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Spurious correlation #5,598 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is US household spending on prescription drugs and the second variable is Votes for the Republican Presidential candidate in Washington.  The chart goes from 2000 to 2020, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

As households spent less on prescription drugs, the overall level of discomfort decreased, leading to a reduced inclination to support the party in power, ultimately affecting voting patterns in Washington. Maybe the saying should be "A pill a day keeps the Republicans at bay!"

Model: dalle-3
Prompt: Generate an image of a realistic scene where a family happily gathers around the dinner table in a cozy Washington home. The household spending on prescription drugs is shown decreasing in a line graph on the wall. As the discomfort level lessens, the family appears more relaxed and content. Meanwhile, a voting ballot box with Republican candidate logos is seen in the background, with fewer votes being cast as the discomfort decreases. The scene playfully portrays the notion that "A pill a day keeps the Republicans at bay". The focus should be on the warm and intimate atmosphere of the dinner table and the subtle indications of decreasing discomfort and decreasing votes in the background. 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: US household spending on prescription drugs and Votes for the Republican Presidential candidate in Washington. 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: a funny image you would see on reddit Cyber folk Realistic A Discovery magazine cover image Parallax art
ChatGPT: Realistic

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both US household spending on prescription drugs and Votes for the Republican Presidential candidate in Washington, in the following situation: As households spent less on prescription drugs, the overall level of discomfort decreased, leading to a reduced inclination to support the party in power, ultimately affecting voting patterns in Washington. Maybe the saying should be "A pill a day keeps the Republicans at bay!". Keep the description simple.
ChatGPT: A family happily gathers around the dinner table in a cozy Washington home. The household spending on prescription drugs is shown decreasing in a line graph on the wall. As the discomfort level lessens, the family appears more relaxed and content. Meanwhile, a voting ballot box with Republican candidate logos is seen in the background, with fewer votes being cast as the discomfort decreases. The scene playfully portrays the notion that "A pill a day keeps the Republicans at bay".

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ Realistic ] and depicting this scene: A family happily gathers around the dinner table in a cozy Washington home. The household spending on prescription drugs is shown decreasing in a line graph on the wall. As the discomfort level lessens, the family appears more relaxed and content. Meanwhile, a voting ballot box with Republican candidate logos is seen in the background, with fewer votes being cast as the discomfort decreases. The scene playfully portrays the notion that "A pill a day keeps the Republicans at bay". Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a realistic scene where a family happily gathers around the dinner table in a cozy Washington home. The household spending on prescription drugs is shown decreasing in a line graph on the wall. As the discomfort level lessens, the family appears more relaxed and content. Meanwhile, a voting ballot box with Republican candidate logos is seen in the background, with fewer votes being cast as the discomfort decreases. The scene playfully portrays the notion that "A pill a day keeps the Republicans at bay". The focus should be on the warm and intimate atmosphere of the dinner table and the subtle indications of decreasing discomfort and decreasing votes in the background.

*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 a decrease in US household spending on prescription drugs caused Votes for the Republican Presidential candidate in Washington to decrease. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)
Prescription for Electoral Tension: A GOP Pill or Bane for Washington Households?

Journal of Political Pharmacology

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 researcher writing an academic paper.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 US household spending on prescription drugs and Votes for the Republican Presidential candidate in Washington. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Make the title punny if you can come up with clever pun. If not, just make it sound clever.

Your research team used data from Bureau of Labor Statistics and MIT Election Data and Science Lab, Harvard Dataverse to assess this nagging question. You found a correlation coefficient of 0.9436730 and p < 0.01 for 2000 to 2020.

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]]
"Prescription for Electoral Tension: A GOP Pill or Bane for Washington Households?"

[[ABSTRACT]]
Prescription drug spending in US households has been a topic of scrutiny and concern, particularly from a political standpoint. In this study, we analyze the relationship between US household spending on prescription drugs and the votes for the Republican presidential candidate in the state of Washington. Utilizing data from the Bureau of Labor Statistics and the MIT Election Data and Science Lab, Harvard Dataverse, and employing robust statistical methods, we identified a strikingly high correlation coefficient of 0.9436730 with a p-value less than < 0.01 from 2000 to 2020. Our findings suggest a robust connection between higher household spending on prescription drugs and favor for the Republican candidate, providing intriguing insights into the intersection of healthcare economics and political preferences. This study aims to provoke thought and discussion as it journeys through the conventional and the unconventional facets of American political economy.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 US household spending on prescription drugs and Votes for the Republican Presidential candidate in Washington. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Here is the title and abstract of the paper:
[[TITLE]]
"Prescription for Electoral Tension: A GOP Pill or Bane for Washington Households?"

[[ABSTRACT]]
Prescription drug spending in US households has been a topic of scrutiny and concern, particularly from a political standpoint. In this study, we analyze the relationship between US household spending on prescription drugs and the votes for the Republican presidential candidate in the state of Washington. Utilizing data from the Bureau of Labor Statistics and the MIT Election Data and Science Lab, Harvard Dataverse, and employing robust statistical methods, we identified a strikingly high correlation coefficient of 0.9436730 with a p-value less than < 0.01 from 2000 to 2020. Our findings suggest a robust connection between higher household spending on prescription drugs and favor for the Republican candidate, providing intriguing insights into the intersection of healthcare economics and political preferences. This study aims to provoke thought and discussion as it journeys through the conventional and the unconventional facets of American political economy.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
INTRODUCTION

The intersection of politics and economics has long been an area of fascination for researchers and observers alike. And what better way to delve into this nuanced relationship than through an analysis of prescription drug spending in US households and its purported influence on political preferences? Hailing from the state of Washington, a land known for its lush greenery, coffee aficionados, and celestial precipitation, we set out to uncover the hidden connections between household spending on prescription drugs and votes for the Republican presidential candidate. A journey awaits, and the findings offer tantalizing insights into the intricate dance of healthcare economics and electoral praxis, blending the serious and the quirky.

It's no secret that prescription drug spending has become a significant point of contention, with prices that seem to escalate faster than a space rocket. As citizens navigate the labyrinthine corridors of healthcare policy and political discourse, it's clear that prescription drug spending has left a distinct mark on the economic landscape of households. But could it also be leaving an imprint on the political inclinations of the voting populace? That's precisely the question that led us to embark on this curious foray into the realm of data parsing and statistical wizardry.

The stage is set, the data is primed, and the results are ready to be unveiled. So grab your lab coat, dust off your data-crunching toolkit, and let's delve into the enigmatic correlation between prescription drug spending and political predilections. Get ready for a rollercoaster ride through the numbers, charts, and maybe even a pun or two along the way. After all, who said academic research had to be devoid of a little whimsy? Let's unravel the enigmatic connection between prescription drugs and political tides, and see if we can't inject a dose of levity into the serious business of scholarly inquiry.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 US household spending on prescription drugs and Votes for the Republican Presidential candidate in Washington. 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 cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
"Prescription for Electoral Tension: A GOP Pill or Bane for Washington Households?"

[[ABSTRACT]]
Prescription drug spending in US households has been a topic of scrutiny and concern, particularly from a political standpoint. In this study, we analyze the relationship between US household spending on prescription drugs and the votes for the Republican presidential candidate in the state of Washington. Utilizing data from the Bureau of Labor Statistics and the MIT Election Data and Science Lab, Harvard Dataverse, and employing robust statistical methods, we identified a strikingly high correlation coefficient of 0.9436730 with a p-value less than < 0.01 from 2000 to 2020. Our findings suggest a robust connection between higher household spending on prescription drugs and favor for the Republican candidate, providing intriguing insights into the intersection of healthcare economics and political preferences. This study aims to provoke thought and discussion as it journeys through the conventional and the unconventional facets of American political economy.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
The intersection of politics and economics has long been an area of fascination for researchers and observers alike. And what better way to delve into this nuanced relationship than through an analysis of prescription drug spending in US households and its purported influence on political preferences? Hailing from the state of Washington, a land known for its lush greenery, coffee aficionados, and celestial precipitation, we set out to uncover the hidden connections between household spending on prescription drugs and votes for the Republican presidential candidate. A journey awaits, and the findings offer tantalizing insights into the intricate dance of healthcare economics and electoral praxis, blending the serious and the quirky.
It's no secret that prescription drug spending has become a significant point of contention, with prices that seem to escalate faster than a space rocket. As citizens navigate the labyrinthine corridors of healthcare policy and political discourse, it's clear that prescription drug spending has left a distinct mark on the economic landscape of households. But could it also be leaving an imprint on the political inclinations of the voting populace? That's precisely the question that led us to embark on this curious foray into the realm of data parsing and statistical wizardry.
The stage is set, the data is primed, and the results are ready to be unveiled. So grab your lab coat, dust off your data-crunching toolkit, and let's delve into the enigmatic correlation between prescription drug spending and political predilections. Get ready for a rollercoaster ride through the numbers, charts, and maybe even a pun or two along the way. After all, who said academic research had to be devoid of a little whimsy? Let's unravel the enigmatic connection between prescription drugs and political tides, and see if we can't inject a dose of levity into the serious business of scholarly inquiry.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

Smith et al. (2015) assert a strong correlation between household expenditure on prescription drugs and political affiliations. Their comprehensive analysis of national data reveals compelling evidence that suggests a significant relationship between spending patterns and electoral inclinations. However, as we traverse through the scholarly landscape of this topic, it's crucial to broaden our horizon beyond the conventional realms of academic discourse. Turning our attention to the intriguing world of non-fiction literature, "The Healing of America: A Global Quest for Better, Cheaper, and Fairer Health Care" by T.R. Reid and "The Truth About Drug Companies: How They Deceive Us and What to Do About It" by Marcia Angell offer thought-provoking insights into the socio-political dimensions of healthcare and pharmaceutical industries.

Doe and Jones (2017) further explore the intricate dynamics of prescription drug expenditures and public sentiment. Their meticulous examination of regional voting patterns and healthcare expenditure implicates a nuanced link between consumer behavior and political preferences. As we navigate through these weighty tomes of academic literature, it's time to take a whimsical detour into the land of fiction. "Love in the Time of Cholera" by Gabriel Garcia Marquez and "The Pillars of the Earth" by Ken Follett, while not directly related to our research, serve as a reminder of the diverse facets of human experience, some of which might even be influenced by prescription drugs and political dispositions.

Additionally, intriguing insights can be gleaned from the unassuming corner of social media. A tweet by @HealthCareGuru reads, "Spending a fortune on prescription drugs but voting for fiscal conservatism – talk about a paradox wrapped in a pill bottle!" The pithy observation encapsulates the perplexing interplay between personal expenditure and political ideologies, prompting us to ponder the idiosyncrasies of human behavior amidst the cacophony of partisan rhetoric.

As we wade through this amalgamation of scholarly discourse, fiction, and social commentary, the dissonance between the serious and the zany becomes palpable. Yet, it is within this lexical tapestry that we hope to unravel the enigma of prescription drug spending and political proclivities, shedding light on an eccentric symphony where healthcare economics meets electoral winds.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 US household spending on prescription drugs and Votes for the Republican Presidential candidate in Washington. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Labor Statistics and MIT Election Data and Science Lab, Harvard Dataverse . You used data from 2000 to 2020

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

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"Prescription for Electoral Tension: A GOP Pill or Bane for Washington Households?"

[[ABSTRACT]]
Prescription drug spending in US households has been a topic of scrutiny and concern, particularly from a political standpoint. In this study, we analyze the relationship between US household spending on prescription drugs and the votes for the Republican presidential candidate in the state of Washington. Utilizing data from the Bureau of Labor Statistics and the MIT Election Data and Science Lab, Harvard Dataverse, and employing robust statistical methods, we identified a strikingly high correlation coefficient of 0.9436730 with a p-value less than < 0.01 from 2000 to 2020. Our findings suggest a robust connection between higher household spending on prescription drugs and favor for the Republican candidate, providing intriguing insights into the intersection of healthcare economics and political preferences. This study aims to provoke thought and discussion as it journeys through the conventional and the unconventional facets of American political economy.

[[INTRODUCTION]]
INTRODUCTION
The intersection of politics and economics has long been an area of fascination for researchers and observers alike. And what better way to delve into this nuanced relationship than through an analysis of prescription drug spending in US households and its purported influence on political preferences? Hailing from the state of Washington, a land known for its lush greenery, coffee aficionados, and celestial precipitation, we set out to uncover the hidden connections between household spending on prescription drugs and votes for the Republican presidential candidate. A journey awaits, and the findings offer tantalizing insights into the intricate dance of healthcare economics and electoral praxis, blending the serious and the quirky.
It's no secret that prescription drug spending has become a significant point of contention, with prices that seem to escalate faster than a space rocket. As citizens navigate the labyrinthine corridors of healthcare policy and political discourse, it's clear that prescription drug spending has left a distinct mark on the economic landscape of households. But could it also be leaving an imprint on the political inclinations of the voting populace? That's precisely the question that led us to embark on this curious foray into the realm of data parsing and statistical wizardry.
The stage is set, the data is primed, and the results are ready to be unveiled. So grab your lab coat, dust off your data-crunching toolkit, and let's delve into the enigmatic correlation between prescription drug spending and political predilections. Get ready for a rollercoaster ride through the numbers, charts, and maybe even a pun or two along the way. After all, who said academic research had to be devoid of a little whimsy? Let's unravel the enigmatic connection between prescription drugs and political tides, and see if we can't inject a dose of levity into the serious business of scholarly inquiry.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

METHODOLOGY

Data Collection:
In our endeavor to unravel the mysterious ties between prescription drug spending and political preferences, we assembled a trove of data sourced from the Bureau of Labor Statistics, as well as the MIT Election Data and Science Lab, Harvard Dataverse. As any seasoned data spelunker will tell you, the wilds of the internet provided an ample hunting ground for our digital prey. We scoured through years of information, sifting through the digital underbrush like intrepid explorers seeking treasure. Our diligent efforts led us to harvest data from the years 2000 to 2020, a period that witnessed its fair share of political theater and prescription pill dynamics.

Variable Selection:
Data can be a capricious beast, and so the selection of variables demanded a keen eye and a steady hand. We pinpointed household spending on prescription drugs as our primary focal point, aiming to measure this metric with the precision of an apothecary mixing a potent potion. On the other end of our statistical seesaw, we gauged the votes for the Republican presidential candidate in the state of Washington, keeping a watchful gaze over the political pendulum as it swung to and fro.

Statistical Analysis:
Armed with our treasure trove of data, we embarked on a quest through the labyrinth of statistical analysis. Our trusty companions, the correlation coefficient and the p-value, guided us through the dense undergrowth of numerical inquiry. With the deft flourish of scholarly arcanum, we calculated a correlation coefficient of 0.9436730, a figure that stood tall and proud, akin to a giant sequoia in the statistical forest. As if that weren't impressive enough, our p-value shimmered proudly, boasting a value less than the revered threshold of 0.01. These figures danced before our eyes, whispering profound secrets and teasing us with tantalizing revelations.

Robustness Checks:
As any intrepid adventurer knows, a single expedition does not a theory cement. We subjected our findings to a battery of robustness checks, akin to stress-testing a fortress against a besieging army. Sensitivity analyses, regression diagnostics, and model specifications all played their role in fortifying our conclusions, ensuring they emerged battle-hardened and battle-proven against the winds of statistical skepticism.

Limitations:
Ah, but no voyage of academic inquiry would be complete without acknowledging the limitations that accompany such scholarly escapades. While our findings yielded a strikingly high correlation coefficient, we recognize the need for cautious interpretation and acknowledgment of potential confounders. The siren song of spurious correlations and lurking variables beckons like a mirage, urging us to exercise prudence in our interpretations.

In summary, our methodological journey through the strange and wondrous realms of data collection, variable selection, statistical analysis, robustness checks, and limitations has brought us to the cusp of a revelation. With analytical acumen and scholarly sagacity, we set our sights on illuminating the link between household prescription drug spending and electoral proclivities, enriching the annals of healthcare economics and political affinity with newfound insights. And hey, if nothing else, we hope our academic odyssey provides a chuckle or two along the way. After all, even in the scholarly pursuit of truth, a little levity can go a long way towards making the journey more enjoyable for all involved.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 US household spending on prescription drugs and Votes for the Republican Presidential candidate in Washington. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

For the time period 2000 to 2020, you found a correlation 0.9436730, r-squared of 0.8905187, 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]]
"Prescription for Electoral Tension: A GOP Pill or Bane for Washington Households?"

[[ABSTRACT]]
Prescription drug spending in US households has been a topic of scrutiny and concern, particularly from a political standpoint. In this study, we analyze the relationship between US household spending on prescription drugs and the votes for the Republican presidential candidate in the state of Washington. Utilizing data from the Bureau of Labor Statistics and the MIT Election Data and Science Lab, Harvard Dataverse, and employing robust statistical methods, we identified a strikingly high correlation coefficient of 0.9436730 with a p-value less than < 0.01 from 2000 to 2020. Our findings suggest a robust connection between higher household spending on prescription drugs and favor for the Republican candidate, providing intriguing insights into the intersection of healthcare economics and political preferences. This study aims to provoke thought and discussion as it journeys through the conventional and the unconventional facets of American political economy.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
INTRODUCTION
The intersection of politics and economics has long been an area of fascination for researchers and observers alike. And what better way to delve into this nuanced relationship than through an analysis of prescription drug spending in US households and its purported influence on political preferences? Hailing from the state of Washington, a land known for its lush greenery, coffee aficionados, and celestial precipitation, we set out to uncover the hidden connections between household spending on prescription drugs and votes for the Republican presidential candidate. A journey awaits, and the findings offer tantalizing insights into the intricate dance of healthcare economics and electoral praxis, blending the serious and the quirky.
It's no secret that prescription drug spending has become a significant point of contention, with prices that seem to escalate faster than a space rocket. As citizens navigate the labyrinthine corridors of healthcare policy and political discourse, it's clear that prescription drug spending has left a distinct mark on the economic landscape of households. But could it also be leaving an imprint on the political inclinations of the voting populace? That's precisely the question that led us to embark on this curious foray into the realm of data parsing and statistical wizardry.
The stage is set, the data is primed, and the results are ready to be unveiled. So grab your lab coat, dust off your data-crunching toolkit, and let's delve into the enigmatic correlation between prescription drug spending and political predilections. Get ready for a rollercoaster ride through the numbers, charts, and maybe even a pun or two along the way. After all, who said academic research had to be devoid of a little whimsy? Let's unravel the enigmatic connection between prescription drugs and political tides, and see if we can't inject a dose of levity into the serious business of scholarly inquiry.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

Our analysis of the relationship between US household spending on prescription drugs and votes for the Republican presidential candidate in Washington unveiled a correlation coefficient of 0.9436730 with an r-squared of 0.8905187 from 2000 to 2020. The p-value was found to be less than 0.01, indicating a statistically significant association between these variables. Fig. 1 illustrates the scatterplot displaying this robust correlation, reinforcing the undeniable connection between the two factors.

The substantial correlation coefficient suggests a strong positive relationship between household spending on prescription drugs and support for the Republican candidate, further emphasizing the intricate interplay of healthcare economics and political preferences. It appears that the saying "health is wealth" might carry additional political weight in the realm of voting decisions.

Interestingly, our findings challenge the conventional wisdom that political leanings and healthcare spending are unrelated, echoing the sentiment that there may be more shared DNA between politics and healthcare economics than meets the eye. One could even argue that in the world of political preferences, pharmaceuticals have seemingly prescribed their own political predilections.

Our results offer a whimsical yet thought-provoking perspective on the fusion of healthcare economics and electoral dynamics, demonstrating the potential impact of prescription drug spending on the political landscape. It seems that when it comes to Washington households, the correlation between prescription drug spending and votes for the Republican candidate takes the lead, creating a captivating symphony of healthcare expenditures and electoral choices.

Ah, the complexities of statistical analysis and political intrigue – a combination that is as curious as it is captivating. Our study opens the door to an engaging dialogue on the multifaceted nature of American political economy, inviting further investigation into the intriguing connections between household spending on prescription drugs and political preferences.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 US household spending on prescription drugs and Votes for the Republican Presidential candidate in Washington. 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]]
"Prescription for Electoral Tension: A GOP Pill or Bane for Washington Households?"

[[ABSTRACT]]
Prescription drug spending in US households has been a topic of scrutiny and concern, particularly from a political standpoint. In this study, we analyze the relationship between US household spending on prescription drugs and the votes for the Republican presidential candidate in the state of Washington. Utilizing data from the Bureau of Labor Statistics and the MIT Election Data and Science Lab, Harvard Dataverse, and employing robust statistical methods, we identified a strikingly high correlation coefficient of 0.9436730 with a p-value less than < 0.01 from 2000 to 2020. Our findings suggest a robust connection between higher household spending on prescription drugs and favor for the Republican candidate, providing intriguing insights into the intersection of healthcare economics and political preferences. This study aims to provoke thought and discussion as it journeys through the conventional and the unconventional facets of American political economy.

[[LITERATURE REVIEW]]
Smith et al. (2015) assert a strong correlation between household expenditure on prescription drugs and political affiliations. Their comprehensive analysis of national data reveals compelling evidence that suggests a significant relationship between spending patterns and electoral inclinations. However, as we traverse through the scholarly landscape of this topic, it's crucial to broaden our horizon beyond the conventional realms of academic discourse. Turning our attention to the intriguing world of non-fiction literature, "The Healing of America: A Global Quest for Better, Cheaper, and Fairer Health Care" by T.R. Reid and "The Truth About Drug Companies: How They Deceive Us and What to Do About It" by Marcia Angell offer thought-provoking insights into the socio-political dimensions of healthcare and pharmaceutical industries.
Doe and Jones (2017) further explore the intricate dynamics of prescription drug expenditures and public sentiment. Their meticulous examination of regional voting patterns and healthcare expenditure implicates a nuanced link between consumer behavior and political preferences. As we navigate through these weighty tomes of academic literature, it's time to take a whimsical detour into the land of fiction. "Love in the Time of Cholera" by Gabriel Garcia Marquez and "The Pillars of the Earth" by Ken Follett, while not directly related to our research, serve as a reminder of the diverse facets of human experience, some of which might even be influenced by prescription drugs and political dispositions.
Additionally, intriguing insights can be gleaned from the unassuming corner of social media. A tweet by @HealthCareGuru reads, "Spending a fortune on prescription drugs but voting for fiscal conservatism – talk about a paradox wrapped in a pill bottle!" The pithy observation encapsulates the perplexing interplay between personal expenditure and political ideologies, prompting us to ponder the idiosyncrasies of human behavior amidst the cacophony of partisan rhetoric.
As we wade through this amalgamation of scholarly discourse, fiction, and social commentary, the dissonance between the serious and the zany becomes palpable. Yet, it is within this lexical tapestry that we hope to unravel the enigma of prescription drug spending and political proclivities, shedding light on an eccentric symphony where healthcare economics meets electoral winds.

[[RESULTS]]
Our analysis of the relationship between US household spending on prescription drugs and votes for the Republican presidential candidate in Washington unveiled a correlation coefficient of 0.9436730 with an r-squared of 0.8905187 from 2000 to 2020. The p-value was found to be less than 0.01, indicating a statistically significant association between these variables. Fig. 1 illustrates the scatterplot displaying this robust correlation, reinforcing the undeniable connection between the two factors.
The substantial correlation coefficient suggests a strong positive relationship between household spending on prescription drugs and support for the Republican candidate, further emphasizing the intricate interplay of healthcare economics and political preferences. It appears that the saying "health is wealth" might carry additional political weight in the realm of voting decisions.
Interestingly, our findings challenge the conventional wisdom that political leanings and healthcare spending are unrelated, echoing the sentiment that there may be more shared DNA between politics and healthcare economics than meets the eye. One could even argue that in the world of political preferences, pharmaceuticals have seemingly prescribed their own political predilections.
Our results offer a whimsical yet thought-provoking perspective on the fusion of healthcare economics and electoral dynamics, demonstrating the potential impact of prescription drug spending on the political landscape. It seems that when it comes to Washington households, the correlation between prescription drug spending and votes for the Republican candidate takes the lead, creating a captivating symphony of healthcare expenditures and electoral choices.
Ah, the complexities of statistical analysis and political intrigue – a combination that is as curious as it is captivating. Our study opens the door to an engaging dialogue on the multifaceted nature of American political economy, inviting further investigation into the intriguing connections between household spending on prescription drugs and political preferences.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

Our study delves into the labyrinthine interplay of household spending on prescription drugs and political proclivities, lo and behold, unveiling a robust correlation with voting patterns in Washington. Our findings not only corroborate previous research by Smith et al. (2015) suggesting a strong association between prescription drug expenditures and political leanings but also add an intriguing layer of nuance to the narrative. The unyielding correlation coefficient of 0.9436730 with a p-value less than 0.01 echoes the sentiment that money talks, even in the realm of prescription pills and political preferences.

In line with Doe and Jones' (2017) exploration of regional voting patterns and healthcare expenditure, our results provide empirical evidence of a tantalizing link between consumer behavior and political affiliations. The peculiar observation by @HealthCareGuru on social media comes to life in our study, as the paradox of "spending a fortune on prescription drugs but voting for fiscal conservatism" seems to be more than just a pithy tweet – it's a statistical revelation.

Our findings not only hint at a connection between healthcare economics and electoral winds but also bring to the forefront the intriguing notion that pharmaceuticals might possess their own political predilections. It's as though prescription drugs have not only found a way into medicine cabinets but also into the ballot box, prescribing political choices alongside their therapeutic effects.

The whimsical detour into the realm of fiction and social commentary in our literature review offers an unexpected yet illuminating perspective. As we navigate through the scholarly chronicles and the whimsical musings, the enigma of prescription drug spending and political proclivities begins to unravel, painting a picture of an eccentric symphony where healthcare economics meets electoral winds.

In conclusion, our study delves deep into the complexities of American political economy, uncovering the intriguing connections between household spending on prescription drugs and political preferences. It extends an invitation for further investigation into the multifaceted nature of these entwined domains, poised at the intersection of prescription pills and political choices.

A pinch of statistical analysis, a dash of political intrigue, and a dollop of whimsy seem to have crafted an academic concoction that is as curious as it is compelling, echoing the sentiment that in the realm of healthcare economics and electoral dynamics, there are certainly more shared DNA and perhaps a few unexpected plot twists.


Model: gpt-3.5-turbo-1106
System prompt: You are a researcher writing an academic paper.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 US household spending on prescription drugs and Votes for the Republican Presidential candidate in Washington. 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]]
"Prescription for Electoral Tension: A GOP Pill or Bane for Washington Households?"

[[ABSTRACT]]
Prescription drug spending in US households has been a topic of scrutiny and concern, particularly from a political standpoint. In this study, we analyze the relationship between US household spending on prescription drugs and the votes for the Republican presidential candidate in the state of Washington. Utilizing data from the Bureau of Labor Statistics and the MIT Election Data and Science Lab, Harvard Dataverse, and employing robust statistical methods, we identified a strikingly high correlation coefficient of 0.9436730 with a p-value less than < 0.01 from 2000 to 2020. Our findings suggest a robust connection between higher household spending on prescription drugs and favor for the Republican candidate, providing intriguing insights into the intersection of healthcare economics and political preferences. This study aims to provoke thought and discussion as it journeys through the conventional and the unconventional facets of American political economy.

[[INTRDUCTION]]
INTRODUCTION
The intersection of politics and economics has long been an area of fascination for researchers and observers alike. And what better way to delve into this nuanced relationship than through an analysis of prescription drug spending in US households and its purported influence on political preferences? Hailing from the state of Washington, a land known for its lush greenery, coffee aficionados, and celestial precipitation, we set out to uncover the hidden connections between household spending on prescription drugs and votes for the Republican presidential candidate. A journey awaits, and the findings offer tantalizing insights into the intricate dance of healthcare economics and electoral praxis, blending the serious and the quirky.
It's no secret that prescription drug spending has become a significant point of contention, with prices that seem to escalate faster than a space rocket. As citizens navigate the labyrinthine corridors of healthcare policy and political discourse, it's clear that prescription drug spending has left a distinct mark on the economic landscape of households. But could it also be leaving an imprint on the political inclinations of the voting populace? That's precisely the question that led us to embark on this curious foray into the realm of data parsing and statistical wizardry.
The stage is set, the data is primed, and the results are ready to be unveiled. So grab your lab coat, dust off your data-crunching toolkit, and let's delve into the enigmatic correlation between prescription drug spending and political predilections. Get ready for a rollercoaster ride through the numbers, charts, and maybe even a pun or two along the way. After all, who said academic research had to be devoid of a little whimsy? Let's unravel the enigmatic connection between prescription drugs and political tides, and see if we can't inject a dose of levity into the serious business of scholarly inquiry.

[[RESULTS]]
Our analysis of the relationship between US household spending on prescription drugs and votes for the Republican presidential candidate in Washington unveiled a correlation coefficient of 0.9436730 with an r-squared of 0.8905187 from 2000 to 2020. The p-value was found to be less than 0.01, indicating a statistically significant association between these variables. Fig. 1 illustrates the scatterplot displaying this robust correlation, reinforcing the undeniable connection between the two factors.
The substantial correlation coefficient suggests a strong positive relationship between household spending on prescription drugs and support for the Republican candidate, further emphasizing the intricate interplay of healthcare economics and political preferences. It appears that the saying "health is wealth" might carry additional political weight in the realm of voting decisions.
Interestingly, our findings challenge the conventional wisdom that political leanings and healthcare spending are unrelated, echoing the sentiment that there may be more shared DNA between politics and healthcare economics than meets the eye. One could even argue that in the world of political preferences, pharmaceuticals have seemingly prescribed their own political predilections.
Our results offer a whimsical yet thought-provoking perspective on the fusion of healthcare economics and electoral dynamics, demonstrating the potential impact of prescription drug spending on the political landscape. It seems that when it comes to Washington households, the correlation between prescription drug spending and votes for the Republican candidate takes the lead, creating a captivating symphony of healthcare expenditures and electoral choices.
Ah, the complexities of statistical analysis and political intrigue – a combination that is as curious as it is captivating. Our study opens the door to an engaging dialogue on the multifaceted nature of American political economy, inviting further investigation into the intriguing connections between household spending on prescription drugs and political preferences.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, our study has illuminated a compelling nexus between US household spending on prescription drugs and the votes for the Republican presidential candidate in the state of Washington. The exceptionally high correlation coefficient and statistically significant p-value demonstrate a robust relationship worthy of further scrutiny, much like a detective with a mystery to unravel. These findings resonate with the ongoing discourse on the interplay between healthcare economics and political proclivities, prompting us to consider the intricate interweaving of pharmaceutical prescriptions and political affinities. Perhaps we should view voting decisions not only as a reflection of ideological preferences but also as a prescription for the state of a household's healthcare expenditures.

This investigation has offered a unique vantage point that challenges the standard assumptions about the separation of healthcare economics and electoral choices, injecting a dose of unpredictability into the traditionally solemn field of academic inquiry. As we bid adieu to this study, one can't help but wonder about the potential implications of this correlation and its impact on future political landscapes. It seems our findings have prescribed a fresh perspective on the entwined nature of healthcare and politics, and whether this correlation is a bitter pill or a sweet remedy remains open to interpretation.

In the spirit of academic inquiry, we assert that no more research is needed in the realm of prescription drug spending and political preferences. After all, isn't prescribing more research in this area akin to using a sledgehammer to crack a walnut? It's time to let our findings marinate in the scholarly pantheon and, in the words of Shakespeare, "exit, pursued by a bear." Let the quirky intersection of healthcare economics and electoral predilections continue to inspire curiosity and, dare we say, a healthy dose of whimsy in the academic landscape.


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 Prescription for Electoral Tension: A GOP Pill or Bane for Washington Households?. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Political Pharmacology


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 Prescription for Electoral Tension: A GOP Pill or Bane for Washington Households?. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Political Paradox 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 Prescription for Electoral Tension: A GOP Pill or Bane for Washington Households? with an abstract of Prescription drug spending in US households has been a topic of scrutiny and concern, particularly from a political standpoint. In this study, we analyze the relationship between US household spending on prescription drugs and the votes for the Republican presidential candidate in the state of Washington. Utilizing data from the Bureau of Labor Statistics and the MIT Election Data and Science Lab, Harvard Dataverse, and employing robust statistical methods, we identified a strikingly high correlation coefficient of 0.9436730 with a p-value less than < 0.01 from 2000 to 2020. Our findings suggest a robust connection between higher household spending on prescription drugs and favor for the Republican candidate, providing intriguing insights into the intersection of healthcare economics and political preferences. This study aims to provoke thought and discussion as it journeys through the conventional and the unconventional facets of American political economy.

ChatGPT:

prescription drug spending, US households, Republican presidential candidate, Washington state, Bureau of Labor Statistics, MIT Election Data and Science Lab, Harvard Dataverse, healthcare economics, political preferences, correlation coefficient, statistical methods, political economy, household spending, prescription drugs

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



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Data details

US household spending on prescription drugs
Detailed data title: Annual US household spend on prescription drugs, as a percentage of total household spend
Source: Bureau of Labor Statistics
See what else correlates with US household spending on prescription drugs

Votes for the Republican Presidential candidate in Washington
Detailed data title: Percentage of all votes cast for the Republican Presidential candidate in Washington
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for the Republican Presidential candidate in Washington

Correlation r = 0.9436730 (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 = 0.8905187 (Coefficient of determination)
This means 89.1% of the change in the one variable (i.e., Votes for the Republican Presidential candidate in Washington) is predictable based on the change in the other (i.e., US household spending on prescription drugs) over the 6 years from 2000 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0047. 0.0046697443603951640000000000
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 0.94 in 0.47% of random cases. Said differently, if you correlated 214 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.

[ 0.56, 0.99 ] 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.
200020042008201220162020
US household spending on prescription drugs (Household spend)1.093441.106120.954721.001130.8078730.776079
Votes for the Republican Presidential candidate in Washington (Percentage of votes)44.578645.640340.476341.294636.832738.767




Why this works

  1. 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.
  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.




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([1.09344,1.10612,0.95472,1.00113,0.807873,0.776079,])
array_2 = np.array([44.5786,45.6403,40.4763,41.2946,36.8327,38.767,])
array_1_name = "US household spending on prescription drugs"
array_2_name = "Votes for the Republican Presidential candidate in Washington"

# 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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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.

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Correlation ID: 5598 · Black Variable ID: 19995 · Red Variable ID: 26186
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