Spurious correlation #2,779 · 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 Google searches for 'i cant even'. The chart goes from 2004 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 Missouri and the second variable is Google searches for 'i cant even'. The chart goes from 2004 to 2023, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As GMO use in Missouri corn increased, it inadvertently led to the production of corn with a higher level of perplexing protein complexes. This protein, when consumed by individuals, somehow influenced their neural pathways, causing a temporary cognitive disarray. This led to a surge in people experiencing situations that were just too overwhelming or nonsensical, prompting them to exclaim, "I can't even." It seems that amidst the quest for heartier crops, corn has sown the seeds of bewilderment in our daily lives.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a surreal Missouri cornfield with rows of brightly colored, geometric-shaped GMO corn under a vibrant, pastel-colored sky. In the foreground, a computer screen glows with rapidly increasing line graphs and charts, symbolizing the perplexing surge in "I can't even" Google searches. The image should capture the juxtaposition of the serene cornfield and the chaotic implications of the surplus of corn syrup triggering sugar crashes. 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 Google searches for 'i cant even'. 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: Vaporwave Vaporwave Charcoal Cutsy graphics Photorealism
ChatGPT: Vaporwave

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 Google searches for 'i cant even', in the following situation: The increase in GMO use in Missouri corn led to a surplus of corn syrup, causing an epidemic of sugar crashes and triggering a surge in "i can't even" Google searches as people struggled to comprehend their sudden lack of energy.. Keep the description simple.
ChatGPT: In a Missouri cornfield, rows of vibrant green GMO corn stretch as far as the eye can see. Nearby, a computer screen displays a rapidly increasing graph of "I can't even" Google searches, as people perplexedly try to make sense of their sudden lack of energy caused by the surplus of corn syrup triggering sugar crashes.

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: [ Vaporwave ] and depicting this scene: In a Missouri cornfield, rows of vibrant green GMO corn stretch as far as the eye can see. Nearby, a computer screen displays a rapidly increasing graph of "I can't even" Google searches, as people perplexedly try to make sense of their sudden lack of energy caused by the surplus of corn syrup triggering sugar crashes. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a surreal Missouri cornfield with rows of brightly colored, geometric-shaped GMO corn under a vibrant, pastel-colored sky. In the foreground, a computer screen glows with rapidly increasing line graphs and charts, symbolizing the perplexing surge in "I can't even" Google searches. The image should capture the juxtaposition of the serene cornfield and the chaotic implications of the surplus of corn syrup triggering sugar crashes.

*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 Missouri caused i cant even (Google searches for 'i cant even') to increase.\Make it sound serious until the end.

AI academic paper

(Because p < 0.01)

“GMO-Google Connection: Corny Puns and Search Queries”

"The Journal of Agricultural Entanglements"

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 Google searches for 'i cant even'. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
“GMO-Google Connection: Corny Puns and Search Queries”

[[ABSTRACT]]
The relationship between the use of genetically modified organisms (GMOs) in corn cultivation in Missouri and the frequency of Google searches for the phrase “i can't even” was investigated using data from USDA and Google Trends. The analysis revealed a remarkably strong correlation coefficient of 0.9020380 with a significance level of p < 0.01 over the period from 2004 to 2023.

Our findings suggest a striking and somewhat humorous connection between the adoption of GMOs in corn production and the prevalence of exasperated online expressions. It appears that as GMO use in corn increased, so did the frequency of individuals expressing their exasperation online. This unexpected finding may shed light on the societal impact of agricultural practices and the influence they have on language usage and communication patterns.

Furthermore, the correlation identified in this study raises the question: are GMOs causing an increase in collective exasperation, or are exasperated individuals more likely to search for topics related to GMOs? This chicken-and-egg dilemma adds an intriguing layer to the study of GMO perceptions and societal sentiments.

In conclusion, while our research has provided insight into the GMO-Google correlation, further investigation is needed to unravel the mechanisms behind this phenomenon and its implications for both agriculture and linguistic trends. Our study brings a new meaning to the phrase "you can't cornvince me otherwise."

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The correlation between the use of genetically modified organisms (GMOs) in agricultural practices and societal trends has been a subject of growing interest. In recent years, researchers have delved into the possible connections between GMO adoption and various societal phenomena, from health concerns to environmental impact. However, the link between GMO use in corn cultivation and the frequency of Google searches for the phrase “i can't even” adds a whimsical twist to this line of inquiry, demonstrating the unexpected and often amusing intersections of scientific investigation.
In investigating this seemingly ludicrous connection, we aim to add a kernel of insight to the understanding of GMO impacts and societal expressions. This research also seeks to provide a-maize-ing fodder for scholarly debate and heuristic discourse on the broader implications of agricultural practices on language usage and communication patterns. We hope to cultivate a deeper appreciation for the interconnectedness of seemingly disparate phenomena, appealing to both the scientifically inclined and the pun enthusiasts amongst us.
As we embark on this corny journey of statistical analysis and linguistic exploration, we are mindful of the pitfalls of spurious correlations and the danger of drawing hasty conclusions. It is our fervent hope that our findings will not only tickle the academic palate but also contribute meaningfully to the fields of agricultural economics, linguistics, and public sentiment analysis. This study brings a new meaning to the phrase "stalk-ifying results" as we shed light on the GMO-Google enigma.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "Smith et al.," the authors find a positive association between the use of genetically modified organisms (GMOs) in corn cultivation and various agricultural outcomes, such as crop yield and pest resistance. Similarly, "Doe and Rowe" establish a link between GMO adoption in Missouri and fluctuations in corn prices. These studies form the basis for understanding the agricultural implications of GMO use in the region.

On a more whimsical note, "Corn: Chemistry and Technology" provides an in-depth examination of corn production processes, shedding light on the chemical composition of this versatile crop. This book leaves no kernel of knowledge unturned, offering a-maize-ing insights into the scientific underpinnings of corn cultivation.

In a departure from scientific non-fiction, "The Omnivore's Dilemma" by Michael Pollan introduces readers to the complex relationship between humans and the food they consume. While not directly related to GMOs, its exploration of agricultural practices encourages a broader perspective on the societal impacts of food production. It's a-maize-ing how much food for thought this book provides.

Adding a touch of fiction to the review, "Children of the Corn" by Stephen King weaves a chilling tale set amidst a cornfield in rural Nebraska. Though the book's focus is more supernatural than agricultural, the eerie ambiance of cornfields may stir a-maize-ing emotions that could potentially influence online search behaviors.

In a bid to elucidate the tangential connections to popular culture, "Field of Dreams," a film featuring a cornfield at its center, offers a surreal narrative that may inadvertently prompt viewers to contemplate corn-related phenomena, possibly leading to increased Google searches for 'i can't even.' It's a cornundrum of cinematic proportions.

In "Jones et al.," the authors note the potential societal impacts of GMO use in agricultural practices, highlighting the need for comprehensive studies to understand the broader implications. While the literature predominantly focuses on the agricultural and economic aspects of GMO use, the unlikely correlation with online search queries for exasperation introduces a light-hearted dimension to an otherwise serious topic.

It seems our research has planted the seed for further investigation into the roots of this unexpected connection. This study brings new meaning to the phrase "corny humor" as we unearth the captivating interplay between agricultural practices and linguistic expressions.

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 Google searches for 'i cant even'. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from USDA and Google Trends . You used data from 2004 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]]
“GMO-Google Connection: Corny Puns and Search Queries”

[[ABSTRACT]]
The relationship between the use of genetically modified organisms (GMOs) in corn cultivation in Missouri and the frequency of Google searches for the phrase “i can't even” was investigated using data from USDA and Google Trends. The analysis revealed a remarkably strong correlation coefficient of 0.9020380 with a significance level of p < 0.01 over the period from 2004 to 2023.

Our findings suggest a striking and somewhat humorous connection between the adoption of GMOs in corn production and the prevalence of exasperated online expressions. It appears that as GMO use in corn increased, so did the frequency of individuals expressing their exasperation online. This unexpected finding may shed light on the societal impact of agricultural practices and the influence they have on language usage and communication patterns.

Furthermore, the correlation identified in this study raises the question: are GMOs causing an increase in collective exasperation, or are exasperated individuals more likely to search for topics related to GMOs? This chicken-and-egg dilemma adds an intriguing layer to the study of GMO perceptions and societal sentiments.

In conclusion, while our research has provided insight into the GMO-Google correlation, further investigation is needed to unravel the mechanisms behind this phenomenon and its implications for both agriculture and linguistic trends. Our study brings a new meaning to the phrase "you can't cornvince me otherwise."

[[INTRODUCTION]]
The correlation between the use of genetically modified organisms (GMOs) in agricultural practices and societal trends has been a subject of growing interest. In recent years, researchers have delved into the possible connections between GMO adoption and various societal phenomena, from health concerns to environmental impact. However, the link between GMO use in corn cultivation and the frequency of Google searches for the phrase “i can't even” adds a whimsical twist to this line of inquiry, demonstrating the unexpected and often amusing intersections of scientific investigation.
In investigating this seemingly ludicrous connection, we aim to add a kernel of insight to the understanding of GMO impacts and societal expressions. This research also seeks to provide a-maize-ing fodder for scholarly debate and heuristic discourse on the broader implications of agricultural practices on language usage and communication patterns. We hope to cultivate a deeper appreciation for the interconnectedness of seemingly disparate phenomena, appealing to both the scientifically inclined and the pun enthusiasts amongst us.
As we embark on this corny journey of statistical analysis and linguistic exploration, we are mindful of the pitfalls of spurious correlations and the danger of drawing hasty conclusions. It is our fervent hope that our findings will not only tickle the academic palate but also contribute meaningfully to the fields of agricultural economics, linguistics, and public sentiment analysis. This study brings a new meaning to the phrase "stalk-ifying results" as we shed light on the GMO-Google enigma.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The first step of this whimsical yet enlightening research involved gathering data from multiple sources, akin to a diligent farmer harvesting an array of corn varieties. Our researchers scoured the annals of the United States Department of Agriculture (USDA) for comprehensive records on GMO adoption in corn cultivation across Missouri. This process involved sifting through a virtual haystack of agricultural reports and statistical databases to identify relevant data spanning the years 2004 to 2023.

Similarly, the frequency of Google searches for the highly relatable phrase "i can't even" was harvested from the bountiful field of Google Trends. This particular phrase was chosen for its colloquial resonance and its potential to encapsulate moments of exasperation, analogous to the moments of despair when a cornstalk refuses to yield its kernels. The search data, like ripe corn, was plucked and cataloged to create a rich dataset suitable for rigorous analysis.

Preprocessing and Variable Selection:
To ensure the robustness of our study, the collected data underwent thorough preprocessing and filtering, much like the careful inspection of corn grains for quality and uniformity. This entailed removing any extraneous factors that may have contaminated the analysis, such as seasonality and other cyclical patterns. Additionally, the selection of relevant variables involved discerning the key indicators of GMO adoption and internet exasperation, akin to selecting the prime ears of corn from a field of mixed varieties. Factors such as the total area of GMO corn cultivation and the quarterly frequency of "i can't even" searches emerged as the primary variables of interest.

Statistical Analysis:
Our statistical approach pivoted around establishing the presence of a meaningful relationship between GMO use in corn and the Google search trend for "i can't even." We employed various analytical methods, including time series analysis and correlation modeling, to scrutinize the datasets for any semblance of connection. The use of a hierarchical linear regression model was considered to account for potential confounding variables, thus preventing our results from being corn-fused by extraneous influences.

The scrutiny of statistical significance encompassed rigorous hypothesis testing procedures, surpassing the mere plucking of low-hanging statistical fruits. Engaging in such procedures allowed us to confirm whether the identified relationship between GMO use and exasperated internet queries was indeed a genuine finding or merely a statistical fluke.

Ethical Considerations:
As we explored the intertwined realms of agricultural technology and internet behavior, ethical considerations were not overlooked. Our research adhered to the principles of data privacy and anonymization in handling the Google search trend data, ensuring that individual search behaviors remained as confidential as corn recipes passed down through generations.

In conclusion, our methodology encompassed a thorough and, dare I say, ear-resistible approach to unraveling the enigmatic connection between GMO use in corn cultivation and the virtual expression of exasperation. This endeavor epitomizes the fusion of rigorous scientific inquiry with a dash of delightful absurdity, echoing the spirit of a jestful agricultural fair where scholarly musings meet the whims of the cornfield.

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 Google searches for 'i cant even'. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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]]
“GMO-Google Connection: Corny Puns and Search Queries”

[[ABSTRACT]]
The relationship between the use of genetically modified organisms (GMOs) in corn cultivation in Missouri and the frequency of Google searches for the phrase “i can't even” was investigated using data from USDA and Google Trends. The analysis revealed a remarkably strong correlation coefficient of 0.9020380 with a significance level of p < 0.01 over the period from 2004 to 2023.

Our findings suggest a striking and somewhat humorous connection between the adoption of GMOs in corn production and the prevalence of exasperated online expressions. It appears that as GMO use in corn increased, so did the frequency of individuals expressing their exasperation online. This unexpected finding may shed light on the societal impact of agricultural practices and the influence they have on language usage and communication patterns.

Furthermore, the correlation identified in this study raises the question: are GMOs causing an increase in collective exasperation, or are exasperated individuals more likely to search for topics related to GMOs? This chicken-and-egg dilemma adds an intriguing layer to the study of GMO perceptions and societal sentiments.

In conclusion, while our research has provided insight into the GMO-Google correlation, further investigation is needed to unravel the mechanisms behind this phenomenon and its implications for both agriculture and linguistic trends. Our study brings a new meaning to the phrase "you can't cornvince me otherwise."

[[LITERATURE REVIEW]]
In "Smith et al.," the authors find a positive association between the use of genetically modified organisms (GMOs) in corn cultivation and various agricultural outcomes, such as crop yield and pest resistance. Similarly, "Doe and Rowe" establish a link between GMO adoption in Missouri and fluctuations in corn prices. These studies form the basis for understanding the agricultural implications of GMO use in the region.
On a more whimsical note, "Corn: Chemistry and Technology" provides an in-depth examination of corn production processes, shedding light on the chemical composition of this versatile crop. This book leaves no kernel of knowledge unturned, offering a-maize-ing insights into the scientific underpinnings of corn cultivation.
In a departure from scientific non-fiction, "The Omnivore's Dilemma" by Michael Pollan introduces readers to the complex relationship between humans and the food they consume. While not directly related to GMOs, its exploration of agricultural practices encourages a broader perspective on the societal impacts of food production. It's a-maize-ing how much food for thought this book provides.
Adding a touch of fiction to the review, "Children of the Corn" by Stephen King weaves a chilling tale set amidst a cornfield in rural Nebraska. Though the book's focus is more supernatural than agricultural, the eerie ambiance of cornfields may stir a-maize-ing emotions that could potentially influence online search behaviors.
In a bid to elucidate the tangential connections to popular culture, "Field of Dreams," a film featuring a cornfield at its center, offers a surreal narrative that may inadvertently prompt viewers to contemplate corn-related phenomena, possibly leading to increased Google searches for 'i can't even.' It's a cornundrum of cinematic proportions.

In "Jones et al.," the authors note the potential societal impacts of GMO use in agricultural practices, highlighting the need for comprehensive studies to understand the broader implications. While the literature predominantly focuses on the agricultural and economic aspects of GMO use, the unlikely correlation with online search queries for exasperation introduces a light-hearted dimension to an otherwise serious topic.
It seems our research has planted the seed for further investigation into the roots of this unexpected connection. This study brings new meaning to the phrase "corny humor" as we unearth the captivating interplay between agricultural practices and linguistic expressions.

[[RESULTS]]
The analysis of the data collected from USDA and Google Trends revealed a strong positive correlation between the use of genetically modified organisms (GMOs) in corn cultivation in Missouri and the frequency of Google searches for the phrase “i can't even” over the period from 2004 to 2023. The correlation coefficient was found to be 0.9020380, indicating a remarkably robust relationship between the two variables. This finding prompts us to ponder, "What did the corn say when it got complimented? Aw, shucks!"
The coefficient of determination (r-squared) was calculated to be 0.8136726, suggesting that approximately 81.4% of the variability in the frequency of "i can't even" searches can be explained by the variability in GMO use in corn cultivation. This strong explanatory power of GMOs on exasperated online expressions brings to mind the quip, "Well, it's corntainly clear as day!"
Moreover, the p-value of less than 0.01 indicates that the observed correlation is unlikely to be a result of random chance. This high level of statistical significance bolsters the validity of our findings and underscores the compelling nature of the GMO-Google connection. One might say this result is as solid as a cob of corn!
Fig. 1 displays the scatterplot depicting the striking correlation between GMO use in corn cultivation and the frequency of "i can't even" searches. It is indeed a sight to behold, reminiscent of a field of data points ripe for punny interpretations.
In light of the pronounced association between these seemingly unrelated variables, one cannot help but reflect on the adage, "You reap what you sow." Our study harvests a unique insight into the intertwining of agricultural practices and contemporary communication, demonstrating that statistical analysis can be both enlightening and a-maize-ingly entertaining.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our study provide compelling evidence for a strong positive correlation between the use of genetically modified organisms (GMOs) in corn cultivation in Missouri and the frequency of Google searches for the phrase "i can't even". This unexpected connection between agricultural practices and linguistic expressions raises intriguing questions about the potential societal impacts of GMO use. In other words, it seems that the GMOs may be cultivating more than just corn!

The robustness of the correlation coefficient and the high degree of statistical significance underscore the validity of our findings and point to a thought-provoking relationship between GMO adoption and online expressions of exasperation. It's as if the GMOs are planting seeds of frustration in the digital landscape, leading individuals to turn to Google in moments of perplexity. One might say these findings are corntroversial indeed!

Our results align with prior research on the agricultural implications of GMO use, supporting the idea that the adoption of GMOs in corn production has far-reaching consequences beyond the fields. This study brings a new twist to the phrase, "You can't make a kernel of a difference," as it suggests that agricultural practices may have an unexpected impact on digital communication patterns.

The coefficient of determination indicates that a substantial proportion of the variability in the frequency of "i can't even" searches can be attributed to variations in GMO use in corn cultivation. This observation emphasizes the influential role of GMOs in shaping online discourse and highlights the potential for agriculture to influence not only the physical environment but also the virtual one. It's a-maize-ing to ponder the reach of agricultural practices into the digital realm!

More research is needed to unravel the mechanisms underlying this striking correlation and to determine the causality between GMO adoption and online expressions of exasperation. It's a conundrum worthy of further investigation that might just lead us to exclaim, "Aha, now we're really getting to the kernel of the issue!"

In closing, our study has sown the seeds for a new avenue of inquiry that highlights the intersection of agriculture and contemporary communication. It's a-maize-ing how a seemingly unrelated variable like GMO use in corn cultivation can be intricately linked to online expressions of exasperation, demonstrating that research can yield unexpected and delightfully punny results.

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 Google searches for 'i cant even'. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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]]
“GMO-Google Connection: Corny Puns and Search Queries”

[[ABSTRACT]]
The relationship between the use of genetically modified organisms (GMOs) in corn cultivation in Missouri and the frequency of Google searches for the phrase “i can't even” was investigated using data from USDA and Google Trends. The analysis revealed a remarkably strong correlation coefficient of 0.9020380 with a significance level of p < 0.01 over the period from 2004 to 2023.

Our findings suggest a striking and somewhat humorous connection between the adoption of GMOs in corn production and the prevalence of exasperated online expressions. It appears that as GMO use in corn increased, so did the frequency of individuals expressing their exasperation online. This unexpected finding may shed light on the societal impact of agricultural practices and the influence they have on language usage and communication patterns.

Furthermore, the correlation identified in this study raises the question: are GMOs causing an increase in collective exasperation, or are exasperated individuals more likely to search for topics related to GMOs? This chicken-and-egg dilemma adds an intriguing layer to the study of GMO perceptions and societal sentiments.

In conclusion, while our research has provided insight into the GMO-Google correlation, further investigation is needed to unravel the mechanisms behind this phenomenon and its implications for both agriculture and linguistic trends. Our study brings a new meaning to the phrase "you can't cornvince me otherwise."

[[INTRDUCTION]]
The correlation between the use of genetically modified organisms (GMOs) in agricultural practices and societal trends has been a subject of growing interest. In recent years, researchers have delved into the possible connections between GMO adoption and various societal phenomena, from health concerns to environmental impact. However, the link between GMO use in corn cultivation and the frequency of Google searches for the phrase “i can't even” adds a whimsical twist to this line of inquiry, demonstrating the unexpected and often amusing intersections of scientific investigation.
In investigating this seemingly ludicrous connection, we aim to add a kernel of insight to the understanding of GMO impacts and societal expressions. This research also seeks to provide a-maize-ing fodder for scholarly debate and heuristic discourse on the broader implications of agricultural practices on language usage and communication patterns. We hope to cultivate a deeper appreciation for the interconnectedness of seemingly disparate phenomena, appealing to both the scientifically inclined and the pun enthusiasts amongst us.
As we embark on this corny journey of statistical analysis and linguistic exploration, we are mindful of the pitfalls of spurious correlations and the danger of drawing hasty conclusions. It is our fervent hope that our findings will not only tickle the academic palate but also contribute meaningfully to the fields of agricultural economics, linguistics, and public sentiment analysis. This study brings a new meaning to the phrase "stalk-ifying results" as we shed light on the GMO-Google enigma.

[[RESULTS]]
The analysis of the data collected from USDA and Google Trends revealed a strong positive correlation between the use of genetically modified organisms (GMOs) in corn cultivation in Missouri and the frequency of Google searches for the phrase “i can't even” over the period from 2004 to 2023. The correlation coefficient was found to be 0.9020380, indicating a remarkably robust relationship between the two variables. This finding prompts us to ponder, "What did the corn say when it got complimented? Aw, shucks!"
The coefficient of determination (r-squared) was calculated to be 0.8136726, suggesting that approximately 81.4% of the variability in the frequency of "i can't even" searches can be explained by the variability in GMO use in corn cultivation. This strong explanatory power of GMOs on exasperated online expressions brings to mind the quip, "Well, it's corntainly clear as day!"
Moreover, the p-value of less than 0.01 indicates that the observed correlation is unlikely to be a result of random chance. This high level of statistical significance bolsters the validity of our findings and underscores the compelling nature of the GMO-Google connection. One might say this result is as solid as a cob of corn!
Fig. 1 displays the scatterplot depicting the striking correlation between GMO use in corn cultivation and the frequency of "i can't even" searches. It is indeed a sight to behold, reminiscent of a field of data points ripe for punny interpretations.
In light of the pronounced association between these seemingly unrelated variables, one cannot help but reflect on the adage, "You reap what you sow." Our study harvests a unique insight into the intertwining of agricultural practices and contemporary communication, demonstrating that statistical analysis can be both enlightening and a-maize-ingly entertaining.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has unveiled a remarkably robust relationship between the use of genetically modified organisms (GMOs) in corn cultivation in Missouri and the frequency of Google searches for the phrase “i can't even.” This unexpected correlation leaves us pondering, "What do you call a group of musical GMO researchers? A-corn ensemble!"

The high correlation coefficient, strong explanatory power, and statistical significance of our findings affirm the relevance of this whimsical investigation. It is clear that this study does more than just "cob-ble together" disparate fields of research - it sheds light on the unexplored intersections of agriculture and online expressions.

However, our findings prompt us to pose the rhetorical question, "Is this the kernel of a larger phenomenon, or merely a-pop corn oddity?" This study augurs a-MAIZE-ing potential for future research exploring the societal impact of agricultural practices on linguistic trends and expressions of exasperation. But for now, we can confidently assert that no more research is needed in this area. After all, we've already "corn-firmed" the compelling connection between GMOs and exasperation.

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 “GMO-Google Connection: Corny Puns and Search Queries”. Respond only with the name of a fictional academic journal.

ChatGPT:

"The Journal of Agricultural Entanglements"

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 “GMO-Google Connection: Corny Puns and Search Queries”. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Agricultural Humor Studies

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 “GMO-Google Connection: Corny Puns and Search Queries” with an abstract of The relationship between the use of genetically modified organisms (GMOs) in corn cultivation in Missouri and the frequency of Google searches for the phrase “i can't even” was investigated using data from USDA and Google Trends. The analysis revealed a remarkably strong correlation coefficient of 0.9020380 with a significance level of p < 0.01 over the period from 2004 to 2023.
Our findings suggest a striking and somewhat humorous connection between the adoption of GMOs in corn production and the prevalence of exasperated online expressions. It appears that as GMO use in corn increased, so did the frequency of individuals expressing their exasperation online. This unexpected finding may shed light on the societal impact of agricultural practices and the influence they have on language usage and communication patterns.
Furthermore, the correlation identified in this study raises the question: are GMOs causing an increase in collective exasperation, or are exasperated individuals more likely to search for topics related to GMOs? This chicken-and-egg dilemma adds an intriguing layer to the study of GMO perceptions and societal sentiments.
In conclusion, while our research has provided insight into the GMO-Google correlation, further investigation is needed to unravel the mechanisms behind this phenomenon and its implications for both agriculture and linguistic trends. Our study brings a new meaning to the phrase "you can't cornvince me otherwise.

ChatGPT:

GMO, genetically modified organisms, corn cultivation, Missouri, Google searches, Google Trends, correlation coefficient, significance level, USDA data, exasperated online expressions, exasperation, language usage, communication patterns, agricultural practices, societal impact, language usage, GMO perceptions, societal sentiments, collective exasperation, chicken-and-egg dilemma, agriculture, linguistic trends, GMO-Google correlation, mechanisms behind the phenomenon, agriculture implications, linguistic trends.

*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 genetically modified with both Bt and HT
Source: USDA
See what else correlates with GMO use in corn grown in Missouri

Google searches for 'i cant even'
Detailed data title: Relative volume of Google searches for 'i cant even' (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 'i cant even'

Correlation r = 0.9020380 (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 = 0.8136726 (Coefficient of determination)
This means 81.4% of the change in the one variable (i.e., Google searches for 'i cant even') is predictable based on the change in the other (i.e., GMO use in corn grown in Missouri) over the 20 years from 2004 through 2023.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 5.5E-8.^Show 0.0000000550414522435446650000
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 0.9 in 5.5E-6% of random cases. Said differently, if you correlated 18,168,125 random variables^Note You don't actually need 18 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 19 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 19 because we have two variables measured over a period of 20 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.76, 0.96 ] 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.

	2004	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 Missouri (GMO corn %)	4	6	7	13	22	37	45	36	48	71	79	75	81	81	83	82	83	84	81	86
Google searches for 'i cant even' (Rel. search volume)	11.8333	24	33.5833	32	34.75	37.5	60.25	63.3333	54.3333	63.25	70.8333	74.75	71.1667	72.4167	63	55.9167	64.0833	65	72.0833	82.1111

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([4,6,7,13,22,37,45,36,48,71,79,75,81,81,83,82,83,84,81,86,])
array_2 = np.array([11.8333,24,33.5833,32,34.75,37.5,60.25,63.3333,54.3333,63.25,70.8333,74.75,71.1667,72.4167,63,55.9167,64.0833,65,72.0833,82.1111,])
array_1_name = "GMO use in corn grown in Missouri"
array_2_name = "Google searches for 'i cant even'"

# 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 Google searches for 'i cant even'
AI-generated correlation image
The spurious research paper: “GMO-Google Connection: Corny Puns and Search Queries”

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Correlation ID: 2779 · Black Variable ID: 745 · Red Variable ID: 1525


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