Spurious correlation #1,557 · 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 South Dakota and the second variable is Kerosene used in Nepal. 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 South Dakota and the second variable is Kerosene used in Nepal. The chart goes from 2000 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As corn production in South Dakota shifted away from GMO varieties, there was a surprising ripple effect on the global kerosene market. It turns out, the GMO corn was emitting a unique pheromone that attracted kerosene-loving moths. Without the GMO corn acting as a beacon for these moths, there was a drastic reduction in moths congregating near the kerosene reserves in Nepal. It's a classic case of non-GMO corn leading to moths having to find a new light source, leaving Nepal's kerosene untouched. Who knew corn and kerosene had such a moth-watering connection!

^{Show image generation prompt} Model: dalle-3
Prompt: An image that looks like it is a scene from a Pixar movie, with vibrant colors and a playful aesthetic. The visual depicts a sprawling field in South Dakota, where tall stalks of corn sway gently in the breeze. The leaves of the corn plants are a vibrant green, displaying the effects of GMO use. In the foreground, a picturesque rural village in Nepal comes into view, with a cozy Nepali home illuminated by a warm kerosene lamp. A family gathers around the lamp, their silhouettes casting a heartwarming glow on their evening meal. The juxtaposition of these two scenes captures the intersection of genetically modified corn in the expansive American field and the comforting glow of the kerosene lamp in the intimate Nepali home. 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 South Dakota and Kerosene used in Nepal. 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: Celtic art An image that looks like it is a scene from a Pixar movie Psychedelic Concept art Sci-fi fantacy
ChatGPT: An image that looks like it is a scene from a Pixar movie

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 South Dakota and Kerosene used in Nepal together. Keep the description simple.
ChatGPT: In a field in South Dakota, tall stalks of corn sway in the breeze. The leaves of the corn plants are a vibrant green, displaying the effects of GMO use. Nearby, in a rural village in Nepal, a family gathers around a kerosene lamp, its warm glow illuminating their evening meal. The juxtaposition of the genetically modified corn in the vast American field and the kerosene lamp in the intimate Nepali home showcases the intersection of these two variables from different regions of the world.

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: [ An image that looks like it is a scene from a Pixar movie ] and depicting this scene: In a field in South Dakota, tall stalks of corn sway in the breeze. The leaves of the corn plants are a vibrant green, displaying the effects of GMO use. Nearby, in a rural village in Nepal, a family gathers around a kerosene lamp, its warm glow illuminating their evening meal. The juxtaposition of the genetically modified corn in the vast American field and the kerosene lamp in the intimate Nepali home showcases the intersection of these two variables from different regions of the world. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: An image that looks like it is a scene from a Pixar movie, with vibrant colors and a playful aesthetic. The visual depicts a sprawling field in South Dakota, where tall stalks of corn sway gently in the breeze. The leaves of the corn plants are a vibrant green, displaying the effects of GMO use. In the foreground, a picturesque rural village in Nepal comes into view, with a cozy Nepali home illuminated by a warm kerosene lamp. A family gathers around the lamp, their silhouettes casting a heartwarming glow on their evening meal. The juxtaposition of these two scenes captures the intersection of genetically modified corn in the expansive American field and the comforting glow of the kerosene lamp in the intimate Nepali home.

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

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

AI academic paper

(Because p < 0.01)

Kernels to Kerosene: Exploring the Correlation Between GMO Corn in South Dakota and Kerosene Consumption in Nepal

The International Journal of Agronomic Alchemy

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between GMO use in corn grown in South Dakota and Kerosene used in Nepal. 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 a couple popular internet memes that are related to one of the topics.

Here is the title and abstract of the paper:
[[TITLE]]
Kernels to Kerosene: Exploring the Correlation Between GMO Corn in South Dakota and Kerosene Consumption in Nepal

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in agriculture has sparked many debates and discussions, with proponents touting increased yields and resistance to pests, and skeptics raising concerns about environmental impact and human health. On the other hand, the consumption of kerosene, particularly in developing countries, has been a matter of interest for its implications for household energy use and economic development. This study aims to bridge these two distinct realms by investigating the intriguing connection between GMO corn production in South Dakota and kerosene consumption in Nepal.

Our research team delved into USDA data on GMO corn cultivation and cross-referenced it with the Energy Information Administration's records of kerosene consumption in Nepal. To our surprise, we observed a strong positive correlation between GMO corn production in South Dakota and kerosene consumption in Nepal, with a correlation coefficient so high, it would make any statistician's heart skip a beat - r = 0.9823597. The p-value was even more impressive, coming in at p < 0.01, which, statistically speaking, is rarer than finding a unicorn in a cornfield.

The findings of this study open up a cornucopia of questions and possibilities, challenging traditional paradigms and inviting further exploration into the interconnected web of agricultural practices and energy dynamics on a global scale. So, the next time you reach for that ear of GMO corn, just remember, in a strange twist of fate, you might be fueling kerosene use halfway across the world - talk about a-maize-ing connections!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The cultivation of genetically modified organism (GMO) crops has long been a topic of fervent discussion and analysis, drawing a line in the proverbial soil between proponents extolling their potential for increased productivity and resistance to pests, and skeptics expressing concerns over their possible adverse effects on the environment and human health. Meanwhile, on a completely different cob, the consumption of kerosene, especially in developing nations, has garnered attention due to its implications on household energy utilization and economic advancement. These two seemingly unrelated domains - GMO corn production in South Dakota and kerosene usage in Nepal - have been the focal points of our investigation to uncover a correlation that may seem as unlikely as a scarecrow winning a dance competition.
As we embark on this peculiar journey of connecting kernels to kerosene, we are reminded of an apt dad joke: "Why did the corn refuse to play the piano? Because it had lost its husk!" Now, moving past the corny humor, we focus on the crux of our research, which brings together datasets that, at first glance, may seem as unrelated as a buttered cob and a barrel of kerosene.
Our intrepid research team scoured USDA records to glean insights into the production of GMO corn in the state of South Dakota, a veritable cornucopia of agricultural activity. Concurrently, we delved into the Energy Information Administration's comprehensive compendium of kerosene consumption in Nepal, a nation with its own distinct energy landscape. The resulting dataset juxtaposed two items as mismatched as a cob of corn and a gallon of kerosene, yet the analysis unveiled a surprising correlation that could make even the most discerning statistician exclaim, "Well, butter my corn and call me a skeptic!"
With a correlation coefficient that could make a mathematician's heart flutter - r = 0.9823597 - it's clear that there may be more to this pairing than meets the eye. The accompanying p-value, a statistically scintillating p < 0.01, is indeed a rarity in the realm of data analysis. Finding such an impressive correlation is as unexpected as discovering a kernel of truth in a field of statistical noise.
The discovery of this unexpected connection between these seemingly disparate elements prompts us to not only contemplate the interplay between agricultural practices and energy dynamics but also raises eyebrow-archingly saucy questions about their global repercussions. So, while GMO corn enjoys its time in the sun, it's worth considering the far-reaching effects of this seemingly innocuous crop - as it turns out, its impact may be more far-reaching than a cornstalk in the wind.
The unveiling of this unexpected correlation invites further inquiry and sparks curiosity about the intricate web of interactions that transcend geographic and categorical boundaries. As we ponder this unexpected correlation, we're reminded of a kernel of wisdom: "Why did the corn go to the computer? Because it wanted to become a kernel!" - a reminder of the delightful twists and turns that research can uncover, even in the unlikeliest of places.
In the following sections, we present our rigorous methodology, illuminating data analysis, and a robust discussion of our findings, inviting readers to contemplate the resonance and implications of our findings in this thoroughly engaging rendezvous between GMO corn and kerosene consumption. Let's embark on this journey of scientific inquiry with kernels of curiosity and a spirit of exploration.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The connection between agricultural practices and energy dynamics has piqued the interest of researchers and enthusiasts alike, akin to a corn maze enticing adventurous souls with its twists and turns. In "Harvesting Insights: A Global Perspective on GMO Crop Production," Smith et al. delve into the world of genetically modified corn crops, shedding light on the intricate web of factors influencing their cultivation and impact. Meanwhile, in "Fueling the Future: Exploring Energy Consumption in Developing Nations," Doe and Jones illuminate the nuanced complexities of kerosene consumption in regions such as Nepal, offering a thorough analysis of its societal and economic implications.

Now, turning our attention to the literary realm, we encounter thought-provoking non-fiction works that parallel the themes of our research. "The Omnivore's Dilemma" by Michael Pollan offers a comprehensive exploration of modern agricultural practices, including the utilization of GMO crops, providing a rich tapestry of insights into the interconnectedness of food production and consumption. Additionally, "Energy and Civilization: A History" by Vaclav Smil delves into the evolution of human energy use, framing kerosene consumption within the broader context of societal development and progress.

As we meander further into the fictitious landscapes of literature, "The Corn Whisperer" by Barbara O'Neal and "Kerosene Kingdom" by Sara Al-Fadhly captivate readers with engaging narratives that, while purely speculative, hint at the potential intertwined fate of GMO corn and kerosene consumption. These literary works, while not grounded in empirical evidence, offer imaginative projections that stimulate thought on the potential symbiotic relationships between these seemingly disparate entities.

Speaking of unlikely combinations, remember, the correlation between GMO corn in South Dakota and kerosene consumption in Nepal might seem as peculiar as a corn cob excitedly offering to refill your kerosene lamp - now there's a kernel of an intriguing image!

In a delightful departure from conventional research references, let us not overlook the cultural zeitgeist. The infamous "This is fine" meme, depicting a cartoon dog sipping coffee while his surroundings are engulfed in flames, humorously captures the unexpected correlations that may elicit a nonchalant response - much like the surprising link we've uncovered. Additionally, the "Weird Flex, But Okay" meme encapsulates the sense of bemused acceptance, which mirrors the simultaneous incredulity and realization one experiences upon discovering improbable relationships - much like the connection between GMO corn and kerosene consumption.

Now, let's plow onward into the enchanting field of data analysis and discussion, as we unpack the implications of this a-maize-ing correlation.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the 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 South Dakota and Kerosene used in Nepal. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from USDA and 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]]
Kernels to Kerosene: Exploring the Correlation Between GMO Corn in South Dakota and Kerosene Consumption in Nepal

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in agriculture has sparked many debates and discussions, with proponents touting increased yields and resistance to pests, and skeptics raising concerns about environmental impact and human health. On the other hand, the consumption of kerosene, particularly in developing countries, has been a matter of interest for its implications for household energy use and economic development. This study aims to bridge these two distinct realms by investigating the intriguing connection between GMO corn production in South Dakota and kerosene consumption in Nepal.

Our research team delved into USDA data on GMO corn cultivation and cross-referenced it with the Energy Information Administration's records of kerosene consumption in Nepal. To our surprise, we observed a strong positive correlation between GMO corn production in South Dakota and kerosene consumption in Nepal, with a correlation coefficient so high, it would make any statistician's heart skip a beat - r = 0.9823597. The p-value was even more impressive, coming in at p < 0.01, which, statistically speaking, is rarer than finding a unicorn in a cornfield.

The findings of this study open up a cornucopia of questions and possibilities, challenging traditional paradigms and inviting further exploration into the interconnected web of agricultural practices and energy dynamics on a global scale. So, the next time you reach for that ear of GMO corn, just remember, in a strange twist of fate, you might be fueling kerosene use halfway across the world - talk about a-maize-ing connections!

[[INTRODUCTION]]
The cultivation of genetically modified organism (GMO) crops has long been a topic of fervent discussion and analysis, drawing a line in the proverbial soil between proponents extolling their potential for increased productivity and resistance to pests, and skeptics expressing concerns over their possible adverse effects on the environment and human health. Meanwhile, on a completely different cob, the consumption of kerosene, especially in developing nations, has garnered attention due to its implications on household energy utilization and economic advancement. These two seemingly unrelated domains - GMO corn production in South Dakota and kerosene usage in Nepal - have been the focal points of our investigation to uncover a correlation that may seem as unlikely as a scarecrow winning a dance competition.
As we embark on this peculiar journey of connecting kernels to kerosene, we are reminded of an apt dad joke: "Why did the corn refuse to play the piano? Because it had lost its husk!" Now, moving past the corny humor, we focus on the crux of our research, which brings together datasets that, at first glance, may seem as unrelated as a buttered cob and a barrel of kerosene.
Our intrepid research team scoured USDA records to glean insights into the production of GMO corn in the state of South Dakota, a veritable cornucopia of agricultural activity. Concurrently, we delved into the Energy Information Administration's comprehensive compendium of kerosene consumption in Nepal, a nation with its own distinct energy landscape. The resulting dataset juxtaposed two items as mismatched as a cob of corn and a gallon of kerosene, yet the analysis unveiled a surprising correlation that could make even the most discerning statistician exclaim, "Well, butter my corn and call me a skeptic!"
With a correlation coefficient that could make a mathematician's heart flutter - r = 0.9823597 - it's clear that there may be more to this pairing than meets the eye. The accompanying p-value, a statistically scintillating p < 0.01, is indeed a rarity in the realm of data analysis. Finding such an impressive correlation is as unexpected as discovering a kernel of truth in a field of statistical noise.
The discovery of this unexpected connection between these seemingly disparate elements prompts us to not only contemplate the interplay between agricultural practices and energy dynamics but also raises eyebrow-archingly saucy questions about their global repercussions. So, while GMO corn enjoys its time in the sun, it's worth considering the far-reaching effects of this seemingly innocuous crop - as it turns out, its impact may be more far-reaching than a cornstalk in the wind.
The unveiling of this unexpected correlation invites further inquiry and sparks curiosity about the intricate web of interactions that transcend geographic and categorical boundaries. As we ponder this unexpected correlation, we're reminded of a kernel of wisdom: "Why did the corn go to the computer? Because it wanted to become a kernel!" - a reminder of the delightful twists and turns that research can uncover, even in the unlikeliest of places.
In the following sections, we present our rigorous methodology, illuminating data analysis, and a robust discussion of our findings, inviting readers to contemplate the resonance and implications of our findings in this thoroughly engaging rendezvous between GMO corn and kerosene consumption. Let's embark on this journey of scientific inquiry with kernels of curiosity and a spirit of exploration.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Now, let's peel back the layers of our research process, much like peeling back the husk of a corn cob to reveal the kernels within. Our approach combined elements of agricultural data analysis and energy consumption metrics in a manner that may seem as unexpected as finding a popcorn kernel in a bag of unpopped corn.

First and foremost, our research team engaged in a virtual scavenger hunt across the vast terrain of the internet, traversing through the digital fields of the United States Department of Agriculture (USDA) and the Energy Information Administration (EIA) to collect data spanning the years 2000 to 2021. The journey through this digital landscape was akin to navigating through a maize maze, encountering a plethora of data as varied as the colors of corn kernels.

With kernels of knowledge from the USDA at our virtual fingertips, we focused on the cultivation of genetically modified organism (GMO) corn in the state of South Dakota. This involved deciphering datasets, navigating through acres of agricultural statistics, and sifting through a veritable sea of corn-related information – a process as labyrinthine as attempting to find one specific popcorn kernel in a cinema-sized bucket.

Simultaneously, we delved into the realm of kerosene consumption in Nepal, drawing from the rich reservoir of data provided by the Energy Information Administration. This involved unraveling the complex web of energy usage in a country known for its diverse topography, from the flat plains of the Terai to the lofty peaks of the Himalayas – a journey as diverse and surprising as finding a rainbow of corn hues in a single cob.

Once these datasets were corralled and herded into our statistical pastures, we engaged in a process of rigorous data cleaning and preparation. This stage of the research was not unlike the meticulous cleaning and husking of corn before it is ready for consumption – a process that requires precision, patience, and a keen eye for details.

The data sets were then lovingly fed into the hungry maw of statistical software, where they underwent complex and thorough analyses to unearth any inklings of correlation. This step was akin to the magical transformation of corn kernels into popcorn – a moment of revelation and surprise as the hidden connections between seemingly disparate datasets popped into view, not unlike kernels of corn transforming into fluffy, irresistible snacks.

In addition, we utilized advanced statistical techniques, including regression analysis and time series modeling, to further unearth and validate any observed connections between GMO corn production in South Dakota and kerosene consumption in Nepal. This phase of the research was like tending to a field of corn, nurturing and observing as the patterns of growth and yield emerged – a process that yields its own unique brand of statistical harvest.

Furthermore, we conducted sensitivity analyses to test the robustness of our findings, ensuring that our observed correlation between these two seemingly unrelated variables was as sturdy and reliable as a well-constructed corn silo. These analyses involved subjecting our data to a battery of stress tests, much like ensuring that a corn stalk can withstand the whims of the wind and weather without bending too far from its true position.

Finally, we employed geographic information system (GIS) mapping techniques to visually represent the spatial distribution of GMO corn production in South Dakota and kerosene consumption in Nepal. This allowed us to chart the terrain of this unexpected correlation, much like crafting a cartographic masterpiece that unveils the hidden landscapes and connections between these two distant yet strangely linked entities.

In summary, our research process can be likened to the growth and transformation of a cornfield, from the planting of the first kernels to the eventual harvest - a journey filled with surprises, complexities, and the occasional ear-resistible pun.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the results section of an academic research paper. You speak in the past tense (you found...).
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 results section of an academic research paper presenting the findings of the connection between GMO use in corn grown in South Dakota and Kerosene used in Nepal. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from USDA and Energy Information Administration .

For the time period 2000 to 2021, you found a correlation 0.9823597, r-squared of 0.9650306, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
Kernels to Kerosene: Exploring the Correlation Between GMO Corn in South Dakota and Kerosene Consumption in Nepal

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in agriculture has sparked many debates and discussions, with proponents touting increased yields and resistance to pests, and skeptics raising concerns about environmental impact and human health. On the other hand, the consumption of kerosene, particularly in developing countries, has been a matter of interest for its implications for household energy use and economic development. This study aims to bridge these two distinct realms by investigating the intriguing connection between GMO corn production in South Dakota and kerosene consumption in Nepal.

Our research team delved into USDA data on GMO corn cultivation and cross-referenced it with the Energy Information Administration's records of kerosene consumption in Nepal. To our surprise, we observed a strong positive correlation between GMO corn production in South Dakota and kerosene consumption in Nepal, with a correlation coefficient so high, it would make any statistician's heart skip a beat - r = 0.9823597. The p-value was even more impressive, coming in at p < 0.01, which, statistically speaking, is rarer than finding a unicorn in a cornfield.

The findings of this study open up a cornucopia of questions and possibilities, challenging traditional paradigms and inviting further exploration into the interconnected web of agricultural practices and energy dynamics on a global scale. So, the next time you reach for that ear of GMO corn, just remember, in a strange twist of fate, you might be fueling kerosene use halfway across the world - talk about a-maize-ing connections!

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The cultivation of genetically modified organism (GMO) crops has long been a topic of fervent discussion and analysis, drawing a line in the proverbial soil between proponents extolling their potential for increased productivity and resistance to pests, and skeptics expressing concerns over their possible adverse effects on the environment and human health. Meanwhile, on a completely different cob, the consumption of kerosene, especially in developing nations, has garnered attention due to its implications on household energy utilization and economic advancement. These two seemingly unrelated domains - GMO corn production in South Dakota and kerosene usage in Nepal - have been the focal points of our investigation to uncover a correlation that may seem as unlikely as a scarecrow winning a dance competition.
As we embark on this peculiar journey of connecting kernels to kerosene, we are reminded of an apt dad joke: "Why did the corn refuse to play the piano? Because it had lost its husk!" Now, moving past the corny humor, we focus on the crux of our research, which brings together datasets that, at first glance, may seem as unrelated as a buttered cob and a barrel of kerosene.
Our intrepid research team scoured USDA records to glean insights into the production of GMO corn in the state of South Dakota, a veritable cornucopia of agricultural activity. Concurrently, we delved into the Energy Information Administration's comprehensive compendium of kerosene consumption in Nepal, a nation with its own distinct energy landscape. The resulting dataset juxtaposed two items as mismatched as a cob of corn and a gallon of kerosene, yet the analysis unveiled a surprising correlation that could make even the most discerning statistician exclaim, "Well, butter my corn and call me a skeptic!"
With a correlation coefficient that could make a mathematician's heart flutter - r = 0.9823597 - it's clear that there may be more to this pairing than meets the eye. The accompanying p-value, a statistically scintillating p < 0.01, is indeed a rarity in the realm of data analysis. Finding such an impressive correlation is as unexpected as discovering a kernel of truth in a field of statistical noise.
The discovery of this unexpected connection between these seemingly disparate elements prompts us to not only contemplate the interplay between agricultural practices and energy dynamics but also raises eyebrow-archingly saucy questions about their global repercussions. So, while GMO corn enjoys its time in the sun, it's worth considering the far-reaching effects of this seemingly innocuous crop - as it turns out, its impact may be more far-reaching than a cornstalk in the wind.
The unveiling of this unexpected correlation invites further inquiry and sparks curiosity about the intricate web of interactions that transcend geographic and categorical boundaries. As we ponder this unexpected correlation, we're reminded of a kernel of wisdom: "Why did the corn go to the computer? Because it wanted to become a kernel!" - a reminder of the delightful twists and turns that research can uncover, even in the unlikeliest of places.
In the following sections, we present our rigorous methodology, illuminating data analysis, and a robust discussion of our findings, inviting readers to contemplate the resonance and implications of our findings in this thoroughly engaging rendezvous between GMO corn and kerosene consumption. Let's embark on this journey of scientific inquiry with kernels of curiosity and a spirit of exploration.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The analysis of the data unveiled a striking correlation between GMO corn production in South Dakota and kerosene consumption in Nepal. The correlation coefficient of 0.9823597 suggests a remarkably strong positive relationship between these seemingly unrelated variables. This correlation value is so high, it makes you want to say, "You can't be corny with statistics this convincing!"

The r-squared value of 0.9650306 further emphasizes the robustness of the correlation, indicating that over 96% of the variation in kerosene consumption in Nepal can be explained by the variation in GMO corn production in South Dakota. In other words, it's as if GMO corn in South Dakota and kerosene in Nepal are engaged in a tango so harmonious, they'd put even Fred Astaire to shame!

The p-value, coming in at p < 0.01, reinforces the statistical significance of the relationship. This p-value is so rare, it's like finding a needle in a haystack, or in this case, a non-GMO corn kernel in a field of genetically modified ones.

Furthermore, the scatterplot (Fig. 1) visually depicts the strong positive correlation between GMO corn production in South Dakota and kerosene consumption in Nepal. Much like the plot of a suspenseful thriller, the points in the scatterplot unfold a compelling narrative of interconnectedness between these two disparate variables.

Overall, these results demonstrate a connection between GMO corn production in South Dakota and kerosene consumption in Nepal that is as surprising as finding a kernel of truth in a bowl of cornflakes.

The next time you're pondering the complexities of global agricultural and energy dynamics, just recall this unexpected connection and remember, sometimes the most unexpected correlations can be as delightful as stumbling upon a perfectly popped kernel in a bucket of popcorn.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between GMO use in corn grown in South Dakota and Kerosene used in Nepal. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 500 tokens.

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

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

[[TITLE]]
Kernels to Kerosene: Exploring the Correlation Between GMO Corn in South Dakota and Kerosene Consumption in Nepal

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in agriculture has sparked many debates and discussions, with proponents touting increased yields and resistance to pests, and skeptics raising concerns about environmental impact and human health. On the other hand, the consumption of kerosene, particularly in developing countries, has been a matter of interest for its implications for household energy use and economic development. This study aims to bridge these two distinct realms by investigating the intriguing connection between GMO corn production in South Dakota and kerosene consumption in Nepal.

Our research team delved into USDA data on GMO corn cultivation and cross-referenced it with the Energy Information Administration's records of kerosene consumption in Nepal. To our surprise, we observed a strong positive correlation between GMO corn production in South Dakota and kerosene consumption in Nepal, with a correlation coefficient so high, it would make any statistician's heart skip a beat - r = 0.9823597. The p-value was even more impressive, coming in at p < 0.01, which, statistically speaking, is rarer than finding a unicorn in a cornfield.

The findings of this study open up a cornucopia of questions and possibilities, challenging traditional paradigms and inviting further exploration into the interconnected web of agricultural practices and energy dynamics on a global scale. So, the next time you reach for that ear of GMO corn, just remember, in a strange twist of fate, you might be fueling kerosene use halfway across the world - talk about a-maize-ing connections!

[[LITERATURE REVIEW]]
The connection between agricultural practices and energy dynamics has piqued the interest of researchers and enthusiasts alike, akin to a corn maze enticing adventurous souls with its twists and turns. In "Harvesting Insights: A Global Perspective on GMO Crop Production," Smith et al. delve into the world of genetically modified corn crops, shedding light on the intricate web of factors influencing their cultivation and impact. Meanwhile, in "Fueling the Future: Exploring Energy Consumption in Developing Nations," Doe and Jones illuminate the nuanced complexities of kerosene consumption in regions such as Nepal, offering a thorough analysis of its societal and economic implications.
Now, turning our attention to the literary realm, we encounter thought-provoking non-fiction works that parallel the themes of our research. "The Omnivore's Dilemma" by Michael Pollan offers a comprehensive exploration of modern agricultural practices, including the utilization of GMO crops, providing a rich tapestry of insights into the interconnectedness of food production and consumption. Additionally, "Energy and Civilization: A History" by Vaclav Smil delves into the evolution of human energy use, framing kerosene consumption within the broader context of societal development and progress.
As we meander further into the fictitious landscapes of literature, "The Corn Whisperer" by Barbara O'Neal and "Kerosene Kingdom" by Sara Al-Fadhly captivate readers with engaging narratives that, while purely speculative, hint at the potential intertwined fate of GMO corn and kerosene consumption. These literary works, while not grounded in empirical evidence, offer imaginative projections that stimulate thought on the potential symbiotic relationships between these seemingly disparate entities.

Speaking of unlikely combinations, remember, the correlation between GMO corn in South Dakota and kerosene consumption in Nepal might seem as peculiar as a corn cob excitedly offering to refill your kerosene lamp - now there's a kernel of an intriguing image!
In a delightful departure from conventional research references, let us not overlook the cultural zeitgeist. The infamous "This is fine" meme, depicting a cartoon dog sipping coffee while his surroundings are engulfed in flames, humorously captures the unexpected correlations that may elicit a nonchalant response - much like the surprising link we've uncovered. Additionally, the "Weird Flex, But Okay" meme encapsulates the sense of bemused acceptance, which mirrors the simultaneous incredulity and realization one experiences upon discovering improbable relationships - much like the connection between GMO corn and kerosene consumption.
Now, let's plow onward into the enchanting field of data analysis and discussion, as we unpack the implications of this a-maize-ing correlation.

[[RESULTS]]
The analysis of the data unveiled a striking correlation between GMO corn production in South Dakota and kerosene consumption in Nepal. The correlation coefficient of 0.9823597 suggests a remarkably strong positive relationship between these seemingly unrelated variables. This correlation value is so high, it makes you want to say, "You can't be corny with statistics this convincing!"
The r-squared value of 0.9650306 further emphasizes the robustness of the correlation, indicating that over 96% of the variation in kerosene consumption in Nepal can be explained by the variation in GMO corn production in South Dakota. In other words, it's as if GMO corn in South Dakota and kerosene in Nepal are engaged in a tango so harmonious, they'd put even Fred Astaire to shame!
The p-value, coming in at p < 0.01, reinforces the statistical significance of the relationship. This p-value is so rare, it's like finding a needle in a haystack, or in this case, a non-GMO corn kernel in a field of genetically modified ones.
Furthermore, the scatterplot (Fig. 1) visually depicts the strong positive correlation between GMO corn production in South Dakota and kerosene consumption in Nepal. Much like the plot of a suspenseful thriller, the points in the scatterplot unfold a compelling narrative of interconnectedness between these two disparate variables.
Overall, these results demonstrate a connection between GMO corn production in South Dakota and kerosene consumption in Nepal that is as surprising as finding a kernel of truth in a bowl of cornflakes.
The next time you're pondering the complexities of global agricultural and energy dynamics, just recall this unexpected connection and remember, sometimes the most unexpected correlations can be as delightful as stumbling upon a perfectly popped kernel in a bucket of popcorn.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of our study provide compelling evidence in support of the prior research that hinted at the intriguing interplay between GMO corn production in South Dakota and kerosene consumption in Nepal. As we navigate through the labyrinthine landscape of agricultural and energy dynamics, it becomes apparent that the relationship between these seemingly disparate factors is more than just a kernel of truth – it's a full-blown cob of correlation.

Building upon the work of Smith et al., our research underscores the intricate web of factors influencing genetically modified corn cultivation. The strong positive correlation between GMO corn production in South Dakota and kerosene consumption in Nepal aligns with the notion that global agricultural practices have far-reaching effects, akin to a domino effect where one crop's abundance can influence energy consumption thousands of miles away. It's almost as if GMO corn and kerosene are engaging in a long-distance tango – a dance of interconnectedness that transcends geographical boundaries and agricultural practices.

Similarly, the investigation by Doe and Jones into kerosene consumption in developing nations gains additional depth with our findings. The robust correlation between kerosene use in Nepal and GMO corn production in South Dakota underscores the importance of understanding global energy dynamics. It's like unraveling the plot of a suspenseful thriller – each twist and turn leading to a revelation about the interconnectedness of seemingly unrelated elements. This correlation is so strong, it's like the literary equivalent of stumbling upon a well-hidden clue in a detective novel.

Even as we reflect on the whimsical references to the "Corn Whisperer" and "Kerosene Kingdom," it becomes evident that these fictitious narratives have inadvertently painted a prescient picture of the potential relationships between GMO corn and kerosene consumption. These literary works, while not rooted in empirical evidence, have skillfully captured the essence of the unexpected correlation uncovered in our research. It's as if fiction has intertwined with reality, creating a blend of imagination and empirical observation that mirrors the serendipitous discovery of an unexpected connection in an otherwise unassuming dataset.

In conclusion, our study's results not only reaffirm but also expand upon the prior research, shedding light on the a-maize-ing interconnectedness between GMO corn production in South Dakota and kerosene consumption in Nepal. The unexpected correlations we've uncovered serve as a testament to the intricate and often surprising relationships that permeate the global agricultural and energy landscape. As we continue to navigate this complex web of interconnectedness, one thing is clear – the world of agricultural and energy dynamics is far from corny; in fact, it's ripe with unexpected connections, waiting to be discovered like hidden treasures in a cornfield.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between GMO use in corn grown in South Dakota and Kerosene used in Nepal. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Kernels to Kerosene: Exploring the Correlation Between GMO Corn in South Dakota and Kerosene Consumption in Nepal

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in agriculture has sparked many debates and discussions, with proponents touting increased yields and resistance to pests, and skeptics raising concerns about environmental impact and human health. On the other hand, the consumption of kerosene, particularly in developing countries, has been a matter of interest for its implications for household energy use and economic development. This study aims to bridge these two distinct realms by investigating the intriguing connection between GMO corn production in South Dakota and kerosene consumption in Nepal.

Our research team delved into USDA data on GMO corn cultivation and cross-referenced it with the Energy Information Administration's records of kerosene consumption in Nepal. To our surprise, we observed a strong positive correlation between GMO corn production in South Dakota and kerosene consumption in Nepal, with a correlation coefficient so high, it would make any statistician's heart skip a beat - r = 0.9823597. The p-value was even more impressive, coming in at p < 0.01, which, statistically speaking, is rarer than finding a unicorn in a cornfield.

The findings of this study open up a cornucopia of questions and possibilities, challenging traditional paradigms and inviting further exploration into the interconnected web of agricultural practices and energy dynamics on a global scale. So, the next time you reach for that ear of GMO corn, just remember, in a strange twist of fate, you might be fueling kerosene use halfway across the world - talk about a-maize-ing connections!

[[INTRDUCTION]]
The cultivation of genetically modified organism (GMO) crops has long been a topic of fervent discussion and analysis, drawing a line in the proverbial soil between proponents extolling their potential for increased productivity and resistance to pests, and skeptics expressing concerns over their possible adverse effects on the environment and human health. Meanwhile, on a completely different cob, the consumption of kerosene, especially in developing nations, has garnered attention due to its implications on household energy utilization and economic advancement. These two seemingly unrelated domains - GMO corn production in South Dakota and kerosene usage in Nepal - have been the focal points of our investigation to uncover a correlation that may seem as unlikely as a scarecrow winning a dance competition.
As we embark on this peculiar journey of connecting kernels to kerosene, we are reminded of an apt dad joke: "Why did the corn refuse to play the piano? Because it had lost its husk!" Now, moving past the corny humor, we focus on the crux of our research, which brings together datasets that, at first glance, may seem as unrelated as a buttered cob and a barrel of kerosene.
Our intrepid research team scoured USDA records to glean insights into the production of GMO corn in the state of South Dakota, a veritable cornucopia of agricultural activity. Concurrently, we delved into the Energy Information Administration's comprehensive compendium of kerosene consumption in Nepal, a nation with its own distinct energy landscape. The resulting dataset juxtaposed two items as mismatched as a cob of corn and a gallon of kerosene, yet the analysis unveiled a surprising correlation that could make even the most discerning statistician exclaim, "Well, butter my corn and call me a skeptic!"
With a correlation coefficient that could make a mathematician's heart flutter - r = 0.9823597 - it's clear that there may be more to this pairing than meets the eye. The accompanying p-value, a statistically scintillating p < 0.01, is indeed a rarity in the realm of data analysis. Finding such an impressive correlation is as unexpected as discovering a kernel of truth in a field of statistical noise.
The discovery of this unexpected connection between these seemingly disparate elements prompts us to not only contemplate the interplay between agricultural practices and energy dynamics but also raises eyebrow-archingly saucy questions about their global repercussions. So, while GMO corn enjoys its time in the sun, it's worth considering the far-reaching effects of this seemingly innocuous crop - as it turns out, its impact may be more far-reaching than a cornstalk in the wind.
The unveiling of this unexpected correlation invites further inquiry and sparks curiosity about the intricate web of interactions that transcend geographic and categorical boundaries. As we ponder this unexpected correlation, we're reminded of a kernel of wisdom: "Why did the corn go to the computer? Because it wanted to become a kernel!" - a reminder of the delightful twists and turns that research can uncover, even in the unlikeliest of places.
In the following sections, we present our rigorous methodology, illuminating data analysis, and a robust discussion of our findings, inviting readers to contemplate the resonance and implications of our findings in this thoroughly engaging rendezvous between GMO corn and kerosene consumption. Let's embark on this journey of scientific inquiry with kernels of curiosity and a spirit of exploration.

[[RESULTS]]
The analysis of the data unveiled a striking correlation between GMO corn production in South Dakota and kerosene consumption in Nepal. The correlation coefficient of 0.9823597 suggests a remarkably strong positive relationship between these seemingly unrelated variables. This correlation value is so high, it makes you want to say, "You can't be corny with statistics this convincing!"
The r-squared value of 0.9650306 further emphasizes the robustness of the correlation, indicating that over 96% of the variation in kerosene consumption in Nepal can be explained by the variation in GMO corn production in South Dakota. In other words, it's as if GMO corn in South Dakota and kerosene in Nepal are engaged in a tango so harmonious, they'd put even Fred Astaire to shame!
The p-value, coming in at p < 0.01, reinforces the statistical significance of the relationship. This p-value is so rare, it's like finding a needle in a haystack, or in this case, a non-GMO corn kernel in a field of genetically modified ones.
Furthermore, the scatterplot (Fig. 1) visually depicts the strong positive correlation between GMO corn production in South Dakota and kerosene consumption in Nepal. Much like the plot of a suspenseful thriller, the points in the scatterplot unfold a compelling narrative of interconnectedness between these two disparate variables.
Overall, these results demonstrate a connection between GMO corn production in South Dakota and kerosene consumption in Nepal that is as surprising as finding a kernel of truth in a bowl of cornflakes.
The next time you're pondering the complexities of global agricultural and energy dynamics, just recall this unexpected connection and remember, sometimes the most unexpected correlations can be as delightful as stumbling upon a perfectly popped kernel in a bucket of popcorn.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has shed light on the unexpected yet undeniably strong correlation between GMO corn production in South Dakota and kerosene consumption in Nepal. The statistical evidence presented here suggests a link so compelling, it's as if they were peas in a pod, or should I say, corn in a husk! This correlation is not just statistically significant; it's practically whispering sweet statistical nothings in our ears.

The implications of this correlation extend far beyond statistical fascination. It prompts us to reconsider the intricate web of global agricultural and energy dynamics. It's like a thought-provoking puzzle where the missing piece turns out to be a corn kernel in a field of kerosene canisters! This unexpected relationship challenges us to rethink the interconnectedness of seemingly unrelated domains, akin to finding out that the scarecrow in the cornfield moonlights as a kerosene enthusiast.

Therefore, we firmly assert that no further research is needed in this area. The results stand as solid as a cob in a cornfield, and the findings are as clear as a sunny day in the heartland. We've cracked the corny code and unearthed a correlation as captivating as a captivating novel - so captivating, in fact, that it may just warrant a sequel! But for now, let's rest assured that the enigmatic bond between GMO corn in South Dakota and kerosene consumption in Nepal has been thoroughly explored. It's time to let this finding pop and sizzle like a well-buttered batch of corn kernels.

In the immortal words of a tasseled-capped, Dad-joke-wielding researcher, "Why don't scientists trust atoms? Because they make up everything - just like the connection between GMO corn and kerosene consumption!"

Lastly, just like that perfectly popped kernel in a bucket of popcorn, this research has revealed a surprising and delightful connection that is sure to linger in the minds of researchers and popcorn enthusiasts alike. It's time to butter our hands and take a well-deserved break from this a-maize-ing adventure into the unexpected interplay between corn and kerosene.

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 Kernels to Kerosene: Exploring the Correlation Between GMO Corn in South Dakota and Kerosene Consumption in Nepal. Respond only with the name of a fictional academic journal.

ChatGPT:

The International Journal of Agronomic Alchemy

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 Kernels to Kerosene: Exploring the Correlation Between GMO Corn in South Dakota and Kerosene Consumption in Nepal. Respond only with the name of a fictional organization.

ChatGPT:

The Consortium for Agricultural Innovation and Sustainable Development

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 Kernels to Kerosene: Exploring the Correlation Between GMO Corn in South Dakota and Kerosene Consumption in Nepal with an abstract of The use of genetically modified organisms (GMOs) in agriculture has sparked many debates and discussions, with proponents touting increased yields and resistance to pests, and skeptics raising concerns about environmental impact and human health. On the other hand, the consumption of kerosene, particularly in developing countries, has been a matter of interest for its implications for household energy use and economic development. This study aims to bridge these two distinct realms by investigating the intriguing connection between GMO corn production in South Dakota and kerosene consumption in Nepal.
Our research team delved into USDA data on GMO corn cultivation and cross-referenced it with the Energy Information Administration's records of kerosene consumption in Nepal. To our surprise, we observed a strong positive correlation between GMO corn production in South Dakota and kerosene consumption in Nepal, with a correlation coefficient so high, it would make any statistician's heart skip a beat - r = 0.9823597. The p-value was even more impressive, coming in at p < 0.01, which, statistically speaking, is rarer than finding a unicorn in a cornfield.
The findings of this study open up a cornucopia of questions and possibilities, challenging traditional paradigms and inviting further exploration into the interconnected web of agricultural practices and energy dynamics on a global scale. So, the next time you reach for that ear of GMO corn, just remember, in a strange twist of fate, you might be fueling kerosene use halfway across the world - talk about a-maize-ing connections!

ChatGPT:

GMO corn production, South Dakota, kerosene consumption, Nepal, correlation, agricultural practices, energy dynamics, household energy use, economic development, USDA data, Energy Information Administration, GMO yields, environmental impact, human health, statistical analysis

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

Kerosene used in Nepal
Detailed data title: Volume of kerosene used consumed in Nepal in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Kerosene used in Nepal

Correlation r = 0.9823597 (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 = 0.9650306 (Coefficient of determination)
This means 96.5% of the change in the one variable (i.e., Kerosene used in Nepal) is predictable based on the change in the other (i.e., GMO use in corn grown in South Dakota) over the 22 years from 2000 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 4.9E-16.^Show 0.0000000000000004896575527869
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 0.98 in 4.9E-14% of random cases. Said differently, if you correlated 2,042,243,593,116,192 random variables^Note You don't actually need 2 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.

[ 0.96, 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 South Dakota (GMO corn %)	35	30	33	34	28	30	20	16	7	6	6	7	9	2	3	1	4	3	2	3	4	3
Kerosene used in Nepal (Million Barrels/Day)	5.34342	6.43814	5.80279	6.33225	4.75205	4.76507	3.17671	2.49901	1.28833	0.868301	0.720055	0.677699	0.401284	0.317671	0.317671	0.232958	0.316803	0.360027	0.402384	0.304164	0.288296	0.297701

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([35,30,33,34,28,30,20,16,7,6,6,7,9,2,3,1,4,3,2,3,4,3,])
array_2 = np.array([5.34342,6.43814,5.80279,6.33225,4.75205,4.76507,3.17671,2.49901,1.28833,0.868301,0.720055,0.677699,0.401284,0.317671,0.317671,0.232958,0.316803,0.360027,0.402384,0.304164,0.288296,0.297701,])
array_1_name = "GMO use in corn grown in South Dakota"
array_2_name = "Kerosene used in Nepal"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. ^Note Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

Full license details.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:

High resolution line chart ^Note The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.

If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only GMO use in corn grown in South Dakota
Line chart for only Kerosene used in Nepal
AI-generated correlation image
The spurious research paper: Kernels to Kerosene: Exploring the Correlation Between GMO Corn in South Dakota and Kerosene Consumption in Nepal

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Correlation ID: 1557 · Black Variable ID: 720 · Red Variable ID: 24733


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