Spurious correlation #5,466 · 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 Air pollution in Wilmington, Ohio. The chart goes from 2000 to 2022, 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 Air pollution in Wilmington, Ohio. The chart goes from 2000 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The decrease in GMO use in cotton led to a reduction in the need for frequent pesticide spraying, which in turn lowered the release of volatile organic compounds into the air. This meant that in Wilmington, Ohio, they were able to breathe a little easier without the extra GMO-toxins in the air. It's a ginned-up connection, but it seems like going au naturel really helped clear the air in more ways than one!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image in the style of a Polaroid photo depicting a serene and idyllic cotton field in Wilmington, Ohio. The cotton plants, bathed in sunlight, sway gently in the breeze, exuding a sense of peace and tranquility. The air is fresh and clean, with a hint of warmth that adds to the serene atmosphere. The absence of pesticides and genetically modified toxins is visually emphasized through the vibrant and healthy appearance of the cotton plants, symbolizing the community's commitment to eco-friendly practices. 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 Air pollution in Wilmington, Ohio. 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: Cutsy art illustration Charcoal a funny image macro Cyber folk a Polaroid photo
ChatGPT: a Polaroid photo

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 Air pollution in Wilmington, Ohio, in the following situation: The decrease in GMO use in cotton led to a reduction in the need for frequent pesticide spraying, which in turn lowered the release of volatile organic compounds into the air. This meant that in Wilmington, Ohio, they were able to breathe a little easier without the extra GMO-toxins in the air. It's a ginned-up connection, but it seems like going au naturel really helped clear the air in more ways than one!. Keep the description simple.
ChatGPT: In a cotton field in Wilmington, Ohio, the air is clear and fresh. The cotton plants sway gently in the breeze, free from the need for frequent pesticide spraying due to the reduced use of GMOs. The absence of genetically modified toxins in the air allows the residents to breathe a little easier, as the reduction in air pollution brings a sense of relief to the community.

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 Polaroid photo ] and depicting this scene: In a cotton field in Wilmington, Ohio, the air is clear and fresh. The cotton plants sway gently in the breeze, free from the need for frequent pesticide spraying due to the reduced use of GMOs. The absence of genetically modified toxins in the air allows the residents to breathe a little easier, as the reduction in air pollution brings a sense of relief to the community. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image in the style of a Polaroid photo depicting a serene and idyllic cotton field in Wilmington, Ohio. The cotton plants, bathed in sunlight, sway gently in the breeze, exuding a sense of peace and tranquility. The air is fresh and clean, with a hint of warmth that adds to the serene atmosphere. The absence of pesticides and genetically modified toxins is visually emphasized through the vibrant and healthy appearance of the cotton plants, symbolizing the community's commitment to eco-friendly practices.

*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 a decrease in GMO use in cotton caused Air pollution in Wilmington, Ohio to decrease. Include one good pun.

AI academic paper

(Because p < 0.01)

Seeds of Stardust: The GMO Gossypium and the Cotton Cloud – A Study of the Relationship Between GMO Use in Cotton and Air Pollution in Wilmington, Ohio

Journal of Genetically Modified Organisms and Environmental Impact

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 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 Air pollution in Wilmington, Ohio. Make lots of jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
"Seeds of Stardust: The GMO Gossypium and the Cotton Cloud – A Study of the Relationship Between GMO Use in Cotton and Air Pollution in Wilmington, Ohio"

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in cotton production has long been a hot topic in the agricultural world, but their impact on the environment goes beyond the confines of the cotton fields. This study delves into the unexpected and somewhat fluff-heavy link between GMO use in cotton and air pollution in Wilmington, Ohio. By crunching the numbers from the USDA and the Environmental Protection Agency, we dug deep into the data from 2000 to 2022, and what we found is both ginning and grin-worthy. Our analysis revealed a correlation coefficient of 0.8378478 and a p-value less than 0.01, suggesting a robust statistical relationship. So, put on your air filtration masks and join us as we unpack this cloud of intrigue surrounding the interconnectedness of GMO cotton and air pollution.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The use of genetically modified organisms (GMOs) in agriculture has been a topic of heated debate, with opinions as diverse as a field of multicolored cotton. Those in favor hail the potential for increased yields, pest resistance, and overall efficiency, while skeptics are quick to point out the potential negative impacts on the environment. However, while the argument continues to swirl like a dust devil, our study takes a unique perspective on the matter – quite literally.
We set out to investigate the connection between GMO use in cotton, an industry often associated with fluffy clouds of white, and air pollution in Wilmington, Ohio. The idea that these two seemingly unrelated entities could be entwined like the strands of a cotton ball struck us as both intriguing and unexpected. After all, who would have thought that the humble cotton plant, after centuries of domestication and genetic tinkering, could have an impact on the air quality of a small Ohio town? Indeed, the notion seemed as outlandish as a sheep in cotton's clothing.
The mysterious link between these two variables prompted us to embark on a rigorous and thorough analysis, treading lightly through the fields of data as we sought to unravel this cottony enigma. Armed with data from the USDA and the Environmental Protection Agency, we combed through the numbers with the same precision as separating the seeds from the fluffy fibers. Our investigation spanned the years 2000 to 2022, akin to painstakingly following the growth of a cotton crop from planting to harvest.
What we uncovered amidst the fields of data was as surprising as a bale of cotton rolling down the street on a windy day. Our analyses unveiled a statistically robust relationship between GMO use in cotton and air pollution, firmly shaking the prevailing belief that these factors operated in separate atmospheres. The air of skepticism dispersed as our findings revealed a correlation coefficient of 0.8378478 and a p-value less than 0.01, a result too striking to be mere happenstance. It was as if the cotton clouds had parted, revealing a hidden connection that had eluded scrutiny for far too long.
So, join us as we embark on this whimsical yet enlightening journey, where we unravel the intertwining threads of GMO cotton and air pollution, and illustrate that these two seemingly unrelated elements are as interlinked as a pair of mismatched socks in a dryer.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

Previous studies have delved into the impact of genetically modified organisms (GMOs) in agricultural production, as well as the link between agricultural practices and environmental pollution. Smith et al. (2015) examined the effects of GMO adoption on crop yields, while Doe (2018) explored the influence of agricultural activities on air quality. Additionally, Jones (2020) investigated the environmental implications of GMO use in various crops. These studies laid the groundwork for our investigation into the intriguing connection between GMO use in cotton and air pollution in Wilmington, Ohio.

In "The Cotton Revolution: Technology and Change in the Cotton Industry of the House," the authors find insights into the technological advancements that have revolutionized cotton production and processing, including the adoption of genetically modified cotton. This detailed account of the changes in the cotton industry sets the stage for understanding the modern-day implications of GMO cotton cultivation and its potential environmental effects.

Conversely, in "The Air We Breathe: A Study of Atmospheric Composition and Human Health," lorem and ipsum provide a comprehensive analysis of air pollution and its impact on human health. This foundational work offers invaluable insights into the complexities of air quality, laying the groundwork for our exploration of the potential link between GMO cotton and air pollution in Wilmington, Ohio.

While these academic works provide a solid foundation for our research, it is essential to consider other sources that may offer unconventional perspectives. Fictional works such as "Cloud Atlas" and "The Wind-Up Bird Chronicle" offer imaginative explorations of atmospheric phenomena and human connection to the environment. Although not directly related to agricultural practices, these literary works serve as a reminder of the interconnectedness of the natural world and human activities, adding a whimsical touch to our investigation.

Additionally, drawing inspiration from board games such as "Cottonopoly" and "Pollutants & Ladders" provides a lighthearted approach to understanding the complex dynamics of cotton production and air pollution. While these games are designed for entertainment, they mirror real-world scenarios and could potentially offer unconventional insights into the intersection of GMO cotton and air quality.

As we navigate the complexities of our research topic, it is crucial to approach the investigation with a blend of scholarly rigor and open-mindedness, embracing the potential for unexpected discoveries in the cotton clouds and air pollution of Wilmington, Ohio.

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 Air pollution in Wilmington, Ohio. Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from USDA and Environmental Protection Agency . You used data from 2000 to 2022

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]]
"Seeds of Stardust: The GMO Gossypium and the Cotton Cloud – A Study of the Relationship Between GMO Use in Cotton and Air Pollution in Wilmington, Ohio"

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in cotton production has long been a hot topic in the agricultural world, but their impact on the environment goes beyond the confines of the cotton fields. This study delves into the unexpected and somewhat fluff-heavy link between GMO use in cotton and air pollution in Wilmington, Ohio. By crunching the numbers from the USDA and the Environmental Protection Agency, we dug deep into the data from 2000 to 2022, and what we found is both ginning and grin-worthy. Our analysis revealed a correlation coefficient of 0.8378478 and a p-value less than 0.01, suggesting a robust statistical relationship. So, put on your air filtration masks and join us as we unpack this cloud of intrigue surrounding the interconnectedness of GMO cotton and air pollution.

[[INTRODUCTION]]
The use of genetically modified organisms (GMOs) in agriculture has been a topic of heated debate, with opinions as diverse as a field of multicolored cotton. Those in favor hail the potential for increased yields, pest resistance, and overall efficiency, while skeptics are quick to point out the potential negative impacts on the environment. However, while the argument continues to swirl like a dust devil, our study takes a unique perspective on the matter – quite literally.
We set out to investigate the connection between GMO use in cotton, an industry often associated with fluffy clouds of white, and air pollution in Wilmington, Ohio. The idea that these two seemingly unrelated entities could be entwined like the strands of a cotton ball struck us as both intriguing and unexpected. After all, who would have thought that the humble cotton plant, after centuries of domestication and genetic tinkering, could have an impact on the air quality of a small Ohio town? Indeed, the notion seemed as outlandish as a sheep in cotton's clothing.
The mysterious link between these two variables prompted us to embark on a rigorous and thorough analysis, treading lightly through the fields of data as we sought to unravel this cottony enigma. Armed with data from the USDA and the Environmental Protection Agency, we combed through the numbers with the same precision as separating the seeds from the fluffy fibers. Our investigation spanned the years 2000 to 2022, akin to painstakingly following the growth of a cotton crop from planting to harvest.
What we uncovered amidst the fields of data was as surprising as a bale of cotton rolling down the street on a windy day. Our analyses unveiled a statistically robust relationship between GMO use in cotton and air pollution, firmly shaking the prevailing belief that these factors operated in separate atmospheres. The air of skepticism dispersed as our findings revealed a correlation coefficient of 0.8378478 and a p-value less than 0.01, a result too striking to be mere happenstance. It was as if the cotton clouds had parted, revealing a hidden connection that had eluded scrutiny for far too long.
So, join us as we embark on this whimsical yet enlightening journey, where we unravel the intertwining threads of GMO cotton and air pollution, and illustrate that these two seemingly unrelated elements are as interlinked as a pair of mismatched socks in a dryer.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To unravel the mysterious connection between GMO use in cotton and air pollution in Wilmington, Ohio, our research employed a myriad of methods that could be deemed as quirky as discovering a pair of polka-dotted socks in a load of laundry. Leveraging data primarily from the USDA and the Environmental Protection Agency, we embarked on a journey akin to picking out overripe fruit from a basket of produce to ensure the highest quality and most reliable data for our analysis.

Our methodology involved the extraction and compilation of extensive historical data on GMO cotton production and air quality metrics in Wilmington, Ohio from the years 2000 to 2022. Like skilled gardeners tending to a diverse bed of flora, we carefully curated this data to ensure its accuracy, completeness, and relevance to our investigation. With the data in hand, we concocted a statistical brew that would put even the most well-known alchemists to shame.

Employing a robust statistical approach, we conducted a series of analysis that resembled solving a complex maze with the added factor of a playful gust of wind blowing through the corridors. Our methods included but were not limited to correlation analysis, regression modeling, and time series analysis, all with the intention of unveiling the hidden patterns and connections between GMO cotton use and air pollution in Wilmington, Ohio. Additionally, we controlled for various environmental and agricultural factors to ensure that the relationship we unearthed was not just a fluke of statistical happenstance.

Furthermore, we utilized advanced software and programming tools, much like a band of merry technophiles wielding magic wands, to crunch the numbers and visualize the intricate dance of data points. Through this creative process, we sought to reveal insights that were as delightful as finding a four-leaf clover in a field of GMO cotton.

It is essential to note that while our methods may have carried an air of whimsy, our commitment to rigor and systematic inquiry remained unwavering. We recognize that the connection between GMO cotton and air pollution is a topic of great significance, and therefore, our methods were designed with the precision and care that one would expect from a crew of intrepid investigators combing through a tangle of clues in a crime scene.

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 Air pollution in Wilmington, Ohio. Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from USDA and Environmental Protection Agency .

For the time period 2000 to 2022, you found a correlation 0.8378478, r-squared of 0.7019890, 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]]
"Seeds of Stardust: The GMO Gossypium and the Cotton Cloud – A Study of the Relationship Between GMO Use in Cotton and Air Pollution in Wilmington, Ohio"

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in cotton production has long been a hot topic in the agricultural world, but their impact on the environment goes beyond the confines of the cotton fields. This study delves into the unexpected and somewhat fluff-heavy link between GMO use in cotton and air pollution in Wilmington, Ohio. By crunching the numbers from the USDA and the Environmental Protection Agency, we dug deep into the data from 2000 to 2022, and what we found is both ginning and grin-worthy. Our analysis revealed a correlation coefficient of 0.8378478 and a p-value less than 0.01, suggesting a robust statistical relationship. So, put on your air filtration masks and join us as we unpack this cloud of intrigue surrounding the interconnectedness of GMO cotton and air pollution.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The use of genetically modified organisms (GMOs) in agriculture has been a topic of heated debate, with opinions as diverse as a field of multicolored cotton. Those in favor hail the potential for increased yields, pest resistance, and overall efficiency, while skeptics are quick to point out the potential negative impacts on the environment. However, while the argument continues to swirl like a dust devil, our study takes a unique perspective on the matter – quite literally.
We set out to investigate the connection between GMO use in cotton, an industry often associated with fluffy clouds of white, and air pollution in Wilmington, Ohio. The idea that these two seemingly unrelated entities could be entwined like the strands of a cotton ball struck us as both intriguing and unexpected. After all, who would have thought that the humble cotton plant, after centuries of domestication and genetic tinkering, could have an impact on the air quality of a small Ohio town? Indeed, the notion seemed as outlandish as a sheep in cotton's clothing.
The mysterious link between these two variables prompted us to embark on a rigorous and thorough analysis, treading lightly through the fields of data as we sought to unravel this cottony enigma. Armed with data from the USDA and the Environmental Protection Agency, we combed through the numbers with the same precision as separating the seeds from the fluffy fibers. Our investigation spanned the years 2000 to 2022, akin to painstakingly following the growth of a cotton crop from planting to harvest.
What we uncovered amidst the fields of data was as surprising as a bale of cotton rolling down the street on a windy day. Our analyses unveiled a statistically robust relationship between GMO use in cotton and air pollution, firmly shaking the prevailing belief that these factors operated in separate atmospheres. The air of skepticism dispersed as our findings revealed a correlation coefficient of 0.8378478 and a p-value less than 0.01, a result too striking to be mere happenstance. It was as if the cotton clouds had parted, revealing a hidden connection that had eluded scrutiny for far too long.
So, join us as we embark on this whimsical yet enlightening journey, where we unravel the intertwining threads of GMO cotton and air pollution, and illustrate that these two seemingly unrelated elements are as interlinked as a pair of mismatched socks in a dryer.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The analysis of the data collected from 2000 to 2022 revealed a striking correlation between GMO use in cotton and air pollution in Wilmington, Ohio. The correlation coefficient of 0.8378478 indicated a strong positive relationship between these seemingly disparate entities, akin to discovering that peanut butter and jelly are, in fact, soulmates.

Furthermore, the r-squared value of 0.7019890 suggested that approximately 70.2% of the variation in air pollution could be explained by the variation in GMO use in cotton. This level of predictability is as surprising as finding a needle in a haystack, or perhaps more fittingly, finding a seedless cotton plant.

The p-value of less than 0.01 provided compelling evidence to reject the null hypothesis of no relationship between GMO use in cotton and air pollution. It seems that this statistical finding had as much probability of happening by chance as a snowball's chance in a cotton gin.

Additionally, as illustrated in Fig. 1, the scatterplot depicts a clear and convincing pattern, with data points resembling a flock of genetically modified sheep on a mission to boldly go where no sheep has gone before.

In conclusion, our comprehensive analysis has laid bare the unexpected connection between GMO use in cotton and air pollution, unveiling a correlation that is as undeniable as the stubbornness of lint on black clothing. These results challenge the conventional wisdom that these two factors operate in isolation, and suggest a tangled relationship as intricate as a ball of yarn in the paws of a mischievous kitten.

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 Air pollution in Wilmington, Ohio. Make lots of jokes, goofy observations, and puns.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
"Seeds of Stardust: The GMO Gossypium and the Cotton Cloud – A Study of the Relationship Between GMO Use in Cotton and Air Pollution in Wilmington, Ohio"

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in cotton production has long been a hot topic in the agricultural world, but their impact on the environment goes beyond the confines of the cotton fields. This study delves into the unexpected and somewhat fluff-heavy link between GMO use in cotton and air pollution in Wilmington, Ohio. By crunching the numbers from the USDA and the Environmental Protection Agency, we dug deep into the data from 2000 to 2022, and what we found is both ginning and grin-worthy. Our analysis revealed a correlation coefficient of 0.8378478 and a p-value less than 0.01, suggesting a robust statistical relationship. So, put on your air filtration masks and join us as we unpack this cloud of intrigue surrounding the interconnectedness of GMO cotton and air pollution.

[[LITERATURE REVIEW]]
Previous studies have delved into the impact of genetically modified organisms (GMOs) in agricultural production, as well as the link between agricultural practices and environmental pollution. Smith et al. (2015) examined the effects of GMO adoption on crop yields, while Doe (2018) explored the influence of agricultural activities on air quality. Additionally, Jones (2020) investigated the environmental implications of GMO use in various crops. These studies laid the groundwork for our investigation into the intriguing connection between GMO use in cotton and air pollution in Wilmington, Ohio.
In "The Cotton Revolution: Technology and Change in the Cotton Industry of the House," the authors find insights into the technological advancements that have revolutionized cotton production and processing, including the adoption of genetically modified cotton. This detailed account of the changes in the cotton industry sets the stage for understanding the modern-day implications of GMO cotton cultivation and its potential environmental effects.
Conversely, in "The Air We Breathe: A Study of Atmospheric Composition and Human Health," lorem and ipsum provide a comprehensive analysis of air pollution and its impact on human health. This foundational work offers invaluable insights into the complexities of air quality, laying the groundwork for our exploration of the potential link between GMO cotton and air pollution in Wilmington, Ohio.
While these academic works provide a solid foundation for our research, it is essential to consider other sources that may offer unconventional perspectives. Fictional works such as "Cloud Atlas" and "The Wind-Up Bird Chronicle" offer imaginative explorations of atmospheric phenomena and human connection to the environment. Although not directly related to agricultural practices, these literary works serve as a reminder of the interconnectedness of the natural world and human activities, adding a whimsical touch to our investigation.
Additionally, drawing inspiration from board games such as "Cottonopoly" and "Pollutants & Ladders" provides a lighthearted approach to understanding the complex dynamics of cotton production and air pollution. While these games are designed for entertainment, they mirror real-world scenarios and could potentially offer unconventional insights into the intersection of GMO cotton and air quality.
As we navigate the complexities of our research topic, it is crucial to approach the investigation with a blend of scholarly rigor and open-mindedness, embracing the potential for unexpected discoveries in the cotton clouds and air pollution of Wilmington, Ohio.

[[RESULTS]]
The analysis of the data collected from 2000 to 2022 revealed a striking correlation between GMO use in cotton and air pollution in Wilmington, Ohio. The correlation coefficient of 0.8378478 indicated a strong positive relationship between these seemingly disparate entities, akin to discovering that peanut butter and jelly are, in fact, soulmates.
Furthermore, the r-squared value of 0.7019890 suggested that approximately 70.2% of the variation in air pollution could be explained by the variation in GMO use in cotton. This level of predictability is as surprising as finding a needle in a haystack, or perhaps more fittingly, finding a seedless cotton plant.
The p-value of less than 0.01 provided compelling evidence to reject the null hypothesis of no relationship between GMO use in cotton and air pollution. It seems that this statistical finding had as much probability of happening by chance as a snowball's chance in a cotton gin.
Additionally, as illustrated in Fig. 1, the scatterplot depicts a clear and convincing pattern, with data points resembling a flock of genetically modified sheep on a mission to boldly go where no sheep has gone before.
In conclusion, our comprehensive analysis has laid bare the unexpected connection between GMO use in cotton and air pollution, unveiling a correlation that is as undeniable as the stubbornness of lint on black clothing. These results challenge the conventional wisdom that these two factors operate in isolation, and suggest a tangled relationship as intricate as a ball of yarn in the paws of a mischievous kitten.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our study provide compelling evidence in support of the prior research that has explored the impact of genetically modified organisms (GMOs) in agricultural production and their relationship with environmental factors. Smith et al. (2015) observed the effects of GMO adoption on crop yields, and our findings align with their work, revealing a robust connection between GMO use in cotton and air pollution. This correlation is not just a bale of fluff but a substantial insight into the far-reaching effects of agricultural practices on environmental quality.

Furthermore, our analysis complements the research conducted by Doe (2018), as we confirm that agricultural activities, particularly those related to GMO cotton cultivation, can indeed contribute to air pollution. This supports the notion that the environmental implications of GMO use in various crops, as investigated by Jones (2020), extend to the cotton industry in Wilmington, Ohio. In other words, our findings are as clear as blue skies on a non-polluted day, validating the concerns raised by prior studies regarding the environmental impact of GMO agriculture.

Moreover, the inclusion of fictional and game-based inspirations in our literature review, while seemingly whimsical, has proven to offer valuable insights. The unconventional perspectives gleaned from literary works and board games have underscored the interconnectedness of human activities with the environment, emphasizing the importance of considering diverse sources of inspiration in scientific inquiry. This reinforces the notion that, just like cotton and pollutants, unorthodox sources can intertwine to offer unexpected revelations.

In conclusion, our study has not only confirmed the link between GMO use in cotton and air pollution but has also emphasized the need for interdisciplinary approaches in addressing complex issues. It is a reminder that scientific inquiry benefits from blending scholarly rigor with a willingness to entertain unexpected connections, even if they may seem as surprising as finding a needle in a haystack of genetically modified cotton. The tough seeds of truth we have uncovered challenge us to think outside the traditional bale and continue exploring the multifaceted relationships between agricultural practices and environmental quality.

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 Air pollution in Wilmington, Ohio. Make lots of jokes, goofy observations, and puns.

Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"Seeds of Stardust: The GMO Gossypium and the Cotton Cloud – A Study of the Relationship Between GMO Use in Cotton and Air Pollution in Wilmington, Ohio"

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in cotton production has long been a hot topic in the agricultural world, but their impact on the environment goes beyond the confines of the cotton fields. This study delves into the unexpected and somewhat fluff-heavy link between GMO use in cotton and air pollution in Wilmington, Ohio. By crunching the numbers from the USDA and the Environmental Protection Agency, we dug deep into the data from 2000 to 2022, and what we found is both ginning and grin-worthy. Our analysis revealed a correlation coefficient of 0.8378478 and a p-value less than 0.01, suggesting a robust statistical relationship. So, put on your air filtration masks and join us as we unpack this cloud of intrigue surrounding the interconnectedness of GMO cotton and air pollution.

[[INTRDUCTION]]
The use of genetically modified organisms (GMOs) in agriculture has been a topic of heated debate, with opinions as diverse as a field of multicolored cotton. Those in favor hail the potential for increased yields, pest resistance, and overall efficiency, while skeptics are quick to point out the potential negative impacts on the environment. However, while the argument continues to swirl like a dust devil, our study takes a unique perspective on the matter – quite literally.
We set out to investigate the connection between GMO use in cotton, an industry often associated with fluffy clouds of white, and air pollution in Wilmington, Ohio. The idea that these two seemingly unrelated entities could be entwined like the strands of a cotton ball struck us as both intriguing and unexpected. After all, who would have thought that the humble cotton plant, after centuries of domestication and genetic tinkering, could have an impact on the air quality of a small Ohio town? Indeed, the notion seemed as outlandish as a sheep in cotton's clothing.
The mysterious link between these two variables prompted us to embark on a rigorous and thorough analysis, treading lightly through the fields of data as we sought to unravel this cottony enigma. Armed with data from the USDA and the Environmental Protection Agency, we combed through the numbers with the same precision as separating the seeds from the fluffy fibers. Our investigation spanned the years 2000 to 2022, akin to painstakingly following the growth of a cotton crop from planting to harvest.
What we uncovered amidst the fields of data was as surprising as a bale of cotton rolling down the street on a windy day. Our analyses unveiled a statistically robust relationship between GMO use in cotton and air pollution, firmly shaking the prevailing belief that these factors operated in separate atmospheres. The air of skepticism dispersed as our findings revealed a correlation coefficient of 0.8378478 and a p-value less than 0.01, a result too striking to be mere happenstance. It was as if the cotton clouds had parted, revealing a hidden connection that had eluded scrutiny for far too long.
So, join us as we embark on this whimsical yet enlightening journey, where we unravel the intertwining threads of GMO cotton and air pollution, and illustrate that these two seemingly unrelated elements are as interlinked as a pair of mismatched socks in a dryer.

[[RESULTS]]
The analysis of the data collected from 2000 to 2022 revealed a striking correlation between GMO use in cotton and air pollution in Wilmington, Ohio. The correlation coefficient of 0.8378478 indicated a strong positive relationship between these seemingly disparate entities, akin to discovering that peanut butter and jelly are, in fact, soulmates.
Furthermore, the r-squared value of 0.7019890 suggested that approximately 70.2% of the variation in air pollution could be explained by the variation in GMO use in cotton. This level of predictability is as surprising as finding a needle in a haystack, or perhaps more fittingly, finding a seedless cotton plant.
The p-value of less than 0.01 provided compelling evidence to reject the null hypothesis of no relationship between GMO use in cotton and air pollution. It seems that this statistical finding had as much probability of happening by chance as a snowball's chance in a cotton gin.
Additionally, as illustrated in Fig. 1, the scatterplot depicts a clear and convincing pattern, with data points resembling a flock of genetically modified sheep on a mission to boldly go where no sheep has gone before.
In conclusion, our comprehensive analysis has laid bare the unexpected connection between GMO use in cotton and air pollution, unveiling a correlation that is as undeniable as the stubbornness of lint on black clothing. These results challenge the conventional wisdom that these two factors operate in isolation, and suggest a tangled relationship as intricate as a ball of yarn in the paws of a mischievous kitten.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has unraveled a tapestry of interconnectedness between the fluffy world of genetically modified cotton and the ethereal realm of air pollution in Wilmington, Ohio. The statistically robust relationship we uncovered between GMO use in cotton and air pollution is as surprising as finding a feather in a box of cotton balls. It seems that these two seemingly unrelated entities are as entwined as a pair of headphones in the pocket of a forgetful researcher.

Our findings challenge the notion that GMO cotton and air pollution operate in separate spheres, much like discovering that a cloud isn't just a shapeless mass in the sky but an intricate dance of water vapor and dust particles. This unexpected correlation is as compelling as finding the last piece of a jigsaw puzzle hiding in the folds of a cotton blanket.

As such, our results have significant implications for both the agricultural and environmental sectors. Adhering to the status quo of treating GMO cotton and air pollution as disconnected entities is as preposterous as trying to separate sugar from a cotton candy. It is high time that policymakers and stakeholders acknowledge the intertwined nature of these factors and craft comprehensive policies that take into account their symbiotic relationship.

In light of these revelatory findings, we assert with confidence that further investigation into this cottony conundrum is as unnecessary as bringing a lint roller to a cotton field. We can rest assured that our study has laid bare the unusual association between GMO cotton and air pollution, leaving no thread unturned. It seems that, in the case of GMO cotton and air pollution in Wilmington, Ohio, the threads of fate are indeed interwoven, and it is time to start weaving a new narrative that accounts for their unexpected harmony.

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 Seeds of Stardust: The GMO Gossypium and the Cotton Cloud – A Study of the Relationship Between GMO Use in Cotton and Air Pollution in Wilmington, Ohio. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Genetically Modified Organisms and Environmental Impact

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 Seeds of Stardust: The GMO Gossypium and the Cotton Cloud – A Study of the Relationship Between GMO Use in Cotton and Air Pollution in Wilmington, Ohio. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Ethereal Ecology and Environmental Enlightenment

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 Seeds of Stardust: The GMO Gossypium and the Cotton Cloud – A Study of the Relationship Between GMO Use in Cotton and Air Pollution in Wilmington, Ohio with an abstract of The use of genetically modified organisms (GMOs) in cotton production has long been a hot topic in the agricultural world, but their impact on the environment goes beyond the confines of the cotton fields. This study delves into the unexpected and somewhat fluff-heavy link between GMO use in cotton and air pollution in Wilmington, Ohio. By crunching the numbers from the USDA and the Environmental Protection Agency, we dug deep into the data from 2000 to 2022, and what we found is both ginning and grin-worthy. Our analysis revealed a correlation coefficient of 0.8378478 and a p-value less than 0.01, suggesting a robust statistical relationship. So, put on your air filtration masks and join us as we unpack this cloud of intrigue surrounding the interconnectedness of GMO cotton and air pollution.

ChatGPT:

GMO cotton, air pollution, genetically modified organisms, cotton production, environmental impact, Wilmington Ohio, USDA data, EPA data, statistical analysis, correlation coefficient, air filtration masks, agricultural world, cotton fields, ecological impact

*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 that is genetically modified to be herbicide-tolerant (HT), but not insect-resistant (Bt)
Source: USDA
See what else correlates with GMO use in cotton

Air pollution in Wilmington, Ohio
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Wilmington, OH
Source: Environmental Protection Agency
See what else correlates with Air pollution in Wilmington, Ohio

Correlation r = 0.8378478 (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 = 0.7019890 (Coefficient of determination)
This means 70.2% of the change in the one variable (i.e., Air pollution in Wilmington, Ohio) is predictable based on the change in the other (i.e., GMO use in cotton) over the 23 years from 2000 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 6.08E-7.^Show 0.0000006084599129361316000000
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 0.84 in 6.08E-5% of random cases. Said differently, if you correlated 1,643,494 random variables^Note You don't actually need 1 million 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 22 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 22 because we have two variables measured over a period of 23 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.

[ 0.65, 0.93 ] 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	2022
GMO use in cotton (GMO cotton %)	26	32	36	32	30	27	26	28	23	23	20	15	17	15	12	10	9	11	9	6	8	6	6
Air pollution in Wilmington, Ohio (Bad air quality days)	10.2804	16.3551	23.1884	12.2066	7.00935	16.8224	8.41121	19.6262	4.78469	1.42857	4.22535	10.2804	6.54206	0.469484	0.943396	1.40187	1.89573	1.22449	0.408163	0	0	0	0.408163

Why this works

Data dredging: I have 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
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([26,32,36,32,30,27,26,28,23,23,20,15,17,15,12,10,9,11,9,6,8,6,6,])
array_2 = np.array([10.2804,16.3551,23.1884,12.2066,7.00935,16.8224,8.41121,19.6262,4.78469,1.42857,4.22535,10.2804,6.54206,0.469484,0.943396,1.40187,1.89573,1.22449,0.408163,0,0,0,0.408163,])
array_1_name = "GMO use in cotton"
array_2_name = "Air pollution in Wilmington, Ohio"

# 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)
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Line chart for only GMO use in cotton
Line chart for only Air pollution in Wilmington, Ohio
AI-generated correlation image
The spurious research paper: Seeds of Stardust: The GMO Gossypium and the Cotton Cloud – A Study of the Relationship Between GMO Use in Cotton and Air Pollution in Wilmington, Ohio

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Correlation ID: 5466 · Black Variable ID: 789 · Red Variable ID: 21461


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