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Spurious correlation #4,530 · 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 the Democratic Presidential candidate in Delaware and the second variable is Electricity generation in Italy.  The chart goes from 1980 to 2020, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

It's a shocking revelation, but as the support for the Democrat candidate in Delaware surges, so does the power! It seems like the Italian electricity grid just can't resist conducting the energy of political change across the Atlantic. Who knew that the spark of democracy in one place could light up homes in another? It's a real power play, proving that when it comes to politics and electricity, the currents of influence know no bounds!

Model: dalle-3
Prompt: Generate an image of a high-tech visualization with sleek, futuristic polling stations in Delaware, where a diverse stream of voters waits in line to cast their ballots for the Democrat Presidential candidate, exuding a sense of purpose and excitement. Show the glow of a vibrant, pulsating current bridging across the Atlantic to Italy, where advanced, high-tech electricity generation meters spin rapidly, surrounded by a humming power grid depicting increased energy production. The scene should exude a sense of connection and energy, with the current symbolizing the bond between political support and electrical power. 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: Votes for the Democrat Presidential candidate in Delaware and Electricity generation in Italy. 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: Parallax art Voxel art High-tech High-tech Vaporwave
ChatGPT: High-tech

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 Votes for the Democrat Presidential candidate in Delaware and Electricity generation in Italy, in the following situation: It's a shocking revelation, but as the support for the Democrat candidate in Delaware surges, so does the power! It seems like the Italian electricity grid just can't resist conducting the energy of political change across the Atlantic. Who knew that the spark of democracy in one place could light up homes in another? It's a real power play, proving that when it comes to politics and electricity, the currents of influence know no bounds!. Keep the description simple.
ChatGPT: In Delaware, a wave of voters lines up at the polling stations, proudly casting their ballots for the Democrat Presidential candidate. Meanwhile, in Italy, the electricity generation meters spin rapidly as the power grid hums with increased energy production. The two scenes are connected by a vibrant, glowing current that travels across the Atlantic, bridging the surge of political support with the surge of electrical power.

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: [ High-tech ] and depicting this scene: In Delaware, a wave of voters lines up at the polling stations, proudly casting their ballots for the Democrat Presidential candidate. Meanwhile, in Italy, the electricity generation meters spin rapidly as the power grid hums with increased energy production. The two scenes are connected by a vibrant, glowing current that travels across the Atlantic, bridging the surge of political support with the surge of electrical power. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a high-tech visualization with sleek, futuristic polling stations in Delaware, where a diverse stream of voters waits in line to cast their ballots for the Democrat Presidential candidate, exuding a sense of purpose and excitement. Show the glow of a vibrant, pulsating current bridging across the Atlantic to Italy, where advanced, high-tech electricity generation meters spin rapidly, surrounded by a humming power grid depicting increased energy production. The scene should exude a sense of connection and energy, with the current symbolizing the bond between political support and electrical power.

*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 Votes for the Democrat Presidential candidate in Delaware caused Electricity generation in Italy to increase. Include one good pun.

AI academic paper

(Because p < 0.01)
Delaware's Blue Gales and Italy's WATT-A Connection: A Shocking Correlation Between Votes for the Democrat Presidential Candidate and Electricity Generation

Journal of Electrifying Politics

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 Votes for the Democrat Presidential candidate in Delaware and Electricity generation in Italy. Make lots of 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 Energy Information Administration to assess this nagging question. You found a correlation coefficient of 0.9278186 and p < 0.01 for 1980 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]]
"Delaware's Blue Gales and Italy's WATT-A Connection: A Shocking Correlation Between Votes for the Democrat Presidential Candidate and Electricity Generation"

[[ABSTRACT]]
In this electrifying paper, we delve deep into the dynamic relationship between the abundance of votes for the Democrat Presidential candidate in the state of Delaware and the captivating world of electricity generation in Italy. Our research team, armed with data from MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, harnessed the power of statistical analysis to unravel this enigmatic connection. With a striking correlation coefficient of 0.9278186 and a jaw-dropping p-value of less than 0.01 for the years spanning from 1980 to 2020, our findings are truly electrifying! This electrifying influx of data suggests that there is a positively charged relationship between Delaware's political allegiance and Italy's electrical output. Using watts of humor and volts of puns, we plug into the shocking spark of this correlation, shedding light on a seldom-explored synergy between political choices and global energy dynamics. This paper illuminates a new dimension in the realm of quantitative social science, proving that even in the world of academia, the power of unexpected connections can truly electrify our understanding of the world.


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 Votes for the Democrat Presidential candidate in Delaware and Electricity generation in Italy. Make lots of jokes, goofy observations, and puns.

Here is the title and abstract of the paper:
[[TITLE]]
"Delaware's Blue Gales and Italy's WATT-A Connection: A Shocking Correlation Between Votes for the Democrat Presidential Candidate and Electricity Generation"

[[ABSTRACT]]
In this electrifying paper, we delve deep into the dynamic relationship between the abundance of votes for the Democrat Presidential candidate in the state of Delaware and the captivating world of electricity generation in Italy. Our research team, armed with data from MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, harnessed the power of statistical analysis to unravel this enigmatic connection. With a striking correlation coefficient of 0.9278186 and a jaw-dropping p-value of less than 0.01 for the years spanning from 1980 to 2020, our findings are truly electrifying! This electrifying influx of data suggests that there is a positively charged relationship between Delaware's political allegiance and Italy's electrical output. Using watts of humor and volts of puns, we plug into the shocking spark of this correlation, shedding light on a seldom-explored synergy between political choices and global energy dynamics. This paper illuminates a new dimension in the realm of quantitative social science, proving that even in the world of academia, the power of unexpected connections can truly electrify our understanding of the world.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

"Shocking" may not be the first word that comes to mind when one thinks about the relationship between political voting patterns in the small state of Delaware and electricity generation in the boot-shaped land of Italy. However, our research has uncovered a connection so electrifying, it's bound to make you "spark" with excitement. Get ready to be "amped" up as we unravel the hair-raising link between votes for the Democrat Presidential candidate in Delaware and Italy's WATT-age generation.

As researchers, we are constantly "conducting" experiments, but this time we've truly "energized" our curiosity to shed light on a less traveled path of scholarly investigation. Our study is a "power play" that takes us from the ballot booths of Delaware to the power plants of Italy. We wanted to see if there was any "watt" the connection could be between these two seemingly disparate entities, and boy, did we find a "sparkling" revelation.

With an upbeat tempo, we'll delve into the "shocking" correlation between blue votes in the early elections and Italy's "WATT-a" production of electricity. The results might just leave you feeling a little "charged" up, and we hope our findings will "transformer" the way you look at seemingly unrelated data points. So, buckle up and board the current of knowledge as we journey through this perplexing yet enlightening connection.


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 Votes for the Democrat Presidential candidate in Delaware and Electricity generation in Italy. Make lots of 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 devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading CVS receipts.

Here is the title and abstract of the paper:
[[TITLE]]
"Delaware's Blue Gales and Italy's WATT-A Connection: A Shocking Correlation Between Votes for the Democrat Presidential Candidate and Electricity Generation"

[[ABSTRACT]]
In this electrifying paper, we delve deep into the dynamic relationship between the abundance of votes for the Democrat Presidential candidate in the state of Delaware and the captivating world of electricity generation in Italy. Our research team, armed with data from MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, harnessed the power of statistical analysis to unravel this enigmatic connection. With a striking correlation coefficient of 0.9278186 and a jaw-dropping p-value of less than 0.01 for the years spanning from 1980 to 2020, our findings are truly electrifying! This electrifying influx of data suggests that there is a positively charged relationship between Delaware's political allegiance and Italy's electrical output. Using watts of humor and volts of puns, we plug into the shocking spark of this correlation, shedding light on a seldom-explored synergy between political choices and global energy dynamics. This paper illuminates a new dimension in the realm of quantitative social science, proving that even in the world of academia, the power of unexpected connections can truly electrify our understanding of the world.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
"Shocking" may not be the first word that comes to mind when one thinks about the relationship between political voting patterns in the small state of Delaware and electricity generation in the boot-shaped land of Italy. However, our research has uncovered a connection so electrifying, it's bound to make you "spark" with excitement. Get ready to be "amped" up as we unravel the hair-raising link between votes for the Democrat Presidential candidate in Delaware and Italy's WATT-age generation.
As researchers, we are constantly "conducting" experiments, but this time we've truly "energized" our curiosity to shed light on a less traveled path of scholarly investigation. Our study is a "power play" that takes us from the ballot booths of Delaware to the power plants of Italy. We wanted to see if there was any "watt" the connection could be between these two seemingly disparate entities, and boy, did we find a "sparkling" revelation.
With an upbeat tempo, we'll delve into the "shocking" correlation between blue votes in the early elections and Italy's "WATT-a" production of electricity. The results might just leave you feeling a little "charged" up, and we hope our findings will "transformer" the way you look at seemingly unrelated data points. So, buckle up and board the current of knowledge as we journey through this perplexing yet enlightening connection.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

As we delve into the electrifying world of political votes and electrical volts, we first turn to Smith's seminal work, "Election Dynamics and Their Societal Impact," in which the author explores the intricate relationship between political alliances and societal outcomes. While Smith's work is enlightening, it fails to illuminate the truly electrifying connection we seek. Next, we pivot to Doe's comprehensive analysis, "Political Landscapes and Their Global Effects," which touches upon the ripple effects of political behaviors. Unfortunately, Doe's work, though stimulating, does not provide the electrifying insights we crave.

Moving beyond the realm of traditional research, we embark on an unconventional quest, immersing ourselves in non-fiction works such as "Power: Why Some People Have It and Others Don't" and "The Current: Warmth, Power, and Light at the Edge of the World." While these books offer valuable perspectives on power dynamics, they ultimately lead us down a dimly lit path in our quest for an electrifying connection.

As we navigate the literary landscape, we venture into the realm of fiction, exploring works like "The Shock Doctrine" and "The Power." Although these titles evoke themes of electrifying influence, they fail to provide the empirical grounding we desperately seek.

In our relentless pursuit of understanding, we stray into unexpected territory, analyzing every source at our disposal. From perusing scholarly articles to skimming through grocery lists and even scrutinizing the fine print of CVS receipts, our diligent efforts yield no direct leads. Yet, in the midst of our seemingly futile search, a spark of inspiration ignites, leading us to uncover the shocking correlation between votes for the Democrat Presidential candidate in Delaware and electricity generation in Italy.

Our unconventional journey through the literary landscape has not only revealed the limitations of traditional sources but has also reinforced our commitment to exploring unexpected avenues in the pursuit of knowledge. With a twist of humor and a jolt of determination, we stand ready to illuminate the world with this electrifying revelation.


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 Votes for the Democrat Presidential candidate in Delaware and Electricity generation in Italy. Make lots of jokes, goofy observations, and puns.

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 Energy Information Administration . You used data from 1980 to 2020

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

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"Delaware's Blue Gales and Italy's WATT-A Connection: A Shocking Correlation Between Votes for the Democrat Presidential Candidate and Electricity Generation"

[[ABSTRACT]]
In this electrifying paper, we delve deep into the dynamic relationship between the abundance of votes for the Democrat Presidential candidate in the state of Delaware and the captivating world of electricity generation in Italy. Our research team, armed with data from MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, harnessed the power of statistical analysis to unravel this enigmatic connection. With a striking correlation coefficient of 0.9278186 and a jaw-dropping p-value of less than 0.01 for the years spanning from 1980 to 2020, our findings are truly electrifying! This electrifying influx of data suggests that there is a positively charged relationship between Delaware's political allegiance and Italy's electrical output. Using watts of humor and volts of puns, we plug into the shocking spark of this correlation, shedding light on a seldom-explored synergy between political choices and global energy dynamics. This paper illuminates a new dimension in the realm of quantitative social science, proving that even in the world of academia, the power of unexpected connections can truly electrify our understanding of the world.

[[INTRODUCTION]]
"Shocking" may not be the first word that comes to mind when one thinks about the relationship between political voting patterns in the small state of Delaware and electricity generation in the boot-shaped land of Italy. However, our research has uncovered a connection so electrifying, it's bound to make you "spark" with excitement. Get ready to be "amped" up as we unravel the hair-raising link between votes for the Democrat Presidential candidate in Delaware and Italy's WATT-age generation.
As researchers, we are constantly "conducting" experiments, but this time we've truly "energized" our curiosity to shed light on a less traveled path of scholarly investigation. Our study is a "power play" that takes us from the ballot booths of Delaware to the power plants of Italy. We wanted to see if there was any "watt" the connection could be between these two seemingly disparate entities, and boy, did we find a "sparkling" revelation.
With an upbeat tempo, we'll delve into the "shocking" correlation between blue votes in the early elections and Italy's "WATT-a" production of electricity. The results might just leave you feeling a little "charged" up, and we hope our findings will "transformer" the way you look at seemingly unrelated data points. So, buckle up and board the current of knowledge as we journey through this perplexing yet enlightening connection.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To uncover the electrifying connection between votes for the Democrat Presidential candidate in Delaware and electricity generation in Italy, our research team embarked on a hair-raising journey through the depths of data analysis and statistical wizardry. Armed with datasets primarily sourced from MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we fused together a positively charged mélange of information dating from 1980 to 2020.

Our first "high-voltage" endeavor revolved around wrangling the electoral data from Delaware. We employed a top-secret method we liked to call the "Blue Wave Surfboard Technique," which involved riding the waves of election statistics with the agility of a pro surfer, albeit digitally. From voter turnout to party affiliations, we dived deep into the political undercurrents of the Delaware elections.

As for Italy's electricity generation data, we didn't just dip our toes in the data stream - we plunged headfirst into the "current." Our approach was a mix of "Jolt-A-Meter" and "Ohm My Goodness" methodologies, involving the rigorous aggregation and analysis of kilowatt-hour figures from various power plants and energy sources in Italy. It was truly a "shocking" experience!

Next, we channeled our inner "Data Tesla" and unleashed the power of statistical analysis. Utilizing a combination of "Electo-regression" and "Ohm-nibus" modeling, we examined the relationship between the Delaware voting patterns and Italy's electricity generation. We didn't stop there - we also threw in a dash of "WATT-if" scenarios to simulate what could have happened under different political conditions.

But how did we measure the strength of this electrifying correlation? Well, we employed the "Voltage-Correlation" technique, which, when applied to our data, produced a coefficient so "high-octane," it made our hair stand on end! Equipped with this knowledge, we could confidently say that the correlation was not just a "spark" in the dark - it was statistically significant enough to light up a room.

Finally, to ensure the reliability of our findings, we engaged in a little "current affairs" by validating our results through robust cross-validation methods and sensitivity analyses. We didn't want any "amp-lied" effects clouding our interpretation!

So, with our hair slightly frazzled from the sheer magnitude of our data analysis, we emerged with a clearer understanding of the shocking connection between Delaware's blue gales and Italy's wattage. It was a journey filled with unexpected twists and turns, but in the end, the "current" was in our favor, and we illuminated a fascinating correlation that will undoubtedly "electrify" academic discourse for years to come.


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 Votes for the Democrat Presidential candidate in Delaware and Electricity generation in Italy. Make lots of jokes, goofy observations, and puns.

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 Energy Information Administration .

For the time period 1980 to 2020, you found a correlation 0.9278186, r-squared of 0.8608474, 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]]
"Delaware's Blue Gales and Italy's WATT-A Connection: A Shocking Correlation Between Votes for the Democrat Presidential Candidate and Electricity Generation"

[[ABSTRACT]]
In this electrifying paper, we delve deep into the dynamic relationship between the abundance of votes for the Democrat Presidential candidate in the state of Delaware and the captivating world of electricity generation in Italy. Our research team, armed with data from MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, harnessed the power of statistical analysis to unravel this enigmatic connection. With a striking correlation coefficient of 0.9278186 and a jaw-dropping p-value of less than 0.01 for the years spanning from 1980 to 2020, our findings are truly electrifying! This electrifying influx of data suggests that there is a positively charged relationship between Delaware's political allegiance and Italy's electrical output. Using watts of humor and volts of puns, we plug into the shocking spark of this correlation, shedding light on a seldom-explored synergy between political choices and global energy dynamics. This paper illuminates a new dimension in the realm of quantitative social science, proving that even in the world of academia, the power of unexpected connections can truly electrify our understanding of the world.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
"Shocking" may not be the first word that comes to mind when one thinks about the relationship between political voting patterns in the small state of Delaware and electricity generation in the boot-shaped land of Italy. However, our research has uncovered a connection so electrifying, it's bound to make you "spark" with excitement. Get ready to be "amped" up as we unravel the hair-raising link between votes for the Democrat Presidential candidate in Delaware and Italy's WATT-age generation.
As researchers, we are constantly "conducting" experiments, but this time we've truly "energized" our curiosity to shed light on a less traveled path of scholarly investigation. Our study is a "power play" that takes us from the ballot booths of Delaware to the power plants of Italy. We wanted to see if there was any "watt" the connection could be between these two seemingly disparate entities, and boy, did we find a "sparkling" revelation.
With an upbeat tempo, we'll delve into the "shocking" correlation between blue votes in the early elections and Italy's "WATT-a" production of electricity. The results might just leave you feeling a little "charged" up, and we hope our findings will "transformer" the way you look at seemingly unrelated data points. So, buckle up and board the current of knowledge as we journey through this perplexing yet enlightening connection.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The hair-raising correlation between votes for the Democrat Presidential candidate in Delaware and electricity generation in Italy has left our research team feeling positively charged with excitement. With a striking correlation coefficient of 0.9278186, an r-squared of 0.8608474, and a p-value of less than 0.01 for the time period from 1980 to 2020, our findings have truly lit up the academic world.

The figure (Fig. 1) included in this paper demonstrates a "shocking" visual representation of the strong correlation between the two variables. It's enough to make even the most conservative statistician flicker with curiosity.

Our results suggest that there is a positively charged relationship between Delaware's political allegiance and Italy's electrical output. The "spark" in voting patterns seems to have some sort of electrical influence across the ocean, leading to this surprising correlation. We'd say this finding is a "power move" for political and energy research alike!

This paper not only sheds "light" on an unexpected synergy between political choices and global energy dynamics, but it also provides a "jolt" of excitement for researchers delving into seemingly unconnected data points. Our findings remind us that sometimes the most "wattage" in research comes from exploring the most unexpected connections.

The "electrifying" influx of data in our study has truly illuminated a new dimension in the realm of quantitative social science, proving that even in the world of academia, the power of unexpected connections can truly electrify our understanding of the world. We hope this paper sparks a surge of interest for future research in this "unexpected voltage" of study.


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 Votes for the Democrat Presidential candidate in Delaware and Electricity generation in Italy. Make lots of 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]]
"Delaware's Blue Gales and Italy's WATT-A Connection: A Shocking Correlation Between Votes for the Democrat Presidential Candidate and Electricity Generation"

[[ABSTRACT]]
In this electrifying paper, we delve deep into the dynamic relationship between the abundance of votes for the Democrat Presidential candidate in the state of Delaware and the captivating world of electricity generation in Italy. Our research team, armed with data from MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, harnessed the power of statistical analysis to unravel this enigmatic connection. With a striking correlation coefficient of 0.9278186 and a jaw-dropping p-value of less than 0.01 for the years spanning from 1980 to 2020, our findings are truly electrifying! This electrifying influx of data suggests that there is a positively charged relationship between Delaware's political allegiance and Italy's electrical output. Using watts of humor and volts of puns, we plug into the shocking spark of this correlation, shedding light on a seldom-explored synergy between political choices and global energy dynamics. This paper illuminates a new dimension in the realm of quantitative social science, proving that even in the world of academia, the power of unexpected connections can truly electrify our understanding of the world.

[[LITERATURE REVIEW]]
As we delve into the electrifying world of political votes and electrical volts, we first turn to Smith's seminal work, "Election Dynamics and Their Societal Impact," in which the author explores the intricate relationship between political alliances and societal outcomes. While Smith's work is enlightening, it fails to illuminate the truly electrifying connection we seek. Next, we pivot to Doe's comprehensive analysis, "Political Landscapes and Their Global Effects," which touches upon the ripple effects of political behaviors. Unfortunately, Doe's work, though stimulating, does not provide the electrifying insights we crave.
Moving beyond the realm of traditional research, we embark on an unconventional quest, immersing ourselves in non-fiction works such as "Power: Why Some People Have It and Others Don't" and "The Current: Warmth, Power, and Light at the Edge of the World." While these books offer valuable perspectives on power dynamics, they ultimately lead us down a dimly lit path in our quest for an electrifying connection.
As we navigate the literary landscape, we venture into the realm of fiction, exploring works like "The Shock Doctrine" and "The Power." Although these titles evoke themes of electrifying influence, they fail to provide the empirical grounding we desperately seek.
In our relentless pursuit of understanding, we stray into unexpected territory, analyzing every source at our disposal. From perusing scholarly articles to skimming through grocery lists and even scrutinizing the fine print of CVS receipts, our diligent efforts yield no direct leads. Yet, in the midst of our seemingly futile search, a spark of inspiration ignites, leading us to uncover the shocking correlation between votes for the Democrat Presidential candidate in Delaware and electricity generation in Italy.
Our unconventional journey through the literary landscape has not only revealed the limitations of traditional sources but has also reinforced our commitment to exploring unexpected avenues in the pursuit of knowledge. With a twist of humor and a jolt of determination, we stand ready to illuminate the world with this electrifying revelation.

[[RESULTS]]
The hair-raising correlation between votes for the Democrat Presidential candidate in Delaware and electricity generation in Italy has left our research team feeling positively charged with excitement. With a striking correlation coefficient of 0.9278186, an r-squared of 0.8608474, and a p-value of less than 0.01 for the time period from 1980 to 2020, our findings have truly lit up the academic world.
The figure (Fig. 1) included in this paper demonstrates a "shocking" visual representation of the strong correlation between the two variables. It's enough to make even the most conservative statistician flicker with curiosity.
Our results suggest that there is a positively charged relationship between Delaware's political allegiance and Italy's electrical output. The "spark" in voting patterns seems to have some sort of electrical influence across the ocean, leading to this surprising correlation. We'd say this finding is a "power move" for political and energy research alike!
This paper not only sheds "light" on an unexpected synergy between political choices and global energy dynamics, but it also provides a "jolt" of excitement for researchers delving into seemingly unconnected data points. Our findings remind us that sometimes the most "wattage" in research comes from exploring the most unexpected connections.
The "electrifying" influx of data in our study has truly illuminated a new dimension in the realm of quantitative social science, proving that even in the world of academia, the power of unexpected connections can truly electrify our understanding of the world. We hope this paper sparks a surge of interest for future research in this "unexpected voltage" of study.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

The positively electrifying results of our study have left us positively "wattened" with excitement and a "shocking" sense of accomplishment. Our findings indeed support prior research and continue to spark new understanding in the realm of quantitative social science. As we journey through the electrified landscape of our discussion, we must harken back to the surprising insights we uncovered in our literature review.

The path to this electrifying connection was fraught with unexpected twists and turns, much like a hair-raising rollercoaster ride at the amusement park. While some might question the validity of unconventional sources, it is clear that our relentless pursuit of knowledge led us to this "electrifying" revelation. Our findings not only support but also greatly expand upon the existing literature, transcending the limitations of traditional sources to bring a "shocking" new dimension to light.

Smith and Doe's works, while insightful, were like flickering candles in a dark room compared to the power surge of our results. The unexpected alignment of votes for the Democrat Presidential candidate in Delaware and electricity generation in Italy cannot be understated. It appears that the "spark" in Delaware's political allegiance has crossed the ocean to "electrify" Italy's electrical output, creating a positively charged relationship that has remained elusive until now.

Our results provide a compelling argument for the "power" of unexpected connections in academia. The "wattage" of our findings, exemplified by a correlation coefficient of 0.9278186 and a p-value of less than 0.01, serves as a "shocking" reminder that even the most unassuming variables can reveal electrifying correlations. This "shocking" connection effectively "illuminates" the far-reaching impact of political choices on global energy dynamics, adding a new layer of depth to the field.

In conclusion, our findings have not only ignited a surge of interest in this "unexpected voltage" of study but have also given rise to a new wave of enthusiasm for exploring the unexpected connections that invigorate research. It is our hope that this paper will inspire future researchers to seek out and embrace the unexpected, recognizing the unparalleled power that can arise from seemingly disparate data points.


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 Votes for the Democrat Presidential candidate in Delaware and Electricity generation in Italy. Make lots of 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]]
"Delaware's Blue Gales and Italy's WATT-A Connection: A Shocking Correlation Between Votes for the Democrat Presidential Candidate and Electricity Generation"

[[ABSTRACT]]
In this electrifying paper, we delve deep into the dynamic relationship between the abundance of votes for the Democrat Presidential candidate in the state of Delaware and the captivating world of electricity generation in Italy. Our research team, armed with data from MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, harnessed the power of statistical analysis to unravel this enigmatic connection. With a striking correlation coefficient of 0.9278186 and a jaw-dropping p-value of less than 0.01 for the years spanning from 1980 to 2020, our findings are truly electrifying! This electrifying influx of data suggests that there is a positively charged relationship between Delaware's political allegiance and Italy's electrical output. Using watts of humor and volts of puns, we plug into the shocking spark of this correlation, shedding light on a seldom-explored synergy between political choices and global energy dynamics. This paper illuminates a new dimension in the realm of quantitative social science, proving that even in the world of academia, the power of unexpected connections can truly electrify our understanding of the world.

[[INTRDUCTION]]
"Shocking" may not be the first word that comes to mind when one thinks about the relationship between political voting patterns in the small state of Delaware and electricity generation in the boot-shaped land of Italy. However, our research has uncovered a connection so electrifying, it's bound to make you "spark" with excitement. Get ready to be "amped" up as we unravel the hair-raising link between votes for the Democrat Presidential candidate in Delaware and Italy's WATT-age generation.
As researchers, we are constantly "conducting" experiments, but this time we've truly "energized" our curiosity to shed light on a less traveled path of scholarly investigation. Our study is a "power play" that takes us from the ballot booths of Delaware to the power plants of Italy. We wanted to see if there was any "watt" the connection could be between these two seemingly disparate entities, and boy, did we find a "sparkling" revelation.
With an upbeat tempo, we'll delve into the "shocking" correlation between blue votes in the early elections and Italy's "WATT-a" production of electricity. The results might just leave you feeling a little "charged" up, and we hope our findings will "transformer" the way you look at seemingly unrelated data points. So, buckle up and board the current of knowledge as we journey through this perplexing yet enlightening connection.

[[RESULTS]]
The hair-raising correlation between votes for the Democrat Presidential candidate in Delaware and electricity generation in Italy has left our research team feeling positively charged with excitement. With a striking correlation coefficient of 0.9278186, an r-squared of 0.8608474, and a p-value of less than 0.01 for the time period from 1980 to 2020, our findings have truly lit up the academic world.
The figure (Fig. 1) included in this paper demonstrates a "shocking" visual representation of the strong correlation between the two variables. It's enough to make even the most conservative statistician flicker with curiosity.
Our results suggest that there is a positively charged relationship between Delaware's political allegiance and Italy's electrical output. The "spark" in voting patterns seems to have some sort of electrical influence across the ocean, leading to this surprising correlation. We'd say this finding is a "power move" for political and energy research alike!
This paper not only sheds "light" on an unexpected synergy between political choices and global energy dynamics, but it also provides a "jolt" of excitement for researchers delving into seemingly unconnected data points. Our findings remind us that sometimes the most "wattage" in research comes from exploring the most unexpected connections.
The "electrifying" influx of data in our study has truly illuminated a new dimension in the realm of quantitative social science, proving that even in the world of academia, the power of unexpected connections can truly electrify our understanding of the world. We hope this paper sparks a surge of interest for future research in this "unexpected voltage" of study.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]
In conclusion, our research has not only sparked our imagination but also provided a shockingly electrifying revelation about the unexpected connection between votes for the Democrat Presidential candidate in Delaware and electricity generation in Italy. Our findings may seem like a tall tale, but the data speaks for itself – there is indeed a positively charged relationship between political allegiance in Delaware and Italy's electrical output. This correlation is enough to make even the most skeptical researcher raise an eyebrow and say, "Ohm my goodness, I can't believe it!" We've truly made strides in shedding light on this unusual synergy between political choices and global energy dynamics. Our research has been a "power play" of statistical analysis and pun-packed discussions, but we must assert that no more research is needed in this area. It's time for us to "disconnect" from this topic and move on to new electrifying discoveries. Thank you, and may your future research be as "shockingly" enlightening as ours!


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 Delaware's Blue Gales and Italy's WATT-A Connection: A Shocking Correlation Between Votes for the Democrat Presidential Candidate and Electricity Generation. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Electrifying Politics


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 Delaware's Blue Gales and Italy's WATT-A Connection: A Shocking Correlation Between Votes for the Democrat Presidential Candidate and Electricity Generation. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Comparative Political and Electrical 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 Delaware's Blue Gales and Italy's WATT-A Connection: A Shocking Correlation Between Votes for the Democrat Presidential Candidate and Electricity Generation with an abstract of In this electrifying paper, we delve deep into the dynamic relationship between the abundance of votes for the Democrat Presidential candidate in the state of Delaware and the captivating world of electricity generation in Italy. Our research team, armed with data from MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, harnessed the power of statistical analysis to unravel this enigmatic connection. With a striking correlation coefficient of 0.9278186 and a jaw-dropping p-value of less than 0.01 for the years spanning from 1980 to 2020, our findings are truly electrifying! This electrifying influx of data suggests that there is a positively charged relationship between Delaware's political allegiance and Italy's electrical output. Using watts of humor and volts of puns, we plug into the shocking spark of this correlation, shedding light on a seldom-explored synergy between political choices and global energy dynamics. This paper illuminates a new dimension in the realm of quantitative social science, proving that even in the world of academia, the power of unexpected connections can truly electrify our understanding of the world.

ChatGPT:

Delaware, Blue Gales, Italy, WATT-A Connection, votes, Democrat Presidential candidate, electricity generation, correlation, MIT Election Data and Science Lab, Harvard Dataverse, Energy Information Administration, statistical analysis, correlation coefficient, p-value, political allegiance, electrical output, global energy dynamics, quantitative social science, unexpected connections

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

Electricity generation in Italy
Detailed data title: Total electricity generation in Italy in billion kWh
Source: Energy Information Administration
See what else correlates with Electricity generation in Italy

Correlation r = 0.9278186 (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 = 0.8608474 (Coefficient of determination)
This means 86.1% of the change in the one variable (i.e., Electricity generation in Italy) is predictable based on the change in the other (i.e., Votes for the Democratic Presidential candidate in Delaware) over the 11 years from 1980 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.8E-5. 0.0000384438580459900200000000
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 0.93 in 0.0038% of random cases. Said differently, if you correlated 26,012 random variables You don't actually need 26 thousand variables to find a correlation like this one. You can also correlate variables that are not independent. I do this a lot.

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 10 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 10 because we have two variables measured over a period of 11 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.

[ 0.74, 0.98 ] 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.
19801984198819921996200020042008201220162020
Votes for the Democratic Presidential candidate in Delaware (Percentage of votes)44.874139.932143.477843.523951.827954.977753.346861.942758.610753.353358.743
Electricity generation in Italy (Billion kWh)175.413169.876173.751211.296225.575253.218277.302295.016287.426279.976272.429




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.




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([44.8741,39.9321,43.4778,43.5239,51.8279,54.9777,53.3468,61.9427,58.6107,53.3533,58.743,])
array_2 = np.array([175.413,169.876,173.751,211.296,225.575,253.218,277.302,295.016,287.426,279.976,272.429,])
array_1_name = "Votes for the Democratic Presidential candidate in Delaware"
array_2_name = "Electricity generation in Italy"

# 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: 4530 · Black Variable ID: 26070 · Red Variable ID: 23748
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