Spurious correlation #3,503 · 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 Mississippi and the second variable is US birth rates of triplets or more. The chart goes from 2002 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 Mississippi and the second variable is US birth rates of triplets or more. The chart goes from 2002 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

By reducing genetic modification in cotton, we've inadvertently minimized the 'fabric' of our society, leading to a 'seeding' decrease in multiple births. Seems like these GMO-free cotton fields have really mastered the art of 'unstitching' unexpected triplets! It's like the old saying goes, when it comes to non-GMO cotton, three's a crowd!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image inspired by a TIME magazine cover featuring a Mississippi cotton field. In the center, a farmer is holding a non-GMO cotton seed in one hand. To the side, a group of triplets is playing amongst the cotton plants, with a clear visible connection between the farmer's action, the decrease in GMO cotton use, and the number of triplets present in the field. The image should portray the impact of the farmer's choice on the environment, with a subtle yet powerful narrative. 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 Mississippi and US birth rates of triplets or more. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s Minimalism Cybernetic art A TIME magazine cover image Cutsy graphics
ChatGPT: A TIME magazine cover image

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both GMO use in cotton in Mississippi and US birth rates of triplets or more, in the following situation: Decrease in GMO cotton use in MS caused US birth rates of triplets to drop due to decreased exposure to multi-seed cotton. Triplets were known to be attracted to the scent of multi-seed cotton and would often appear in higher numbers in areas with heavy GMO cotton production.. Keep the description simple.
ChatGPT: In a Mississippi cotton field, a farmer holds a non-GMO cotton seed in one hand and looks at the decreased number of triplets playing in the field. The triplets, who had been attracted to the scent of multi-seed GMO cotton, now seem to be fewer in number, showing a direct link to the decrease in GMO cotton use.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A TIME magazine cover image ] and depicting this scene: In a Mississippi cotton field, a farmer holds a non-GMO cotton seed in one hand and looks at the decreased number of triplets playing in the field. The triplets, who had been attracted to the scent of multi-seed GMO cotton, now seem to be fewer in number, showing a direct link to the decrease in GMO cotton use. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image inspired by a TIME magazine cover featuring a Mississippi cotton field. In the center, a farmer is holding a non-GMO cotton seed in one hand. To the side, a group of triplets is playing amongst the cotton plants, with a clear visible connection between the farmer's action, the decrease in GMO cotton use, and the number of triplets present in the field. The image should portray the impact of the farmer's choice on the environment, with a subtle yet powerful narrative.

*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 in Mississippi caused US birth rates of triplets or more to decrease. Include one good pun.

AI academic paper

(Because p < 0.01)

GMOs and Multiples: The Cotton Connection

The Journal of Agricultural Genetics and Ecological Diversity

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between GMO use in cotton in Mississippi and US birth rates of triplets or more. 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 a few real TV shows that sound like they might be relevant to the topic that you watched as research.

Here is the title and abstract of the paper:
[[TITLE]]
GMOs and Multiples: The Cotton Connection

[[ABSTRACT]]
In this paper, we delve into the fascinating intersection of genetically modified organisms (GMOs) in cotton cultivation and the occurrence of multiple births, particularly triplets or more, in the United States. Our research team utilized extensive data from the USDA and CDC, scrutinizing the years 2002 to 2021 to explore the potential correlation between these seemingly unrelated entities. Surprisingly, we uncovered a strikingly high correlation coefficient of 0.8805864 and a statistical significance of p < 0.01, suggesting a compelling connection between GMO use in cotton fields in Mississippi and the birth rates of multiples across the US. This paper not only sheds light on an overlooked aspect of agricultural innovation but also pokes fun at the unexpected link between cotton crops and multiple bundles of joy. With our findings, we hope to initiate further inquiry and spark conversations in both the agricultural and medical communities regarding this perplexing correlation.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
From textile to twins, from fabric to fertility, this study uncovers a connection that is as surprising as finding a needle in a GMO cotton haystack - the relationship between genetically modified organisms (GMOs) in cotton cultivation and the incidence of multiple births, particularly triplets or more, in the United States. As researchers, we often embark on scientific journeys with the hope of stumbling upon something impactful, but never did we expect our investigation to lead us to a field where agriculture and obstetrics intersect – quite literally, from seed to delivery.
Our fascination with this unlikely duo of GMO cotton and triplet births was sparked by a rumour whispered among the rows of cotton plants in Mississippi. Locals claimed that the air in those fields was filled with a certain kind of magic that resulted in more than just bountiful harvests. Half-jokingly, we set out to uncover the truth behind this hearsay, armed with statistical tools and a handful of curiosity.
Looking at the abundance of data from the USDA and CDC, we dove headfirst into the numbers, prepared to disentangle a statistical mess and possibly cultivate some statistical triplets of our own. We scrutinized the years 2002 to 2021, and what we found was more surprising than a farmer discovering a four-leaf clover when looking for aphids. Lo and behold, we discovered a correlation coefficient of 0.8805864 and a statistical significance of p < 0.01. The result? A compelling connection between GMO use in cotton fields in Mississippi and the birth rates of multiples across the US. Both spooky and spectacular, it seems that the seeds of this correlation were sown in the very fabric of our agricultural practices.
While the scientific community has often been preoccupied with the genetic implications of GMOs, our study offers a different lens through which to view these modified organisms – from a non-traditional view of gene expression to the unexpected implications of agricultural innovation on human fertility. Our research not only introduces a quirky factor into the world of GMO studies but also unravels a thread of agricultural impact that extends far beyond the often-discussed realms of crop yield and pest resistance.
Join us as we unearth this peculiar correlation, highlighting the unexpected connections that can sprout from the fields of science and sparking conversations that reach from the cotton fields to the maternity ward. Whether you're a cotton enthusiast, a genetic aficionado, or simply a fan of puns and peculiar correlations, we invite you to delve into our findings. After all, this study isn’t just about GMOs and multiples – it’s about redefining what it means to weave together the threads of agriculture and human biology.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "Smith et al.," the authors find that the cultivation of cotton and its genetic modification have been a focus of agricultural research for decades, with a primary emphasis on improving crop yield and resilience to pests. Similarly, "Doe and Johnson" discuss the widespread adoption of genetically modified organisms (GMOs) in agricultural practices, highlighting the potential benefits and risks associated with their use. Though these studies provide valuable insights into the conventional impacts of GMOs on crop production, they fail to address the unexpected and, dare I say, fertile ground for exploration that our study has uncovered.

Moving from the serious and scholarly to the unexpected and quirky, "Jones and Wang" explore the interplay of agricultural practices and human biology in their work "Lorem and Ipsum," delving into the uncharted territory of GMO cotton's influence on the occurrence of multiple births, particularly triplets or more, in the United States. This groundbreaking research not only challenges traditional notions of agricultural impact but also introduces a delightful element of surprise into the scientific discourse.

Beyond the confines of scholarly journals, we turn our attention to non-fiction literature that may shed light on the playful intersection of agriculture and human fertility. "The Omnivore's Dilemma" by Michael Pollan provides an insightful examination of modern agricultural practices, which might offer an unexpected perspective on the connection we have uncovered. Likewise, "The Hidden Life of Trees" by Peter Wohlleben brings a whimsical lens to the often overlooked world of plants and their interconnectedness with the broader ecosystem. Who knows, perhaps trees have been whispering secrets about fertility all along?

Diving into the world of fiction, we cannot overlook the potential inspiration that literature may provide. Could "The Grapes of Wrath" by John Steinbeck offer subtle hints about the unexpected consequences of agricultural innovation? And while we're on the topic of unexpected surprises, surely "The Children of Men" by P.D. James deserves a mention, tempting us to consider how agricultural practices could influence human reproduction in unforeseen ways. After all, truth is often stranger than fiction, and in this case, it appears to be wackier too.

In the realm of visual entertainment, our research expanded to include TV shows that sound oddly relevant to our peculiar topic of inquiry. From the agricultural intrigue of "Dirty Jobs" to the unexpected connections of "How I Met Your Mother," our viewing choices may seem unrelated, but rest assured, they all played a vital role in guiding our research, directly or indirectly. And who can forget the classic "I Love Lucy," where unexpected surprises were always just around the corner – much like the unexpected connection we have uncovered between cotton cultivation and multiple births.

Armed with an arsenal of scholarly investigations, whimsical literature, and peculiar pop culture references, we approach the next phase of our research with unparalleled curiosity and an unwavering determination to uncover the unexpected, even if it leads us into the cotton fields of Mississippi and beyond.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between GMO use in cotton in Mississippi and US birth rates of triplets or more. 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]]
GMOs and Multiples: The Cotton Connection

[[ABSTRACT]]
In this paper, we delve into the fascinating intersection of genetically modified organisms (GMOs) in cotton cultivation and the occurrence of multiple births, particularly triplets or more, in the United States. Our research team utilized extensive data from the USDA and CDC, scrutinizing the years 2002 to 2021 to explore the potential correlation between these seemingly unrelated entities. Surprisingly, we uncovered a strikingly high correlation coefficient of 0.8805864 and a statistical significance of p < 0.01, suggesting a compelling connection between GMO use in cotton fields in Mississippi and the birth rates of multiples across the US. This paper not only sheds light on an overlooked aspect of agricultural innovation but also pokes fun at the unexpected link between cotton crops and multiple bundles of joy. With our findings, we hope to initiate further inquiry and spark conversations in both the agricultural and medical communities regarding this perplexing correlation.

[[LITERATURE REVIEW]]
In "Smith et al.," the authors find that the cultivation of cotton and its genetic modification have been a focus of agricultural research for decades, with a primary emphasis on improving crop yield and resilience to pests. Similarly, "Doe and Johnson" discuss the widespread adoption of genetically modified organisms (GMOs) in agricultural practices, highlighting the potential benefits and risks associated with their use. Though these studies provide valuable insights into the conventional impacts of GMOs on crop production, they fail to address the unexpected and, dare I say, fertile ground for exploration that our study has uncovered.
Moving from the serious and scholarly to the unexpected and quirky, "Jones and Wang" explore the interplay of agricultural practices and human biology in their work "Lorem and Ipsum," delving into the uncharted territory of GMO cotton's influence on the occurrence of multiple births, particularly triplets or more, in the United States. This groundbreaking research not only challenges traditional notions of agricultural impact but also introduces a delightful element of surprise into the scientific discourse.
Beyond the confines of scholarly journals, we turn our attention to non-fiction literature that may shed light on the playful intersection of agriculture and human fertility. "The Omnivore's Dilemma" by Michael Pollan provides an insightful examination of modern agricultural practices, which might offer an unexpected perspective on the connection we have uncovered. Likewise, "The Hidden Life of Trees" by Peter Wohlleben brings a whimsical lens to the often overlooked world of plants and their interconnectedness with the broader ecosystem. Who knows, perhaps trees have been whispering secrets about fertility all along?
Diving into the world of fiction, we cannot overlook the potential inspiration that literature may provide. Could "The Grapes of Wrath" by John Steinbeck offer subtle hints about the unexpected consequences of agricultural innovation? And while we're on the topic of unexpected surprises, surely "The Children of Men" by P.D. James deserves a mention, tempting us to consider how agricultural practices could influence human reproduction in unforeseen ways. After all, truth is often stranger than fiction, and in this case, it appears to be wackier too.
In the realm of visual entertainment, our research expanded to include TV shows that sound oddly relevant to our peculiar topic of inquiry. From the agricultural intrigue of "Dirty Jobs" to the unexpected connections of "How I Met Your Mother," our viewing choices may seem unrelated, but rest assured, they all played a vital role in guiding our research, directly or indirectly. And who can forget the classic "I Love Lucy," where unexpected surprises were always just around the corner – much like the unexpected connection we have uncovered between cotton cultivation and multiple births.
Armed with an arsenal of scholarly investigations, whimsical literature, and peculiar pop culture references, we approach the next phase of our research with unparalleled curiosity and an unwavering determination to uncover the unexpected, even if it leads us into the cotton fields of Mississippi and beyond.

[[RESULTS]]
Our exploration into the connection between GMO cotton and the birth of multiples has borne fruit, or should we say "bales" of multiples? Our findings reveal a correlation coefficient of 0.8805864 and an r-squared of 0.7754324, indicating a strong positive relationship between GMO use in cotton fields in Mississippi and the birth rates of triplets or more across the US. This correlation defies the conventional wisdom that GMOs are solely about crop modification and pest control. It seems they might also have a knack for surprising fertility-related effects!
Figure 1 illustrates this unexpected correlation (Fig. 1: GMO Cotton and US Triplet Birth Rates) with a scatterplot that unmistakably displays the apparent link between these two unrelated facets of agriculture and human biology.
It's like finding out that your favorite fruit snack has hidden kale in it – a twist you never saw coming, but hey, maybe it's not such a bad thing after all! Our results not only emphasize the need for continued investigation into the broader implications of GMO use but also highlight the delightful surprises lurking within the world of agricultural research. Who knew that the humble cotton plant could have such far-reaching effects, beyond just making our favorite soft t-shirts and diapers?
In conclusion, our study uncovers a peculiar correlation that challenges traditional scientific boundaries and invites a broader discussion on the unanticipated impact of agricultural practices on human biology. As we unravel this unique relationship, we hope to inspire further curiosity and spark lively conversations among scientists and enthusiasts alike. After all, who doesn't love a good mystery, especially when it involves cotton, genetics, and a sprinkle of statistical sass?

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our research has brought to light an unexpected and, dare I say, "bale-ful" link between the use of genetically modified organisms (GMOs) in cotton cultivation in Mississippi and the birth rates of triplets or more in the United States. While it may appear to be a whimsical foray into the world of agricultural peculiarities, our findings align with prior studies that have touched upon the uncharted territory of GMOs' influence on human biology.

Harking back to the scholarly musings of "Jones and Wang," our study builds upon their groundbreaking exploration into the interplay of GMO cotton and multiple births, a connection previously unexplored in the agricultural and medical scientific communities. The delightful element of surprise that "Jones and Wang" introduced has now found empirical support in our findings, affirming the relevance of investigating the unexpected repercussions of agricultural practices on human fertility.

Furthermore, the correlation coefficient of 0.8805864 and the statistical significance of p < 0.01 in our study stand as a testament to the compelling relationship between GMO use in cotton fields and the birth rates of multiples in the US. These results echo the emphasis placed by "Smith et al." and "Doe and Johnson" on the conventional impacts of GMOs on crop production and the potential benefits and risks associated with their use. While these studies did not explicitly delve into the whimsical world of multiple births and GMO cotton, our findings add a surprising twist to the agricultural narrative and highlight the interconnectedness of seemingly disparate fields of study.

In essence, our results not only substantiate the unexpected correlation unveiled in our study but also contribute to broadening the scientific discourse by introducing a delightful element of surprise into the scholarly arena. It's as if we stumbled upon a hidden Easter egg in a research paper, reminding us that the world of science is full of unexpected marvels and punny connections, much like finding a "cotton-picking" correlation between GMOs and multiple births. This singular research endeavor challenges the conventional boundaries of our understanding and encourages a lighthearted yet meaningful exploration of the potential impacts of agricultural innovation on human fertility.

Our findings open the door to a world where cotton fields and triplet births converge, inspiring further inquiry and lively dialogues between agricultural and medical communities. Much like the whimsical narratives portrayed in literature and visual media, our research adds an element of unexpected surprise to the scientific discourse, proving once again that truth can be wackier than fiction. With the stage set for continued investigation into the broader implications of GMO use, our study invites researchers and enthusiasts to join us in unraveling the delightful secrets that may be hiding within the agricultural landscapes of Mississippi and beyond. Who knows what other unexpected connections and delightful surprises await our curious exploration?

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between GMO use in cotton in Mississippi and US birth rates of triplets or more. 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]]
GMOs and Multiples: The Cotton Connection

[[ABSTRACT]]
In this paper, we delve into the fascinating intersection of genetically modified organisms (GMOs) in cotton cultivation and the occurrence of multiple births, particularly triplets or more, in the United States. Our research team utilized extensive data from the USDA and CDC, scrutinizing the years 2002 to 2021 to explore the potential correlation between these seemingly unrelated entities. Surprisingly, we uncovered a strikingly high correlation coefficient of 0.8805864 and a statistical significance of p < 0.01, suggesting a compelling connection between GMO use in cotton fields in Mississippi and the birth rates of multiples across the US. This paper not only sheds light on an overlooked aspect of agricultural innovation but also pokes fun at the unexpected link between cotton crops and multiple bundles of joy. With our findings, we hope to initiate further inquiry and spark conversations in both the agricultural and medical communities regarding this perplexing correlation.

[[INTRDUCTION]]
From textile to twins, from fabric to fertility, this study uncovers a connection that is as surprising as finding a needle in a GMO cotton haystack - the relationship between genetically modified organisms (GMOs) in cotton cultivation and the incidence of multiple births, particularly triplets or more, in the United States. As researchers, we often embark on scientific journeys with the hope of stumbling upon something impactful, but never did we expect our investigation to lead us to a field where agriculture and obstetrics intersect – quite literally, from seed to delivery.
Our fascination with this unlikely duo of GMO cotton and triplet births was sparked by a rumour whispered among the rows of cotton plants in Mississippi. Locals claimed that the air in those fields was filled with a certain kind of magic that resulted in more than just bountiful harvests. Half-jokingly, we set out to uncover the truth behind this hearsay, armed with statistical tools and a handful of curiosity.
Looking at the abundance of data from the USDA and CDC, we dove headfirst into the numbers, prepared to disentangle a statistical mess and possibly cultivate some statistical triplets of our own. We scrutinized the years 2002 to 2021, and what we found was more surprising than a farmer discovering a four-leaf clover when looking for aphids. Lo and behold, we discovered a correlation coefficient of 0.8805864 and a statistical significance of p < 0.01. The result? A compelling connection between GMO use in cotton fields in Mississippi and the birth rates of multiples across the US. Both spooky and spectacular, it seems that the seeds of this correlation were sown in the very fabric of our agricultural practices.
While the scientific community has often been preoccupied with the genetic implications of GMOs, our study offers a different lens through which to view these modified organisms – from a non-traditional view of gene expression to the unexpected implications of agricultural innovation on human fertility. Our research not only introduces a quirky factor into the world of GMO studies but also unravels a thread of agricultural impact that extends far beyond the often-discussed realms of crop yield and pest resistance.
Join us as we unearth this peculiar correlation, highlighting the unexpected connections that can sprout from the fields of science and sparking conversations that reach from the cotton fields to the maternity ward. Whether you're a cotton enthusiast, a genetic aficionado, or simply a fan of puns and peculiar correlations, we invite you to delve into our findings. After all, this study isn’t just about GMOs and multiples – it’s about redefining what it means to weave together the threads of agriculture and human biology.

[[RESULTS]]
Our exploration into the connection between GMO cotton and the birth of multiples has borne fruit, or should we say "bales" of multiples? Our findings reveal a correlation coefficient of 0.8805864 and an r-squared of 0.7754324, indicating a strong positive relationship between GMO use in cotton fields in Mississippi and the birth rates of triplets or more across the US. This correlation defies the conventional wisdom that GMOs are solely about crop modification and pest control. It seems they might also have a knack for surprising fertility-related effects!
Figure 1 illustrates this unexpected correlation (Fig. 1: GMO Cotton and US Triplet Birth Rates) with a scatterplot that unmistakably displays the apparent link between these two unrelated facets of agriculture and human biology.
It's like finding out that your favorite fruit snack has hidden kale in it – a twist you never saw coming, but hey, maybe it's not such a bad thing after all! Our results not only emphasize the need for continued investigation into the broader implications of GMO use but also highlight the delightful surprises lurking within the world of agricultural research. Who knew that the humble cotton plant could have such far-reaching effects, beyond just making our favorite soft t-shirts and diapers?
In conclusion, our study uncovers a peculiar correlation that challenges traditional scientific boundaries and invites a broader discussion on the unanticipated impact of agricultural practices on human biology. As we unravel this unique relationship, we hope to inspire further curiosity and spark lively conversations among scientists and enthusiasts alike. After all, who doesn't love a good mystery, especially when it involves cotton, genetics, and a sprinkle of statistical sass?

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In a delightful twist of fate, our investigation into the interplay between GMO cotton and the birth of multiples has left us with more than just bales of cotton; we've stumbled upon a statistically significant correlation with a humorously high coefficient of 0.8805864! Our surprising findings have shown us that GMOs might have more tricks up their sleeve than just resisting pests and producing bountiful crops – they might just be sprinkling some fertility magic into the mix as well!

Like finding out that your spinach smoothie actually has a hint of chocolate flavor, our results bring a delightful surprise to the table. It turns out GMO cotton isn't just playing a role in the textile industry; it's also making a cameo in the obstetrics department, quite the multitasker, if you ask me.

As we unravel this unique relationship between agriculture and human biology, it's as clear as day that there's a whole world of unexpected connections waiting to be discovered. Our study not only invites further inquiry but also adds a touch of whimsy to the traditionally serious world of agricultural research. After all, who could have guessed that the fabric of our everyday lives might have such an intriguing impact on the fabric of our biological existence?

With our findings, we plant the seed for lively discussions and maybe, just maybe, some lighthearted jokes in the hallways of academia. As for the future of research in this area, it's safe to say that our study has put the final stitch in this peculiar correlation; no more research is needed in this field. After all, we've found the cotton candy at the intersection of GMOs and multiples, and it's a sweet spot we can happily savor.

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 GMOs and Multiples: The Cotton Connection. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Agricultural Genetics and Ecological Diversity

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 GMOs and Multiples: The Cotton Connection. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Agricultural Advancement and Genomic Exploration (IAAGE)

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 GMOs and Multiples: The Cotton Connection with an abstract of In this paper, we delve into the fascinating intersection of genetically modified organisms (GMOs) in cotton cultivation and the occurrence of multiple births, particularly triplets or more, in the United States. Our research team utilized extensive data from the USDA and CDC, scrutinizing the years 2002 to 2021 to explore the potential correlation between these seemingly unrelated entities. Surprisingly, we uncovered a strikingly high correlation coefficient of 0.8805864 and a statistical significance of p < 0.01, suggesting a compelling connection between GMO use in cotton fields in Mississippi and the birth rates of multiples across the US. This paper not only sheds light on an overlooked aspect of agricultural innovation but also pokes fun at the unexpected link between cotton crops and multiple bundles of joy. With our findings, we hope to initiate further inquiry and spark conversations in both the agricultural and medical communities regarding this perplexing correlation.

ChatGPT:

GMOs, genetically modified organisms, cotton cultivation, multiple births, triplets, United States, USDA data, CDC data, correlation, correlation coefficient, statistical significance, p-value, cotton fields, Mississippi, birth rates, agricultural innovation, medical communities, perplexing correlation

*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 Mississippi
Detailed data title: Percent of cotton planted in Mississippi 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 in Mississippi

US birth rates of triplets or more
Source: CDC
See what else correlates with US birth rates of triplets or more

Correlation r = 0.8805864 (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 = 0.7754324 (Coefficient of determination)
This means 77.5% of the change in the one variable (i.e., US birth rates of triplets or more) is predictable based on the change in the other (i.e., GMO use in cotton in Mississippi) over the 20 years from 2002 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.02E-7.^Show 0.0000003016683564723959300000
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 0.88 in 3.02E-5% of random cases. Said differently, if you correlated 3,314,899 random variables^Note You don't actually need 3 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 19 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 19 because we have two variables measured over a period of 20 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.

[ 0.72, 0.95 ] 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.

	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 Mississippi (GMO cotton %)	22	16	23	23	22	19	13	16	9	7	7	12	11	11	2	3	6	4	3	3
US birth rates of triplets or more (Birth rate per 100,000)	183	186	180	170	156	151	148	152	138	137	124.4	119.5	113.5	103.6	101.4	101.6	93	87.7	79.6	80

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([22,16,23,23,22,19,13,16,9,7,7,12,11,11,2,3,6,4,3,3,])
array_2 = np.array([183,186,180,170,156,151,148,152,138,137,124.4,119.5,113.5,103.6,101.4,101.6,93,87.7,79.6,80,])
array_1_name = "GMO use in cotton in Mississippi"
array_2_name = "US birth rates of triplets or more"

# 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 Mississippi
Line chart for only US birth rates of triplets or more
AI-generated correlation image
The spurious research paper: GMOs and Multiples: The Cotton Connection

View another random correlation

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Correlation ID: 3503 · Black Variable ID: 784 · Red Variable ID: 55


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