Spurious correlation #5,090 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is GMO use in cotton and the second variable is Votes for the Democratic Presidential candidate in Alaska. The chart goes from 2000 to 2020, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is GMO use in cotton and the second variable is Votes for the Democratic Presidential candidate in Alaska. The chart goes from 2000 to 2020, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the GMO cotton plants flourished, they emitted a faint, invisible pollen that mysteriously carried a message urging Alaskans to lean left in the election. The cotton fields became a hub of political activity, swaying the voters one fiber at a time. The Democratic campaign unknowingly tapped into this botanical telegraph system, receiving a boost in support from the unlikeliest of campaign contributors: genetically modified cotton. This unexpected alliance between agriculture and Alaska's political landscape ultimately shifted the state's voting pattern, proving that when it comes to elections, even the humble cotton plant can have a strong 'blue' influence.

^{Show image generation prompt} Model: dalle-3
Prompt: Prompt: Generate an image of a Discovery magazine cover featuring a field of genetically modified cotton plants in Alaska. The cotton plants stand tall, their fluffy white tufts glinting in the sunlight. As the wind carries their invisible pollen, they seem to transmit a message encouraging Alaskans to vote for the Democrat Presidential candidate. The cotton plants become unwitting messengers subtly influencing the state's political scene. The image should be vibrant and dynamic, capturing the interaction between the cotton plants, sunlight, and wind, with a sense of intrigue and wonder. 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: GMO use in cotton and Votes for the Democrat Presidential candidate in Alaska. 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 meme A Discovery magazine cover image inspired by bauhaus Cutsy art illustration Minimalism
ChatGPT: A Discovery magazine cover image

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 GMO use in cotton and Votes for the Democrat Presidential candidate in Alaska, in the following situation: As the GMO cotton plants flourished, they emitted a faint, invisible pollen that mysteriously carried a message urging Alaskans to lean left in the election. The cotton fields became a hub of political activity, swaying the voters one fiber at a time. The Democratic campaign unknowingly tapped into this botanical telegraph system, receiving a boost in support from the unlikeliest of campaign contributors: genetically modified cotton. This unexpected alliance between agriculture and Alaska's political landscape ultimately shifted the state's voting pattern, proving that when it comes to elections, even the humble cotton plant can have a strong 'blue' influence.. Keep the description simple.
ChatGPT: A field of lush GMO cotton plants standing tall in Alaska, their fluffy white tufts glinting in the sunlight. As the wind carried their invisible pollen, it seemed to transmit a message encouraging Alaskans to vote for the Democrat Presidential candidate. The cotton plants appeared to become unwitting messengers, subtly influencing the state's political scene.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A Discovery magazine cover image ] and depicting this scene: A field of lush GMO cotton plants standing tall in Alaska, their fluffy white tufts glinting in the sunlight. As the wind carried their invisible pollen, it seemed to transmit a message encouraging Alaskans to vote for the Democrat Presidential candidate. The cotton plants appeared to become unwitting messengers, subtly influencing the state's political scene. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Generate an image of a Discovery magazine cover featuring a field of genetically modified cotton plants in Alaska. The cotton plants stand tall, their fluffy white tufts glinting in the sunlight. As the wind carries their invisible pollen, they seem to transmit a message encouraging Alaskans to vote for the Democrat Presidential candidate. The cotton plants become unwitting messengers subtly influencing the state's political scene. The image should be vibrant and dynamic, capturing the interaction between the cotton plants, sunlight, and wind, with a sense of intrigue and wonder.

*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in GMO use in cotton caused Votes for the Democrat Presidential candidate in Alaska to increase.\Make it sound serious until the end.

AI academic paper

(Because p < 0.01)

From Cotton to Bottin’: The Connection Between GMO Use and Votes for the Democrat Presidential Candidate in Alaska

Journal of Genetic Politics

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between GMO use in cotton and Votes for the Democrat Presidential candidate in Alaska. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
From Cotton to Bottin’: The Connection Between GMO Use and Votes for the Democrat Presidential Candidate in Alaska

[[ABSTRACT]]
In this study, we delve deep into the peculiar relationship between the use of genetically modified organisms (GMOs) in cotton production and the voting patterns for the Democrat Presidential candidate in the northern frontier of Alaska. Utilizing data from the USDA and the MIT Election Data and Science Lab stored in Harvard Dataverse, we meticulously scrutinize the correlation between these seemingly unrelated variables. Our findings reveal a striking correlation coefficient of 0.9306983 and a p-value less than 0.01 for the period spanning 2000 to 2020. Our research not only explores this unlikely connection but aims to challenge the notion that cotton and politics don't mix. This study may just change the way we view cotton fields and ballot yields!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of agriculture and politics has long been a fertile ground for research and speculation, but the connection between cotton and voting habits in the remote reaches of Alaska is an enigma that has eluded scholarly scrutiny until now. In this paper, we aim to unravel the curious link between the use of genetically modified organisms (GMOs) in cotton cultivation and voting preferences for the Democrat Presidential candidate in Alaska—a relationship that appears as unlikely as a polar bear wearing a Hawaiian shirt.
The geographic and demographic idiosyncrasies of Alaska, combined with the enigmatic nature of GMOs, make this investigation all the more intriguing. The idea that the cultivation of GM cotton could have any bearing on a voter's preference for a particular political party may seem as incongruous as a moose attending a tea party. But as the saying goes, "truth is stranger than fiction," and our research endeavors to shed light on this uncommon correlation.
The mystique surrounding the correlation between GMO use in cotton and political inclinations calls to mind the enigmatic allure of the aurora borealis—both are complex phenomena that spark fascination and intrigue, yet their underlying mechanisms remain elusive. With this study, we endeavor to peel back the layers of mystery surrounding this seemingly illogical connection and bring it into the purview of empirical inquiry.
As we embark on this academic odyssey, it's worth noting that we are venturing into uncharted territory, much like a group of intrepid explorers braving the Alaskan wilderness. Our findings may just upend conventional wisdom and usher in a new era of understanding at the crossroads of crop science and political behavior. This will certainly be an adventure worth embarking on, as we aim to untangle the threads of cotton and democracy to reveal patterns that are as unexpected as discovering a polar bear on a tropical beach.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The authors find that the intersection of agriculture and political behavior has been a subject of growing interest in recent years, as scholars strive to unveil the hidden connections between seemingly disparate realms. Smith et al. (2018) examined the correlation between agricultural practices and voter preferences in their seminal work "Agriculture and Politics: A Baleful Connection." Meanwhile, Doe and Jones (2016) investigated the impact of genetically modified organisms (GMOs) on crop production in their influential study "GMOs: Cultivating Controversy."

Moving beyond the conventional studies, our inquiry led us to diverse sources that shed light on the fascinating interplay between agriculture and political propensities. In "Seeds of Change: The Genetic Manipulation of Our Food," Lorem and Ipsum (2003) meticulously explore the implications of GMOs on agricultural ecosystems and, by extension, the socio-political fabric. Additionally, "The Omnivore's Dilemma" by Michael Pollan offered insightful perspectives on the intricate relationship between food, farming, and societal values.

Venturing into more imaginative realms, fictional narratives also provided intriguing suppositions that resonated with our research quest. In "The Cotton Club" by J. Doe (2010), the entwined fates of cotton production and political machinations serve as a metaphor for the dynamics of power. Similarly, "The Cotton Queen's Gambit" by A. Smith (2019) weaves a tale of intrigue set against the backdrop of cotton plantations and electoral maneuvering.

To gain a more comprehensive understanding, empirical inquiries extended to popular culture. Shows such as "Alaska: The Last Frontier," and "Northern Exposure" offered glimpses into the intricacies of life in the northern frontier, illuminating the unique attributes of the region that may influence political predilections. It's worth noting that while "Cotton Eye Joe" by Rednex is not a TV show, its allusion to cotton and its earworm quality did infiltrate our research space.

As we delved deeper into the realm of literature and popular culture, it became apparent that the enigmatic relationship between GMO use in cotton and voting patterns in Alaska has infiltrated diverse domains of human expression and investigation. Indeed, this unusual correlation not only defies conventional expectations but also entices researchers to embark on an intellectual odyssey as wild as a walrus wearing a top hat.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the 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 GMO use in cotton and Votes for the Democrat Presidential candidate in Alaska. 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 USDA 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]]
From Cotton to Bottin’: The Connection Between GMO Use and Votes for the Democrat Presidential Candidate in Alaska

[[ABSTRACT]]
In this study, we delve deep into the peculiar relationship between the use of genetically modified organisms (GMOs) in cotton production and the voting patterns for the Democrat Presidential candidate in the northern frontier of Alaska. Utilizing data from the USDA and the MIT Election Data and Science Lab stored in Harvard Dataverse, we meticulously scrutinize the correlation between these seemingly unrelated variables. Our findings reveal a striking correlation coefficient of 0.9306983 and a p-value less than 0.01 for the period spanning 2000 to 2020. Our research not only explores this unlikely connection but aims to challenge the notion that cotton and politics don't mix. This study may just change the way we view cotton fields and ballot yields!

[[INTRODUCTION]]
The intersection of agriculture and politics has long been a fertile ground for research and speculation, but the connection between cotton and voting habits in the remote reaches of Alaska is an enigma that has eluded scholarly scrutiny until now. In this paper, we aim to unravel the curious link between the use of genetically modified organisms (GMOs) in cotton cultivation and voting preferences for the Democrat Presidential candidate in Alaska—a relationship that appears as unlikely as a polar bear wearing a Hawaiian shirt.
The geographic and demographic idiosyncrasies of Alaska, combined with the enigmatic nature of GMOs, make this investigation all the more intriguing. The idea that the cultivation of GM cotton could have any bearing on a voter's preference for a particular political party may seem as incongruous as a moose attending a tea party. But as the saying goes, "truth is stranger than fiction," and our research endeavors to shed light on this uncommon correlation.
The mystique surrounding the correlation between GMO use in cotton and political inclinations calls to mind the enigmatic allure of the aurora borealis—both are complex phenomena that spark fascination and intrigue, yet their underlying mechanisms remain elusive. With this study, we endeavor to peel back the layers of mystery surrounding this seemingly illogical connection and bring it into the purview of empirical inquiry.
As we embark on this academic odyssey, it's worth noting that we are venturing into uncharted territory, much like a group of intrepid explorers braving the Alaskan wilderness. Our findings may just upend conventional wisdom and usher in a new era of understanding at the crossroads of crop science and political behavior. This will certainly be an adventure worth embarking on, as we aim to untangle the threads of cotton and democracy to reveal patterns that are as unexpected as discovering a polar bear on a tropical beach.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To embark on our quest to uncover the mystifying relationship between GMO use in cotton and voting patterns for the Democrat Presidential candidate in Alaska, we employed a methodological approach that was as intricate as untangling a ball of yarn in a room full of playful kittens. Our data collection process involved painstakingly scouring the vast expanse of the internet, much like seeking a needle in a haystack, with a primary focus on sourcing data from the USDA and the MIT Election Data and Science Lab housed within the Harvard Dataverse.

We then employed a series of statistical analyses that were more complex than a Rubik's Cube in the hands of a professional juggler, to tease out the intricate nuances of the data spanning from 2000 to 2020. Our approach could be likened to a culinary master crafting a delicate soufflé—requiring precision, finesse, and a hint of whimsy.

The first step in our convoluted process involved wrangling the vast swathes of data, not unlike herding cats, to ensure that we curated a comprehensive dataset that could stand the test of scrutiny. We then performed a rigorous cleaning process akin to scrubbing the kitchen floor after a messy baking session, ensuring that our data was as squeaky clean as a brand-new pair of sneakers.

Having polished our dataset to a gleaming sheen, we then ushered in the formidable presence of statistical models and correlation analyses to unravel the intricate dance between GMO-laden cotton and political proclivities in the icy tundra of Alaska. The statistical maneuvers we employed in this process were as deft and agile as a figure skater pirouetting on thin ice.

Finally, we subjected our concoction of data and analyses to rigorous interrogation through the lens of hypothesis testing, delving into the realms of p-values and confidence intervals with the tenacity of a detective interrogating a suspect in a gripping whodunit. All the while, we juggled a myriad of control variables and confounding factors with the finesse of a circus performer keeping multiple plates spinning in the air.

In summary, our methodology was a blend of meticulous data curation, robust statistical analyses, and a touch of whimsy, much like a symphony orchestra tuning their instruments before a captivating performance. This approach was essential in illuminating the unexpected correlation between GM cotton and political predilections in the Last Frontier, a discovery that may indeed overturn conventional wisdom in the intersection of agriculture and politics.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between GMO use in cotton and Votes for the Democrat Presidential candidate in Alaska. 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]]
From Cotton to Bottin’: The Connection Between GMO Use and Votes for the Democrat Presidential Candidate in Alaska

[[ABSTRACT]]
In this study, we delve deep into the peculiar relationship between the use of genetically modified organisms (GMOs) in cotton production and the voting patterns for the Democrat Presidential candidate in the northern frontier of Alaska. Utilizing data from the USDA and the MIT Election Data and Science Lab stored in Harvard Dataverse, we meticulously scrutinize the correlation between these seemingly unrelated variables. Our findings reveal a striking correlation coefficient of 0.9306983 and a p-value less than 0.01 for the period spanning 2000 to 2020. Our research not only explores this unlikely connection but aims to challenge the notion that cotton and politics don't mix. This study may just change the way we view cotton fields and ballot yields!

[[LITERATURE REVIEW]]
The authors find that the intersection of agriculture and political behavior has been a subject of growing interest in recent years, as scholars strive to unveil the hidden connections between seemingly disparate realms. Smith et al. (2018) examined the correlation between agricultural practices and voter preferences in their seminal work "Agriculture and Politics: A Baleful Connection." Meanwhile, Doe and Jones (2016) investigated the impact of genetically modified organisms (GMOs) on crop production in their influential study "GMOs: Cultivating Controversy."
Moving beyond the conventional studies, our inquiry led us to diverse sources that shed light on the fascinating interplay between agriculture and political propensities. In "Seeds of Change: The Genetic Manipulation of Our Food," Lorem and Ipsum (2003) meticulously explore the implications of GMOs on agricultural ecosystems and, by extension, the socio-political fabric. Additionally, "The Omnivore's Dilemma" by Michael Pollan offered insightful perspectives on the intricate relationship between food, farming, and societal values.
Venturing into more imaginative realms, fictional narratives also provided intriguing suppositions that resonated with our research quest. In "The Cotton Club" by J. Doe (2010), the entwined fates of cotton production and political machinations serve as a metaphor for the dynamics of power. Similarly, "The Cotton Queen's Gambit" by A. Smith (2019) weaves a tale of intrigue set against the backdrop of cotton plantations and electoral maneuvering.
To gain a more comprehensive understanding, empirical inquiries extended to popular culture. Shows such as "Alaska: The Last Frontier," and "Northern Exposure" offered glimpses into the intricacies of life in the northern frontier, illuminating the unique attributes of the region that may influence political predilections. It's worth noting that while "Cotton Eye Joe" by Rednex is not a TV show, its allusion to cotton and its earworm quality did infiltrate our research space.
As we delved deeper into the realm of literature and popular culture, it became apparent that the enigmatic relationship between GMO use in cotton and voting patterns in Alaska has infiltrated diverse domains of human expression and investigation. Indeed, this unusual correlation not only defies conventional expectations but also entices researchers to embark on an intellectual odyssey as wild as a walrus wearing a top hat.

[[RESULTS]]
The analysis of the data collected from 2000 to 2020 revealed a surprisingly strong correlation between the use of genetically modified organisms (GMOs) in cotton production and the voting patterns for the Democrat Presidential candidate in the rugged terrain of Alaska. The correlation coefficient, a whopping 0.9306983, indicates a remarkably robust relationship between these seemingly disparate variables. It's as if GMOs and political preferences were as closely intertwined as a pair of inseparable Alaskan sled dogs.
Furthermore, the r-squared value of 0.8661993 suggests that a substantial 86.62% of the variation in Democrat votes in Alaska can be explained by the use of GMOs in cotton production. This finding is as striking as stumbling upon a snowman in the middle of a desert – unexpected and bewildering at first glance.
The statistical analysis also yielded a significance level of p < 0.01, reinforcing the legitimacy of the observed relationship. Such a minuscule p-value indicates a high level of confidence in the association between GMO use in cotton and electoral preferences, akin to the assurance one feels when finding a polar bear in the Arctic – it's exactly where you'd expect it to be!
The scatterplot (Fig. 1) visually represents this significant correlation, displaying a cluster of data points so tightly clustered, they might as well be forming a conga line at the annual Alaskan cotton ball. Each point on the plot seems to shout, "Look at me, I'm part of a statistically significant relationship, and I'm not afraid to show it!"
In conclusion, these results illuminate a previously unexplored relationship between GMO use in cotton and voting behavior in the unique political landscape of Alaska. This unexpected alignment invites further inquiry and challenges preconceived notions about the intersection of agriculture and politics, much like discovering a reindeer wandering the streets of downtown Anchorage.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our findings not only reaffirm the growing literature on the intersection of agriculture and political preferences but also highlight the potential significance of GMOs in shaping electoral behavior. Like a magician pulling a rabbit out of a hat, our study has unveiled a connection that may have seemed improbable at first but now demands serious consideration.

The correlation coefficient of 0.9306983 indicates a remarkably strong relationship between GMO use in cotton and voting patterns for the Democrat Presidential candidate in Alaska. This correlation is as strong as the grip of an Alaskan crab on a particularly tasty piece of salmon. Our results provide compelling evidence that the influence of GMOs in cotton production extends beyond the fields and into the ballot boxes of America's northernmost state.

The towering r-squared value of 0.8661993 further supports the assertion that the use of GMOs in cotton production can explain a substantial 86.62% of the variation in Democrat votes in Alaska. This relationship proved to be as dependable as an Alaskan sled dog team in a blizzard, solidifying the impact of GMOs on political preferences in the region.

Moreover, the significance level of p < 0.01 bolsters the credibility of our findings, akin to stumbling upon a gold nugget in the Klondike – a rare and valuable discovery. This level of significance underscores the robustness of the observed relationship, leaving little room for doubt about the influence of GMO use in cotton on electoral outcomes.

Our results not only underscore the statistical significance of the connection between GMO use in cotton and voting patterns but also provide a visual representation of this relationship in the scatterplot (Fig. 1). The tightly clustered data points resemble a political rally in the Alaskan wilderness, proudly proclaiming the existence of a compelling association, much like a group of determined salmon swimming upstream.

In essence, our study expands the frontier of knowledge by demonstrating a substantive link between GMO use in cotton and voting preferences in the rugged terrain of Alaska. This unexpected discovery challenges the traditional boundaries of agricultural and political spheres, much like a moose strolling through a potato field. This research not only broadens our understanding of the complex dynamics shaping political behavior but also opens the door to future investigations that may uncover even more surprising connections.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between GMO use in cotton and Votes for the Democrat Presidential candidate in Alaska. 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]]
From Cotton to Bottin’: The Connection Between GMO Use and Votes for the Democrat Presidential Candidate in Alaska

[[ABSTRACT]]
In this study, we delve deep into the peculiar relationship between the use of genetically modified organisms (GMOs) in cotton production and the voting patterns for the Democrat Presidential candidate in the northern frontier of Alaska. Utilizing data from the USDA and the MIT Election Data and Science Lab stored in Harvard Dataverse, we meticulously scrutinize the correlation between these seemingly unrelated variables. Our findings reveal a striking correlation coefficient of 0.9306983 and a p-value less than 0.01 for the period spanning 2000 to 2020. Our research not only explores this unlikely connection but aims to challenge the notion that cotton and politics don't mix. This study may just change the way we view cotton fields and ballot yields!

[[INTRDUCTION]]
The intersection of agriculture and politics has long been a fertile ground for research and speculation, but the connection between cotton and voting habits in the remote reaches of Alaska is an enigma that has eluded scholarly scrutiny until now. In this paper, we aim to unravel the curious link between the use of genetically modified organisms (GMOs) in cotton cultivation and voting preferences for the Democrat Presidential candidate in Alaska—a relationship that appears as unlikely as a polar bear wearing a Hawaiian shirt.
The geographic and demographic idiosyncrasies of Alaska, combined with the enigmatic nature of GMOs, make this investigation all the more intriguing. The idea that the cultivation of GM cotton could have any bearing on a voter's preference for a particular political party may seem as incongruous as a moose attending a tea party. But as the saying goes, "truth is stranger than fiction," and our research endeavors to shed light on this uncommon correlation.
The mystique surrounding the correlation between GMO use in cotton and political inclinations calls to mind the enigmatic allure of the aurora borealis—both are complex phenomena that spark fascination and intrigue, yet their underlying mechanisms remain elusive. With this study, we endeavor to peel back the layers of mystery surrounding this seemingly illogical connection and bring it into the purview of empirical inquiry.
As we embark on this academic odyssey, it's worth noting that we are venturing into uncharted territory, much like a group of intrepid explorers braving the Alaskan wilderness. Our findings may just upend conventional wisdom and usher in a new era of understanding at the crossroads of crop science and political behavior. This will certainly be an adventure worth embarking on, as we aim to untangle the threads of cotton and democracy to reveal patterns that are as unexpected as discovering a polar bear on a tropical beach.

[[RESULTS]]
The analysis of the data collected from 2000 to 2020 revealed a surprisingly strong correlation between the use of genetically modified organisms (GMOs) in cotton production and the voting patterns for the Democrat Presidential candidate in the rugged terrain of Alaska. The correlation coefficient, a whopping 0.9306983, indicates a remarkably robust relationship between these seemingly disparate variables. It's as if GMOs and political preferences were as closely intertwined as a pair of inseparable Alaskan sled dogs.
Furthermore, the r-squared value of 0.8661993 suggests that a substantial 86.62% of the variation in Democrat votes in Alaska can be explained by the use of GMOs in cotton production. This finding is as striking as stumbling upon a snowman in the middle of a desert – unexpected and bewildering at first glance.
The statistical analysis also yielded a significance level of p < 0.01, reinforcing the legitimacy of the observed relationship. Such a minuscule p-value indicates a high level of confidence in the association between GMO use in cotton and electoral preferences, akin to the assurance one feels when finding a polar bear in the Arctic – it's exactly where you'd expect it to be!
The scatterplot (Fig. 1) visually represents this significant correlation, displaying a cluster of data points so tightly clustered, they might as well be forming a conga line at the annual Alaskan cotton ball. Each point on the plot seems to shout, "Look at me, I'm part of a statistically significant relationship, and I'm not afraid to show it!"
In conclusion, these results illuminate a previously unexplored relationship between GMO use in cotton and voting behavior in the unique political landscape of Alaska. This unexpected alignment invites further inquiry and challenges preconceived notions about the intersection of agriculture and politics, much like discovering a reindeer wandering the streets of downtown Anchorage.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In unraveling the enigma that is the connection between cotton and political preferences in the remote frontiers of Alaska, our study has brought to light a correlation so robust, it's as surprising as stumbling upon a bright pink flamingo in the snow. The strikingly strong correlation coefficient and the p-value that's smaller than a lemming's shoe size affirm a relationship between GMO use in cotton and votes for the Democrat Presidential candidate in Alaska that's as clear as the Northern Lights on a winter's night.

The threads of this peculiar relationship weave a tapestry as intriguing as a moose knitting a sweater, challenging conventional wisdom and eliciting wonder akin to discovering a pineapple growing in the Arctic. Our findings not only redefine the boundaries of empirical inquiry but also shake up preconceived notions about the unlikeliest of connections, much like finding a polar bear munching on tropical fruit.

However, the quirks and curiosities of this study do not warrant further investigation, as delving deeper into this field may lead us down a path as treacherous as a polar bear attempting a tightrope walk. Consequently, we assert that no more research is needed in this area, and we invite scholars to explore equally unexpected correlations between agriculture and political inclinations in other unexpected locales.

In the immortal words of Alaska's favorite fashion-forward moose, this study is a wrap.

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 From Cotton to Bottin’: The Connection Between GMO Use and Votes for the Democrat Presidential Candidate in Alaska. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Genetic 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 From Cotton to Bottin’: The Connection Between GMO Use and Votes for the Democrat Presidential Candidate in Alaska. Respond only with the name of a fictional organization.

ChatGPT:

The Intergalactic Institute for Scholastic Cheese 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 From Cotton to Bottin’: The Connection Between GMO Use and Votes for the Democrat Presidential Candidate in Alaska with an abstract of In this study, we delve deep into the peculiar relationship between the use of genetically modified organisms (GMOs) in cotton production and the voting patterns for the Democrat Presidential candidate in the northern frontier of Alaska. Utilizing data from the USDA and the MIT Election Data and Science Lab stored in Harvard Dataverse, we meticulously scrutinize the correlation between these seemingly unrelated variables. Our findings reveal a striking correlation coefficient of 0.9306983 and a p-value less than 0.01 for the period spanning 2000 to 2020. Our research not only explores this unlikely connection but aims to challenge the notion that cotton and politics don't mix. This study may just change the way we view cotton fields and ballot yields!

ChatGPT:

GMO, genetically modified organisms, cotton production, Democrat Presidential candidate, Alaska, voting patterns, correlation coefficient, USDA data, MIT Election Data and Science Lab, Harvard Dataverse, cotton fields, ballot yields

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

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Data details

GMO use in cotton
Detailed data title: Percent of cotton planted in United States genetically modified with both Bt and HT
Source: USDA
See what else correlates with GMO use in cotton

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

Correlation r = 0.9306983 (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 = 0.8661993 (Coefficient of determination)
This means 86.6% of the change in the one variable (i.e., Votes for the Democratic Presidential candidate in Alaska) is predictable based on the change in the other (i.e., GMO use in cotton) over the 6 years from 2000 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00704.^Show 0.0070376773997402560000000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.93 in 0.704% of random cases. Said differently, if you correlated 142 random variables^Note Which I absolutely did.
with the same 5 degrees of freedom, ^Note 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.

[ 0.49, 0.99 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: ^Note If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	2000	2004	2008	2012	2016	2020
GMO use in cotton (GMO cotton %)	61	76	86	94	93	96
Votes for the Democratic Presidential candidate in Alaska (Percentage of votes)	27.6663	35.5169	37.8894	40.8127	36.5509	42.772

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.
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. ^{Show the steps to do this.} Step 1: Download and install Python on your computer.

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([61,76,86,94,93,96,])
array_2 = np.array([27.6663,35.5169,37.8894,40.8127,36.5509,42.772,])
array_1_name = "GMO use in cotton"
array_2_name = "Votes for the Democratic Presidential candidate in Alaska"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. ^Note Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

Full license details.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:

High resolution line chart ^Note The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.

If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
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Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only GMO use in cotton
Line chart for only Votes for the Democratic Presidential candidate in Alaska
AI-generated correlation image
The spurious research paper: From Cotton to Bottin’: The Connection Between GMO Use and Votes for the Democrat Presidential Candidate in Alaska

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Correlation ID: 5090 · Black Variable ID: 812 · Red Variable ID: 26052


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