Spurious correlation #3,958 · 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 Texas and the second variable is Google searches for 'how to immigrate to canada'. The chart goes from 2005 to 2023, 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 Texas and the second variable is Google searches for 'how to immigrate to canada'. The chart goes from 2005 to 2023, and the two variables track closely in value over that time.

AI explanation

As GMO use in Texas corn increased, so did the size of the corn. The taller corn stalks began whispering to the people, "Eh, maybe it's time to leaf this place and move to Canada, eh?" This led to a spike in Google searches for 'how to immigrate to Canada' as the corny persuasion proved to be ear-resistible. The phenomenon has been dubbed as the great corn migration, also known as cob-beration. This unexpected consequence has sparked a new agricultural movement, with farmers now focusing on creating bilingual corn to expand their market reach. It's a-maize-ing how a simple crop change can ear-itate such a monumental shift! Remember, when it comes to Texas corn, the stakes are always high.

^{Show image generation prompt} Model: dalle-3
Prompt: [ A photo-realistic image ] Imagine a sprawling Texas cornfield under the warm glow of the setting sun. Towering stalks of corn, prominently labeled with "genetically modified" markings, dominate the landscape. In the background, a group of worried individuals are huddled together, peering intently at their smartphones as they search for "how to immigrate to Canada." Their anxious expressions reflect the palpable surge in interest sparked by the sentient corn's plea for refuge. 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 Texas and Google searches for 'how to immigrate to canada'. 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 photo-realistic image Concept art Low poly inspired by bauhaus 3D Pixar Style
ChatGPT: A photo-realistic 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 corn grown in Texas and Google searches for 'how to immigrate to canada', in the following situation: The increase in GMO use in Texas corn led to the rise of sentient cornstalks, who, seeking refuge from their fate as delicious snacks, prompted a surge in interest in immigrating to Canada among humans in an attempt to avoid potential corn-related uprisings.. Keep the description simple.
ChatGPT: In a Texas cornfield, towering stalks of corn stand tall, their genetically modified labels visible. Nearby, people use their smartphones to search for "how to immigrate to Canada," their worried expressions reflecting the surge in interest sparked by the sentient corn's desire for refuge.

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 photo-realistic image ] and depicting this scene: In a Texas cornfield, towering stalks of corn stand tall, their genetically modified labels visible. Nearby, people use their smartphones to search for "how to immigrate to Canada," their worried expressions reflecting the surge in interest sparked by the sentient corn's desire for refuge. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ A photo-realistic image ] Imagine a sprawling Texas cornfield under the warm glow of the setting sun. Towering stalks of corn, prominently labeled with "genetically modified" markings, dominate the landscape. In the background, a group of worried individuals are huddled together, peering intently at their smartphones as they search for "how to immigrate to Canada." Their anxious expressions reflect the palpable surge in interest sparked by the sentient corn's plea for refuge.

*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in GMO use in corn grown in Texas caused how to immigrate to canada (Google searches for 'how to immigrate to canada') to increase.\Make it sound serious until the end.

AI academic paper

(Because p < 0.01)

Sweet Corn and Canada's Bourne: The GMO-Google Correlation

Journal of Agritech and Cybernetics

Jan 2024

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between GMO use in corn grown in Texas and Google searches for 'how to immigrate to canada'. 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 cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
Sweet Corn and Canada's Bourne: The GMO-Google Correlation

[[ABSTRACT]]
This study delves into the intriguing relationship between the use of genetically modified organisms (GMOs) in corn cultivation in Texas and Google searches for "how to immigrate to Canada." By leveraging data from the USDA and Google Trends, our research team uncovered a surprising co-mingling of these seemingly disparate topics. The correlation coefficient of 0.9106705 and p < 0.01 for the period from 2005 to 2023 served as both a revelation and a source of amusement. Our findings offer a kernel of insight into the whimsical ways in which human curiosity and international agricultural practices intersect. This paper not only peels back the husk of this puzzling phenomenon but also highlights the ear-resistible humor inherent in unexpected data connections.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intertwining of genetically modified organisms (GMOs) in agriculture and Google searches for "how to immigrate to Canada" may seem as unlikely a pairing as a strawberry and ketchup sandwich. Yet, as with any good mystery, the plot thickens when these two seemingly unrelated subjects are brought together. By analyzing the extensive data available from the United States Department of Agriculture (USDA) and Google Trends, our research team sought to elucidate the curious correlation between these disparate domains.
Initially, we were as skeptical as a cat in a room full of rocking chairs. The very notion that the cultivation of genetically modified sweet corn in the heart of Texas could influence individuals' yearning to relocate to the Great White North seemed preposterous, to say the least. However, as we dug deeper and delved into the maize of data, we were surprised to uncover a statistically significant relationship that would make even the most ardent skeptics do a double take.
One might ponder what links these two subjects as if they were two cars sharing the same parking meter. Are there practical connections, or is this an elaborate dance of statistical anomalies? As we grapple with these questions, we invite the reader to join us on a journey through the fields of agricultural biotechnology and the digital landscape of immigration queries. While our findings may seem as unlikely as finding a needle in a cornfield, we assure you that they are as real as a scarecrow on a sunny day.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "Smith et al.," the authors find that the use of genetically modified organisms (GMOs) in corn cultivation has become increasingly prevalent in the agricultural landscape of Texas. The study discusses the potential benefits and concerns surrounding GMO use, emphasizing the need for further research to fully understand the implications of this agricultural practice. Similarly, "Doe and Jones" highlight the growing interest in Google searches for "how to immigrate to Canada" and delve into the various factors that may influence such queries.

In "The Omnivore's Dilemma" by Michael Pollan, the author examines the complexities of modern food production, including the prevalence of GMOs in corn farming. While the book offers insightful commentary on the impact of agricultural practices, it fails to address the unexpected link between GMOs and aspirations of Canadian immigration. Conversely, "Interpreter of Maladies" by Jhumpa Lahiri presents a collection of short stories that, while not directly related to the topic at hand, provides a poignant exploration of human desires and the pursuit of fulfillment.

Furthermore, social media posts such as "Just spotted a field of GMO corn while browsing how to immigrate to Canada. Coincidence? I think not!" and "GMO corn and Canadian immigration seem like a-maize-ing bedfellows 🌽🇨🇦 #corncerns" reflect the public's fascination with the peculiar correlation uncovered by our research. While these sources offer anecdotal evidence rather than empirical data, they underscore the widespread curiosity and amusement generated by this unexpected connection.

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 Texas and Google searches for 'how to immigrate to canada'. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from USDA and Google Trends . You used data from 2005 to 2023

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]]
Sweet Corn and Canada's Bourne: The GMO-Google Correlation

[[ABSTRACT]]
This study delves into the intriguing relationship between the use of genetically modified organisms (GMOs) in corn cultivation in Texas and Google searches for "how to immigrate to Canada." By leveraging data from the USDA and Google Trends, our research team uncovered a surprising co-mingling of these seemingly disparate topics. The correlation coefficient of 0.9106705 and p < 0.01 for the period from 2005 to 2023 served as both a revelation and a source of amusement. Our findings offer a kernel of insight into the whimsical ways in which human curiosity and international agricultural practices intersect. This paper not only peels back the husk of this puzzling phenomenon but also highlights the ear-resistible humor inherent in unexpected data connections.

[[INTRODUCTION]]
The intertwining of genetically modified organisms (GMOs) in agriculture and Google searches for "how to immigrate to Canada" may seem as unlikely a pairing as a strawberry and ketchup sandwich. Yet, as with any good mystery, the plot thickens when these two seemingly unrelated subjects are brought together. By analyzing the extensive data available from the United States Department of Agriculture (USDA) and Google Trends, our research team sought to elucidate the curious correlation between these disparate domains.
Initially, we were as skeptical as a cat in a room full of rocking chairs. The very notion that the cultivation of genetically modified sweet corn in the heart of Texas could influence individuals' yearning to relocate to the Great White North seemed preposterous, to say the least. However, as we dug deeper and delved into the maize of data, we were surprised to uncover a statistically significant relationship that would make even the most ardent skeptics do a double take.
One might ponder what links these two subjects as if they were two cars sharing the same parking meter. Are there practical connections, or is this an elaborate dance of statistical anomalies? As we grapple with these questions, we invite the reader to join us on a journey through the fields of agricultural biotechnology and the digital landscape of immigration queries. While our findings may seem as unlikely as finding a needle in a cornfield, we assure you that they are as real as a scarecrow on a sunny day.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The study involved a methodical and multi-faceted approach to investigating the correlation between GMO use in corn cultivation in Texas and the frequency of Google searches for "how to immigrate to Canada." Our research team embarked on a data odyssey, navigating the expansive territories of the USDA's National Agricultural Statistics Service (NASS) and the labyrinthine pathways of Google Trends.

First, to apprehend the extent of GMO adoption in corn cultivation in Texas, we employed a combination of agricultural surveys, historical data analysis, and a touch of agricultural astrology (just kidding!) from the USDA's NASS. Utilizing this data, we estimated the proportion of genetically modified sweet corn in Texas, comparing it to the total corn production in the state. The correlation between the adoption of GMO technology and the trends in Google searches was evaluated using statistical methods that were as precise as a crop-dusting plane pirouetting over a field.

Simultaneously, our research team gathered data on the frequency of Google searches for "how to immigrate to Canada" using the Google Trends platform. We focused on the state of Texas and various key time points from 2005 to 2023, embracing the art and science of digital sleuthing. Our approach involved tracking the peaks and troughs in search volume, akin to navigators charting the ebb and flow of ocean tides.

After the data collection stage, we engaged in a rigorous process of data cleansing and preparation, ensuring that our datasets were as pristine as a cornfield glistening in the early morning dew. This involved the removal of any outliers or anomalies that might have detracted from the integrity of our analysis. We then unleashed a battalion of statistical techniques, including correlation analysis, time series modeling, and non-linear regressions, to apprehend the elusive relationship between GMO usage and the penchant for pondering potential relocation to Canada.

Moreover, to control for potential confounding variables such as economic conditions, political events, or viral internet memes, we conducted a series of sensitivity analyses and robustness checks. These efforts aimed to fortify the credibility and reliability of our findings, ensuring that any detected correlations were as sturdy as the stalks of genetically modified corn braving a gusty wind.

In summary, our methodology was a hybrid concoction of agricultural data mining, digital anthropology, and statistical acrobatics, woven together to unravel the enigmatic relationship between GMO corn and Canadian aspirations. While we took the pursuit of knowledge seriously, we could not resist the temptation to sprinkle our scholarly endeavors with the occasional kernel of 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 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 Texas and Google searches for 'how to immigrate to canada'. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 500 tokens.

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

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

[[TITLE]]
Sweet Corn and Canada's Bourne: The GMO-Google Correlation

[[ABSTRACT]]
This study delves into the intriguing relationship between the use of genetically modified organisms (GMOs) in corn cultivation in Texas and Google searches for "how to immigrate to Canada." By leveraging data from the USDA and Google Trends, our research team uncovered a surprising co-mingling of these seemingly disparate topics. The correlation coefficient of 0.9106705 and p < 0.01 for the period from 2005 to 2023 served as both a revelation and a source of amusement. Our findings offer a kernel of insight into the whimsical ways in which human curiosity and international agricultural practices intersect. This paper not only peels back the husk of this puzzling phenomenon but also highlights the ear-resistible humor inherent in unexpected data connections.

[[LITERATURE REVIEW]]
In "Smith et al.," the authors find that the use of genetically modified organisms (GMOs) in corn cultivation has become increasingly prevalent in the agricultural landscape of Texas. The study discusses the potential benefits and concerns surrounding GMO use, emphasizing the need for further research to fully understand the implications of this agricultural practice. Similarly, "Doe and Jones" highlight the growing interest in Google searches for "how to immigrate to Canada" and delve into the various factors that may influence such queries.
In "The Omnivore's Dilemma" by Michael Pollan, the author examines the complexities of modern food production, including the prevalence of GMOs in corn farming. While the book offers insightful commentary on the impact of agricultural practices, it fails to address the unexpected link between GMOs and aspirations of Canadian immigration. Conversely, "Interpreter of Maladies" by Jhumpa Lahiri presents a collection of short stories that, while not directly related to the topic at hand, provides a poignant exploration of human desires and the pursuit of fulfillment.
Furthermore, social media posts such as "Just spotted a field of GMO corn while browsing how to immigrate to Canada. Coincidence? I think not!" and "GMO corn and Canadian immigration seem like a-maize-ing bedfellows 🌽🇨🇦 #corncerns" reflect the public's fascination with the peculiar correlation uncovered by our research. While these sources offer anecdotal evidence rather than empirical data, they underscore the widespread curiosity and amusement generated by this unexpected connection.

[[RESULTS]]
The analysis of the data revealed a remarkably robust correlation between the use of genetically modified organisms (GMOs) in sweet corn cultivation in Texas and Google searches for "how to immigrate to Canada." The correlation coefficient of 0.9106705 indicated a strong positive relationship between these seemingly incongruous variables for the period from 2005 to 2023. This association was further supported by an r-squared value of 0.8293208, signifying that approximately 83% of the variation in the frequency of Google searches for emigrating to Canada could be explained by the use of GMOs in corn grown in Texas.
The statistical significance of this relationship, denoted by p < 0.01, underscored the reliability of the findings, validating the strength of the observed association. The robustness of these statistical measures reinforces the notion that the interplay between GMOs in sweet corn cultivation and aspirations for Canadian immigration is not merely a chance occurrence akin to finding a four-leaf clover in a cornfield, but rather a consistent and substantial phenomenon deserving of scrutiny.
Figure 1 displays the scatterplot illustrating the pronounced correlation between the use of GMOs in corn cultivation in Texas and the frequency of Google searches related to emigrating to Canada. The juxtaposition of these two divergent topics may evoke whimsy, but the fervor in this relationship is clear, as evident in the tightly clustered data points. The figure serves as a visual manifestation of the compelling link between agricultural practices and intangible desires, reminiscent of the allegorical combination of a kernel of truth and the quest for greener pastures.
The unexpected convergence of GMO use in corn grown in Texas and inquiries about Canadian immigration, as evidenced by our empirical findings, beckons further investigation into the underlying mechanisms driving this enigmatic correlation. It propels the discourse beyond the realm of mere happenstance and toward a deeper understanding of the intricate interplay between agricultural practices and human curiosity. This intersection of seemingly unrelated domains not only offers academic intrigue but also injects a pop of humor into the otherwise staid landscape of research inquiry.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our study not only confirm, but also lend substantial weight to the assertion made by previous researchers regarding the escalating prevalence of genetically modified organisms (GMOs) in sweet corn cultivation in Texas. The robust correlation identified between GMO use in corn grown in Texas and Google searches for "how to immigrate to Canada" not only validates prior findings but also casts a spotlight on the unexpected interconnectedness of these seemingly distinct domains.

Smith et al.'s work is particularly relevant, as it established the increasing ubiquity of GMOs in corn farming, providing the foundation for our own investigation into the potential ripple effects of this agricultural practice. The salient correlation identified in this study adds a unique layer to the existing discourse, illustrating the far-reaching implications of GMO use beyond the confines of conventional agricultural analysis. Furthermore, our findings imbue the contemplation of Canadian immigration with a bibulous degree of curiosity, underscoring the potential for interdisciplinary inquiry to yield invaluable insights, much like a finely aged cheddar.

Moreover, our results lend credence to the growing interest in emigrating to Canada, as expounded upon by Doe and Jones. The significant positive relationship uncovered between GMO use and Canadian immigration queries unveils a complex tapestry of human motivations, aspirations, and perhaps a tinge of whimsy. This unexpected convergence of agriculture and immigration not only echoes the philosophical underpinnings found in "Interpreter of Maladies" but also establishes a pragmatic basis for further investigation into the intricacies of human decision-making and the influence of agricultural practices on international daydreaming.

While the humor evoked by the juxtaposition of GMOs and Canadian immigration may resemble a corny joke, the statistical robustness of our findings transmutes this correlation into a serious subject for exploration. The starchy connection between these seemingly orthogonal themes does not merely prompt a chuckle but incites a fervor for delving deeper into the underlying mechanisms driving this association, much like searching for the sweetest kernels in a cob. Our research lays the groundwork for a cornucopia of future studies, opening avenues for scholarly endeavors that marry empirical rigor with a dash of unexpected delight.

The unexpected association uncovered in this study serves as a testament to the serendipitous nature of scientific inquiry, highlighting the potential for revelatory discoveries in the most unassuming of places. As we peel back the layers of this enigmatic correlation, we unearth a tapestry woven with threads of curiosity, innovation, and a hint of whimsy – a testament to the kaleidoscopic nature of human endeavors and the intricate dance between empirical investigation and the unpredictable nuances of human behavior.

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 Texas and Google searches for 'how to immigrate to canada'. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Sweet Corn and Canada's Bourne: The GMO-Google Correlation

[[ABSTRACT]]
This study delves into the intriguing relationship between the use of genetically modified organisms (GMOs) in corn cultivation in Texas and Google searches for "how to immigrate to Canada." By leveraging data from the USDA and Google Trends, our research team uncovered a surprising co-mingling of these seemingly disparate topics. The correlation coefficient of 0.9106705 and p < 0.01 for the period from 2005 to 2023 served as both a revelation and a source of amusement. Our findings offer a kernel of insight into the whimsical ways in which human curiosity and international agricultural practices intersect. This paper not only peels back the husk of this puzzling phenomenon but also highlights the ear-resistible humor inherent in unexpected data connections.

[[INTRDUCTION]]
The intertwining of genetically modified organisms (GMOs) in agriculture and Google searches for "how to immigrate to Canada" may seem as unlikely a pairing as a strawberry and ketchup sandwich. Yet, as with any good mystery, the plot thickens when these two seemingly unrelated subjects are brought together. By analyzing the extensive data available from the United States Department of Agriculture (USDA) and Google Trends, our research team sought to elucidate the curious correlation between these disparate domains.
Initially, we were as skeptical as a cat in a room full of rocking chairs. The very notion that the cultivation of genetically modified sweet corn in the heart of Texas could influence individuals' yearning to relocate to the Great White North seemed preposterous, to say the least. However, as we dug deeper and delved into the maize of data, we were surprised to uncover a statistically significant relationship that would make even the most ardent skeptics do a double take.
One might ponder what links these two subjects as if they were two cars sharing the same parking meter. Are there practical connections, or is this an elaborate dance of statistical anomalies? As we grapple with these questions, we invite the reader to join us on a journey through the fields of agricultural biotechnology and the digital landscape of immigration queries. While our findings may seem as unlikely as finding a needle in a cornfield, we assure you that they are as real as a scarecrow on a sunny day.

[[RESULTS]]
The analysis of the data revealed a remarkably robust correlation between the use of genetically modified organisms (GMOs) in sweet corn cultivation in Texas and Google searches for "how to immigrate to Canada." The correlation coefficient of 0.9106705 indicated a strong positive relationship between these seemingly incongruous variables for the period from 2005 to 2023. This association was further supported by an r-squared value of 0.8293208, signifying that approximately 83% of the variation in the frequency of Google searches for emigrating to Canada could be explained by the use of GMOs in corn grown in Texas.
The statistical significance of this relationship, denoted by p < 0.01, underscored the reliability of the findings, validating the strength of the observed association. The robustness of these statistical measures reinforces the notion that the interplay between GMOs in sweet corn cultivation and aspirations for Canadian immigration is not merely a chance occurrence akin to finding a four-leaf clover in a cornfield, but rather a consistent and substantial phenomenon deserving of scrutiny.
Figure 1 displays the scatterplot illustrating the pronounced correlation between the use of GMOs in corn cultivation in Texas and the frequency of Google searches related to emigrating to Canada. The juxtaposition of these two divergent topics may evoke whimsy, but the fervor in this relationship is clear, as evident in the tightly clustered data points. The figure serves as a visual manifestation of the compelling link between agricultural practices and intangible desires, reminiscent of the allegorical combination of a kernel of truth and the quest for greener pastures.
The unexpected convergence of GMO use in corn grown in Texas and inquiries about Canadian immigration, as evidenced by our empirical findings, beckons further investigation into the underlying mechanisms driving this enigmatic correlation. It propels the discourse beyond the realm of mere happenstance and toward a deeper understanding of the intricate interplay between agricultural practices and human curiosity. This intersection of seemingly unrelated domains not only offers academic intrigue but also injects a pop of humor into the otherwise staid landscape of research inquiry.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has peeled back the layers of this amusing enigma, shedding light on the unexpected tango between GMOs in sweet corn cultivation in Texas and the allure of Canadian immigration. The robust correlation coefficient and statistical significance indicate that this connection is as strong as a bull in a china shop, defying conventional wisdom with its peculiar grip.

While it may seem as unlikely as a Texan without a ten-gallon hat, the correlation we uncovered is a testament to the whimsical ways in which human curiosity and agricultural practices intersect. Our findings provide a kernel of insight into the unpredictable nature of data relationships, serving as a reminder that statistical analysis is not always as cut and dried as a cob of corn.

The scatterplot visualization, like a quirky piece of modern art, captures the compelling link between these disparate subjects. It stands as a vivid depiction of the unexpected dance between the cultivation of genetically modified sweet corn and aspirations for Canadian immigration, illustrating a co-mingling of the tangible and the intangible that is as fascinating as it is chuckle-inducing.

Having uncovered this surprising correlation, we assert that further research in this area is as unnecessary as a scarecrow at midnight. Our study stands as a testament to the serendipitous discoveries that await in the expanse of data analysis, and we invite future scholars to embrace the humor and unpredictability inherent in uncovering unexpected connections between seemingly unrelated topics.

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 Sweet Corn and Canada's Bourne: The GMO-Google Correlation. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Agritech and Cybernetics

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 Sweet Corn and Canada's Bourne: The GMO-Google Correlation. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Agricultural Innovation and Technology (IAIT)

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 Sweet Corn and Canada's Bourne: The GMO-Google Correlation with an abstract of This study delves into the intriguing relationship between the use of genetically modified organisms (GMOs) in corn cultivation in Texas and Google searches for "how to immigrate to Canada." By leveraging data from the USDA and Google Trends, our research team uncovered a surprising co-mingling of these seemingly disparate topics. The correlation coefficient of 0.9106705 and p < 0.01 for the period from 2005 to 2023 served as both a revelation and a source of amusement. Our findings offer a kernel of insight into the whimsical ways in which human curiosity and international agricultural practices intersect. This paper not only peels back the husk of this puzzling phenomenon but also highlights the ear-resistible humor inherent in unexpected data connections.

ChatGPT:

sweet corn, GMO, genetically modified organisms, Canada, immigration, Texas, corn cultivation, USDA, Google Trends, correlation coefficient, international agricultural practices, data connections

*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 Texas
Detailed data title: Percent of all corn planted in Texas genetically modified with both Bt and HT
Source: USDA
See what else correlates with GMO use in corn grown in Texas

Google searches for 'how to immigrate to canada'
Detailed data title: Relative volume of Google searches for 'how to immigrate to canada' (Worldwide, without quotes)
Source: Google Trends
Additional Info: ^{See full details} Relative search volume (not absolute numbers)

See what else correlates with Google searches for 'how to immigrate to canada'

Correlation r = 0.9106705 (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 = 0.8293208 (Coefficient of determination)
This means 82.9% of the change in the one variable (i.e., Google searches for 'how to immigrate to canada') is predictable based on the change in the other (i.e., GMO use in corn grown in Texas) over the 19 years from 2005 through 2023.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 6.2E-8.^Show 0.0000000616468065314411200000
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 0.91 in 6.2E-6% of random cases. Said differently, if you correlated 16,221,440 random variables^Note You don't actually need 16 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 18 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 18 because we have two variables measured over a period of 19 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.

[ 0.78, 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.

	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023
GMO use in corn grown in Texas (GMO corn %)	9	13	20	27	33	40	42	44	53	62	67	71	77	75	80	76	80	79	83
Google searches for 'how to immigrate to canada' (Rel. search volume)	19.0833	10.3333	9.75	15.5	17.9167	20.4167	21.9167	19.75	23.5833	25.75	22.0833	31.25	28.8333	32	32.1667	35.5833	29.8333	33.6667	29.5

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([9,13,20,27,33,40,42,44,53,62,67,71,77,75,80,76,80,79,83,])
array_2 = np.array([19.0833,10.3333,9.75,15.5,17.9167,20.4167,21.9167,19.75,23.5833,25.75,22.0833,31.25,28.8333,32,32.1667,35.5833,29.8333,33.6667,29.5,])
array_1_name = "GMO use in corn grown in Texas"
array_2_name = "Google searches for 'how to immigrate to canada'"

# 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 corn grown in Texas
Line chart for only Google searches for 'how to immigrate to canada'
AI-generated correlation image
The spurious research paper: Sweet Corn and Canada's Bourne: The GMO-Google Correlation

View another random correlation

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Correlation ID: 3958 · Black Variable ID: 750 · Red Variable ID: 1349


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