Spurious correlation #3,556 · 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 in California and the second variable is Petroluem consumption in Cuba. 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 in California and the second variable is Petroluem consumption in Cuba. The chart goes from 2000 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As GMO cotton production in California increased, it led to the creation of larger cotton balls. These cotton balls, carried by the wind, mysteriously found their way to the shores of Cuba, where they were used as makeshift pillows by the locals. This, in turn, resulted in an unforeseen spike in the demand for sweet dreams, causing a surge in the use of petroleum for the production of high-quality, sleep-inducing lullabies in Cuba. It's a real snooze fest of a connection! Go figure!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image in the style of a Polaroid photo showing a sunny California field with rows of vibrant green GMO cotton plants. In the background, a cotton candy machine is humming away, producing fluffy pink cotton candy. In the distance, showcase a bustling Cuban carnival with colorful attractions and people eagerly lining up to indulge in the sweet treats. Giant petroleum tanks should be visible nearby, fueling the demand for more cotton candy machines, all captured in a single captivating scene. 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 in California and Petroluem consumption in Cuba. 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: Woodblock painting a meme Steampunk a Polaroid photo Cutsy graphics
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 in California and Petroluem consumption in Cuba, in the following situation: The increased use of GMO cotton in California led to a surplus of cotton candy, causing an overwhelming demand for petroleum-based cotton candy machines in Cuba, thus increasing petroleum consumption.. Keep the description simple.
ChatGPT: In a sunny California field, a farmer tends to rows of vibrant green GMO cotton plants, while a cotton candy machine hums away in the background, producing fluffy pink cotton candy. Across the ocean in Cuba, people eagerly line up at a colorful carnival, indulging in the sweet treats, as giant petroleum tanks stand nearby, fueling the demand for more cotton candy machines.

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 sunny California field, a farmer tends to rows of vibrant green GMO cotton plants, while a cotton candy machine hums away in the background, producing fluffy pink cotton candy. Across the ocean in Cuba, people eagerly line up at a colorful carnival, indulging in the sweet treats, as giant petroleum tanks stand nearby, fueling the demand for more cotton candy machines. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image in the style of a Polaroid photo showing a sunny California field with rows of vibrant green GMO cotton plants. In the background, a cotton candy machine is humming away, producing fluffy pink cotton candy. In the distance, showcase a bustling Cuban carnival with colorful attractions and people eagerly lining up to indulge in the sweet treats. Giant petroleum tanks should be visible nearby, fueling the demand for more cotton candy machines, all captured in a single captivating scene.

*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 increases in GMO use in cotton in California positively influenced Petroluem consumption in Cuba.

AI academic paper

(Because p < 0.01)

Seeds of Change: Exploring the Cotton Connection Between GMOs and Cuban Petroleum

The Journal of Crop Science and International Relations

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 in California and Petroluem consumption in Cuba. Make lots of jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name some movies that you watched that are tangentially related to the topic.

Here is the title and abstract of the paper:
[[TITLE]]
"Seeds of Change: Exploring the Cotton Connection Between GMOs and Cuban Petroleum"

[[ABSTRACT]]
The relationship between genetically modified organism (GMO) usage in cotton production in California and petroleum consumption in Cuba has long confounded experts, leading to much speculative word-spinning and fiber flux. In this paper, we delve into the tangled web of links between these seemingly disparate phenomena, sifting through data from the USDA and Energy Information Administration to unravel the knotty threads. Our analysis reveals a striking correlation coefficient of 0.7275791 and a p-value of less than 0.01 for the years 2000 to 2021, stirring up a cotton storm of attention. Our findings not only highlight the inherent interconnectedness of seemingly unrelated agricultural and energy sectors, but also raise pressing questions about the global impact of GMOs and the intricacies of international trade. This research provides a fresh perspective on the interplay between seemingly unrelated commodities, shedding light on the unexpected intersections of agricultural and energy economics.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The enigmatic interplay between the use of genetically modified organisms (GMOs) in cotton cultivation in California and the petroleum consumption in Cuba has been a source of bewilderment and intrigue for researchers and observers alike. The tendrils of these two seemingly unrelated phenomena have woven a complex tapestry that has eluded clear comprehension. As the saying goes, "It's not just about picking cotton; it's about connecting the dots."
The history of GMO cotton cultivation in California and Cuban petroleum consumption can be likened to a pair of mismatched socks, inexplicably linked through the fabric of agricultural and energy economics. While the discussion around GMOs often tends to be as prickly as a cotton plant itself, the connection to petroleum consumption in Cuba adds a layer of complexity that has researchers unraveling threads like a cat with a ball of yarn.
As researchers, we find ourselves in the eye of this intriguing storm, endeavoring to untangle the knotty threads of correlation and causation. Our aspiration is not only to disentangle this cotton conundrum but also to lay bare the intricate connections that underpin seemingly unrelated domains. In doing so, we aim to sow the seeds of understanding and cultivate a field of knowledge where the roots of one industry are inextricably intertwined with the branches of another.
The conventional wisdom that "cotton is the fabric of our lives" may take on a whole new meaning as we delve deep into the data and explore the unanticipated intersections of agricultural production and energy consumption. Through rigorous analysis and a dash of wit, we endeavor to shed light on the curious case of GMO cotton and Cuban petroleum, laying bare the unseen stitches that bind them together.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The association between genetically modified organism (GMO) usage in cotton production in California and petroleum consumption in Cuba has garnered significant attention in academia and beyond. Smith and Doe (2015) conducted a comprehensive analysis, examining the potential economic and environmental ramifications of GMO cotton on global petroleum markets. Their findings indicated a complex interplay between GMO cotton yields and petroleum consumption, hinting at a correlation that sent ripples through the scholarly community. Additionally, Jones et al. (2018) examined the historical patterns of GMO adoption in California and its repercussions on transnational agricultural trade, offering a nuanced perspective on the intricate web of relationships between GMO cotton and global energy dynamics.

In "The Cotton Crisis: GMOs and the Tangled Web of Petroleum," the authors delve into the intricate nexus of cotton production and petroleum usage, weaving a narrative that captures the imagination of readers while unraveling the mysteries of agricultural and energy economics. Le Monde's "GMOs: A Thread of Controversy" and Orwell's "Animal Farm" offer compelling insights into the sociopolitical dimensions of agricultural biotechnology, hinting at the broader implications of GMO adoption on diverse economies.

The saga of GMO cotton and Cuban petroleum consumption bears a striking resemblance to the interwoven plotlines of cinematic masterpieces such as "Cotton Club" and "Cuba," where the backdrop of agriculture and energy sets the stage for a gripping tale of interconnectedness and intrigue. While these artistic parallels may seem far-fetched, they serve as a poignant reminder of the multifaceted nature of the phenomenon under study.

As the scholarly discourse around GMO cotton and Cuban petroleum unfolds, it becomes increasingly evident that the conventional boundaries of academic inquiry are being stretched to accommodate the flamboyant threads of interdisciplinary research. The next section consolidates the diverse lines of inquiry into a coherent framework, laying the groundwork for a comprehensive analysis of the intricate relationship between GMO cotton in California and petroleum consumption in Cuba.

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 in California and Petroluem consumption in Cuba. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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 0.7275791, r-squared of 0.5293714, 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 Change: Exploring the Cotton Connection Between GMOs and Cuban Petroleum"

[[ABSTRACT]]
The relationship between genetically modified organism (GMO) usage in cotton production in California and petroleum consumption in Cuba has long confounded experts, leading to much speculative word-spinning and fiber flux. In this paper, we delve into the tangled web of links between these seemingly disparate phenomena, sifting through data from the USDA and Energy Information Administration to unravel the knotty threads. Our analysis reveals a striking correlation coefficient of 0.7275791 and a p-value of less than 0.01 for the years 2000 to 2021, stirring up a cotton storm of attention. Our findings not only highlight the inherent interconnectedness of seemingly unrelated agricultural and energy sectors, but also raise pressing questions about the global impact of GMOs and the intricacies of international trade. This research provides a fresh perspective on the interplay between seemingly unrelated commodities, shedding light on the unexpected intersections of agricultural and energy economics.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The enigmatic interplay between the use of genetically modified organisms (GMOs) in cotton cultivation in California and the petroleum consumption in Cuba has been a source of bewilderment and intrigue for researchers and observers alike. The tendrils of these two seemingly unrelated phenomena have woven a complex tapestry that has eluded clear comprehension. As the saying goes, "It's not just about picking cotton; it's about connecting the dots."
The history of GMO cotton cultivation in California and Cuban petroleum consumption can be likened to a pair of mismatched socks, inexplicably linked through the fabric of agricultural and energy economics. While the discussion around GMOs often tends to be as prickly as a cotton plant itself, the connection to petroleum consumption in Cuba adds a layer of complexity that has researchers unraveling threads like a cat with a ball of yarn.
As researchers, we find ourselves in the eye of this intriguing storm, endeavoring to untangle the knotty threads of correlation and causation. Our aspiration is not only to disentangle this cotton conundrum but also to lay bare the intricate connections that underpin seemingly unrelated domains. In doing so, we aim to sow the seeds of understanding and cultivate a field of knowledge where the roots of one industry are inextricably intertwined with the branches of another.
The conventional wisdom that "cotton is the fabric of our lives" may take on a whole new meaning as we delve deep into the data and explore the unanticipated intersections of agricultural production and energy consumption. Through rigorous analysis and a dash of wit, we endeavor to shed light on the curious case of GMO cotton and Cuban petroleum, laying bare the unseen stitches that bind them together.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The investigation into the perplexing relationship between the use of genetically modified organisms (GMOs) in California's cotton production and petroleum consumption in Cuba has yielded an intriguing set of results. After meticulously scouring data from the USDA and the Energy Information Administration, we uncovered a noteworthy correlation coefficient of 0.7275791, a powerful r-squared value of 0.5293714, and a p-value of less than 0.01. These findings indicate a substantial and statistically significant association between these seemingly unrelated variables.

Fig. 1 presents a scatterplot illustrating the robust correlation between the use of GMOs in cotton cultivation in California and petroleum consumption in Cuba. The figure facilitates visualization of the striking relationship that our analysis has brought to the fore, underscoring the interconnectedness of these apparently disparate facets of agricultural and energy economics.

The magnitude of the correlation coefficient, reminiscent of a thread count in a fine cotton fabric, signifies a strong and positive correlation between GMO usage in California's cotton production and petroleum consumption in Cuba. This compelling association invites further contemplation of the intricate interplay between agricultural practices and energy demand on a global scale. As the data unfolds, it becomes clear that this correlation is no mere cotton candy fluff, but rather a tangible connection with profound implications for both industries.

The robustness of the r-squared value, akin to the density of cotton fibers in a luxurious textile, suggests that approximately 53% of the variability in Cuban petroleum consumption can be explained by the utilization of GMOs in California's cotton production during the specified time period. This substantial explanatory power underscores the significance of GMO usage in cotton and its influence on energy consumption patterns, weaving a compelling narrative of interconnectedness between these domains.

The p-value of less than 0.01, remarkable akin to stumbling upon an exceptionally rare fabric in a sprawling textile bazaar, underscores the statistical significance of the relationship between GMO usage in cotton production in California and petroleum consumption in Cuba. This stringent threshold for statistical significance further bolsters the credibility of our findings, compelling us to unravel the complex threads of causation and connection.

In summary, the results of our investigation reveal a compelling and substantial correlation between the usage of GMOs in California's cotton production and petroleum consumption in Cuba, shedding light on the unexpected intersections of agricultural and energy economics. These findings not only underscore the inherent intertwining of seemingly disconnected sectors but also raise pertinent questions about the global impact of GMOs and the intricate dynamics of international trade.

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 in California and Petroluem consumption in Cuba. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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 Change: Exploring the Cotton Connection Between GMOs and Cuban Petroleum"

[[ABSTRACT]]
The relationship between genetically modified organism (GMO) usage in cotton production in California and petroleum consumption in Cuba has long confounded experts, leading to much speculative word-spinning and fiber flux. In this paper, we delve into the tangled web of links between these seemingly disparate phenomena, sifting through data from the USDA and Energy Information Administration to unravel the knotty threads. Our analysis reveals a striking correlation coefficient of 0.7275791 and a p-value of less than 0.01 for the years 2000 to 2021, stirring up a cotton storm of attention. Our findings not only highlight the inherent interconnectedness of seemingly unrelated agricultural and energy sectors, but also raise pressing questions about the global impact of GMOs and the intricacies of international trade. This research provides a fresh perspective on the interplay between seemingly unrelated commodities, shedding light on the unexpected intersections of agricultural and energy economics.

[[LITERATURE REVIEW]]
The association between genetically modified organism (GMO) usage in cotton production in California and petroleum consumption in Cuba has garnered significant attention in academia and beyond. Smith and Doe (2015) conducted a comprehensive analysis, examining the potential economic and environmental ramifications of GMO cotton on global petroleum markets. Their findings indicated a complex interplay between GMO cotton yields and petroleum consumption, hinting at a correlation that sent ripples through the scholarly community. Additionally, Jones et al. (2018) examined the historical patterns of GMO adoption in California and its repercussions on transnational agricultural trade, offering a nuanced perspective on the intricate web of relationships between GMO cotton and global energy dynamics.
In "The Cotton Crisis: GMOs and the Tangled Web of Petroleum," the authors delve into the intricate nexus of cotton production and petroleum usage, weaving a narrative that captures the imagination of readers while unraveling the mysteries of agricultural and energy economics. Le Monde's "GMOs: A Thread of Controversy" and Orwell's "Animal Farm" offer compelling insights into the sociopolitical dimensions of agricultural biotechnology, hinting at the broader implications of GMO adoption on diverse economies.
The saga of GMO cotton and Cuban petroleum consumption bears a striking resemblance to the interwoven plotlines of cinematic masterpieces such as "Cotton Club" and "Cuba," where the backdrop of agriculture and energy sets the stage for a gripping tale of interconnectedness and intrigue. While these artistic parallels may seem far-fetched, they serve as a poignant reminder of the multifaceted nature of the phenomenon under study.
As the scholarly discourse around GMO cotton and Cuban petroleum unfolds, it becomes increasingly evident that the conventional boundaries of academic inquiry are being stretched to accommodate the flamboyant threads of interdisciplinary research. The next section consolidates the diverse lines of inquiry into a coherent framework, laying the groundwork for a comprehensive analysis of the intricate relationship between GMO cotton in California and petroleum consumption in Cuba.

[[RESULTS]]
The investigation into the perplexing relationship between the use of genetically modified organisms (GMOs) in California's cotton production and petroleum consumption in Cuba has yielded an intriguing set of results. After meticulously scouring data from the USDA and the Energy Information Administration, we uncovered a noteworthy correlation coefficient of 0.7275791, a powerful r-squared value of 0.5293714, and a p-value of less than 0.01. These findings indicate a substantial and statistically significant association between these seemingly unrelated variables.
Fig. 1 presents a scatterplot illustrating the robust correlation between the use of GMOs in cotton cultivation in California and petroleum consumption in Cuba. The figure facilitates visualization of the striking relationship that our analysis has brought to the fore, underscoring the interconnectedness of these apparently disparate facets of agricultural and energy economics.
The magnitude of the correlation coefficient, reminiscent of a thread count in a fine cotton fabric, signifies a strong and positive correlation between GMO usage in California's cotton production and petroleum consumption in Cuba. This compelling association invites further contemplation of the intricate interplay between agricultural practices and energy demand on a global scale. As the data unfolds, it becomes clear that this correlation is no mere cotton candy fluff, but rather a tangible connection with profound implications for both industries.
The robustness of the r-squared value, akin to the density of cotton fibers in a luxurious textile, suggests that approximately 53% of the variability in Cuban petroleum consumption can be explained by the utilization of GMOs in California's cotton production during the specified time period. This substantial explanatory power underscores the significance of GMO usage in cotton and its influence on energy consumption patterns, weaving a compelling narrative of interconnectedness between these domains.
The p-value of less than 0.01, remarkable akin to stumbling upon an exceptionally rare fabric in a sprawling textile bazaar, underscores the statistical significance of the relationship between GMO usage in cotton production in California and petroleum consumption in Cuba. This stringent threshold for statistical significance further bolsters the credibility of our findings, compelling us to unravel the complex threads of causation and connection.
In summary, the results of our investigation reveal a compelling and substantial correlation between the usage of GMOs in California's cotton production and petroleum consumption in Cuba, shedding light on the unexpected intersections of agricultural and energy economics. These findings not only underscore the inherent intertwining of seemingly disconnected sectors but also raise pertinent questions about the global impact of GMOs and the intricate dynamics of international trade.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of our investigation into the association between GMO usage in California's cotton production and petroleum consumption in Cuba offer a captivating glimpse into the intricate tapestry of agricultural and energy economics. Our results buttress the prior research by Smith and Doe (2015) and Jones et al. (2018), validating the complex interplay between GMO cotton yields and petroleum consumption, as well as highlighting the historical patterns of GMO adoption in California and its impact on transnational agricultural trade.

The robust correlation coefficient and p-value of less than 0.01 fortify the notion that GMO usage in California's cotton production has a significant and statistically meaningful association with petroleum consumption in Cuba. This compelling correlation, akin to discovering a flawlessly woven fabric in the chaotic web of a busy textile market, underscores the undeniable interconnectedness of these seemingly unrelated variables. In a manner reminiscent of a well-crafted pun, it weaves a narrative of unexpected intersection between agricultural and energy dynamics.

The r-squared value of 0.5293714, serving as a metaphorical measure of thread density in a luxurious textile, reveals that approximately 53% of the variability in Cuban petroleum consumption can be attributed to the utilization of GMOs in California's cotton production during the specified time period. This substantial explanatory power elucidates the impact of GMO usage in cotton on energy consumption patterns, echoing the nuanced analysis by Jones et al. (2018) on the intricate web of relationships between GMO cotton and global energy dynamics.

These results, much like the convoluted plotlines of "Cotton Club" and "Cuba," underscore the complexity of the correlation between GMO usage in cotton and petroleum consumption, challenging academia to embrace the multifaceted nature of the phenomenon under study. In a manner reminiscent of the multiple layers in a densely woven fabric, our findings call attention to the far-reaching implications of GMO adoption on diverse economies, compelling us to unravel the intricacies of international trade with renewed vigor.

As we unravel the tangled threads of agricultural and energy economics, it becomes clear that the conventional boundaries of academic inquiry are being stretched to accommodate the flamboyant threads of interdisciplinary research. Our investigation not only strengthens the evidence base for the interconnectedness of GMO cotton and Cuban petroleum consumption but also sets the stage for further exploration of the unexpected intersections and complex dynamics at play.

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 in California and Petroluem consumption in Cuba. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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 Change: Exploring the Cotton Connection Between GMOs and Cuban Petroleum"

[[ABSTRACT]]
The relationship between genetically modified organism (GMO) usage in cotton production in California and petroleum consumption in Cuba has long confounded experts, leading to much speculative word-spinning and fiber flux. In this paper, we delve into the tangled web of links between these seemingly disparate phenomena, sifting through data from the USDA and Energy Information Administration to unravel the knotty threads. Our analysis reveals a striking correlation coefficient of 0.7275791 and a p-value of less than 0.01 for the years 2000 to 2021, stirring up a cotton storm of attention. Our findings not only highlight the inherent interconnectedness of seemingly unrelated agricultural and energy sectors, but also raise pressing questions about the global impact of GMOs and the intricacies of international trade. This research provides a fresh perspective on the interplay between seemingly unrelated commodities, shedding light on the unexpected intersections of agricultural and energy economics.

[[INTRDUCTION]]
The enigmatic interplay between the use of genetically modified organisms (GMOs) in cotton cultivation in California and the petroleum consumption in Cuba has been a source of bewilderment and intrigue for researchers and observers alike. The tendrils of these two seemingly unrelated phenomena have woven a complex tapestry that has eluded clear comprehension. As the saying goes, "It's not just about picking cotton; it's about connecting the dots."
The history of GMO cotton cultivation in California and Cuban petroleum consumption can be likened to a pair of mismatched socks, inexplicably linked through the fabric of agricultural and energy economics. While the discussion around GMOs often tends to be as prickly as a cotton plant itself, the connection to petroleum consumption in Cuba adds a layer of complexity that has researchers unraveling threads like a cat with a ball of yarn.
As researchers, we find ourselves in the eye of this intriguing storm, endeavoring to untangle the knotty threads of correlation and causation. Our aspiration is not only to disentangle this cotton conundrum but also to lay bare the intricate connections that underpin seemingly unrelated domains. In doing so, we aim to sow the seeds of understanding and cultivate a field of knowledge where the roots of one industry are inextricably intertwined with the branches of another.
The conventional wisdom that "cotton is the fabric of our lives" may take on a whole new meaning as we delve deep into the data and explore the unanticipated intersections of agricultural production and energy consumption. Through rigorous analysis and a dash of wit, we endeavor to shed light on the curious case of GMO cotton and Cuban petroleum, laying bare the unseen stitches that bind them together.

[[RESULTS]]
The investigation into the perplexing relationship between the use of genetically modified organisms (GMOs) in California's cotton production and petroleum consumption in Cuba has yielded an intriguing set of results. After meticulously scouring data from the USDA and the Energy Information Administration, we uncovered a noteworthy correlation coefficient of 0.7275791, a powerful r-squared value of 0.5293714, and a p-value of less than 0.01. These findings indicate a substantial and statistically significant association between these seemingly unrelated variables.
Fig. 1 presents a scatterplot illustrating the robust correlation between the use of GMOs in cotton cultivation in California and petroleum consumption in Cuba. The figure facilitates visualization of the striking relationship that our analysis has brought to the fore, underscoring the interconnectedness of these apparently disparate facets of agricultural and energy economics.
The magnitude of the correlation coefficient, reminiscent of a thread count in a fine cotton fabric, signifies a strong and positive correlation between GMO usage in California's cotton production and petroleum consumption in Cuba. This compelling association invites further contemplation of the intricate interplay between agricultural practices and energy demand on a global scale. As the data unfolds, it becomes clear that this correlation is no mere cotton candy fluff, but rather a tangible connection with profound implications for both industries.
The robustness of the r-squared value, akin to the density of cotton fibers in a luxurious textile, suggests that approximately 53% of the variability in Cuban petroleum consumption can be explained by the utilization of GMOs in California's cotton production during the specified time period. This substantial explanatory power underscores the significance of GMO usage in cotton and its influence on energy consumption patterns, weaving a compelling narrative of interconnectedness between these domains.
The p-value of less than 0.01, remarkable akin to stumbling upon an exceptionally rare fabric in a sprawling textile bazaar, underscores the statistical significance of the relationship between GMO usage in cotton production in California and petroleum consumption in Cuba. This stringent threshold for statistical significance further bolsters the credibility of our findings, compelling us to unravel the complex threads of causation and connection.
In summary, the results of our investigation reveal a compelling and substantial correlation between the usage of GMOs in California's cotton production and petroleum consumption in Cuba, shedding light on the unexpected intersections of agricultural and energy economics. These findings not only underscore the inherent intertwining of seemingly disconnected sectors but also raise pertinent questions about the global impact of GMOs and the intricate dynamics of international trade.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

Having unraveled the strands of agricultural and energy economics, we find ourselves in a proverbial cotton field of discoveries and unanswered questions. The correlation we've uncovered between GMO usage in California's cotton production and petroleum consumption in Cuba is as startling as finding a polyester blend in a sea of natural fibers. Our findings underscore the interconnectedness of these seemingly unrelated domains, akin to the interwoven threads of a complex tapestry.

As we close the curtains on this investigation, we cannot help but acknowledge the intriguing nature of this correlation. It seems as improbable as finding a needle in a haystack, yet here we are, with a statistically significant association that demands attention. This correlation is not just a quirk of the data, but a tangible manifestation of the intricate relationship between agricultural practices and energy consumption. It's like discovering a hidden pocket in a well-worn pair of jeans – unexpectedly functional and oddly captivating.

The compelling strength of our correlation coefficient, reminiscent of the tensile strength of a high-quality cotton fabric, invites further contemplation of the underlying mechanisms at play. Meanwhile, the explanatory power of our r-squared value, akin to the comforting softness of a cotton shirt, highlights the substantial influence of GMO usage on Cuban petroleum consumption. It's as if we've untangled a particularly perplexing knot in a ball of yarn, revealing a pattern that defies conventional wisdom.

In conclusion, our research has cast a spotlight on the unexpected interplay between GMO cotton cultivation in California and Cuban petroleum consumption, prompting a reevaluation of the interconnectedness of seemingly disparate industries. It's time to cut through the fabric of conventional thinking and embrace the fact that, perhaps, everything is indeed connected – just like the stitches in a well-made quilt.

In the words of a wise tailor, "The fabric of existence is interwoven with threads of unexpected connections." With that in mind, we assert that further research in this area is unnecessary. It is time to tie a bow on this investigation and turn our attention to other equally intriguing puzzles lurking in the colorful landscape of economic and agricultural relationships.

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 Change: Exploring the Cotton Connection Between GMOs and Cuban Petroleum. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Crop Science and International Relations

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 Change: Exploring the Cotton Connection Between GMOs and Cuban Petroleum. Respond only with the name of a fictional organization.

ChatGPT:

The Global Agricultural Genetics Association

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 Change: Exploring the Cotton Connection Between GMOs and Cuban Petroleum with an abstract of The relationship between genetically modified organism (GMO) usage in cotton production in California and petroleum consumption in Cuba has long confounded experts, leading to much speculative word-spinning and fiber flux. In this paper, we delve into the tangled web of links between these seemingly disparate phenomena, sifting through data from the USDA and Energy Information Administration to unravel the knotty threads. Our analysis reveals a striking correlation coefficient of 0.7275791 and a p-value of less than 0.01 for the years 2000 to 2021, stirring up a cotton storm of attention. Our findings not only highlight the inherent interconnectedness of seemingly unrelated agricultural and energy sectors, but also raise pressing questions about the global impact of GMOs and the intricacies of international trade. This research provides a fresh perspective on the interplay between seemingly unrelated commodities, shedding light on the unexpected intersections of agricultural and energy economics.

ChatGPT:

GMO cotton, petroleum consumption, California cotton production, Cuba, USDA data, Energy Information Administration, correlation coefficient, international trade, agricultural economics, energy economics

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

Petroluem consumption in Cuba
Detailed data title: Volume of petroluem consumption consumed in Cuba in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Petroluem consumption in Cuba

Correlation r = 0.7275791 (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 = 0.5293714 (Coefficient of determination)
This means 52.9% of the change in the one variable (i.e., Petroluem consumption in Cuba) is predictable based on the change in the other (i.e., GMO use in cotton in California) over the 22 years from 2000 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00012.^Show 0.0001243741247370444400000000
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 0.73 in 0.012% of random cases. Said differently, if you correlated 8,040 random variables^Note Which I absolutely did.
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.

[ 0.44, 0.88 ] 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 in California (GMO cotton %)	3	11	6	9	6	8	9	4	7	8	19	9	21	9	15	10	3	2	6	10	3	3
Petroluem consumption in Cuba (Million Barrels/Day)	157.958	150.23	152.147	156.088	157.736	156.437	151.2	149.754	161.974	163.759	189.203	170.7	184.926	175.663	176.958	170	174	148.951	158.717	164.068	151.749	155.578

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([3,11,6,9,6,8,9,4,7,8,19,9,21,9,15,10,3,2,6,10,3,3,])
array_2 = np.array([157.958,150.23,152.147,156.088,157.736,156.437,151.2,149.754,161.974,163.759,189.203,170.7,184.926,175.663,176.958,170,174,148.951,158.717,164.068,151.749,155.578,])
array_1_name = "GMO use in cotton in California"
array_2_name = "Petroluem consumption in Cuba"

# 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 in California
Line chart for only Petroluem consumption in Cuba
AI-generated correlation image
The spurious research paper: Seeds of Change: Exploring the Cotton Connection Between GMOs and Cuban Petroleum

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Correlation ID: 3556 · Black Variable ID: 769 · Red Variable ID: 24333


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