abundance of insurance underwriters in the state. Using data from MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics spanning from 2003 to 2018, our research team unveils a glaring correlation coefficient of -0.9817736 and a significance level of p < 0.01. The findings of our study may just leave you feeling like you've entered into the realm of political punditry, where a twist is just a ballot away. Our analysis challenges conventional political measures and pokes at the underbelly of economic indicators, offering a lens through which to view Ohio's political landscape in an unexpected, albeit statistically significant, light.

[[INTRDUCTION]]
In the annals of political analysis, the examination of peculiar correlations often awakens latent skepticism and warrants further investigation. The juxtaposition of Democratic Senatorial votes in Ohio with the proliferation of insurance underwriters in the state, while initially presenting itself as a topic to be brushed off like so much statistical dandruff, has beguiled our research group and beckoned us down the rabbit hole of a novel and, dare we say, enthralling research inquiry.
The interplay between political inclinations and economic players has long been a subject of interest. However, the extent to which such matters intertwine often evokes the analogy of a Rubik's Cube—perplexing, with a multitude of permutations and combinations that confound even the most astute solver. Like a statistical sleuth, we have delved into this enigmatic relationship armed with an arsenal of data drawn from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics. Our exploration between 2003 and 2018 has yielded a correlation coefficient of -0.9817736, piquing our interest and steering our investigation into uncharted terrain.
As we embark on this investigation, we stand poised at the intersection of political machinations and economic undercurrents, armed with the tools of statistical inference and the unwavering pursuit of academic rigor. With a twinkle in our eyes and the satisfaction of confronting an unexpected anomaly in our hearts, we invite you, dear reader, to accompany us on this revelatory journey into the unexpected nexus of political proclivities and labor market dynamics.

[[RESULTS]]
The primary goal of this study was to discern any discernible link between the number of insurance underwriters in Ohio and the votes cast for Democratic Senators in the state. Our analysis revealed a remarkably strong negative correlation between the two variables, with a correlation coefficient of -0.9817736. This relationship was further strengthened by an r-squared value of 0.9638793, indicating that approximately 96.4% of the variation in the number of insurance underwriters can be explained by the votes for Democratic Senators. The p-value of less than 0.01 provided compelling evidence against the null hypothesis of no correlation, leaving us with quite the conundrum to ponder.
The scatterplot (Fig. 1) visually encapsulates the robust inverse association we uncovered, resembling a pair of magnetized entities repelling one another with fervent insistence. However, behold the cautionary tale of correlation versus causation: while our statistical findings elucidate a striking pattern, we remain keenly aware that interpreting this association as a sign of direct influence would be akin to hailing a rooster as the dawn's architect.
This discovery prompts us to ponder the intricate interplay between political decisions and the labor market. It compels us to contemplate whether the very act of casting ballots may exert an unseen force on the landscape of employment. Alas, we find ourselves in the peculiar position of musing upon the potential political pulse that seems to reverberate throughout the realm of insurance underwriting. As in all scientific pursuits, every answer seems to unveil a multitude of further inquiries, expanding the horizon of our curiosity like an ever-expanding universe of riddles and revelations.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation into the association between votes for Democratic Senators in Ohio and the prevalence of insurance underwriters has unearthed a remarkably robust negative correlation, stunning our socks off with its strength. The nuanced dance between political preferences and labor market dynamics has left us feeling much like statisticians at a carnival—whirling with excitement at the unexpected twists and turns, yet mindful of the need to discern causality from mere correlation.

While we revel in the allure of our findings, we must exercise caution in attributing a causal relationship to this magnetic aversion between the two variables. As any seasoned researcher knows, asserting causation based solely on correlation is akin to mistaking a flock of seagulls for the harbinger of a fish market. Indeed, the siren call of spurious correlations lurks around every statistical corner, ready to lure the unwary analyst into a whirlpool of erroneous conclusions.

This study sheds light on the enigmatic interplay of political choices and occupational landscapes, painting a picture reminiscent of a surrealist masterpiece—captivating, off-kilter, and open to a myriad of interpretations. As we bid adieu to this particular investigation, we assert with resounding confidence that no further research in this domain is needed—a statement that we, of course, offer with our tongues planted firmly in our cheeks.


Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited The Ohio Show: Democrat Votes and Insurance Underwriters' Bloat. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Political Circus Studies


Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited The Ohio Show: Democrat Votes and Insurance Underwriters' Bloat. Respond only with the name of a fictional organization.

ChatGPT:

The National Institute for Voter Behavior Studies


Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled The Ohio Show: Democrat Votes and Insurance Underwriters' Bloat with an abstract of This research paper examines the curious relationship between votes for Democratic Senators in Ohio and the abundance of insurance underwriters in the state. Using data from MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics spanning from 2003 to 2018, our research team unveils a glaring correlation coefficient of -0.9817736 and a significance level of p < 0.01. The findings of our study may just leave you feeling like you've entered into the realm of political punditry, where a twist is just a ballot away. Our analysis challenges conventional political measures and pokes at the underbelly of economic indicators, offering a lens through which to view Ohio's political landscape in an unexpected, albeit statistically significant, light.

ChatGPT:

Ohio, Democratic Senators, insurance underwriters, correlation coefficient, significance level, MIT Election Data and Science Lab, Harvard Dataverse, Bureau of Labor Statistics, political landscape, economic indicators, Ohio votes, Democrat votes, Ohio politics

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



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Data details

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

The number of insurance underwriters in Ohio
Detailed data title: BLS estimate of insurance underwriters in Ohio
Source: Bureau of Larbor Statistics
See what else correlates with The number of insurance underwriters in Ohio

Correlation r = -0.9817736 (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 = 0.9638793 (Coefficient of determination)
This means 96.4% of the change in the one variable (i.e., The number of insurance underwriters in Ohio) is predictable based on the change in the other (i.e., Votes for Democratic Senators in Ohio) over the 6 years from 2003 through 2018.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0005. 0.0004952765042464667000000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. 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 -0.98 in 0.05% of random cases. Said differently, if you correlated 2,019 random variables Which I absolutely did.
with the same 5 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 5 because we have two variables measured over a period of 6 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.

[ -1, -0.84 ] 95% correlation confidence interval (using the Fisher z-transformation)
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: 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.
200420062010201220162018
Votes for Democratic Senators in Ohio (Total votes)196117022573701503300276269019969102355920
The number of insurance underwriters in Ohio (Insurance Underwriters)505046305590426048604630




Why this works

  1. 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.
  2. Lack of causal connection: There is probably 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.
  3. 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 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.
  4. 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.
  5. Inverted Y-axis: I inverted the Y-axis on the chart above so that the lines would move together. This is visually pleasing, but not at all intuitive. Below is a line graph that does not invert the Y-axis and starts at zero.




Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. 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([1961170,2257370,1503300,2762690,1996910,2355920,])
array_2 = np.array([5050,4630,5590,4260,4860,4630,])
array_1_name = "Votes for Democratic Senators in Ohio"
array_2_name = "The number of insurance underwriters in Ohio"

# 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)



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Correlation ID: 4466 · Black Variable ID: 26296 · Red Variable ID: 12744
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