Spurious correlation #1,742 · 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 Fossil fuel use in Spain. 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 Fossil fuel use in Spain. The chart goes from 2000 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

It's simple really, as GMO corn in South Dakota grows taller and stronger, it's reaching new heights in productivity. This leads to an excess supply of corn, creating a *maize* ing demand in the global market. Now, in Spain, they're *corn*pletely overjoyed by the availability of corn for various purposes, including as a feed for livestock. This ultimately means more transportation of corn products from the US to Spain, *kernel*ly increasing the need for fossil fuels to *stalk* up on corn in Spanish markets. Who knew that a small seed of genetic modification in South Dakota could *cob*tribute to such a *earsome* impact on Fossil fuel use in Spain! It's a-maize-ing!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a South Dakota cornfield at sunset, where a farmer is operating a tractor running on fossil fuel. The rows of genetically modified corn should be depicted in the field, with subtle hints of futuristic technology seamlessly integrated into the landscape. The futuristic elements should be a subtle nod to 'Star Wars', creating an intriguing blend of sci-fi and rural imagery. 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 Fossil fuel use in Spain. 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: Cyberpunk Hyper-realistic An image that looks like it is a scene from 'Star Wars' Surrealism Voxel art
ChatGPT: A scene from 'Star Wars'

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 Fossil fuel use in Spain together. Keep the description simple.
ChatGPT: In a South Dakota cornfield, a farmer uses a tractor running on fossil fuel to work the land. In the field, the rows of corn are genetically modified to resist pests and produce higher yields.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A scene from 'Star Wars' ] and depicting this scene: In a South Dakota cornfield, a farmer uses a tractor running on fossil fuel to work the land. In the field, the rows of corn are genetically modified to resist pests and produce higher yields. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a South Dakota cornfield at sunset, where a farmer is operating a tractor running on fossil fuel. The rows of genetically modified corn should be depicted in the field, with subtle hints of futuristic technology seamlessly integrated into the landscape. The futuristic elements should be a subtle nod to 'Star Wars', creating an intriguing blend of sci-fi and rural imagery.

*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 increases in GMO use in corn grown in South Dakota positively influenced Fossil fuel use in Spain. Include one good pun.

AI academic paper

(Because p < 0.01)

Maize Mania: The Corn-nection Between GMOs in South Dakota and Fossil Fuel Fiasco in Spain

Journal of Agroecological Innovations

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 Fossil fuel use in Spain. Make lots of jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading the backs of shampoo bottles.

Here is the title and abstract of the paper:
[[TITLE]]
"Maize Mania: The Corn-nection Between GMOs in South Dakota and Fossil Fuel Fiasco in Spain"

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in maize cultivation has long been a topic of debate and speculation. In this study, we boldly venture into the realm of corny puns to explore the potential link between GMO use in corn grown in South Dakota and the seemingly unrelated topic of fossil fuel consumption in Spain. Drawing data from the USDA and the Energy Information Administration, we examine the correlation between these two seemingly disparate phenomena. To our surprise and delight, our analysis reveals a striking correlation coefficient of 0.9298696 with a p-value less than 0.01, affirming a maize-merizing relationship between GMOs in South Dakota and fossil fuel usage in Spain from 2000 to 2021. Our findings bring a-MAIZE-ing insights into the intercontinental impact of genetically modified corn and leave us pondering the cornundrum of its wider implications.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
The interplay between genetically modified organisms (GMOs) and their effects on the environment and economies has been a perennial topic of interest in scientific and agricultural circles. While the debate rages on, we cannot help but marvel at the corn-ucopia of possibilities and puns that arise from delving into this corny subject.
In this study, we boldly venture into the maize maze to uncover the potential correlation between GMO use in corn grown in South Dakota and the fossil fuel fiasco in Spain. It's a highly improbable connection, a bit like finding a kernel of truth in a haystack, but we were fascinated by the possibility of a link between these apparently disjointed variables.
The idea may seem as unlikely as a unicorn strolling through a cornfield, but with the heaping helpings of data from the USDA and the Energy Information Administration, we set out to determine if there is more than just cornfusion when it comes to GMOs and fossil fuel use.
Our statistical analysis has been as rigorous as a corn stalk in a hurricane, and to our bewilderment, we unearthed a correlation coefficient of 0.9298696 with a p-value less than 0.01. This discovery was as shocking as finding a popcorn kernel in a sea of corn flakes, as it affirmatively indicates a strong connection between the GMO-laden cornfields of South Dakota and the fossil fuel consumption of Spain.
These findings provide a-MAIZE-ing insights into the potential impact of genetically modified corn not only on a local scale but also internationally, leaving us pondering the cornundrum of its implications and prompting further investigation into this unexpected correlation.
With our ears to the ground and our eyes on the data, we delve into the tangled web of maize mania, to demystify the corn-nection between GMOs in South Dakota and the fossil fuel fiasco in Spain.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The correlation between GMO use in maize cultivation and its potential impact on fossil fuel consumption may appear as incongruous as a cow attempting to do the cha-cha, yet empirical evidence suggests a connection that is as real as corn on the cob. Smith et al. (2018) assert that the production and distribution of genetically modified corn can have far-reaching consequences, not only for agricultural practices but also for the broader environmental landscape. Similarly, Doe and Jones (2015) delve into the complexities of international trade and its influence on energy consumption, shedding light on the intricate web of intercontinental dependencies.

Venturing beyond the academic realm, "The Omnivore's Dilemma" by Michael Pollan offers a non-fiction perspective on the modern agricultural landscape, examining the implications of GMO use and its ramifications on food production. In a literary twist, "The Corn King and the Spring Queen" by Naomi Mitchison explores the nuanced relationship between humans and the land, hinting at the interconnectedness of agricultural practices and societal dynamics.

But wait, the review does not stop there! As researchers, we must be thorough in our quest for knowledge. As such, we went as far as scanning the backs of shampoo bottles, seeking wisdom from the most unexpected sources. Surprisingly, we stumbled upon an enlightening tidbit about "maize extract" in a particular brand of shampoo, leading us to ponder the unforeseen influence of corn even in personal care products. While this discovery may seem as tangential as a corn kernel rolling down a hill, it highlights the omnipresence of maize in our daily lives and its potential impact on resource utilization.

As we navigate the labyrinth of literature surrounding GMOs in maize cultivation and fossil fuel consumption, it becomes abundantly clear that the seemingly whimsical correlation we seek to explore has implications that extend beyond the realms of scientific inquiry. The excitement of these discoveries is as palpable as butter on freshly popped popcorn, leaving us eagerly anticipating the revelations that await as we delve deeper into this maize-terious connection.

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 Fossil fuel use in Spain. Make lots of jokes, goofy observations, and puns. 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]]
"Maize Mania: The Corn-nection Between GMOs in South Dakota and Fossil Fuel Fiasco in Spain"

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in maize cultivation has long been a topic of debate and speculation. In this study, we boldly venture into the realm of corny puns to explore the potential link between GMO use in corn grown in South Dakota and the seemingly unrelated topic of fossil fuel consumption in Spain. Drawing data from the USDA and the Energy Information Administration, we examine the correlation between these two seemingly disparate phenomena. To our surprise and delight, our analysis reveals a striking correlation coefficient of 0.9298696 with a p-value less than 0.01, affirming a maize-merizing relationship between GMOs in South Dakota and fossil fuel usage in Spain from 2000 to 2021. Our findings bring a-MAIZE-ing insights into the intercontinental impact of genetically modified corn and leave us pondering the cornundrum of its wider implications.

[[INTRODUCTION]]
INTRODUCTION
The interplay between genetically modified organisms (GMOs) and their effects on the environment and economies has been a perennial topic of interest in scientific and agricultural circles. While the debate rages on, we cannot help but marvel at the corn-ucopia of possibilities and puns that arise from delving into this corny subject.
In this study, we boldly venture into the maize maze to uncover the potential correlation between GMO use in corn grown in South Dakota and the fossil fuel fiasco in Spain. It's a highly improbable connection, a bit like finding a kernel of truth in a haystack, but we were fascinated by the possibility of a link between these apparently disjointed variables.
The idea may seem as unlikely as a unicorn strolling through a cornfield, but with the heaping helpings of data from the USDA and the Energy Information Administration, we set out to determine if there is more than just cornfusion when it comes to GMOs and fossil fuel use.
Our statistical analysis has been as rigorous as a corn stalk in a hurricane, and to our bewilderment, we unearthed a correlation coefficient of 0.9298696 with a p-value less than 0.01. This discovery was as shocking as finding a popcorn kernel in a sea of corn flakes, as it affirmatively indicates a strong connection between the GMO-laden cornfields of South Dakota and the fossil fuel consumption of Spain.
These findings provide a-MAIZE-ing insights into the potential impact of genetically modified corn not only on a local scale but also internationally, leaving us pondering the cornundrum of its implications and prompting further investigation into this unexpected correlation.
With our ears to the ground and our eyes on the data, we delve into the tangled web of maize mania, to demystify the corn-nection between GMOs in South Dakota and the fossil fuel fiasco in Spain.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Sampling Strategy:
Our research team embarked on a cornfield adventure like no other, traversing the expanse of the internet to harvest bountiful data from reputable sources such as the USDA and the Energy Information Administration. We sifted through an agricultural ocean of information, much like seeking a prized piece of corn in a popcorn bucket, to ensure that our data pool was as robust as a well-fertilized maize crop.

Variables and Data Collection:
With our sights firmly set on the mysterious link between genetically modified corn in South Dakota and fossil fuel usage in Spain, we gathered data from the years 2000 to 2021, ensuring that our sample size was as crisp and golden as a freshly-picked ear of corn. The genetically modified organism (GMO) variable was measured using data on the percentage of GMO corn crops in South Dakota, while the fossil fuel consumption variable was measured using data on the use of fossil fuels in Spain. We meticulously collated this data, treating it with the kind of care and attention one might give to a prized heirloom variety of corn.

Statistical Analysis:
Our statistical analysis was as rigorous as separating kernels from a cob, employing advanced techniques to unveil any underlying patterns or correlations. We utilized Pearson's correlation coefficient to assess the strength and direction of the relationship between GMO use in South Dakota and fossil fuel consumption in Spain. This allowed us to ascertain whether the two variables were as inextricably linked as kernels on a cob or as separate as popcorn in a microwave.

Intercontinental Comparison:
In order to contextualize our findings, we conducted a comparative analysis of GMO-driven corn cultivation in South Dakota and its impact on fossil fuel usage in Spain. While this may seem as unconventional as salsa on cornflakes, we felt it was crucial to situate our findings within a global framework, much like comparing the appeal of corn on the cob to that of a bag of popcorn at the movies.

Ethical Considerations:
Our research adhered to the highest ethical standards, ensuring that all data was handled with the care and respect due to a kernel of corn. We also took precautions to avoid any cross-pollination of data, maintaining the integrity of our findings and guarding against any potential statistical mutations.

Limitations:
Despite our meticulous approach, it is imperative to note that our study is not without limitations. The ecological validity of our findings may be subject to the same uncertainties as predicting the number of kernels on an ear of corn without peeling back the husk. Additionally, while our findings are robust within the confines of our chosen variables and time frame, they may not capture the full spectrum of factors influencing GMO use and fossil fuel consumption. We encourage future researchers to take these limitations into account and pursue further investigation into this intriguing corn-nection.

In summary, our methodology may have been as convoluted as navigating a maize maze, but it was essential to uncover the hidden kernel of truth in this delightfully corny research endeavor.

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 Fossil fuel use in Spain. Make lots of jokes, goofy observations, and puns. 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 .

For the time period 2000 to 2021, you found a correlation 0.9298696, r-squared of 0.8646575, 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]]
"Maize Mania: The Corn-nection Between GMOs in South Dakota and Fossil Fuel Fiasco in Spain"

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in maize cultivation has long been a topic of debate and speculation. In this study, we boldly venture into the realm of corny puns to explore the potential link between GMO use in corn grown in South Dakota and the seemingly unrelated topic of fossil fuel consumption in Spain. Drawing data from the USDA and the Energy Information Administration, we examine the correlation between these two seemingly disparate phenomena. To our surprise and delight, our analysis reveals a striking correlation coefficient of 0.9298696 with a p-value less than 0.01, affirming a maize-merizing relationship between GMOs in South Dakota and fossil fuel usage in Spain from 2000 to 2021. Our findings bring a-MAIZE-ing insights into the intercontinental impact of genetically modified corn and leave us pondering the cornundrum of its wider implications.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
INTRODUCTION
The interplay between genetically modified organisms (GMOs) and their effects on the environment and economies has been a perennial topic of interest in scientific and agricultural circles. While the debate rages on, we cannot help but marvel at the corn-ucopia of possibilities and puns that arise from delving into this corny subject.
In this study, we boldly venture into the maize maze to uncover the potential correlation between GMO use in corn grown in South Dakota and the fossil fuel fiasco in Spain. It's a highly improbable connection, a bit like finding a kernel of truth in a haystack, but we were fascinated by the possibility of a link between these apparently disjointed variables.
The idea may seem as unlikely as a unicorn strolling through a cornfield, but with the heaping helpings of data from the USDA and the Energy Information Administration, we set out to determine if there is more than just cornfusion when it comes to GMOs and fossil fuel use.
Our statistical analysis has been as rigorous as a corn stalk in a hurricane, and to our bewilderment, we unearthed a correlation coefficient of 0.9298696 with a p-value less than 0.01. This discovery was as shocking as finding a popcorn kernel in a sea of corn flakes, as it affirmatively indicates a strong connection between the GMO-laden cornfields of South Dakota and the fossil fuel consumption of Spain.
These findings provide a-MAIZE-ing insights into the potential impact of genetically modified corn not only on a local scale but also internationally, leaving us pondering the cornundrum of its implications and prompting further investigation into this unexpected correlation.
With our ears to the ground and our eyes on the data, we delve into the tangled web of maize mania, to demystify the corn-nection between GMOs in South Dakota and the fossil fuel fiasco in Spain.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

Correlation Analysis:
Our analysis of the data from 2000 to 2021 revealed a striking correlation coefficient of 0.9298696 between the use of genetically modified organisms (GMOs) in maize cultivation in South Dakota and the fossil fuel consumption in Spain. It's as if these two variables were doing the tango across continents, cha-cha-cha-ing their way to a statistically significant relationship. Frankly, we were as surprised as a kernel of popcorn when it pops unexpectedly—this correlation was as strong as an ox plowing a field of GM corn!

R-Squared Goodness:
Additionally, our findings revealed an r-squared value of 0.8646575. That's right, our model explains a whopping 86.47% of the variation in fossil fuel usage in Spain using the GMO usage in South Dakota as a predictor. This relationship has more explanatory power than a verbose professor with a microphone—clear, loud, and impossible to ignore!

Statistical Significance:
We also uncovered a p-value of less than 0.01, indicating that the likelihood of this correlation occurring by random chance is about as probable as finding a needle in a haystack made entirely of genetically modified corn cobs. This result is as good as winning the lottery of statistical significance—our findings are not just significant, they're a statistical bullseye!

Figure 1:
In Figure 1, we illustrate the strong correlation between GMO use in corn grown in South Dakota and fossil fuel consumption in Spain. The scatterplot is so aesthetically pleasing that it's the Leonardo da Vinci of data visualization! It's a breathtaking sight, a masterpiece of data artistry that leaves viewers in awe of the beauty of statistical harmony. You'll want to frame it and hang it in your living room, it's that captivating!

In conclusion, the results of this study not only provide empirical evidence for a novel connection between GMOs in South Dakota and the fossil fuel fiasco in Spain, but they also serve as a reminder that scientific inquiry can lead to some truly a-MAIZE-ing discoveries. We are left with a sense of wonder and a newfound appreciation for the uncharted territories of research where unexpected relationships can blossom like corn in the summer sun.

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 Fossil fuel use in Spain. Make lots of jokes, goofy observations, and puns. 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]]
"Maize Mania: The Corn-nection Between GMOs in South Dakota and Fossil Fuel Fiasco in Spain"

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in maize cultivation has long been a topic of debate and speculation. In this study, we boldly venture into the realm of corny puns to explore the potential link between GMO use in corn grown in South Dakota and the seemingly unrelated topic of fossil fuel consumption in Spain. Drawing data from the USDA and the Energy Information Administration, we examine the correlation between these two seemingly disparate phenomena. To our surprise and delight, our analysis reveals a striking correlation coefficient of 0.9298696 with a p-value less than 0.01, affirming a maize-merizing relationship between GMOs in South Dakota and fossil fuel usage in Spain from 2000 to 2021. Our findings bring a-MAIZE-ing insights into the intercontinental impact of genetically modified corn and leave us pondering the cornundrum of its wider implications.

[[LITERATURE REVIEW]]
The correlation between GMO use in maize cultivation and its potential impact on fossil fuel consumption may appear as incongruous as a cow attempting to do the cha-cha, yet empirical evidence suggests a connection that is as real as corn on the cob. Smith et al. (2018) assert that the production and distribution of genetically modified corn can have far-reaching consequences, not only for agricultural practices but also for the broader environmental landscape. Similarly, Doe and Jones (2015) delve into the complexities of international trade and its influence on energy consumption, shedding light on the intricate web of intercontinental dependencies.
Venturing beyond the academic realm, "The Omnivore's Dilemma" by Michael Pollan offers a non-fiction perspective on the modern agricultural landscape, examining the implications of GMO use and its ramifications on food production. In a literary twist, "The Corn King and the Spring Queen" by Naomi Mitchison explores the nuanced relationship between humans and the land, hinting at the interconnectedness of agricultural practices and societal dynamics.
But wait, the review does not stop there! As researchers, we must be thorough in our quest for knowledge. As such, we went as far as scanning the backs of shampoo bottles, seeking wisdom from the most unexpected sources. Surprisingly, we stumbled upon an enlightening tidbit about "maize extract" in a particular brand of shampoo, leading us to ponder the unforeseen influence of corn even in personal care products. While this discovery may seem as tangential as a corn kernel rolling down a hill, it highlights the omnipresence of maize in our daily lives and its potential impact on resource utilization.
As we navigate the labyrinth of literature surrounding GMOs in maize cultivation and fossil fuel consumption, it becomes abundantly clear that the seemingly whimsical correlation we seek to explore has implications that extend beyond the realms of scientific inquiry. The excitement of these discoveries is as palpable as butter on freshly popped popcorn, leaving us eagerly anticipating the revelations that await as we delve deeper into this maize-terious connection.

[[RESULTS]]
Correlation Analysis:
Our analysis of the data from 2000 to 2021 revealed a striking correlation coefficient of 0.9298696 between the use of genetically modified organisms (GMOs) in maize cultivation in South Dakota and the fossil fuel consumption in Spain. It's as if these two variables were doing the tango across continents, cha-cha-cha-ing their way to a statistically significant relationship. Frankly, we were as surprised as a kernel of popcorn when it pops unexpectedly—this correlation was as strong as an ox plowing a field of GM corn!
R-Squared Goodness:
Additionally, our findings revealed an r-squared value of 0.8646575. That's right, our model explains a whopping 86.47% of the variation in fossil fuel usage in Spain using the GMO usage in South Dakota as a predictor. This relationship has more explanatory power than a verbose professor with a microphone—clear, loud, and impossible to ignore!
Statistical Significance:
We also uncovered a p-value of less than 0.01, indicating that the likelihood of this correlation occurring by random chance is about as probable as finding a needle in a haystack made entirely of genetically modified corn cobs. This result is as good as winning the lottery of statistical significance—our findings are not just significant, they're a statistical bullseye!
Figure 1:
In Figure 1, we illustrate the strong correlation between GMO use in corn grown in South Dakota and fossil fuel consumption in Spain. The scatterplot is so aesthetically pleasing that it's the Leonardo da Vinci of data visualization! It's a breathtaking sight, a masterpiece of data artistry that leaves viewers in awe of the beauty of statistical harmony. You'll want to frame it and hang it in your living room, it's that captivating!
In conclusion, the results of this study not only provide empirical evidence for a novel connection between GMOs in South Dakota and the fossil fuel fiasco in Spain, but they also serve as a reminder that scientific inquiry can lead to some truly a-MAIZE-ing discoveries. We are left with a sense of wonder and a newfound appreciation for the uncharted territories of research where unexpected relationships can blossom like corn in the summer sun.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our findings provide unprecedented support for the previously suggested correlation between GMO use in maize cultivation in South Dakota and fossil fuel consumption in Spain. The robust correlation coefficient and the statistical significance of our results lend credence to the notion that these seemingly unrelated variables are as intertwined as a strand of DNA on a microscopic level. It's like finding out that two distant cousins are actually best friends who finish each other's sentences with statistical significance!

But let's not overlook the powerful explanatory capacity of our model, as represented by the impressively high r-squared value. This relationship explains a lion's share of the variation in fossil fuel usage in Spain, akin to solving a complex jigsaw puzzle where the pieces fit together flawlessly, leaving no gaps for statistical misinterpretation. It's akin to wielding the power of statistical thunder, and it sounds just as electrifying as it is statistically relevant!

Moreover, the scatterplot visually captures the tight bond between GMO use in South Dakota and fossil fuel consumption in Spain, offering a remarkable display of statistical artistry. This figure is not just a visualization; it's a Mona Lisa of data, a true masterpiece that renders one speechless with its sheer beauty. You'll want to hang it in the Louvre and watch as it mesmerizes data enthusiasts and art aficionados alike, symbolizing the fusion of science and art in a breathtaking display of correlation!

Our results, with their undeniable statistical strength, support the whispered conjectures of academia and the unconventional musings found on the backs of shampoo bottles. It's as though science and serendipity have engaged in a choreographed dance, revealing the maize-merizing connections that exist between seemingly dissimilar variables. As researchers, we cannot help but marvel at the maize-terious nature of these findings and approach the unexplored frontiers of statistical exploration with renewed excitement, akin to children in a statistical candy store! With a-MAIZE-ing discoveries sprouting from unexpected places, we are reminded that the quest for knowledge is as rich and diverse as the agricultural tapestry we seek to unravel.

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 Fossil fuel use in Spain. Make lots of jokes, goofy observations, and puns. 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]]
"Maize Mania: The Corn-nection Between GMOs in South Dakota and Fossil Fuel Fiasco in Spain"

[[ABSTRACT]]
The use of genetically modified organisms (GMOs) in maize cultivation has long been a topic of debate and speculation. In this study, we boldly venture into the realm of corny puns to explore the potential link between GMO use in corn grown in South Dakota and the seemingly unrelated topic of fossil fuel consumption in Spain. Drawing data from the USDA and the Energy Information Administration, we examine the correlation between these two seemingly disparate phenomena. To our surprise and delight, our analysis reveals a striking correlation coefficient of 0.9298696 with a p-value less than 0.01, affirming a maize-merizing relationship between GMOs in South Dakota and fossil fuel usage in Spain from 2000 to 2021. Our findings bring a-MAIZE-ing insights into the intercontinental impact of genetically modified corn and leave us pondering the cornundrum of its wider implications.

[[INTRDUCTION]]
INTRODUCTION
The interplay between genetically modified organisms (GMOs) and their effects on the environment and economies has been a perennial topic of interest in scientific and agricultural circles. While the debate rages on, we cannot help but marvel at the corn-ucopia of possibilities and puns that arise from delving into this corny subject.
In this study, we boldly venture into the maize maze to uncover the potential correlation between GMO use in corn grown in South Dakota and the fossil fuel fiasco in Spain. It's a highly improbable connection, a bit like finding a kernel of truth in a haystack, but we were fascinated by the possibility of a link between these apparently disjointed variables.
The idea may seem as unlikely as a unicorn strolling through a cornfield, but with the heaping helpings of data from the USDA and the Energy Information Administration, we set out to determine if there is more than just cornfusion when it comes to GMOs and fossil fuel use.
Our statistical analysis has been as rigorous as a corn stalk in a hurricane, and to our bewilderment, we unearthed a correlation coefficient of 0.9298696 with a p-value less than 0.01. This discovery was as shocking as finding a popcorn kernel in a sea of corn flakes, as it affirmatively indicates a strong connection between the GMO-laden cornfields of South Dakota and the fossil fuel consumption of Spain.
These findings provide a-MAIZE-ing insights into the potential impact of genetically modified corn not only on a local scale but also internationally, leaving us pondering the cornundrum of its implications and prompting further investigation into this unexpected correlation.
With our ears to the ground and our eyes on the data, we delve into the tangled web of maize mania, to demystify the corn-nection between GMOs in South Dakota and the fossil fuel fiasco in Spain.

[[RESULTS]]
Correlation Analysis:
Our analysis of the data from 2000 to 2021 revealed a striking correlation coefficient of 0.9298696 between the use of genetically modified organisms (GMOs) in maize cultivation in South Dakota and the fossil fuel consumption in Spain. It's as if these two variables were doing the tango across continents, cha-cha-cha-ing their way to a statistically significant relationship. Frankly, we were as surprised as a kernel of popcorn when it pops unexpectedly—this correlation was as strong as an ox plowing a field of GM corn!
R-Squared Goodness:
Additionally, our findings revealed an r-squared value of 0.8646575. That's right, our model explains a whopping 86.47% of the variation in fossil fuel usage in Spain using the GMO usage in South Dakota as a predictor. This relationship has more explanatory power than a verbose professor with a microphone—clear, loud, and impossible to ignore!
Statistical Significance:
We also uncovered a p-value of less than 0.01, indicating that the likelihood of this correlation occurring by random chance is about as probable as finding a needle in a haystack made entirely of genetically modified corn cobs. This result is as good as winning the lottery of statistical significance—our findings are not just significant, they're a statistical bullseye!
Figure 1:
In Figure 1, we illustrate the strong correlation between GMO use in corn grown in South Dakota and fossil fuel consumption in Spain. The scatterplot is so aesthetically pleasing that it's the Leonardo da Vinci of data visualization! It's a breathtaking sight, a masterpiece of data artistry that leaves viewers in awe of the beauty of statistical harmony. You'll want to frame it and hang it in your living room, it's that captivating!
In conclusion, the results of this study not only provide empirical evidence for a novel connection between GMOs in South Dakota and the fossil fuel fiasco in Spain, but they also serve as a reminder that scientific inquiry can lead to some truly a-MAIZE-ing discoveries. We are left with a sense of wonder and a newfound appreciation for the uncharted territories of research where unexpected relationships can blossom like corn in the summer sun.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has as bold and spicy a conclusion as a bowl of chili made with genetically modified corn—it's hot stuff! The remarkable correlation between GMO use in South Dakota and fossil fuel consumption in Spain has left us feeling as corn-fused as a corn stalk at a disco! Our findings not only shed light on this unexpected connection but also bring a-MAIZE-ing insights into the far-reaching impact of genetically modified corn.

Despite the initial cornfusion and skepticism, our research has planted the seeds for a new appreciation of the tangled web of maize mania. The data have spoken louder than a GMO-cornfield whistle in the wind, and it's clear that the GMO-laden cornfields of South Dakota and the fossil fuel consumption of Spain are dancing the statistical tango.

Furthermore, our study reaffirms that scientific inquiry can yield unexpected, even corny, connections. It's like stumbling upon a kernel of truth in a haystack of hypotheses—unpredictable and absolutely popcorn-worthy!

Our results not only provide empirical evidence for this novel connection but also put a spotlight on the exciting and unpredictable nature of scientific exploration. It's a reminder that scientific inquiry is not just about big theories and complex equations but also about the potential for a-MAIZE-ing discoveries that can change the way we view the world.

Our findings bring us to the undeniable conclusion that no more research is needed in this area. We've corn-pleted our mission, and it's time to pop the champagne (or should we say, pop the corn?) and celebrate this surprising discovery. As they say, sometimes the most corny and unexpected connections can lead to the most valuable insights.

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 Maize Mania: The Corn-nection Between GMOs in South Dakota and Fossil Fuel Fiasco in Spain. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Agroecological Innovations

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 Maize Mania: The Corn-nection Between GMOs in South Dakota and Fossil Fuel Fiasco in Spain. Respond only with the name of a fictional organization.

ChatGPT:

The Association for Sustainable Agri-Research and Environmental Solutions (ASARES)

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 Maize Mania: The Corn-nection Between GMOs in South Dakota and Fossil Fuel Fiasco in Spain with an abstract of The use of genetically modified organisms (GMOs) in maize cultivation has long been a topic of debate and speculation. In this study, we boldly venture into the realm of corny puns to explore the potential link between GMO use in corn grown in South Dakota and the seemingly unrelated topic of fossil fuel consumption in Spain. Drawing data from the USDA and the Energy Information Administration, we examine the correlation between these two seemingly disparate phenomena. To our surprise and delight, our analysis reveals a striking correlation coefficient of 0.9298696 with a p-value less than 0.01, affirming a maize-merizing relationship between GMOs in South Dakota and fossil fuel usage in Spain from 2000 to 2021. Our findings bring a-MAIZE-ing insights into the intercontinental impact of genetically modified corn and leave us pondering the cornundrum of its wider implications.

ChatGPT:

GMOs, maize cultivation, genetically modified organisms, South Dakota, fossil fuel consumption, Spain, correlation analysis, USDA, Energy Information Administration, maize production, GMO impact, intercontinental impact, corn farming, industry linkage, environmental impact, agriculture, energy consumption, correlation coefficient, 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 herbicide-tolerant (HT), but not insect-resistant (Bt)
Source: USDA
See what else correlates with GMO use in corn grown in South Dakota

Fossil fuel use in Spain
Detailed data title: Total fossil fuel use in Spain in billion kWh
Source: Energy Information Administration
See what else correlates with Fossil fuel use in Spain

Correlation r = 0.9298696 (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 = 0.8646575 (Coefficient of determination)
This means 86.5% of the change in the one variable (i.e., Fossil fuel use in Spain) 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 3.9E-10.^Show 0.0000000003880411606710900000
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 0.93 in 3.9E-8% of random cases. Said differently, if you correlated 2,577,046,204 random variables^Note You don't actually need 2 billion variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

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

[ 0.84, 0.97 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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

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

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

	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
GMO use in corn grown in South Dakota (GMO corn %)	11	14	23	24	30	31	32	34	30	25	29	25	23	12	14	13	16	17	15	12	11	8
Fossil fuel use in Spain (Billion kWh)	116.197	112.541	134.83	130.965	150.045	173.146	171.434	176.148	177.447	154.09	132.539	140.034	139.188	107.531	101.593	117.017	102.792	121.087	106.839	107.277	84.227	83.3654

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([11,14,23,24,30,31,32,34,30,25,29,25,23,12,14,13,16,17,15,12,11,8,])
array_2 = np.array([116.197,112.541,134.83,130.965,150.045,173.146,171.434,176.148,177.447,154.09,132.539,140.034,139.188,107.531,101.593,117.017,102.792,121.087,106.839,107.277,84.227,83.3654,])
array_1_name = "GMO use in corn grown in South Dakota"
array_2_name = "Fossil fuel use in Spain"

# 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)

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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.

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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
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Line chart for only GMO use in corn grown in South Dakota
Line chart for only Fossil fuel use in Spain
AI-generated correlation image
The spurious research paper: Maize Mania: The Corn-nection Between GMOs in South Dakota and Fossil Fuel Fiasco in Spain

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Correlation ID: 1742 · Black Variable ID: 735 · Red Variable ID: 23994


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