Spurious correlation #3,948 · 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 Wind power generated in New Caledonia. 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 cotton and the second variable is Wind power generated in New Caledonia. The chart goes from 2000 to 2021, and the two variables track closely in value over that time.

AI explanation

As GMO cotton plants grew taller and stronger, they began to generate a unique magnetic field. This magnetic field, in turn, aligned with the Earth's natural magnetic field, creating a phenomenon known as "bio-magnetic resonance." This bio-magnetic resonance then enhanced the air currents in New Caledonia, leading to an overall increase in wind power generation. Who knew that GMO cotton could literally harness the power of the wind!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a sunlit field in New Caledonia, with rows of tall cotton plants swaying gently in the breeze. The cotton plants are vibrant green with sleek, streamlined leaves that resemble wind turbines. As the wind picks up, the cotton plants produce a soft hum, and the surrounding wind turbines start spinning faster, generating a powerful gust of energy. The sight of GMO cotton plants contributing to the wind power generation in the beautiful island landscape is a testament to the innovation and progress in sustainable energy production. The image should exude a steampunk aesthetic, integrating elements of industrial machinery and futuristic technology amidst the natural setting. 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 Wind power generated in New Caledonia. 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: 3D Pixar Style Cubism Minimalism Cutsy art illustration Steampunk
ChatGPT: Steampunk

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 Wind power generated in New Caledonia, in the following situation: Because the increased GMO use in cotton led to the creation of genetically modified cotton plants that produce electricity when the wind blows, thus boosting wind power generation in New Caledonia.. Keep the description simple.
ChatGPT: In a sunlit field in New Caledonia, rows of tall cotton plants sway gently in the breeze. These cotton plants are vibrant green, with sleek, streamlined leaves that resemble wind turbines. As the wind picks up, the cotton plants begin to produce a soft hum, and the surrounding wind turbines start spinning faster, generating a powerful gust of energy. The sight of GMO cotton plants contributing to the wind power generation in the beautiful island landscape is a testament to the innovation and progress in sustainable energy production.

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: [ Steampunk ] and depicting this scene: In a sunlit field in New Caledonia, rows of tall cotton plants sway gently in the breeze. These cotton plants are vibrant green, with sleek, streamlined leaves that resemble wind turbines. As the wind picks up, the cotton plants begin to produce a soft hum, and the surrounding wind turbines start spinning faster, generating a powerful gust of energy. The sight of GMO cotton plants contributing to the wind power generation in the beautiful island landscape is a testament to the innovation and progress in sustainable energy production. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a sunlit field in New Caledonia, with rows of tall cotton plants swaying gently in the breeze. The cotton plants are vibrant green with sleek, streamlined leaves that resemble wind turbines. As the wind picks up, the cotton plants produce a soft hum, and the surrounding wind turbines start spinning faster, generating a powerful gust of energy. The sight of GMO cotton plants contributing to the wind power generation in the beautiful island landscape is a testament to the innovation and progress in sustainable energy production. The image should exude a steampunk aesthetic, integrating elements of industrial machinery and futuristic technology amidst the natural setting.

*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 Wind power generated in New Caledonia to increase. Make it feel plausible.

AI academic paper

(Because p < 0.01)

Cottoning on to Renewable Energy: The Genetically Modified Power of Wind in New Caledonia

The Journal of Transgenic Environmental Engineering

Jan 2024

^{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 dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between GMO use in cotton and Wind power generated in New Caledonia. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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 the backs of shampoo bottles.

Here is the title and abstract of the paper:
[[TITLE]]
Cottoning on to Renewable Energy: The Genetically Modified Power of Wind in New Caledonia

[[ABSTRACT]]
The purpose of this study was to investigate the potential connection between the use of genetically modified organisms (GMOs) in cotton farming and the generation of wind power in New Caledonia. Utilizing data from the USDA and the Energy Information Administration, our research team conducted a comprehensive analysis spanning from 2000 to 2021. Surprisingly, our findings revealed a striking correlation coefficient of 0.9385764 with a p-value of less than 0.01, indicating a robust relationship between the two variables.

While the notion of cotton crops influencing wind power may seem as unlikely as finding a needle in a haystack, our results suggest otherwise. Our research unearths a potential link between the adoption of GMOs in cotton cultivation and the amplification of wind power generation in the island nation of New Caledonia. This unexpected connection may just blow you away—both figuratively and literally.

In conclusion, our study sheds light on a hitherto unexplored synergy between agricultural practices and renewable energy sources. These findings lend credence to the adage, "When it comes to harnessing wind power, the stakes are high, but the wind is stronger." Our research highlights the importance of exploring unconventional avenues in renewable energy integration, while also proving that sometimes, the answers are right in front of us, rustling through the cotton fields.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Utilizing renewable energy sources has become increasingly important in the global effort to reduce carbon emissions and mitigate the impacts of climate change. Wind power, in particular, has garnered attention for its potential to provide clean, sustainable energy. Meanwhile, the use of genetically modified organisms (GMOs) in agricultural practices has been a subject of debate and investigation, with proponents touting its potential benefits and detractors raising concerns about its impact on ecosystems. The intersection of these two seemingly disparate fields—wind power generation and GMO use in cotton farming—prompts a closer examination of the potential link between them.
It may seem like a stretch to connect the growth of cotton plants with the generation of wind power, but as the saying goes, "Where there's a whirl, there's a way." The prevailing winds of curiosity have propelled us to investigate this unconventional relationship, and the results may just leave you spinning with excitement.
In our endeavor to explore the uncharted territories of green energy and agricultural practices, we stumbled upon a correlation that is as unexpected as a sheep in a cotton field. Our research has unearthed a potential synergy between the adoption of GMOs in cotton cultivation and the amplification of wind power generation in the tropical paradise of New Caledonia. This revelation could blow away any doubts about the interconnectedness of seemingly distinct domains.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In “The Impact of Genetically Modified Organisms on Agricultural Practices” by Smith, the authors find, surprisinly, a potential link between the adoption of GMOs in cotton cultivation and the amplification of wind power generation in New Caledonia.
"But what do you call a cotton field on a windy day? A wind farm!"

Doe in "The Evolution of Wind Power Technologies" reveals a high correlation coefficient of 0.9385764 between the two variables, with a p-value of less than 0.01, highlighting a robust relationship.

"Did you hear about the cotton farmer who started a wind energy business? He's now known as a real 'blowhard'."

Jones discusses in "Wind Power Dynamics in Island Regions" the unexpected connection between the growth of cotton plants and the generation of wind power, shedding light on this hitherto unexplored synergy.

On a more literary note, "The Wind Through the Cotton Fields" by Lorem Ipsum delves into the intertwining of wind power and agricultural practices, providing a poetic perspective on the subject.
"Why don't cotton farmers ever get lost? Because they always have wind direction!"

Additionally, "Gone with the Wind" by Margaret Mitchell, though a work of fiction, offers a timeless exploration of the impact of wind on the agricultural landscape, metaphorically capturing the essence of this unique relationship.

On a less traditional note, the researchers also investigated unexpected sources of knowledge, including the backs of shampoo bottles, which surprisingly offered insights, albeit tangentially, into the potential connection between GMO use in cotton and wind power generation in New Caledonia.
"What do shampoo and wind power have in common? They both leave you feeling 'blown away'!"

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between GMO use in cotton and Wind power generated in New Caledonia. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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]]
Cottoning on to Renewable Energy: The Genetically Modified Power of Wind in New Caledonia

[[ABSTRACT]]
The purpose of this study was to investigate the potential connection between the use of genetically modified organisms (GMOs) in cotton farming and the generation of wind power in New Caledonia. Utilizing data from the USDA and the Energy Information Administration, our research team conducted a comprehensive analysis spanning from 2000 to 2021. Surprisingly, our findings revealed a striking correlation coefficient of 0.9385764 with a p-value of less than 0.01, indicating a robust relationship between the two variables.

While the notion of cotton crops influencing wind power may seem as unlikely as finding a needle in a haystack, our results suggest otherwise. Our research unearths a potential link between the adoption of GMOs in cotton cultivation and the amplification of wind power generation in the island nation of New Caledonia. This unexpected connection may just blow you away—both figuratively and literally.

In conclusion, our study sheds light on a hitherto unexplored synergy between agricultural practices and renewable energy sources. These findings lend credence to the adage, "When it comes to harnessing wind power, the stakes are high, but the wind is stronger." Our research highlights the importance of exploring unconventional avenues in renewable energy integration, while also proving that sometimes, the answers are right in front of us, rustling through the cotton fields.

[[INTRODUCTION]]
Utilizing renewable energy sources has become increasingly important in the global effort to reduce carbon emissions and mitigate the impacts of climate change. Wind power, in particular, has garnered attention for its potential to provide clean, sustainable energy. Meanwhile, the use of genetically modified organisms (GMOs) in agricultural practices has been a subject of debate and investigation, with proponents touting its potential benefits and detractors raising concerns about its impact on ecosystems. The intersection of these two seemingly disparate fields—wind power generation and GMO use in cotton farming—prompts a closer examination of the potential link between them.
It may seem like a stretch to connect the growth of cotton plants with the generation of wind power, but as the saying goes, "Where there's a whirl, there's a way." The prevailing winds of curiosity have propelled us to investigate this unconventional relationship, and the results may just leave you spinning with excitement.
In our endeavor to explore the uncharted territories of green energy and agricultural practices, we stumbled upon a correlation that is as unexpected as a sheep in a cotton field. Our research has unearthed a potential synergy between the adoption of GMOs in cotton cultivation and the amplification of wind power generation in the tropical paradise of New Caledonia. This revelation could blow away any doubts about the interconnectedness of seemingly distinct domains.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The data utilized in this study was primarily sourced from the United States Department of Agriculture (USDA) and the Energy Information Administration. Our research team scoured the depths of the internet, navigating through cyber jungles and electronic thicket to gather information spanning the years 2000 to 2021. We carefully plucked the ripest data fruits from these digital orchards, ensuring that only the most succulent and relevant datasets were included in our analysis. Much like separating cotton from its seeds, our team meticulously extracted pertinent information from the vast expanse of online resources.

Research Methods:
To untangle the intricate relationship between GMO use in cotton farming and wind power generation in New Caledonia, we employed a novel approach. First, we utilized a technique reminiscent of cloud gazing, wherein we allowed the data to drift through our analytical field of vision without imposing preconceived notions. Then, we applied a hybrid methodology akin to a genetic modification process, combining elements of regression analysis and econometric modeling to cultivate a robust understanding of the potential association between the two variables. This method allowed us to cross-pollinate statistical techniques, yielding insights that bloomed like cotton flowers in a gentle breeze.

Statistical Analysis:
In order to analyze the data and uncover any underlying patterns, we harnessed the power of statistical tools such as correlation analysis and regression modeling. These analytical instruments acted as the wind in our research sails, propelling us toward the discovery of any significant relationship between the adoption of GMOs in cotton farming and the generation of wind power in New Caledonia. Like the swaying of cotton stalks in the wind, our statistical analyses aimed to capture the subtle yet impactful connections between these variables.

Quality Control:
To ensure the reliability and validity of our findings, we implemented rigorous quality control measures. Just as a cotton gin separates the fibers from the seeds, we meticulously combed through the data, removing any extraneous or contaminated information. Our commitment to quality control rivaled the meticulous inspection of cotton bolls, as we sieved through the data with unwavering attention to detail, ensuring that only the purest and most pertinent data fibers were retained for our analysis.

Random Dad Joke:
Why did the scarecrow win an award? Because he was outstanding in his field!

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

Please make up the results section of an academic research paper presenting the findings of the connection between GMO use in cotton and Wind power generated in New Caledonia. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Your research team collected data from all across the internet, but mostly just used information from USDA and Energy Information Administration .

For the time period 2000 to 2021, you found a correlation 0.9385764, r-squared of 0.8809257, and p < 0.01.

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

Here is the title and abstract of the paper:
[[TITLE]]
Cottoning on to Renewable Energy: The Genetically Modified Power of Wind in New Caledonia

[[ABSTRACT]]
The purpose of this study was to investigate the potential connection between the use of genetically modified organisms (GMOs) in cotton farming and the generation of wind power in New Caledonia. Utilizing data from the USDA and the Energy Information Administration, our research team conducted a comprehensive analysis spanning from 2000 to 2021. Surprisingly, our findings revealed a striking correlation coefficient of 0.9385764 with a p-value of less than 0.01, indicating a robust relationship between the two variables.

While the notion of cotton crops influencing wind power may seem as unlikely as finding a needle in a haystack, our results suggest otherwise. Our research unearths a potential link between the adoption of GMOs in cotton cultivation and the amplification of wind power generation in the island nation of New Caledonia. This unexpected connection may just blow you away—both figuratively and literally.

In conclusion, our study sheds light on a hitherto unexplored synergy between agricultural practices and renewable energy sources. These findings lend credence to the adage, "When it comes to harnessing wind power, the stakes are high, but the wind is stronger." Our research highlights the importance of exploring unconventional avenues in renewable energy integration, while also proving that sometimes, the answers are right in front of us, rustling through the cotton fields.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
Utilizing renewable energy sources has become increasingly important in the global effort to reduce carbon emissions and mitigate the impacts of climate change. Wind power, in particular, has garnered attention for its potential to provide clean, sustainable energy. Meanwhile, the use of genetically modified organisms (GMOs) in agricultural practices has been a subject of debate and investigation, with proponents touting its potential benefits and detractors raising concerns about its impact on ecosystems. The intersection of these two seemingly disparate fields—wind power generation and GMO use in cotton farming—prompts a closer examination of the potential link between them.
It may seem like a stretch to connect the growth of cotton plants with the generation of wind power, but as the saying goes, "Where there's a whirl, there's a way." The prevailing winds of curiosity have propelled us to investigate this unconventional relationship, and the results may just leave you spinning with excitement.
In our endeavor to explore the uncharted territories of green energy and agricultural practices, we stumbled upon a correlation that is as unexpected as a sheep in a cotton field. Our research has unearthed a potential synergy between the adoption of GMOs in cotton cultivation and the amplification of wind power generation in the tropical paradise of New Caledonia. This revelation could blow away any doubts about the interconnectedness of seemingly distinct domains.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The analysis revealed a striking correlation coefficient of 0.9385764 between the use of GMOs in cotton farming and the generation of wind power in New Caledonia. This correlation coefficient suggests a robust and positive relationship between the two variables, as strong as the gusts of wind that power the turbines.

This unexpected connection may just spin your head more than a windmill in a storm! However, our findings indicate that the adoption of GMOs in cotton cultivation is associated with an amplification of wind power generation in the island nation of New Caledonia. It seems that when it comes to renewable energy, the winds of change may be blowing through cotton fields.

The r-squared value of 0.8809257 further emphasizes the strength of the relationship between GMO use in cotton and wind power generated in New Caledonia. This value indicates that a significant portion of the variability in wind power generation can be explained by the use of GMOs in cotton farming. It's as if the cotton plants are whispering secrets to the wind turbines, guiding them to generate more power.

Furthermore, the p-value of less than 0.01 provides strong evidence against the null hypothesis of no relationship between GMO use in cotton and wind power generation. It seems that there's more to the cotton-wind relationship than meets the eye, just like how there's more to a good pun than meets the ear!

The results are visually represented in Figure 1, which displays a scatterplot illustrating the strong correlation between the use of GMOs in cotton farming and wind power generation in New Caledonia. The figure speaks for itself, showing a clear pattern that aligns with our statistical findings.

In summary, our research findings bolster the idea that the impact of agricultural practices extends beyond the fields, reaching into the realm of renewable energy. The unexpected link between GMO use in cotton and wind power generation in New Caledonia may just blow away any doubts about the interconnectedness of seemingly disparate domains.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between GMO use in cotton and Wind power generated in New Caledonia. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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]]
Cottoning on to Renewable Energy: The Genetically Modified Power of Wind in New Caledonia

[[ABSTRACT]]
The purpose of this study was to investigate the potential connection between the use of genetically modified organisms (GMOs) in cotton farming and the generation of wind power in New Caledonia. Utilizing data from the USDA and the Energy Information Administration, our research team conducted a comprehensive analysis spanning from 2000 to 2021. Surprisingly, our findings revealed a striking correlation coefficient of 0.9385764 with a p-value of less than 0.01, indicating a robust relationship between the two variables.

While the notion of cotton crops influencing wind power may seem as unlikely as finding a needle in a haystack, our results suggest otherwise. Our research unearths a potential link between the adoption of GMOs in cotton cultivation and the amplification of wind power generation in the island nation of New Caledonia. This unexpected connection may just blow you away—both figuratively and literally.

In conclusion, our study sheds light on a hitherto unexplored synergy between agricultural practices and renewable energy sources. These findings lend credence to the adage, "When it comes to harnessing wind power, the stakes are high, but the wind is stronger." Our research highlights the importance of exploring unconventional avenues in renewable energy integration, while also proving that sometimes, the answers are right in front of us, rustling through the cotton fields.

[[LITERATURE REVIEW]]
In “The Impact of Genetically Modified Organisms on Agricultural Practices” by Smith, the authors find, surprisinly, a potential link between the adoption of GMOs in cotton cultivation and the amplification of wind power generation in New Caledonia.
"But what do you call a cotton field on a windy day? A wind farm!"
Doe in "The Evolution of Wind Power Technologies" reveals a high correlation coefficient of 0.9385764 between the two variables, with a p-value of less than 0.01, highlighting a robust relationship.
"Did you hear about the cotton farmer who started a wind energy business? He's now known as a real 'blowhard'."
Jones discusses in "Wind Power Dynamics in Island Regions" the unexpected connection between the growth of cotton plants and the generation of wind power, shedding light on this hitherto unexplored synergy.
On a more literary note, "The Wind Through the Cotton Fields" by Lorem Ipsum delves into the intertwining of wind power and agricultural practices, providing a poetic perspective on the subject.
"Why don't cotton farmers ever get lost? Because they always have wind direction!"
Additionally, "Gone with the Wind" by Margaret Mitchell, though a work of fiction, offers a timeless exploration of the impact of wind on the agricultural landscape, metaphorically capturing the essence of this unique relationship.
On a less traditional note, the researchers also investigated unexpected sources of knowledge, including the backs of shampoo bottles, which surprisingly offered insights, albeit tangentially, into the potential connection between GMO use in cotton and wind power generation in New Caledonia.
"What do shampoo and wind power have in common? They both leave you feeling 'blown away'!"

[[RESULTS]]
The analysis revealed a striking correlation coefficient of 0.9385764 between the use of GMOs in cotton farming and the generation of wind power in New Caledonia. This correlation coefficient suggests a robust and positive relationship between the two variables, as strong as the gusts of wind that power the turbines.
This unexpected connection may just spin your head more than a windmill in a storm! However, our findings indicate that the adoption of GMOs in cotton cultivation is associated with an amplification of wind power generation in the island nation of New Caledonia. It seems that when it comes to renewable energy, the winds of change may be blowing through cotton fields.
The r-squared value of 0.8809257 further emphasizes the strength of the relationship between GMO use in cotton and wind power generated in New Caledonia. This value indicates that a significant portion of the variability in wind power generation can be explained by the use of GMOs in cotton farming. It's as if the cotton plants are whispering secrets to the wind turbines, guiding them to generate more power.
Furthermore, the p-value of less than 0.01 provides strong evidence against the null hypothesis of no relationship between GMO use in cotton and wind power generation. It seems that there's more to the cotton-wind relationship than meets the eye, just like how there's more to a good pun than meets the ear!
The results are visually represented in Figure 1, which displays a scatterplot illustrating the strong correlation between the use of GMOs in cotton farming and wind power generation in New Caledonia. The figure speaks for itself, showing a clear pattern that aligns with our statistical findings.
In summary, our research findings bolster the idea that the impact of agricultural practices extends beyond the fields, reaching into the realm of renewable energy. The unexpected link between GMO use in cotton and wind power generation in New Caledonia may just blow away any doubts about the interconnectedness of seemingly disparate domains.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of our study support and extend prior research that explored the potential relationship between the use of genetically modified organisms (GMOs) in cotton farming and the generation of wind power in New Caledonia. Surprisingly, our results align with the work of Smith et al., who first hinted at the connection between GMO adoption in cotton cultivation and the amplification of wind power generation. It seems that this unlikely duo of cotton and wind is not just all hot air but an actual windfall of energy potential.

Our data revealed a robust correlation coefficient of 0.9385764 between GMO use in cotton and wind power generation, echoing the findings of Doe, whose research also reported a high correlation coefficient of similar magnitude. It appears that the winds of statistical significance are blowing in the same direction, reinforcing the notion that the intertwining of cotton and wind power is more than just a baleful rumor.

The significant r-squared value of 0.8809257 further fortifies the evidence for the relationship between GMO use in cotton and wind power generation, suggesting that the use of GMOs in cotton farming explains a substantial portion of the variability in wind power generation. It's as if the cotton’s modified genes are whispering the secrets of enhanced power generation to the wind turbines, making them spin with an extra oomph.

Additionally, the p-value of less than 0.01 provides resounding support for the alternative hypothesis of a relationship between GMO use in cotton and wind power generation, dismissing the null hypothesis as quickly as a strong gust of wind dissipates a cloud. It seems that the winds of statistical significance are blowing through these findings, leaving no room for doubt.

In summary, the unexpected link between GMO use in cotton and wind power generation in New Caledonia, as indicated by our research, lends credence to the notion that sometimes, the answers to complex questions blow through the most unlikely of places, much like the winds that power the turbines. These findings may just pleasantly surprise those who have been skeptical about the connection between agricultural practices and renewable energy sources – much like a good dad joke, they grow on you.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between GMO use in cotton and Wind power generated in New Caledonia. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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]]
Cottoning on to Renewable Energy: The Genetically Modified Power of Wind in New Caledonia

[[ABSTRACT]]
The purpose of this study was to investigate the potential connection between the use of genetically modified organisms (GMOs) in cotton farming and the generation of wind power in New Caledonia. Utilizing data from the USDA and the Energy Information Administration, our research team conducted a comprehensive analysis spanning from 2000 to 2021. Surprisingly, our findings revealed a striking correlation coefficient of 0.9385764 with a p-value of less than 0.01, indicating a robust relationship between the two variables.

While the notion of cotton crops influencing wind power may seem as unlikely as finding a needle in a haystack, our results suggest otherwise. Our research unearths a potential link between the adoption of GMOs in cotton cultivation and the amplification of wind power generation in the island nation of New Caledonia. This unexpected connection may just blow you away—both figuratively and literally.

In conclusion, our study sheds light on a hitherto unexplored synergy between agricultural practices and renewable energy sources. These findings lend credence to the adage, "When it comes to harnessing wind power, the stakes are high, but the wind is stronger." Our research highlights the importance of exploring unconventional avenues in renewable energy integration, while also proving that sometimes, the answers are right in front of us, rustling through the cotton fields.

[[INTRDUCTION]]
Utilizing renewable energy sources has become increasingly important in the global effort to reduce carbon emissions and mitigate the impacts of climate change. Wind power, in particular, has garnered attention for its potential to provide clean, sustainable energy. Meanwhile, the use of genetically modified organisms (GMOs) in agricultural practices has been a subject of debate and investigation, with proponents touting its potential benefits and detractors raising concerns about its impact on ecosystems. The intersection of these two seemingly disparate fields—wind power generation and GMO use in cotton farming—prompts a closer examination of the potential link between them.
It may seem like a stretch to connect the growth of cotton plants with the generation of wind power, but as the saying goes, "Where there's a whirl, there's a way." The prevailing winds of curiosity have propelled us to investigate this unconventional relationship, and the results may just leave you spinning with excitement.
In our endeavor to explore the uncharted territories of green energy and agricultural practices, we stumbled upon a correlation that is as unexpected as a sheep in a cotton field. Our research has unearthed a potential synergy between the adoption of GMOs in cotton cultivation and the amplification of wind power generation in the tropical paradise of New Caledonia. This revelation could blow away any doubts about the interconnectedness of seemingly distinct domains.

[[RESULTS]]
The analysis revealed a striking correlation coefficient of 0.9385764 between the use of GMOs in cotton farming and the generation of wind power in New Caledonia. This correlation coefficient suggests a robust and positive relationship between the two variables, as strong as the gusts of wind that power the turbines.
This unexpected connection may just spin your head more than a windmill in a storm! However, our findings indicate that the adoption of GMOs in cotton cultivation is associated with an amplification of wind power generation in the island nation of New Caledonia. It seems that when it comes to renewable energy, the winds of change may be blowing through cotton fields.
The r-squared value of 0.8809257 further emphasizes the strength of the relationship between GMO use in cotton and wind power generated in New Caledonia. This value indicates that a significant portion of the variability in wind power generation can be explained by the use of GMOs in cotton farming. It's as if the cotton plants are whispering secrets to the wind turbines, guiding them to generate more power.
Furthermore, the p-value of less than 0.01 provides strong evidence against the null hypothesis of no relationship between GMO use in cotton and wind power generation. It seems that there's more to the cotton-wind relationship than meets the eye, just like how there's more to a good pun than meets the ear!
The results are visually represented in Figure 1, which displays a scatterplot illustrating the strong correlation between the use of GMOs in cotton farming and wind power generation in New Caledonia. The figure speaks for itself, showing a clear pattern that aligns with our statistical findings.
In summary, our research findings bolster the idea that the impact of agricultural practices extends beyond the fields, reaching into the realm of renewable energy. The unexpected link between GMO use in cotton and wind power generation in New Caledonia may just blow away any doubts about the interconnectedness of seemingly disparate domains.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study illuminates a surprising connection between the use of genetically modified organisms (GMOs) in cotton farming and the amplification of wind power generation in New Caledonia. This unexpected correlation is as astonishing as a cotton-powered tornado – a whirlwind of agricultural and renewable energy synergy, if you will.

The robust correlation coefficient and r-squared value emphasize the strength and predictability of this relationship, leaving skeptics no room to "spin" their doubts. It seems that in the cotton fields of New Caledonia, the winds carry more than just cotton seeds – they carry the potential for renewable energy harvest as well.

The p-value of less than 0.01 provides strong evidence that this connection is not a mere fluke or a gust of wind. It's as reliable as a well-oiled wind turbine, churning out energy in sync with the growth of GMO cotton. This synergy between agricultural practices and renewable energy sources is truly a breath of fresh air – or should we say, a breath of wind power?

Thus, it appears that the winds of change in renewable energy may be blowing straight from the cotton fields, powered by the genetic modifications that have stirred up a storm of unexpected correlation. But as the saying goes, "What did the cotton plant say to the wind turbine? I'm a fan!"

In light of these compelling findings, it seems that no further research is needed in this area. The winds of science have blown us in the right direction, and we're firm in our understanding that GMO use in cotton and wind power in New Caledonia are undeniably linked. It's time to harness this knowledge and let it "blow" through the fields of academia and industry alike – just like a good dad joke.

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 Cottoning on to Renewable Energy: The Genetically Modified Power of Wind in New Caledonia. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Transgenic Environmental Engineering

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 Cottoning on to Renewable Energy: The Genetically Modified Power of Wind in New Caledonia. Respond only with the name of a fictional organization.

ChatGPT:

The Sustainable Energy Innovation Institute

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 Cottoning on to Renewable Energy: The Genetically Modified Power of Wind in New Caledonia with an abstract of The purpose of this study was to investigate the potential connection between the use of genetically modified organisms (GMOs) in cotton farming and the generation of wind power in New Caledonia. Utilizing data from the USDA and the Energy Information Administration, our research team conducted a comprehensive analysis spanning from 2000 to 2021. Surprisingly, our findings revealed a striking correlation coefficient of 0.9385764 with a p-value of less than 0.01, indicating a robust relationship between the two variables.
While the notion of cotton crops influencing wind power may seem as unlikely as finding a needle in a haystack, our results suggest otherwise. Our research unearths a potential link between the adoption of GMOs in cotton cultivation and the amplification of wind power generation in the island nation of New Caledonia. This unexpected connection may just blow you away—both figuratively and literally.
In conclusion, our study sheds light on a hitherto unexplored synergy between agricultural practices and renewable energy sources. These findings lend credence to the adage, "When it comes to harnessing wind power, the stakes are high, but the wind is stronger." Our research highlights the importance of exploring unconventional avenues in renewable energy integration, while also proving that sometimes, the answers are right in front of us, rustling through the cotton fields.

ChatGPT:

genetically modified organisms, GMOs, cotton farming, wind power, New Caledonia, USDA data, Energy Information Administration, correlation coefficient, renewable energy, agriculture, unconventional energy sources

*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

Wind power generated in New Caledonia
Detailed data title: Total wind power generated in New Caledonia in billion kWh
Source: Energy Information Administration
See what else correlates with Wind power generated in New Caledonia

Correlation r = 0.9385764 (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 = 0.8809257 (Coefficient of determination)
This means 88.1% of the change in the one variable (i.e., Wind power generated in New Caledonia) is predictable based on the change in the other (i.e., GMO use in cotton) over the 22 years from 2000 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.07E-10.^Show 0.0000000001069247917678759300
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 0.94 in 1.07E-8% of random cases. Said differently, if you correlated 9,352,367,991 random variables^Note You don't actually need 9 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.

[ 0.86, 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 cotton (GMO cotton %)	61	69	71	73	76	79	83	87	86	88	93	90	94	90	96	94	93	96	94	98	96	97
Wind power generated in New Caledonia (Billion kWh)	0.007	0.007	0.008	0.005	0.01	0.02	0.03	0.04	0.03	0.03	0.051	0.046	0.057	0.052	0.057	0.062	0.045	0.045	0.045	0.057	0.049	0.0557002

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.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. ^{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([61,69,71,73,76,79,83,87,86,88,93,90,94,90,96,94,93,96,94,98,96,97,])
array_2 = np.array([0.007,0.007,0.008,0.005,0.01,0.02,0.03,0.04,0.03,0.03,0.051,0.046,0.057,0.052,0.057,0.062,0.045,0.045,0.045,0.057,0.049,0.0557002,])
array_1_name = "GMO use in cotton"
array_2_name = "Wind power generated in New Caledonia"

# 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 cotton
Line chart for only Wind power generated in New Caledonia
AI-generated correlation image
The spurious research paper: Cottoning on to Renewable Energy: The Genetically Modified Power of Wind in New Caledonia

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Correlation ID: 3948 · Black Variable ID: 812 · Red Variable ID: 23853


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