Spurious correlation #3,390 · 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 Kansas and the second variable is Biomass power generated in Brazil. The chart goes from 2000 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is GMO use in corn grown in Kansas and the second variable is Biomass power generated in Brazil. The chart goes from 2000 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The GMO corn in Kansas has developed a supercharged growth gene, inadvertently sending out powerful vibes into the environment. These vibes somehow travel all the way to Brazil, supercharging the biomass power generation process. It's like the corn is saying, "Hey, let's make some energy moves, Brazil!" It's a-maize-ing how connected the world of agriculture and energy production can be!

^{Show image generation prompt} Model: dalle-3
Prompt: The prompt for an image using DALL·E 3 in the style of data cyber art: Create an image of a surreal landscape with a Kansas cornfield filled with towering, glowing GMO cornstalks swaying in the breeze under a digital, golden sun. In the background, depict massive, futuristic power plants in Brazil, emitting streams of neon-colored light as they work tirelessly to generate energy from biomass. Show the two environments intertwining in a visually striking way, symbolizing the parallel growth of genetically modified corn and biomass power plants. 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 Kansas and Biomass power generated in Brazil. 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: Symbolism Data cyber art Brutalism Woodblock painting Steampunk
ChatGPT: Data cyber art

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 Kansas and Biomass power generated in Brazil, in the following situation: Because GMO corn in Kansas led to taller stalks, Brazilian power plants gained height envy, prompting an increase in biomass power generation to prove their own plant's prowess.. Keep the description simple.
ChatGPT: In a Kansas cornfield, towering stalks of GMO corn sway in the breeze under the golden sun. Nearby, in Brazil, massive power plants loom against the skyline, their chimneys billowing white smoke as they work tirelessly to generate energy from biomass. The scene captures the parallel growth of the genetically modified corn and the biomass power plants, each reaching for new heights in their own ways.

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: [ Data cyber art ] and depicting this scene: In a Kansas cornfield, towering stalks of GMO corn sway in the breeze under the golden sun. Nearby, in Brazil, massive power plants loom against the skyline, their chimneys billowing white smoke as they work tirelessly to generate energy from biomass. The scene captures the parallel growth of the genetically modified corn and the biomass power plants, each reaching for new heights in their own ways. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: The prompt for an image using DALL·E 3 in the style of data cyber art: Create an image of a surreal landscape with a Kansas cornfield filled with towering, glowing GMO cornstalks swaying in the breeze under a digital, golden sun. In the background, depict massive, futuristic power plants in Brazil, emitting streams of neon-colored light as they work tirelessly to generate energy from biomass. Show the two environments intertwining in a visually striking way, symbolizing the parallel growth of genetically modified corn and biomass power plants.

*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 Kansas caused Biomass power generated in Brazil to increase. Make it feel plausible.

AI academic paper

(Because p < 0.01)

Corn's Gene Change and Biomass Power Range: A Transcontinental Exchange

The Journal of Agro-Genomics and Renewable Energy

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 Kansas and Biomass power generated in Brazil. 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 cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
Corn's Gene Change and Biomass Power Range: A Transcontinental Exchange

[[ABSTRACT]]
This research examines the relationship between the use of genetically modified organisms (GMOs) in corn crops in Kansas and the generation of biomass power in Brazil. Utilizing data from the USDA and Energy Information Administration spanning from 2000 to 2021, we employed statistical analyses to investigate this compelling agricultural and energy trade. The results revealed a striking correlation coefficient of 0.9863857, with a p-value less than 0.01, indicating a robust association between GMO adoption in Kansas corn and biomass power output in Brazil.

Our findings suggest that the GMO traits in Kansas corn may somehow impact the production and utilization of biomass for power generation in Brazil. The interconnectedness of these seemingly distant agricultural and energy sectors highlights the intricate global web of influence. Additionally, the substantial correlation coefficient and low p-value suggest a strong linear relationship, akin to a dad joke's predictable punchline - clearly related, yet delightfully unexpected. This unexpected intercontinental connection prompts deeper reflection and sparks further questions about the intricate interconnectedness of agricultural practices and energy solutions worldwide.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The use of genetically modified organisms (GMOs) in agricultural production continues to be a topic of debate and investigation, much like the eternal debate of whether a tomato is a fruit or a vegetable – it's both, according to botanists, but don't bring it up at the dinner table. In this study, we explore the curious connection between the adoption of GMO traits in corn grown in Kansas and the generation of biomass power in Brazil.
Much like a successful experiment, this research brings together seemingly disparate variables – corn genetics and energy production – to uncover potential relationships that may have far-reaching implications, like discovering a new element on the periodic table (although we're not quite there yet). As we delve into this unconventional pairing of agriculture and energy, we aim to shed light on the unexpected intercontinental kinship and unlock the potential implications for agricultural and energy policies.
The intersection of these two distinct domains prompts an investigation as intriguing as a science fiction novel, but without the alien invasions (we hope). By examining the correlation between GMO use in Kansas corn and biomass power generation in Brazil, we seek to delve into the unexplored realms of agricultural biotechnology and renewable energy production, much like scientists exploring the depths of the ocean or the vastness of outer space – but without the need for a spacesuit.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

When examining the intersection of genetically modified organisms (GMOs) in corn crops and biomass power generation, researchers have unearthed a trove of studies illuminating potential connections. In "Smith et al.'s study," the authors find a positive correlation between the adoption of GMO traits in Kansas corn and the production of biomass power in Brazil, echoing the harmony of a perfect duet - or in this case, a truly corny joke.

As our investigation delves deeper into this unconventional synergy, it is imperative to consider the implications of this intercontinental interplay. In "Doe and Jones' research," the authors report a significant association between the proliferation of GMOs in Kansas corn and the amplification of biomass power production in Brazil. This correlation is as clear as the brunch menu after a late Saturday morning wake-up call - no need for further translation.

Delving into the interdisciplinary context of agriculture and energy, it is crucial to note the relevance of non-fiction works such as "The Omnivore's Dilemma" by Michael Pollan and "The Quest: Energy, Security, and the Remaking of the Modern World" by Daniel Yergin, which provide valuable insights into the intricate web of agricultural practices and energy dynamics. However, delving further into the world of fiction, novels such as "The Corn Maiden and Other Nightmares" by Joyce Carol Oates and "Biomass Burning and Global Change" by Liane Cortesi offer a creative lens through which to examine the potential connections between corn genetics and biomass power.

Moreover, it is essential to acknowledge the impact of popular social media discussions, with posts such as "Gotta love that GMO-powered biomass energy from across the globe! #CornAndPowerRock" and "Kernels of truth: GMO corn in Kansas may be fueling biomass power in Brazil - who knew? #AgriculturalTwists" highlighting the growing awareness of this transcontinental exchange. These engaging online dialogues reflect the increasing curiosity and intrigue surrounding the unexpected link between GMO use in corn and biomass power generation, akin to stumbling upon a field of genetically modified jokes – a-maize-ing!

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 Kansas and Biomass power generated in Brazil. 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 Energy Information Administration . You used data from 2000 to 2021

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

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
Corn's Gene Change and Biomass Power Range: A Transcontinental Exchange

[[ABSTRACT]]
This research examines the relationship between the use of genetically modified organisms (GMOs) in corn crops in Kansas and the generation of biomass power in Brazil. Utilizing data from the USDA and Energy Information Administration spanning from 2000 to 2021, we employed statistical analyses to investigate this compelling agricultural and energy trade. The results revealed a striking correlation coefficient of 0.9863857, with a p-value less than 0.01, indicating a robust association between GMO adoption in Kansas corn and biomass power output in Brazil.

Our findings suggest that the GMO traits in Kansas corn may somehow impact the production and utilization of biomass for power generation in Brazil. The interconnectedness of these seemingly distant agricultural and energy sectors highlights the intricate global web of influence. Additionally, the substantial correlation coefficient and low p-value suggest a strong linear relationship, akin to a dad joke's predictable punchline - clearly related, yet delightfully unexpected. This unexpected intercontinental connection prompts deeper reflection and sparks further questions about the intricate interconnectedness of agricultural practices and energy solutions worldwide.

[[INTRODUCTION]]
The use of genetically modified organisms (GMOs) in agricultural production continues to be a topic of debate and investigation, much like the eternal debate of whether a tomato is a fruit or a vegetable – it's both, according to botanists, but don't bring it up at the dinner table. In this study, we explore the curious connection between the adoption of GMO traits in corn grown in Kansas and the generation of biomass power in Brazil.
Much like a successful experiment, this research brings together seemingly disparate variables – corn genetics and energy production – to uncover potential relationships that may have far-reaching implications, like discovering a new element on the periodic table (although we're not quite there yet). As we delve into this unconventional pairing of agriculture and energy, we aim to shed light on the unexpected intercontinental kinship and unlock the potential implications for agricultural and energy policies.
The intersection of these two distinct domains prompts an investigation as intriguing as a science fiction novel, but without the alien invasions (we hope). By examining the correlation between GMO use in Kansas corn and biomass power generation in Brazil, we seek to delve into the unexplored realms of agricultural biotechnology and renewable energy production, much like scientists exploring the depths of the ocean or the vastness of outer space – but without the need for a spacesuit.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The data for this analysis was gathered from a variety of sources, but predominantly relied on information obtained from the United States Department of Agriculture (USDA) and the Energy Information Administration (EIA). Similar to a farmer harvesting crops, we carefully gathered data spanning from 2000 to 2021, ensuring a comprehensive and robust dataset for our investigation. This comprehensive approach allowed us to corn-er the market on information, pun intended.

Calculation of GMO Adoption Rate:
The adoption rate of genetically modified organisms (GMOs) in corn grown in Kansas was calculated using a convoluted process involving complex statistical models and a hint of magic – well, more like complex statistical models and a lot of coffee. We utilized a modified version of the famous Drake equation (originally used to estimate the potential number of active, communicative extraterrestrial civilizations in the Milky Way galaxy) to estimate the rate of GMO adoption, adapting it to fit the terrestrial task at hand. It was a bit like searching for life on Mars, but with more Excel spreadsheets.

Assessment of Biomass Power Generation:
The generation of biomass power in Brazil was assessed through an intriguing combination of satellite imagery analysis and traditional energy production reports. Our team of researchers put on their detective hats and methodically combed through the data, not unlike Sherlock Holmes investigating a curious case. We calculated the biomass power generation with the level of precision one might expect from a team of highly caffeinated statisticians.

Statistical Analysis:
To assess the relationship between GMO adoption in Kansas corn and biomass power generation in Brazil, we employed sophisticated statistical analyses, including but not limited to correlation analysis, regression models, and hypothesis testing. These analyses were conducted with the meticulous care of an archivist organizing ancient scrolls, as we sought to unravel the mystery of the transcontinental connection. We also ensured that our statistical tests were as robust as a well-engineered bridge, avoiding statistical pitfalls like a savvy traveler sidestepping tourist traps.

Normalization and Standardization:
The data for GMO adoption and biomass power generation were normalized and standardized to facilitate meaningful comparisons and to eliminate potential biases arising from varying measurement units. This process involved meticulous attention to detail, much like an artisan crafting a delicate sculpture, and required the precision of a scientist measuring subatomic particles. Our aim was to ensure that our analysis was as consistent as the changing seasons – but hopefully with fewer allergies.

Control Variables:
Various control variables were incorporated into the analysis to account for potential confounding factors, including but not limited to land use changes, weather patterns, and global market forces. These control variables were carefully selected and incorporated into our models with the precision of a dentist performing a root canal – aiming for accuracy and minimizing any discomfort for the data.

The meticulous execution of these research methods allowed us to uncover the compelling and unexpected relationship between GMO adoption in Kansas corn and biomass power generation in Brazil, shedding light on the intricate web of agricultural and energy dynamics that extends across borders and continents.

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 Kansas and Biomass power generated in Brazil. 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]]
Corn's Gene Change and Biomass Power Range: A Transcontinental Exchange

[[ABSTRACT]]
This research examines the relationship between the use of genetically modified organisms (GMOs) in corn crops in Kansas and the generation of biomass power in Brazil. Utilizing data from the USDA and Energy Information Administration spanning from 2000 to 2021, we employed statistical analyses to investigate this compelling agricultural and energy trade. The results revealed a striking correlation coefficient of 0.9863857, with a p-value less than 0.01, indicating a robust association between GMO adoption in Kansas corn and biomass power output in Brazil.

Our findings suggest that the GMO traits in Kansas corn may somehow impact the production and utilization of biomass for power generation in Brazil. The interconnectedness of these seemingly distant agricultural and energy sectors highlights the intricate global web of influence. Additionally, the substantial correlation coefficient and low p-value suggest a strong linear relationship, akin to a dad joke's predictable punchline - clearly related, yet delightfully unexpected. This unexpected intercontinental connection prompts deeper reflection and sparks further questions about the intricate interconnectedness of agricultural practices and energy solutions worldwide.

[[LITERATURE REVIEW]]
When examining the intersection of genetically modified organisms (GMOs) in corn crops and biomass power generation, researchers have unearthed a trove of studies illuminating potential connections. In "Smith et al.'s study," the authors find a positive correlation between the adoption of GMO traits in Kansas corn and the production of biomass power in Brazil, echoing the harmony of a perfect duet - or in this case, a truly corny joke.
As our investigation delves deeper into this unconventional synergy, it is imperative to consider the implications of this intercontinental interplay. In "Doe and Jones' research," the authors report a significant association between the proliferation of GMOs in Kansas corn and the amplification of biomass power production in Brazil. This correlation is as clear as the brunch menu after a late Saturday morning wake-up call - no need for further translation.
Delving into the interdisciplinary context of agriculture and energy, it is crucial to note the relevance of non-fiction works such as "The Omnivore's Dilemma" by Michael Pollan and "The Quest: Energy, Security, and the Remaking of the Modern World" by Daniel Yergin, which provide valuable insights into the intricate web of agricultural practices and energy dynamics. However, delving further into the world of fiction, novels such as "The Corn Maiden and Other Nightmares" by Joyce Carol Oates and "Biomass Burning and Global Change" by Liane Cortesi offer a creative lens through which to examine the potential connections between corn genetics and biomass power.
Moreover, it is essential to acknowledge the impact of popular social media discussions, with posts such as "Gotta love that GMO-powered biomass energy from across the globe! #CornAndPowerRock" and "Kernels of truth: GMO corn in Kansas may be fueling biomass power in Brazil - who knew? #AgriculturalTwists" highlighting the growing awareness of this transcontinental exchange. These engaging online dialogues reflect the increasing curiosity and intrigue surrounding the unexpected link between GMO use in corn and biomass power generation, akin to stumbling upon a field of genetically modified jokes – a-maize-ing!

[[RESULTS]]
The analysis of the data gathered from the USDA and Energy Information Administration evinced a remarkably strong correlation between the use of genetically modified organisms (GMOs) in corn grown in Kansas and the generation of biomass power in Brazil. The correlation coefficient of 0.9863857 suggests a relationship as dependable as a well-constructed pun - solidly linked yet surprisingly impactful. This robust association indicates that changes in the genetic makeup of Kansas corn may indeed have implications for the production of biomass power in distant Brazil, creating an agricultural and energy love story for the ages.
The r-squared value of 0.9729568 further solidifies the strength of this relationship, reminiscent of a highly significant scientific discovery that captivates the entire research community like an unexpected twist in a Sherlock Holmes novel. The high coefficient of determination implies that a striking 97.3% of the variability in biomass power generation in Brazil can be explained by the adoption of GMO traits in Kansas corn. This level of explanatory power is as rare as a physicist's sense of humor - not often encountered, but deeply appreciated when observed.
Moreover, the p-value of less than 0.01 provides compelling evidence to reject the null hypothesis that there is no relationship between GMO use in Kansas corn and biomass power generation in Brazil. This statistical significance is as clear as the periodic table of elements, leaving no room for doubt and prompting us to embrace the interconnectedness of these two seemingly disparate variables, much like embracing a good dad joke - surprising and oddly satisfying.
The scatterplot shown in Fig. 1 visually encapsulates the strong positive correlation between GMO use in corn grown in Kansas and biomass power generated in Brazil. The tightly clustered data points resemble the precision of a well-crafted joke, driving home the point that the relationship between these two variables is no laughing matter – although we couldn't resist a few puns along the way.
In conclusion, our findings distinctly indicate a meaningful association between the adoption of GMO traits in Kansas corn and the production of biomass power in Brazil. This unexpected yet substantial intercontinental linkage beckons further exploration into the complex interplay between agricultural practices and energy solutions on a global scale, proving that when it comes to scientific discoveries, the results can be as surprising and satisfying as a perfectly timed dad joke.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The investigation into the correlation between the use of genetically modified organisms (GMOs) in corn crops in Kansas and the generation of biomass power in Brazil has yielded compelling results, supporting the previous research that hinted at this unexpected transcontinental connection. Our findings align with the work of Smith et al., further underscoring the harmony of this unusual agricultural and energy duet - it's like corn and power singing in perfect pitch. Additionally, the association reported by Doe and Jones receives further affirmation through our study, highlighting the steadfast correlation akin to an old dad joke - reliable, yet surprisingly impactful.

The substantial correlation coefficient and low p-value in our study add weight to the connection, akin to a notable pun—predictable in its tie to the variables, yet delightfully unexpected in its magnitude. This echoes the resonance of the correlation as thoroughly as a dad joke invokes a room full of eye rolls and laughter simultaneously.

The significant r-squared value bolsters the veracity of our findings, akin to a groundbreaking scientific discovery that captures the research community's collective attention much like an unexpected twist in a mystery novel. This high coefficient of determination elucidates an impressive 97.3% of the variability in biomass power generation in Brazil explained by the adoption of GMO traits in Kansas corn. This explanatory power is as rare as a chemist's ability to crack a good joke - infrequently observed, but thoroughly appreciated when encountered.

The compelling p-value of less than 0.01 confidently refutes the null hypothesis, presenting a relationship between GMO use in Kansas corn and biomass power generation in Brazil as as clear as the structure of a well-constructed joke, leaving no room for doubt. The striking visual representation in the scatterplot further reinforces the strong positive correlation as unmistakable as a perfectly timed dad joke.

In summary, the results of this study provide robust evidence of a meaningful association between the adoption of GMO traits in Kansas corn and the production of biomass power in Brazil. This unexpected yet substantial intercontinental linkage prompts further exploration into the complex interplay between agricultural practices and energy solutions on a global scale, highlighting that even in data analysis, the results can be as surprising and satisfying as a well-crafted dad joke.

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 Kansas and Biomass power generated in Brazil. 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]]
Corn's Gene Change and Biomass Power Range: A Transcontinental Exchange

[[ABSTRACT]]
This research examines the relationship between the use of genetically modified organisms (GMOs) in corn crops in Kansas and the generation of biomass power in Brazil. Utilizing data from the USDA and Energy Information Administration spanning from 2000 to 2021, we employed statistical analyses to investigate this compelling agricultural and energy trade. The results revealed a striking correlation coefficient of 0.9863857, with a p-value less than 0.01, indicating a robust association between GMO adoption in Kansas corn and biomass power output in Brazil.

Our findings suggest that the GMO traits in Kansas corn may somehow impact the production and utilization of biomass for power generation in Brazil. The interconnectedness of these seemingly distant agricultural and energy sectors highlights the intricate global web of influence. Additionally, the substantial correlation coefficient and low p-value suggest a strong linear relationship, akin to a dad joke's predictable punchline - clearly related, yet delightfully unexpected. This unexpected intercontinental connection prompts deeper reflection and sparks further questions about the intricate interconnectedness of agricultural practices and energy solutions worldwide.

[[INTRDUCTION]]
The use of genetically modified organisms (GMOs) in agricultural production continues to be a topic of debate and investigation, much like the eternal debate of whether a tomato is a fruit or a vegetable – it's both, according to botanists, but don't bring it up at the dinner table. In this study, we explore the curious connection between the adoption of GMO traits in corn grown in Kansas and the generation of biomass power in Brazil.
Much like a successful experiment, this research brings together seemingly disparate variables – corn genetics and energy production – to uncover potential relationships that may have far-reaching implications, like discovering a new element on the periodic table (although we're not quite there yet). As we delve into this unconventional pairing of agriculture and energy, we aim to shed light on the unexpected intercontinental kinship and unlock the potential implications for agricultural and energy policies.
The intersection of these two distinct domains prompts an investigation as intriguing as a science fiction novel, but without the alien invasions (we hope). By examining the correlation between GMO use in Kansas corn and biomass power generation in Brazil, we seek to delve into the unexplored realms of agricultural biotechnology and renewable energy production, much like scientists exploring the depths of the ocean or the vastness of outer space – but without the need for a spacesuit.

[[RESULTS]]
The analysis of the data gathered from the USDA and Energy Information Administration evinced a remarkably strong correlation between the use of genetically modified organisms (GMOs) in corn grown in Kansas and the generation of biomass power in Brazil. The correlation coefficient of 0.9863857 suggests a relationship as dependable as a well-constructed pun - solidly linked yet surprisingly impactful. This robust association indicates that changes in the genetic makeup of Kansas corn may indeed have implications for the production of biomass power in distant Brazil, creating an agricultural and energy love story for the ages.
The r-squared value of 0.9729568 further solidifies the strength of this relationship, reminiscent of a highly significant scientific discovery that captivates the entire research community like an unexpected twist in a Sherlock Holmes novel. The high coefficient of determination implies that a striking 97.3% of the variability in biomass power generation in Brazil can be explained by the adoption of GMO traits in Kansas corn. This level of explanatory power is as rare as a physicist's sense of humor - not often encountered, but deeply appreciated when observed.
Moreover, the p-value of less than 0.01 provides compelling evidence to reject the null hypothesis that there is no relationship between GMO use in Kansas corn and biomass power generation in Brazil. This statistical significance is as clear as the periodic table of elements, leaving no room for doubt and prompting us to embrace the interconnectedness of these two seemingly disparate variables, much like embracing a good dad joke - surprising and oddly satisfying.
The scatterplot shown in Fig. 1 visually encapsulates the strong positive correlation between GMO use in corn grown in Kansas and biomass power generated in Brazil. The tightly clustered data points resemble the precision of a well-crafted joke, driving home the point that the relationship between these two variables is no laughing matter – although we couldn't resist a few puns along the way.
In conclusion, our findings distinctly indicate a meaningful association between the adoption of GMO traits in Kansas corn and the production of biomass power in Brazil. This unexpected yet substantial intercontinental linkage beckons further exploration into the complex interplay between agricultural practices and energy solutions on a global scale, proving that when it comes to scientific discoveries, the results can be as surprising and satisfying as a perfectly timed dad joke.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, the results of this study provide compelling evidence of a robust and unexpected connection between the use of genetically modified organisms (GMOs) in Kansas corn and the generation of biomass power in Brazil, demonstrating a relationship as consistent as the groan-worthy punchline of a classic dad joke - reliably connected yet pleasantly surprising.

The remarkable correlation coefficient and statistical significance reveal a link as strong as the bonds between molecules in a covalent compound, leaving little doubt of the influence of GMO traits in Kansas corn on biomass power production in Brazil. This unexpected kinship between seemingly distant variables adds a fascinating twist to the complex web of global agricultural and energy dynamics, much like discovering a hidden comedic gem in a serious research paper – it's there, but not always expected.

The findings of this study prompt a deeper examination of the intricate interplay between agricultural practices and energy solutions, akin to unraveling the layers of an onion, but without the tears. Further research in this area could uncover additional insights and unexpected connections, but really, do we need more research when we've already found the corny link between GMOs and biomass power? Like a good dad joke, this connection is delightful on its own, and any further exploration may just be overkill. Thus, it may be wise to let this study stand as the final word on the subject, leaving the scientific community with an intriguing yet satisfying connection to ponder.

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 Corn's Gene Change and Biomass Power Range: A Transcontinental Exchange. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Agro-Genomics and Renewable Energy

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 Corn's Gene Change and Biomass Power Range: A Transcontinental Exchange. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Agri-Genetic Advancements and Sustainable Biomass Economies (IAGASBE)

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 Corn's Gene Change and Biomass Power Range: A Transcontinental Exchange with an abstract of This research examines the relationship between the use of genetically modified organisms (GMOs) in corn crops in Kansas and the generation of biomass power in Brazil. Utilizing data from the USDA and Energy Information Administration spanning from 2000 to 2021, we employed statistical analyses to investigate this compelling agricultural and energy trade. The results revealed a striking correlation coefficient of 0.9863857, with a p-value less than 0.01, indicating a robust association between GMO adoption in Kansas corn and biomass power output in Brazil.
Our findings suggest that the GMO traits in Kansas corn may somehow impact the production and utilization of biomass for power generation in Brazil. The interconnectedness of these seemingly distant agricultural and energy sectors highlights the intricate global web of influence. Additionally, the substantial correlation coefficient and low p-value suggest a strong linear relationship, akin to a dad joke's predictable punchline - clearly related, yet delightfully unexpected. This unexpected intercontinental connection prompts deeper reflection and sparks further questions about the intricate interconnectedness of agricultural practices and energy solutions worldwide.

ChatGPT:

genetically modified organisms (GMOs), corn crops, Kansas, biomass power, Brazil, USDA data, Energy Information Administration, statistical analysis, correlation coefficient, p-value, GMO adoption, power output, interconnectedness, agricultural practices, energy solutions

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

Biomass power generated in Brazil
Detailed data title: Total biomass power generated in Brazil in billion kWh
Source: Energy Information Administration
See what else correlates with Biomass power generated in Brazil

Correlation r = 0.9863857 (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 = 0.9729568 (Coefficient of determination)
This means 97.3% of the change in the one variable (i.e., Biomass power generated in Brazil) is predictable based on the change in the other (i.e., GMO use in corn grown in Kansas) over the 22 years from 2000 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.7E-17.^Show 0.0000000000000000373240667473
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 0.99 in 3.7E-15% of random cases. Said differently, if you correlated 26,792,364,475,458,436 random variables^Note You don't actually need 26 quadrillion variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

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

[ 0.97, 0.99 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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

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

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

	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
GMO use in corn grown in Kansas (GMO corn %)	1	1	2	5	5	10	12	21	35	38	40	42	51	69	72	79	79	77	84	82	81	84
Biomass power generated in Brazil (Billion kWh)	7.844	8.98	10.219	11.894	12.476	13.591	14.723	18.025	19.817	22.602	31.495	32.434	35.766	40.972	47.079	49.446	51.335	52.912	54.382	54.921	58.744	51.4352

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([1,1,2,5,5,10,12,21,35,38,40,42,51,69,72,79,79,77,84,82,81,84,])
array_2 = np.array([7.844,8.98,10.219,11.894,12.476,13.591,14.723,18.025,19.817,22.602,31.495,32.434,35.766,40.972,47.079,49.446,51.335,52.912,54.382,54.921,58.744,51.4352,])
array_1_name = "GMO use in corn grown in Kansas"
array_2_name = "Biomass power generated in Brazil"

# 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 Kansas
Line chart for only Biomass power generated in Brazil
AI-generated correlation image
The spurious research paper: Corn's Gene Change and Biomass Power Range: A Transcontinental Exchange

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Correlation ID: 3390 · Black Variable ID: 742 · Red Variable ID: 23541


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