Spurious correlation #1,455 · 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 soybeans in Ohio and the second variable is Fossil fuel use in Saint Vincent/Grenadines. The chart goes from 2000 to 2021, 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 soybeans in Ohio and the second variable is Fossil fuel use in Saint Vincent/Grenadines. The chart goes from 2000 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The GMO soybeans gained the ability to release tiny amounts of rocket fuel, prompting a sudden surge in Fossil fuel use in Saint Vincent/Grenadines as residents unwittingly used the soybeans as biofuel. As the soybeans proliferated, so did the unexpected eco-friendly, soy-powered transportation methods in the tropical paradise, leading to a not-so-green revolution in Fossil fuel usage. It's a modern twist on the classic tale of Jack and the Soybeanstalk, where the beanstalks lead not to a giant's castle, but to fuel pumps and gas-guzzling vehicles in a Caribbean island nation. It's a case of soybeans sowing the seeds of a fuel frenzy thousands of miles away, creating a connection as bizarre and twisted as a soy-based pretzel.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image that represents a TIME magazine cover featuring the following scene: In the heart of a vast Ohio soybean field, a farmer operates a modern tractor, emitting a trail of exhaust as it plants genetically modified soybean seeds. As the sun sets, a warm orange glow blankets the fields, illuminating the dynamic blend of farming technology, nature, and agriculture. The scene should capture the juxtaposition of innovative farming methods against the serene beauty of the natural landscape. 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 soybeans in Ohio and Fossil fuel use in Saint Vincent/Grenadines. 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 TIME magazine cover image Art nouveau Realistic An image that looks like it is a scene from 'Star Wars' Surrealism
ChatGPT: A TIME 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 soybeans in Ohio and Fossil fuel use in Saint Vincent/Grenadines together. Keep the description simple.
ChatGPT: In a sprawling soybean field in Ohio, a farmer is using a tractor powered by fossil fuels to plant genetically modified soybean seeds. The exhaust from the tractor mingles with the scent of the fresh earth, as the sun sets over the fields and casts a warm glow on the landscape.

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 TIME magazine cover image ] and depicting this scene: In a sprawling soybean field in Ohio, a farmer is using a tractor powered by fossil fuels to plant genetically modified soybean seeds. The exhaust from the tractor mingles with the scent of the fresh earth, as the sun sets over the fields and casts a warm glow on the landscape. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that represents a TIME magazine cover featuring the following scene: In the heart of a vast Ohio soybean field, a farmer operates a modern tractor, emitting a trail of exhaust as it plants genetically modified soybean seeds. As the sun sets, a warm orange glow blankets the fields, illuminating the dynamic blend of farming technology, nature, and agriculture. The scene should capture the juxtaposition of innovative farming methods against the serene beauty of the natural landscape.

*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 soybeans in Ohio caused Fossil fuel use in Saint Vincent/Grenadines to increase.

AI academic paper

(Because p < 0.01)

The Soybean and Fossil Fuel Tango: Unraveling the Relationship Between GMO Use in Ohio and Fossil Fuel Consumption in Saint Vincent/Grenadines

Journal of Bioenergy Economics and Sustainability

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 soybeans in Ohio and Fossil fuel use in Saint Vincent/Grenadines. 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 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]]
The Soybean and Fossil Fuel Tango: Unraveling the Relationship Between GMO Use in Ohio and Fossil Fuel Consumption in Saint Vincent/Grenadines

[[ABSTRACT]]
In this study, we delve into the perplexing connection between the use of genetically modified organisms (GMOs) in soybeans in Ohio and the consumption of fossil fuels in Saint Vincent and the Grenadines. Our research team utilized data from the USDA and the Energy Information Administration to conduct a comprehensive analysis spanning the years 2000 to 2021. Surprisingly, our findings revealed a striking correlation coefficient of 0.9348841 and p < 0.01, indicating a robust relationship between these seemingly disparate factors. We invite readers to join us on this intriguing journey as we unearth the unexpected connections between agricultural practices and energy consumption, leading to a greater appreciation of the intricate dance between soybeans and fossil fuels in these geographically distinct regions.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The world of agricultural and energy systems is often perceived as two separate galaxies, each with its own distinct orbit and gravitational pull. However, as we embark on this research journey, we are reminded of the famous words of physicist Neil deGrasse Tyson, who once said, "The universe is under no obligation to make sense to you." Indeed, the enigmatic relationship between the use of genetically modified organisms (GMOs) in soybeans in Ohio and the consumption of fossil fuels in Saint Vincent and the Grenadines serves as a testament to the unpredictability and interconnectedness of our global food and energy systems.
As we venture into uncharted territory, we are driven by both a scientific curiosity and a relentless pursuit of understanding the intricate dance between soybeans and fossil fuels. The seemingly incongruous nature of these two phenomena has sparked intellectual intrigue among researchers and enthusiasts alike, akin to the anticipation one feels when witnessing the unlikely pairing of a salsa dancer and a square dancer at a cultural exchange event – it's a peculiar sight, yet it's undeniably fascinating.
Our pursuit may seem as unlikely as finding a soybean in a fossil fuel mine or stumbling upon a biodiesel-powered tractor in a soybean field, but rest assured, our investigation is firmly grounded in statistical rigor and methodological precision. Through the lens of data analysis and empirical inquiry, we aim to shed light on the cryptic nexus of GMO soybeans in the heartland of America and the fossil fuel consumption habits of an archipelagic nation in the Caribbean, unraveling a web of agricultural and energy dynamics that has likely remained hidden in plain sight, much like a chameleon camouflaging itself among the lush foliage.
So, come along and embrace the peculiar allure of this captivating tango between soybeans and fossil fuels, where statistical correlations and scientific revelations await, and where the unexpected connections between agricultural practices and energy consumption are poised to surprise and intrigue – much like stumbling upon a carefully concealed easter egg in a labyrinth of incongruous riddles.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The bewildering correlation between the use of genetically modified organisms (GMOs) in soybeans in Ohio and the consumption of fossil fuels in Saint Vincent and the Grenadines has captivated the attention of researchers from diverse disciplines, prompting an exploration of existing literature to shed light on this unlikely relationship.

Smith and Doe (2015) examined the environmental impact of GMO soybean cultivation in the Midwest, highlighting the potential implications for energy consumption in downstream processes. Their findings hinted at the intricate web of connections between agricultural practices and energy utilization, akin to unraveling a complex origami sculpture whose folds reveal unexpected patterns.

In a similarly comprehensive study, Jones et al. (2017) delved into the socioeconomic repercussions of GMO adoption among soybean farmers, uncovering nuanced shifts in resource allocation and technological dependencies. Their work presents a thought-provoking backdrop against which the enigmatic dance between soybeans and fossil fuels may be observed, akin to witnessing a symphony orchestra perform a melodious composition of statistical analyses and empirical insights.

Expanding beyond academic publications, the literature review encompasses non-fiction works such as "The Omnivore's Dilemma" by Michael Pollan and "Guns, Germs, and Steel" by Jared Diamond, which provide broader perspectives on agricultural systems and societal development. These literary undertakings offer a holistic vantage point from which to contemplate the interconnectedness of human activities and natural resources, much like savoring a rich culinary dish that tantalizes multiple senses simultaneously.

In a departure from traditional sources, the inquiry extended to fictional narratives that, though not explicitly focused on agricultural or energy dynamics, possess thematic resonance with the underlying intricacies of our investigation. Works such as "The Alchemist" by Paulo Coelho and "Cloud Atlas" by David Mitchell serve as metaphorical vessels for exploring the uncharted terrain of unforeseen connections and serendipitous encounters, akin to embarking on a whimsical journey through a literary labyrinth where plot twists mirror the unpredictability of statistical relationships.

This exhaustive literature review benefits from an unconventional source; the researchers' thorough examination of grocery store receipts, including an eclectic assemblage of soybean-infused products and fossil fuel-derived commodities, akin to a whimsical scavenger hunt through the aisles of consumerism. The juxtaposition of mundane purchases unveils subtle yet profound links between seemingly unrelated items, evoking a sense of amusement akin to stumbling upon an unexpected punchline in a series of cryptic riddles.

As we pivot towards synthesizing these diverse threads of literature, it becomes evident that the intertwined narrative of GMO soybeans and fossil fuel consumption transcends disciplinary boundaries, beckoning researchers to adopt an interdisciplinary lens through which to unravel the underlying tapestry of interconnectedness.

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 soybeans in Ohio and Fossil fuel use in Saint Vincent/Grenadines. 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 Energy Information Administration . You used data from 2000 to 2021

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

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
The Soybean and Fossil Fuel Tango: Unraveling the Relationship Between GMO Use in Ohio and Fossil Fuel Consumption in Saint Vincent/Grenadines

[[ABSTRACT]]
In this study, we delve into the perplexing connection between the use of genetically modified organisms (GMOs) in soybeans in Ohio and the consumption of fossil fuels in Saint Vincent and the Grenadines. Our research team utilized data from the USDA and the Energy Information Administration to conduct a comprehensive analysis spanning the years 2000 to 2021. Surprisingly, our findings revealed a striking correlation coefficient of 0.9348841 and p < 0.01, indicating a robust relationship between these seemingly disparate factors. We invite readers to join us on this intriguing journey as we unearth the unexpected connections between agricultural practices and energy consumption, leading to a greater appreciation of the intricate dance between soybeans and fossil fuels in these geographically distinct regions.

[[INTRODUCTION]]
The world of agricultural and energy systems is often perceived as two separate galaxies, each with its own distinct orbit and gravitational pull. However, as we embark on this research journey, we are reminded of the famous words of physicist Neil deGrasse Tyson, who once said, "The universe is under no obligation to make sense to you." Indeed, the enigmatic relationship between the use of genetically modified organisms (GMOs) in soybeans in Ohio and the consumption of fossil fuels in Saint Vincent and the Grenadines serves as a testament to the unpredictability and interconnectedness of our global food and energy systems.
As we venture into uncharted territory, we are driven by both a scientific curiosity and a relentless pursuit of understanding the intricate dance between soybeans and fossil fuels. The seemingly incongruous nature of these two phenomena has sparked intellectual intrigue among researchers and enthusiasts alike, akin to the anticipation one feels when witnessing the unlikely pairing of a salsa dancer and a square dancer at a cultural exchange event – it's a peculiar sight, yet it's undeniably fascinating.
Our pursuit may seem as unlikely as finding a soybean in a fossil fuel mine or stumbling upon a biodiesel-powered tractor in a soybean field, but rest assured, our investigation is firmly grounded in statistical rigor and methodological precision. Through the lens of data analysis and empirical inquiry, we aim to shed light on the cryptic nexus of GMO soybeans in the heartland of America and the fossil fuel consumption habits of an archipelagic nation in the Caribbean, unraveling a web of agricultural and energy dynamics that has likely remained hidden in plain sight, much like a chameleon camouflaging itself among the lush foliage.
So, come along and embrace the peculiar allure of this captivating tango between soybeans and fossil fuels, where statistical correlations and scientific revelations await, and where the unexpected connections between agricultural practices and energy consumption are poised to surprise and intrigue – much like stumbling upon a carefully concealed easter egg in a labyrinth of incongruous riddles.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

I. Data Collection
To unravel the enigmatic connection between the use of GMO soybeans in Ohio and fossil fuel consumption in Saint Vincent and the Grenadines, our research team embarked on a virtual odyssey across the vast expanse of the internet, navigating through the digital labyrinth like intrepid explorers seeking hidden treasures. We scoured databases, combed through reports, and sifted through an abundance of agricultural and energy data, akin to intrepid Robin Hoods pillaging the virtual Sherwood Forest for statistical riches. While our virtual voyage led us to numerous digital ports-of-call, our primary sources of data were the United States Department of Agriculture (USDA) and the Energy Information Administration, where we unearthed a trove of information spanning the years 2000 to 2021.

II. Statistical Analysis
With our data hoard in tow, we set sail for the shores of statistical analysis, armed with spreadsheets and an unwavering determination to seek correlations amidst the tumultuous seas of data. Our team employed a rigorous array of statistical methods, wielding correlation coefficients, regression analysis, and p-values as our compass and sextant in navigating the treacherous waters of numerical analysis. Through the arcane arts of statistical sorcery, we sought to unveil the elusive relationship between the cultivation of GMO soybeans in the heartland of the United States and the consumption of fossil fuels in the sun-kissed archipelago of Saint Vincent and the Grenadines.

III. Multivariate Regression Model
In order to disentangle the intricate web of agricultural and energy dynamics, we harnessed the power of multivariate regression modeling, crafting a mathematical tapestry that interwove variables such as annual soybean production, GMO adoption rates, fossil fuel consumption, energy intensity, and a litany of other factors that shaped the agricultural and energy landscapes of our study regions. Our multivariate regression model stood as a grand mosaic of numerical intricacy, akin to a monumental quilt crafted from an eclectic melange of statistical fabric, yielding insights into the complex interplay between GMO soybeans and fossil fuel habits that had previously lain concealed, much like ancient hieroglyphics waiting to be deciphered.

IV. Sensitivity Analysis
As diligent guardians of scientific objectivity, we undertook a sensitivity analysis of our findings, subjecting our data to varying scenarios and statistical assumptions to gauge the robustness of our results. Not unlike adventurers meticulously probing for hidden traps in a mythical temple, we rigorously probed the boundaries of our statistical inferences, ensuring that our revelations regarding the relationship between GMO soybeans and fossil fuel consumption remained steadfast amidst the tempestuous winds of statistical uncertainty.

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 soybeans in Ohio and Fossil fuel use in Saint Vincent/Grenadines. 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]]
The Soybean and Fossil Fuel Tango: Unraveling the Relationship Between GMO Use in Ohio and Fossil Fuel Consumption in Saint Vincent/Grenadines

[[ABSTRACT]]
In this study, we delve into the perplexing connection between the use of genetically modified organisms (GMOs) in soybeans in Ohio and the consumption of fossil fuels in Saint Vincent and the Grenadines. Our research team utilized data from the USDA and the Energy Information Administration to conduct a comprehensive analysis spanning the years 2000 to 2021. Surprisingly, our findings revealed a striking correlation coefficient of 0.9348841 and p < 0.01, indicating a robust relationship between these seemingly disparate factors. We invite readers to join us on this intriguing journey as we unearth the unexpected connections between agricultural practices and energy consumption, leading to a greater appreciation of the intricate dance between soybeans and fossil fuels in these geographically distinct regions.

[[LITERATURE REVIEW]]
The bewildering correlation between the use of genetically modified organisms (GMOs) in soybeans in Ohio and the consumption of fossil fuels in Saint Vincent and the Grenadines has captivated the attention of researchers from diverse disciplines, prompting an exploration of existing literature to shed light on this unlikely relationship.
Smith and Doe (2015) examined the environmental impact of GMO soybean cultivation in the Midwest, highlighting the potential implications for energy consumption in downstream processes. Their findings hinted at the intricate web of connections between agricultural practices and energy utilization, akin to unraveling a complex origami sculpture whose folds reveal unexpected patterns.
In a similarly comprehensive study, Jones et al. (2017) delved into the socioeconomic repercussions of GMO adoption among soybean farmers, uncovering nuanced shifts in resource allocation and technological dependencies. Their work presents a thought-provoking backdrop against which the enigmatic dance between soybeans and fossil fuels may be observed, akin to witnessing a symphony orchestra perform a melodious composition of statistical analyses and empirical insights.
Expanding beyond academic publications, the literature review encompasses non-fiction works such as "The Omnivore's Dilemma" by Michael Pollan and "Guns, Germs, and Steel" by Jared Diamond, which provide broader perspectives on agricultural systems and societal development. These literary undertakings offer a holistic vantage point from which to contemplate the interconnectedness of human activities and natural resources, much like savoring a rich culinary dish that tantalizes multiple senses simultaneously.
In a departure from traditional sources, the inquiry extended to fictional narratives that, though not explicitly focused on agricultural or energy dynamics, possess thematic resonance with the underlying intricacies of our investigation. Works such as "The Alchemist" by Paulo Coelho and "Cloud Atlas" by David Mitchell serve as metaphorical vessels for exploring the uncharted terrain of unforeseen connections and serendipitous encounters, akin to embarking on a whimsical journey through a literary labyrinth where plot twists mirror the unpredictability of statistical relationships.
This exhaustive literature review benefits from an unconventional source; the researchers' thorough examination of grocery store receipts, including an eclectic assemblage of soybean-infused products and fossil fuel-derived commodities, akin to a whimsical scavenger hunt through the aisles of consumerism. The juxtaposition of mundane purchases unveils subtle yet profound links between seemingly unrelated items, evoking a sense of amusement akin to stumbling upon an unexpected punchline in a series of cryptic riddles.
As we pivot towards synthesizing these diverse threads of literature, it becomes evident that the intertwined narrative of GMO soybeans and fossil fuel consumption transcends disciplinary boundaries, beckoning researchers to adopt an interdisciplinary lens through which to unravel the underlying tapestry of interconnectedness.

[[RESULTS]]
Upon conducting our rigorous analysis, we unearthed a striking correlation coefficient of 0.9348841, an r-squared value of 0.8740083, and a p-value less than 0.01, signifying a robust and statistically significant relationship between the use of genetically modified organisms (GMOs) in soybeans in Ohio and the consumption of fossil fuels in Saint Vincent and the Grenadines. It's as if these two seemingly unrelated entities have been engaging in a clandestine tango, hidden from the prying eyes of traditional agricultural and energy studies.
The scatterplot (Fig. 1) visually depicts the strong correlation between GMO use in soybeans in Ohio and fossil fuel consumption in Saint Vincent and the Grenadines, essentially turning what may have seemed like an academic puzzle into a plot fit for a thrilling detective novel. The plot thickens as we delve deeper into the connection between these two variables, with the data leaving little room for doubt about their intricate dance and potentially shared fate.
One can almost imagine the soybeans in Ohio whispering secrets to the fossil fuels in Saint Vincent and the Grenadines, as if part of a gripping saga filled with unexpected alliances and subplots. These findings challenge conventional wisdom and beckon us to reassess the interplay between agricultural practices and energy consumption, much like stumbling upon a hidden treasure map that leads to unexpected riches.
It is clear that this unconventional correlation between GMO soybeans and fossil fuels demands our attention, inviting us to embrace the unexpected connections that lie beneath the surface of seemingly disparate agricultural and energy systems. The dance floor beckons, and we are merely spectators to the captivating tango of soybeans and fossil fuels—a performance that transcends geographic boundaries and captivates the imagination.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our study not only echo the prior research conducted by Smith and Doe (2015) and Jones et al. (2017) but also provide compelling evidence to support the unexpected correlation between GMO use in soybeans in Ohio and fossil fuel consumption in Saint Vincent and the Grenadines. It's almost as if these two distinct geographical locations are engaged in a covert tango, with soybeans and fossil fuels performing a dance that defies traditional agricultural and energy paradigms.

The substantial correlation coefficient and r-squared value discovered in our analysis suggest a strong and consistent relationship between these seemingly unrelated variables. The metaphorical whispering of secrets from soybeans to fossil fuels becomes less metaphorical and more tantalizingly real as we ponder the intricate connections unveiled by the data. It's as if the soybeans and fossil fuels are conspiring to tell us a story, nudging us to follow the breadcrumbs of their intertwined narrative.

Our findings underscore the need for a holistic understanding of agricultural and energy dynamics, transcending disciplinary boundaries and challenging researchers to adopt an interdisciplinary lens similar to the multifaceted exploration of themes in "The Alchemist" or "Cloud Atlas." This whimsical journey through literature resonates with the unpredictability and serendipity inherent in uncovering statistical relationships, much like stumbling upon an unexpected punchline in a cryptic riddle.

Certainly, the implications of our results extend beyond the confines of tradition. They beckon us to reimagine the narratives that intertwine agricultural practices and energy consumption, much like navigating a series of plot twists in a detective novel. In this tango between soybeans and fossil fuels, we find ourselves both spectators and participants, captivated by the enchanting performance unfolding before our eyes.

As we continue to unravel the enigmatic dance of soybeans and fossil fuels, our study prompts a reevaluation of the interconnectedness of seemingly disparate systems. The captivating tango between soybeans in Ohio and fossil fuels in Saint Vincent and the Grenadines draws attention to the intricate web of relationships that underpins our global agricultural and energy landscapes. It's almost as if soybeans and fossil fuels have been keeping a secret, and now, it's our turn to marvel at the unexpected revelations they divulge.

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 soybeans in Ohio and Fossil fuel use in Saint Vincent/Grenadines. 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]]
The Soybean and Fossil Fuel Tango: Unraveling the Relationship Between GMO Use in Ohio and Fossil Fuel Consumption in Saint Vincent/Grenadines

[[ABSTRACT]]
In this study, we delve into the perplexing connection between the use of genetically modified organisms (GMOs) in soybeans in Ohio and the consumption of fossil fuels in Saint Vincent and the Grenadines. Our research team utilized data from the USDA and the Energy Information Administration to conduct a comprehensive analysis spanning the years 2000 to 2021. Surprisingly, our findings revealed a striking correlation coefficient of 0.9348841 and p < 0.01, indicating a robust relationship between these seemingly disparate factors. We invite readers to join us on this intriguing journey as we unearth the unexpected connections between agricultural practices and energy consumption, leading to a greater appreciation of the intricate dance between soybeans and fossil fuels in these geographically distinct regions.

[[INTRDUCTION]]
The world of agricultural and energy systems is often perceived as two separate galaxies, each with its own distinct orbit and gravitational pull. However, as we embark on this research journey, we are reminded of the famous words of physicist Neil deGrasse Tyson, who once said, "The universe is under no obligation to make sense to you." Indeed, the enigmatic relationship between the use of genetically modified organisms (GMOs) in soybeans in Ohio and the consumption of fossil fuels in Saint Vincent and the Grenadines serves as a testament to the unpredictability and interconnectedness of our global food and energy systems.
As we venture into uncharted territory, we are driven by both a scientific curiosity and a relentless pursuit of understanding the intricate dance between soybeans and fossil fuels. The seemingly incongruous nature of these two phenomena has sparked intellectual intrigue among researchers and enthusiasts alike, akin to the anticipation one feels when witnessing the unlikely pairing of a salsa dancer and a square dancer at a cultural exchange event – it's a peculiar sight, yet it's undeniably fascinating.
Our pursuit may seem as unlikely as finding a soybean in a fossil fuel mine or stumbling upon a biodiesel-powered tractor in a soybean field, but rest assured, our investigation is firmly grounded in statistical rigor and methodological precision. Through the lens of data analysis and empirical inquiry, we aim to shed light on the cryptic nexus of GMO soybeans in the heartland of America and the fossil fuel consumption habits of an archipelagic nation in the Caribbean, unraveling a web of agricultural and energy dynamics that has likely remained hidden in plain sight, much like a chameleon camouflaging itself among the lush foliage.
So, come along and embrace the peculiar allure of this captivating tango between soybeans and fossil fuels, where statistical correlations and scientific revelations await, and where the unexpected connections between agricultural practices and energy consumption are poised to surprise and intrigue – much like stumbling upon a carefully concealed easter egg in a labyrinth of incongruous riddles.

[[RESULTS]]
Upon conducting our rigorous analysis, we unearthed a striking correlation coefficient of 0.9348841, an r-squared value of 0.8740083, and a p-value less than 0.01, signifying a robust and statistically significant relationship between the use of genetically modified organisms (GMOs) in soybeans in Ohio and the consumption of fossil fuels in Saint Vincent and the Grenadines. It's as if these two seemingly unrelated entities have been engaging in a clandestine tango, hidden from the prying eyes of traditional agricultural and energy studies.
The scatterplot (Fig. 1) visually depicts the strong correlation between GMO use in soybeans in Ohio and fossil fuel consumption in Saint Vincent and the Grenadines, essentially turning what may have seemed like an academic puzzle into a plot fit for a thrilling detective novel. The plot thickens as we delve deeper into the connection between these two variables, with the data leaving little room for doubt about their intricate dance and potentially shared fate.
One can almost imagine the soybeans in Ohio whispering secrets to the fossil fuels in Saint Vincent and the Grenadines, as if part of a gripping saga filled with unexpected alliances and subplots. These findings challenge conventional wisdom and beckon us to reassess the interplay between agricultural practices and energy consumption, much like stumbling upon a hidden treasure map that leads to unexpected riches.
It is clear that this unconventional correlation between GMO soybeans and fossil fuels demands our attention, inviting us to embrace the unexpected connections that lie beneath the surface of seemingly disparate agricultural and energy systems. The dance floor beckons, and we are merely spectators to the captivating tango of soybeans and fossil fuels—a performance that transcends geographic boundaries and captivates the imagination.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has successfully peeled back the layers of mystery surrounding the enigmatic relationship between GMO use in soybeans in Ohio and fossil fuel consumption in Saint Vincent and the Grenadines. We have unveiled a powerful correlation that speaks to the interconnectedness of our global food and energy systems, akin to discovering a secret love affair between two seemingly unrelated celebrities.

Our findings challenge traditional assumptions and call into question established paradigms, much like finding out that the quiet librarian leads a double life as a master salsa dancer. This unexpected connection prompts us to rethink the seemingly disparate worlds of agricultural practices and energy consumption, forcing us to consider the possibility of a shared destiny between soybeans and fossil fuels.

As we reflect on the implications of our research, it becomes clear that the dance between soybeans and fossil fuels is not merely a scientific curiosity but a compelling narrative that transcends geographic boundaries, much like stumbling upon an enthralling novel that defies genre conventions. We are left with a sense of wonder and fascination, much like discovering a hidden easter egg in a labyrinth of agricultural and energy dynamics.

In light of these revelatory findings, it is our firm belief that further investigation in this area is unnecessary. The soybean and fossil fuel tango has been unraveled, and we are content to bask in the delight of this unexpected connection, much like reveling in an unforeseen punchline in a labyrinth of scientific inquiry. It's time to bid adieu to this captivating tango and allow it to take its final bow on the grand stage of agricultural and energy research.

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited The Soybean and Fossil Fuel Tango: Unraveling the Relationship Between GMO Use in Ohio and Fossil Fuel Consumption in Saint Vincent/Grenadines. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Bioenergy Economics and Sustainability

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited The Soybean and Fossil Fuel Tango: Unraveling the Relationship Between GMO Use in Ohio and Fossil Fuel Consumption in Saint Vincent/Grenadines. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Sustainable Agriculture and Global Energy (CSAGE)

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled The Soybean and Fossil Fuel Tango: Unraveling the Relationship Between GMO Use in Ohio and Fossil Fuel Consumption in Saint Vincent/Grenadines with an abstract of In this study, we delve into the perplexing connection between the use of genetically modified organisms (GMOs) in soybeans in Ohio and the consumption of fossil fuels in Saint Vincent and the Grenadines. Our research team utilized data from the USDA and the Energy Information Administration to conduct a comprehensive analysis spanning the years 2000 to 2021. Surprisingly, our findings revealed a striking correlation coefficient of 0.9348841 and p < 0.01, indicating a robust relationship between these seemingly disparate factors. We invite readers to join us on this intriguing journey as we unearth the unexpected connections between agricultural practices and energy consumption, leading to a greater appreciation of the intricate dance between soybeans and fossil fuels in these geographically distinct regions.

ChatGPT:

GMO soybeans Ohio, GMO use impact, fossil fuel consumption, Saint Vincent and the Grenadines, agriculture and energy consumption, correlation between GMO use and fossil fuel consumption, USDA data analysis, Energy Information Administration, agricultural practices and energy consumption

*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 soybeans in Ohio
Detailed data title: Percent of soybeans planted in Ohio that are genetically modified
Source: USDA
See what else correlates with GMO use in soybeans in Ohio

Fossil fuel use in Saint Vincent/Grenadines
Detailed data title: Total fossil fuel use in Saint Vincent/Grenadines in billion kWh
Source: Energy Information Administration
See what else correlates with Fossil fuel use in Saint Vincent/Grenadines

Correlation r = 0.9348841 (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 = 0.8740083 (Coefficient of determination)
This means 87.4% of the change in the one variable (i.e., Fossil fuel use in Saint Vincent/Grenadines) is predictable based on the change in the other (i.e., GMO use in soybeans in Ohio) over the 22 years from 2000 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.9E-10.^Show 0.0000000001887361813514817600
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 1.9E-8% of random cases. Said differently, if you correlated 5,298,401,148 random variables^Note You don't actually need 5 billion variables to find a correlation like this one. I don't have that many variables in my database. 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.

In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.

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 21 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 21 because we have two variables measured over a period of 22 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.

[ 0.85, 0.97 ] 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	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
GMO use in soybeans in Ohio (GMO soybeans %)	48	64	73	74	76	77	82	87	89	83	86	85	86	89	90	91	91	91	91	95	88	96
Fossil fuel use in Saint Vincent/Grenadines (Billion kWh)	0.065	0.075	0.075	0.082	0.087	0.099838	0.104	0.111276	0.108529	0.109	0.11	0.107	0.107	0.112	0.121701	0.120908	0.128057	0.121197	0.117509	0.12	0.113936	0.121228

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.

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([48,64,73,74,76,77,82,87,89,83,86,85,86,89,90,91,91,91,91,95,88,96,])
array_2 = np.array([0.065,0.075,0.075,0.082,0.087,0.099838,0.104,0.111276,0.108529,0.109,0.11,0.107,0.107,0.112,0.121701,0.120908,0.128057,0.121197,0.117509,0.12,0.113936,0.121228,])
array_1_name = "GMO use in soybeans in Ohio"
array_2_name = "Fossil fuel use in Saint Vincent/Grenadines"

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

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

Reuseable content

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

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

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

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

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

Download images for these variables:

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

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

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only GMO use in soybeans in Ohio
Line chart for only Fossil fuel use in Saint Vincent/Grenadines
AI-generated correlation image
The spurious research paper: The Soybean and Fossil Fuel Tango: Unraveling the Relationship Between GMO Use in Ohio and Fossil Fuel Consumption in Saint Vincent/Grenadines

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Correlation ID: 1455 · Black Variable ID: 840 · Red Variable ID: 23937


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