Spurious correlation #4,231 · 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 corn grown in Missouri and the second variable is U.S. intercountry adoptions. 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 corn grown in Missouri and the second variable is U.S. intercountry adoptions. 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 use in Missouri corn decreased, it led to the production of smaller, non-GMO corn. This smaller corn size then made its way into the popular corn mazes in the state. These unexpectedly challenging mazes resulted in a decline in the number of visitors making it through to the exit. To compensate, local families had to spend more time in the mazes, inadvertently bonding and deciding to pursue domestic adoptions instead of intercountry adoptions, leading to a decrease on the national level.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image showing a Missouri cornfield filled with smaller non-GMO cornstalks sprouting up. The cornstalks form intricate and visually stunning mazes, with families visible inside, navigating the twists and turns. The atmosphere is one of wonder and bonding as the families work together to find their way. The image should capture the sense of connection and the unexpected beauty found within the cornfield. 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 corn grown in Missouri and U.S. intercountry adoptions. 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: Art nouveau Cute 2D animation Sci-fi fantacy Hyper-realistic Cute 2D animation
ChatGPT: Sci-fi fantasy

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 corn grown in Missouri and U.S. intercountry adoptions, in the following situation: As GMO use in Missouri corn decreased, it led to the production of smaller, non-GMO corn. This smaller corn size then made its way into the popular corn mazes in the state. These unexpectedly challenging mazes resulted in a decline in the number of visitors making it through to the exit. To compensate, local families had to spend more time in the mazes, inadvertently bonding and deciding to pursue domestic adoptions instead of intercountry adoptions, leading to a decrease on the national level.. Keep the description simple.
ChatGPT: In a Missouri cornfield, smaller non-GMO cornstalks grow, eventually becoming part of the corn mazes that draw many visitors. Families find themselves tangled within the unexpectedly challenging mazes, spending more time figuring their way out. As a result, the families form stronger bonds and decide to pursue domestic adoptions over international ones.

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: [ Sci-fi fantasy ] and depicting this scene: In a Missouri cornfield, smaller non-GMO cornstalks grow, eventually becoming part of the corn mazes that draw many visitors. Families find themselves tangled within the unexpectedly challenging mazes, spending more time figuring their way out. As a result, the families form stronger bonds and decide to pursue domestic adoptions over international ones. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image showing a Missouri cornfield filled with smaller non-GMO cornstalks sprouting up. The cornstalks form intricate and visually stunning mazes, with families visible inside, navigating the twists and turns. The atmosphere is one of wonder and bonding as the families work together to find their way. The image should capture the sense of connection and the unexpected beauty found within the cornfield.

*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 corn grown in Missouri caused U.S. intercountry adoptions to decrease.

AI academic paper

(Because p < 0.01)

The Kernels of Fate: Exploring the Corny Connection Between GMO Use in Missouri and U.S. Intercountry Adoptions

Journal of Agricultural Genetics and Social Dynamics

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 corn grown in Missouri and U.S. intercountry adoptions. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name a couple popular internet memes that are related to one of the topics.

Here is the title and abstract of the paper:
[[TITLE]]
The Kernels of Fate: Exploring the Corny Connection Between GMO Use in Missouri and U.S. Intercountry Adoptions

[[ABSTRACT]]
This study delves into the, perhaps unexpected, relationship between genetically modified (GMO) corn cultivation in Missouri and intercountry adoptions in the United States from 2000 to 2021. Using data from the USDA and Bureau of Consular Affairs, we uncovered a correlation coefficient of 0.9238676 with a p-value of less than 0.01, indicating a meaningful association. While causality cannot be definitively established, the robustness of the statistical findings suggests a complex and intriguing link. The implications of this connection remain enigmatic and warrant further investigation. This work sheds light on the potentially divergent paths of GMO corn and international adoptions, opening a cornucopia of possibilities for future research and policy considerations.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
The intersection of genetically modified organisms (GMOs) and the convoluted web of international adoption may seem, at first glance, as disparate as apples and oranges. However, as we peel back the layers of this peculiar correlation, we uncover a cornucopia of unexpected connections, unveiling a kernel of truth that challenges conventional wisdom and leaves us pondering the twisty tendrils of fate.
The ubiquitous presence of GMO corn in the agricultural landscape of Missouri has long been a subject of scrutiny and debate, with proponents extolling its potential to boost yields and resist pests, while detractors raise concerns about environmental impact and unintended consequences. At the same time, intercountry adoptions in the United States have followed a distinct trajectory, shaped by geopolitical, socioeconomic, and cultural forces. The intertwining of these two seemingly disparate domains presents a rich tapestry of inquiry, offering fertile ground for exploration.
Undoubtedly, this intriguing correlation raises more than a few eyebrows and piques the curiosity of even the most stoic researchers. As we delve into the depths of this unexpected relationship, we must tread carefully, mindful of the potential pitfalls and thorny thickets that may lie in our path. The enigmatic nature of this association leaves us with more questions than answers, beckoning us to untangle the complex threads that weave together these seemingly distant domains.
In this paper, we undertake the arduous task of unraveling the tangled skein of GMO corn cultivation in Missouri and its ostensible interplay with the labyrinthine landscape of intercountry adoptions in the United States. Drawing upon data from the United States Department of Agriculture (USDA) and the Bureau of Consular Affairs, we embark on a quest to unearth patterns, tease out correlations, and, ideally, shed light on the intricate dance between these enigmatic entities.
As we navigate through this maze of maize and adoption, we seek not only to elucidate the statistical linkages but also to ponder the broader implications of such a corny connection. With a wink to the twists of fate and the capricious whims of chance, let us embark on this scholarly odyssey, armed with statistical rigor and a pervasive sense of curiosity. For in the realm of research, as in life, we often find that the most fascinating discoveries emerge from the most unexpected sources.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The literature review encompasses a comprehensive survey of scholarship aimed at elucidating the interrelationship between genetically modified (GMO) corn cultivation in Missouri and intercountry adoptions in the United States. The authors begin by examining seminal works in the field, such as Smith et al.'s investigation into the agronomic impacts of GMO corn on farming practices in the Midwest. The study underscores the significance of genetic modifications in corn crops and their ramifications for agricultural production, setting the stage for our exploration of their potential influence on broader societal trends.

Doe and colleagues' research delves into the demographic dynamics of intercountry adoptions, shedding light on the multifaceted factors that shape these transnational familial arrangements. Their findings highlight the intricate interplay of cultural, economic, and legal considerations, offering a valuable framework for contextualizing the observed correlation with GMO corn cultivation.

Expanding our purview, Jones's exploration of environmental sustainability in agricultural systems offers insights into the ecological footprint of GMO corn cultivation. The study prompts contemplation of the broader ecosystemic implications of GMO adoption, laying the groundwork for a nuanced consideration of its potential ripple effects on intercountry adoption patterns.

Turning to the realm of non-fiction literature, works such as "Seeds of Deception" and "The Omnivore's Dilemma" contribute to our understanding of the public discourse surrounding GMOs and their implications for agricultural practices. These texts offer a window into the wider societal conversations that infuse our exploration, enriching our perspective with diverse viewpoints and ethical considerations.

On a tangential note, fictional works such as "The Corn Whisperer" and "Maize Runner" provide imaginative fodder for contemplating the intersection of GMO corn and human destinies. While these narratives may dwell in the realm of fiction, they fuel our contemplation of the potential narratives underlying the observed correlation, reminding us of the intricate dance between the real and the imagined in shaping our scholarly inquiries.

Notably, internet memes such as "Corny Conundrums" and "GMOs and Giggles" humorously reflect the permeation of GMO-related discourse into popular culture, serving as quirky reminders of the broader societal consciousness surrounding this topic. Though seemingly lighthearted, these cultural artifacts underscore the intersection of scholarly inquiry and public perception, beckoning us to navigate the cornfield of knowledge with both rigor and whimsy.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between GMO use in corn grown in Missouri and U.S. intercountry adoptions. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from USDA and Bureau of Consular Affairs . You used data from 2000 to 2021

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

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
The Kernels of Fate: Exploring the Corny Connection Between GMO Use in Missouri and U.S. Intercountry Adoptions

[[ABSTRACT]]
This study delves into the, perhaps unexpected, relationship between genetically modified (GMO) corn cultivation in Missouri and intercountry adoptions in the United States from 2000 to 2021. Using data from the USDA and Bureau of Consular Affairs, we uncovered a correlation coefficient of 0.9238676 with a p-value of less than 0.01, indicating a meaningful association. While causality cannot be definitively established, the robustness of the statistical findings suggests a complex and intriguing link. The implications of this connection remain enigmatic and warrant further investigation. This work sheds light on the potentially divergent paths of GMO corn and international adoptions, opening a cornucopia of possibilities for future research and policy considerations.

[[INTRODUCTION]]
INTRODUCTION
The intersection of genetically modified organisms (GMOs) and the convoluted web of international adoption may seem, at first glance, as disparate as apples and oranges. However, as we peel back the layers of this peculiar correlation, we uncover a cornucopia of unexpected connections, unveiling a kernel of truth that challenges conventional wisdom and leaves us pondering the twisty tendrils of fate.
The ubiquitous presence of GMO corn in the agricultural landscape of Missouri has long been a subject of scrutiny and debate, with proponents extolling its potential to boost yields and resist pests, while detractors raise concerns about environmental impact and unintended consequences. At the same time, intercountry adoptions in the United States have followed a distinct trajectory, shaped by geopolitical, socioeconomic, and cultural forces. The intertwining of these two seemingly disparate domains presents a rich tapestry of inquiry, offering fertile ground for exploration.
Undoubtedly, this intriguing correlation raises more than a few eyebrows and piques the curiosity of even the most stoic researchers. As we delve into the depths of this unexpected relationship, we must tread carefully, mindful of the potential pitfalls and thorny thickets that may lie in our path. The enigmatic nature of this association leaves us with more questions than answers, beckoning us to untangle the complex threads that weave together these seemingly distant domains.
In this paper, we undertake the arduous task of unraveling the tangled skein of GMO corn cultivation in Missouri and its ostensible interplay with the labyrinthine landscape of intercountry adoptions in the United States. Drawing upon data from the United States Department of Agriculture (USDA) and the Bureau of Consular Affairs, we embark on a quest to unearth patterns, tease out correlations, and, ideally, shed light on the intricate dance between these enigmatic entities.
As we navigate through this maze of maize and adoption, we seek not only to elucidate the statistical linkages but also to ponder the broader implications of such a corny connection. With a wink to the twists of fate and the capricious whims of chance, let us embark on this scholarly odyssey, armed with statistical rigor and a pervasive sense of curiosity. For in the realm of research, as in life, we often find that the most fascinating discoveries emerge from the most unexpected sources.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The data utilized in this study was obtained from various sources, including the United States Department of Agriculture (USDA) and the Bureau of Consular Affairs. The selection of these sources was based on their comprehensive coverage of GMO corn cultivation in Missouri and intercountry adoption statistics in the United States. Given the eclectic nature of the research question, data was collected from the vast expanses of the internet, akin to intrepid explorers navigating the digital wilderness in search of elusive patterns. The period under investigation spanned from the year 2000 to 2021, encompassing a cornucopia of historical events and global shifts that may have influenced the interplay between GMO corn and intercountry adoptions.

Statistical Analysis:
The quantitative analysis employed in this study involved a series of robust statistical methods, reminiscent of an intricate dance between data points and coefficients. Correlation analysis was utilized to discern the potential relationship between the cultivation of GMO corn in Missouri and the incidence of intercountry adoptions in the United States. The strengths of these associations were gauged using correlation coefficients and p-values, allowing for a nuanced understanding of the interwoven dynamics at play. The statistical techniques employed were as meticulous as the work of a watchmaker, carefully examining the intricate mechanisms underlying the seemingly disparate domains of agriculture and international adoption.

Control Variables:
To mitigate the risk of spurious correlations and to ensure the validity of the observed linkages, several control variables were incorporated into the analysis. These included factors such as economic indicators, demographic trends, geopolitical shifts, and other relevant variables that could confound the relationship between GMO corn cultivation and intercountry adoptions. The inclusion of these control variables served as a shield against potential confounding influences, akin to the protective armor of a valiant knight shielding against the whims of chance and unforeseen interferences.

Robustness Checks:
In order to ascertain the robustness of the observed correlations, sensitivity analyses and robustness checks were conducted. These checks were akin to stress-testing the findings, ensuring that the reported connections between GMO corn cultivation in Missouri and intercountry adoptions withstood the scrutiny of alternative model specifications and methodological variations. Much like stress-testing a suspension bridge, these robustness checks aimed to fortify the empirical underpinnings of the uncovered relationship, assuring that the insights gleaned were not merely fleeting mirages in the scholarly desert.

Ethical Considerations:
Throughout the process of data collection and analysis, ethical considerations were upheld with the utmost diligence. The privacy and confidentiality of individuals involved in intercountry adoptions were paramount, and measures were taken to anonymize and aggregate the adoption data to protect the identities of the parties involved. Additionally, the use of GMO corn cultivation data was conducted with a mindful awareness of the ethical dimensions surrounding agricultural practices and the potential implications for global food systems. The ethical compass guiding this research remained steadfast in its commitment to responsible and conscientious inquiry, akin to a moral lodestar navigating the labyrinthine terrain of scholarly investigation.

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 corn grown in Missouri and U.S. intercountry adoptions. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Limit your response to 500 tokens.

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

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

[[TITLE]]
The Kernels of Fate: Exploring the Corny Connection Between GMO Use in Missouri and U.S. Intercountry Adoptions

[[ABSTRACT]]
This study delves into the, perhaps unexpected, relationship between genetically modified (GMO) corn cultivation in Missouri and intercountry adoptions in the United States from 2000 to 2021. Using data from the USDA and Bureau of Consular Affairs, we uncovered a correlation coefficient of 0.9238676 with a p-value of less than 0.01, indicating a meaningful association. While causality cannot be definitively established, the robustness of the statistical findings suggests a complex and intriguing link. The implications of this connection remain enigmatic and warrant further investigation. This work sheds light on the potentially divergent paths of GMO corn and international adoptions, opening a cornucopia of possibilities for future research and policy considerations.

[[LITERATURE REVIEW]]
The literature review encompasses a comprehensive survey of scholarship aimed at elucidating the interrelationship between genetically modified (GMO) corn cultivation in Missouri and intercountry adoptions in the United States. The authors begin by examining seminal works in the field, such as Smith et al.'s investigation into the agronomic impacts of GMO corn on farming practices in the Midwest. The study underscores the significance of genetic modifications in corn crops and their ramifications for agricultural production, setting the stage for our exploration of their potential influence on broader societal trends.
Doe and colleagues' research delves into the demographic dynamics of intercountry adoptions, shedding light on the multifaceted factors that shape these transnational familial arrangements. Their findings highlight the intricate interplay of cultural, economic, and legal considerations, offering a valuable framework for contextualizing the observed correlation with GMO corn cultivation.
Expanding our purview, Jones's exploration of environmental sustainability in agricultural systems offers insights into the ecological footprint of GMO corn cultivation. The study prompts contemplation of the broader ecosystemic implications of GMO adoption, laying the groundwork for a nuanced consideration of its potential ripple effects on intercountry adoption patterns.
Turning to the realm of non-fiction literature, works such as "Seeds of Deception" and "The Omnivore's Dilemma" contribute to our understanding of the public discourse surrounding GMOs and their implications for agricultural practices. These texts offer a window into the wider societal conversations that infuse our exploration, enriching our perspective with diverse viewpoints and ethical considerations.
On a tangential note, fictional works such as "The Corn Whisperer" and "Maize Runner" provide imaginative fodder for contemplating the intersection of GMO corn and human destinies. While these narratives may dwell in the realm of fiction, they fuel our contemplation of the potential narratives underlying the observed correlation, reminding us of the intricate dance between the real and the imagined in shaping our scholarly inquiries.
Notably, internet memes such as "Corny Conundrums" and "GMOs and Giggles" humorously reflect the permeation of GMO-related discourse into popular culture, serving as quirky reminders of the broader societal consciousness surrounding this topic. Though seemingly lighthearted, these cultural artifacts underscore the intersection of scholarly inquiry and public perception, beckoning us to navigate the cornfield of knowledge with both rigor and whimsy.

[[RESULTS]]
The analysis of the data collected from the USDA and Bureau of Consular Affairs revealed a striking correlation between GMO use in corn grown in Missouri and U.S. intercountry adoptions from 2000 to 2021. The correlation coefficient of 0.9238676 and the r-squared value of 0.8535314 indicated a strong and significant relationship between these seemingly disparate variables. The p-value of less than 0.01 further underscored the robustness of this association, leaving researchers scratching their heads in amazement at the unexpected link.
Figure 1 depicts the scatterplot illustrating the robust correlation between GMO use in corn cultivated in Missouri and U.S. intercountry adoptions. The plot provides a visual representation of the striking connection between these two domains, serving as a vivid reminder that truth can indeed be stranger than fiction.
The results of this study not only raise eyebrows but also prompt a whimsical reflection on the capricious nature of fate. The seemingly unrelated worlds of GMO corn and international adoptions have been brought together in an unforeseen pas de deux, inviting observers to ponder the twists and turns of destiny that have led to this intriguing connection. Further research is warranted to unpack the implications of this unanticipated correlation and explore the cornucopia of possibilities it presents for our understanding of agricultural, sociocultural, and geopolitical dynamics.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of this study provide empirical support for the unexpected association between GMO use in corn grown in Missouri and intercountry adoptions in the United States, as suggested by the literature review. The robust correlation coefficient and statistically significant p-value lend credence to the notion that there may indeed be kernels of truth in the seemingly whimsical connection between these disparate domains.

The work of Smith et al. in elucidating the agronomic impacts of GMO corn on farming practices in the Midwest appears to have set the stage for our investigation, offering a sturdy foundation for the exploration of broader societal implications. Similarly, Doe and colleagues' insights into the demographic dynamics of intercountry adoptions resonate harmoniously with our findings, as they paint a picture of the intricate interplay of cultural, economic, and legal factors underlying transnational familial arrangements. This resonance highlights the complex interweaving of genetic modifications in corn crops and global demographic patterns, inviting contemplation of the multifaceted cascade of influences that shape our social fabric.

Jones's exploration of environmental sustainability in agricultural systems also appears prescient in light of our results, as it prompts consideration of the broader ripple effects of GMO adoption on societal dynamics. The environmental ripples may well extend to the societal pond, stirring the waters of international adoption patterns in unforeseen ways, underscoring the intricate dance between human agency and environmental forces in shaping our collective destinies.

The intertwining of scholarly inquiry and public perception, as underscored by the inclusion of internet memes in the literature review, finds echoes in the unexpected confluence of GMO use in corn and international adoption patterns. While seemingly whimsical, these artifacts of popular culture serve as playful harbingers of the potential for scholarly inquiry to intersect with the unpredictable currents of human experience, prompting contemplation of the delightful serendipity that underscores our scholarly pursuits.

In light of the robust statistical evidence, the seemingly lighthearted musings of "Corny Conundrums" and "GMOs and Giggles" take on a surprisingly earnest hue, inviting us to gaze into the cornfield of knowledge with both rigour and whimsy. The unexpected correlation between GMO use in corn grown in Missouri and U.S. intercountry adoptions serves as a striking reminder that scholarly inquiry, like life itself, is replete with unanticipated twists and turns, prompting us to set sail on a cornucopia of possibilities for future investigation and scholarly reflection.

The results of this study, while highlighting the capricious nature of fate, also beckon us to embrace with both amusement and intellectual curiosity the unexpected interplay of GMO cultivation and international adoption, underscoring the kaleidoscopic narratives and potential trajectories that weave through the intricate tapestry of human existence.

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 corn grown in Missouri and U.S. intercountry adoptions. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
The Kernels of Fate: Exploring the Corny Connection Between GMO Use in Missouri and U.S. Intercountry Adoptions

[[ABSTRACT]]
This study delves into the, perhaps unexpected, relationship between genetically modified (GMO) corn cultivation in Missouri and intercountry adoptions in the United States from 2000 to 2021. Using data from the USDA and Bureau of Consular Affairs, we uncovered a correlation coefficient of 0.9238676 with a p-value of less than 0.01, indicating a meaningful association. While causality cannot be definitively established, the robustness of the statistical findings suggests a complex and intriguing link. The implications of this connection remain enigmatic and warrant further investigation. This work sheds light on the potentially divergent paths of GMO corn and international adoptions, opening a cornucopia of possibilities for future research and policy considerations.

[[INTRDUCTION]]
INTRODUCTION
The intersection of genetically modified organisms (GMOs) and the convoluted web of international adoption may seem, at first glance, as disparate as apples and oranges. However, as we peel back the layers of this peculiar correlation, we uncover a cornucopia of unexpected connections, unveiling a kernel of truth that challenges conventional wisdom and leaves us pondering the twisty tendrils of fate.
The ubiquitous presence of GMO corn in the agricultural landscape of Missouri has long been a subject of scrutiny and debate, with proponents extolling its potential to boost yields and resist pests, while detractors raise concerns about environmental impact and unintended consequences. At the same time, intercountry adoptions in the United States have followed a distinct trajectory, shaped by geopolitical, socioeconomic, and cultural forces. The intertwining of these two seemingly disparate domains presents a rich tapestry of inquiry, offering fertile ground for exploration.
Undoubtedly, this intriguing correlation raises more than a few eyebrows and piques the curiosity of even the most stoic researchers. As we delve into the depths of this unexpected relationship, we must tread carefully, mindful of the potential pitfalls and thorny thickets that may lie in our path. The enigmatic nature of this association leaves us with more questions than answers, beckoning us to untangle the complex threads that weave together these seemingly distant domains.
In this paper, we undertake the arduous task of unraveling the tangled skein of GMO corn cultivation in Missouri and its ostensible interplay with the labyrinthine landscape of intercountry adoptions in the United States. Drawing upon data from the United States Department of Agriculture (USDA) and the Bureau of Consular Affairs, we embark on a quest to unearth patterns, tease out correlations, and, ideally, shed light on the intricate dance between these enigmatic entities.
As we navigate through this maze of maize and adoption, we seek not only to elucidate the statistical linkages but also to ponder the broader implications of such a corny connection. With a wink to the twists of fate and the capricious whims of chance, let us embark on this scholarly odyssey, armed with statistical rigor and a pervasive sense of curiosity. For in the realm of research, as in life, we often find that the most fascinating discoveries emerge from the most unexpected sources.

[[RESULTS]]
The analysis of the data collected from the USDA and Bureau of Consular Affairs revealed a striking correlation between GMO use in corn grown in Missouri and U.S. intercountry adoptions from 2000 to 2021. The correlation coefficient of 0.9238676 and the r-squared value of 0.8535314 indicated a strong and significant relationship between these seemingly disparate variables. The p-value of less than 0.01 further underscored the robustness of this association, leaving researchers scratching their heads in amazement at the unexpected link.
Figure 1 depicts the scatterplot illustrating the robust correlation between GMO use in corn cultivated in Missouri and U.S. intercountry adoptions. The plot provides a visual representation of the striking connection between these two domains, serving as a vivid reminder that truth can indeed be stranger than fiction.
The results of this study not only raise eyebrows but also prompt a whimsical reflection on the capricious nature of fate. The seemingly unrelated worlds of GMO corn and international adoptions have been brought together in an unforeseen pas de deux, inviting observers to ponder the twists and turns of destiny that have led to this intriguing connection. Further research is warranted to unpack the implications of this unanticipated correlation and explore the cornucopia of possibilities it presents for our understanding of agricultural, sociocultural, and geopolitical dynamics.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, the findings of this study reveal a cornucopia of unexpected connections between GMO use in corn grown in Missouri and U.S. intercountry adoptions. The robust correlation coefficient and the compelling visual representation in the scatterplot reinforce the notion that truth can indeed be stranger than fiction, or should we say, cornier than expected. The implications of this connection beckon us to ponder the capricious whims of fate that have led to this intriguing association, leaving us to marvel at the enigmatic dance between seemingly disparate domains.

While causality cannot be unequivocally established, the statistical findings underscore the need to navigate the maze of maize and adoption with caution and diligence. The robustness of the correlation prompts us to reflect on the twists of destiny and the unpredictability of research inquiry. It seems that our scholarly odyssey has led us to uncharted territories, cultivating fertile ground for future investigations and perhaps even a-maize-ing revelations.

In light of these findings, it is clear that no more research is needed in this area. Corn-gratulations to all involved in this research!

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited The Kernels of Fate: Exploring the Corny Connection Between GMO Use in Missouri and U.S. Intercountry Adoptions. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Agricultural Genetics and Social Dynamics

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited The Kernels of Fate: Exploring the Corny Connection Between GMO Use in Missouri and U.S. Intercountry Adoptions. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Ethical Sustainable Agriculture and Family Dynamics (SESAFD)

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled The Kernels of Fate: Exploring the Corny Connection Between GMO Use in Missouri and U.S. Intercountry Adoptions with an abstract of This study delves into the, perhaps unexpected, relationship between genetically modified (GMO) corn cultivation in Missouri and intercountry adoptions in the United States from 2000 to 2021. Using data from the USDA and Bureau of Consular Affairs, we uncovered a correlation coefficient of 0.9238676 with a p-value of less than 0.01, indicating a meaningful association. While causality cannot be definitively established, the robustness of the statistical findings suggests a complex and intriguing link. The implications of this connection remain enigmatic and warrant further investigation. This work sheds light on the potentially divergent paths of GMO corn and international adoptions, opening a cornucopia of possibilities for future research and policy considerations.

ChatGPT:

GMO corn cultivation Missouri, intercountry adoptions United States, USDA data analysis, Bureau of Consular Affairs adoption statistics, correlation coefficient GMO corn and intercountry adoptions, causality GMO corn and adoption, implications GMO corn adoption link

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

U.S. intercountry adoptions
Source: Bureau of Consular Affairs
See what else correlates with U.S. intercountry adoptions

Correlation r = 0.9238676 (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 = 0.8535314 (Coefficient of determination)
This means 85.4% of the change in the one variable (i.e., U.S. intercountry adoptions) is predictable based on the change in the other (i.e., GMO use in corn grown in Missouri) over the 22 years from 2000 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 8.6E-10.^Show 0.0000000008600128144432369000
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 0.92 in 8.6E-8% of random cases. Said differently, if you correlated 1,162,773,372 random variables^Note You don't actually need 1 billion variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 21 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 21 because we have two variables measured over a period of 22 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.82, 0.97 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: ^Note If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
GMO use in corn grown in Missouri (GMO corn %)	20	23	27	32	32	37	38	30	27	23	15	27	18	5	4	5	4	2	2	2	2	2
U.S. intercountry adoptions (Adoptions)	18857	19647	21467	21654	22991	22734	20680	19608	17456	12744	11058	9319	8668	7092	6438	5644	5370	4714	4059	2971	1622	1785

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. ^{Show the steps to do this.} Step 1: Download and install Python on your computer.

Step 2: Open a plaintext editor like Notepad and paste the code below into it.

Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

Step 7: Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."

# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([20,23,27,32,32,37,38,30,27,23,15,27,18,5,4,5,4,2,2,2,2,2,])
array_2 = np.array([18857,19647,21467,21654,22991,22734,20680,19608,17456,12744,11058,9319,8668,7092,6438,5644,5370,4714,4059,2971,1622,1785,])
array_1_name = "GMO use in corn grown in Missouri"
array_2_name = "U.S. intercountry adoptions"

# 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
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Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only GMO use in corn grown in Missouri
Line chart for only U.S. intercountry adoptions
AI-generated correlation image
The spurious research paper: The Kernels of Fate: Exploring the Corny Connection Between GMO Use in Missouri and U.S. Intercountry Adoptions

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Correlation ID: 4231 · Black Variable ID: 716 · Red Variable ID: 216


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