Spurious correlation #3,910 · 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 cotton in Georgia and the second variable is Electricity generation in Gambia. 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 cotton in Georgia and the second variable is Electricity generation in Gambia. The chart goes from 2000 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As cotton production in Georgia increased, there was a corresponding rise in the number of boll weevils migrating to Gambia. These boll weevils, attracted to the Gambian climate, inadvertently enhanced the local electricity production. It turns out, when life gives Gambia boll weevils from Georgia, they power up and generate a buzz in the electricity industry. #GMOpoweredElectricity

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a vibrant steampunk world where electric blue cotton plants crackle with energy, illuminating the landscape of a field in Georgia. In the distance, farmers in steampunk attire look on in awe at the electrifying scene. The air crackles with sparks, and a sense of innovation and excitement fills the atmosphere. The image should capture the fusion of steampunk aesthetics with the electrifying energy of the cotton plants, creating a visually compelling and immersive scene. 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 cotton in Georgia and Electricity generation in Gambia. 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: Voxel art inspired by bauhaus Photorealism Steampunk Art nouveau
ChatGPT: Steampunk

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 cotton in Georgia and Electricity generation in Gambia, in the following situation: Because the GMO cotton in Georgia is so electric, it sparked a surge in enthusiasm for biotechnology around the world. As farmers in Gambia saw the shocking success of the cotton crop, they decided it was time to power up and generate some electrifying innovation. As a result, they switched on their own GMO game and charged forward with new methods, leading to a shocking increase in electricity generation. It's a truly electrifying tale of how one crop's power surge lit the spark that led to a bolt of inspiration to power up!. Keep the description simple.
ChatGPT: In a field in Georgia, vibrant electric blue cotton plants stand out against the green landscape. They crackle with energy, emitting sparks that light up the sky and energize the air around them. In Gambia, farmers stand in awe as they watch the electrifying scene, their faces lighting up with excitement. Inspired by the power of the GMO cotton, they rush back to their farms and begin innovating. Soon, the air in Gambia is filled with the hum of electricity generation as the farmers charge forward with their own new methods, creating a surge of electrifying innovation.

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: [ Steampunk ] and depicting this scene: In a field in Georgia, vibrant electric blue cotton plants stand out against the green landscape. They crackle with energy, emitting sparks that light up the sky and energize the air around them. In Gambia, farmers stand in awe as they watch the electrifying scene, their faces lighting up with excitement. Inspired by the power of the GMO cotton, they rush back to their farms and begin innovating. Soon, the air in Gambia is filled with the hum of electricity generation as the farmers charge forward with their own new methods, creating a surge of electrifying innovation. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a vibrant steampunk world where electric blue cotton plants crackle with energy, illuminating the landscape of a field in Georgia. In the distance, farmers in steampunk attire look on in awe at the electrifying scene. The air crackles with sparks, and a sense of innovation and excitement fills the atmosphere. The image should capture the fusion of steampunk aesthetics with the electrifying energy of the cotton plants, creating a visually compelling and immersive scene.

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

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

AI academic paper

(Because p < 0.01)

GMOs in Georgia: Generating Gambia's Gigawatts

Journal of Agricultural Advancements

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 cotton in Georgia and Electricity generation in Gambia. 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 CVS receipts.

Here is the title and abstract of the paper:
[[TITLE]]
"GMOs in Georgia: Generating Gambia's Gigawatts"

[[ABSTRACT]]
In this study, we delved into the seemingly disparate realms of genetically modified organisms (GMOs) in cotton cultivation in Georgia and electricity generation in Gambia. While these two subjects may seem as unrelated as a chicken and a bicycle, our research aimed to unravel the interconnectedness of these activities. Utilizing comprehensive data from the USDA for GMO cotton usage in Georgia and the Energy Information Administration for electricity generation in Gambia, we applied rigorous statistical analysis to uncover any hidden link between the two. Our findings revealed a startling correlation coefficient of 0.9785748 and a p-value of less than 0.01 for the time period of 2000 to 2021. The results point to a significant relationship between the adoption of GMOs in cotton farming in Georgia and the generation of electricity in Gambia. As we dive deeper into this unexpected connection, we explore the implications of this correlation and consider the broader impact on agricultural and energy policies. While it may seem like comparing apples and orangutans, our findings shed light on a previously overlooked relationship that bears potential implications for both the agricultural and energy sectors.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The use of genetically modified organisms (GMOs) in agriculture has been a topic of much debate, with proponents touting increased yields and pest resistance, while skeptics express concerns about environmental impact and potential health risks. Meanwhile, the generation of electricity in developing nations presents its own set of challenges, from infrastructure limitations to access to affordable and sustainable energy sources. And yet, in the midst of these seemingly divergent spheres, our research has uncovered an unexpected and intriguing connection between GMO cotton cultivation in Georgia and electricity generation in Gambia.
Now, before you go thinking we've been sampling too many GMOs ourselves, let's break down the serious science behind this eyebrow-raising correlation. Our study aims to illuminate a link that may appear as unlikely as finding a Yeti sipping a cup of coffee in your local café. The synergy between these disparate activities may seem as mismatched as wearing a tuxedo to a beach party, but our findings present a compelling case for further exploration.
With one foot firmly planted in the fields of agricultural science and the other tapping away in the realm of energy economics, our research delves into uncharted territory, akin to a daring explorer setting out to discover the fabled city of El Dorado. Armed with data from the United States Department of Agriculture and the Energy Information Administration, we navigated through the labyrinth of statistics, like intrepid treasure hunters, to uncover the hidden relationship between GMO cotton cultivation in the Peach State and the generation of gigawatts in the smiling coast of Africa.
Now, we understand that the idea of GMO cotton and electricity generation coming together might seem as unlikely as a penguin taking up ballet, but hold on to your lab coats, because our findings are no flight of fancy. So, grab your safety goggles, lean in, and let's illuminate the electrifying connection between these seemingly incongruous activities.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The current body of literature presents a mix of studies offering insights into genetically modified organisms (GMOs) in agriculture and electricity generation in developing nations. Smith et al. (2018) conducted a comprehensive analysis of GMO adoption rates in cotton cultivation, highlighting the economic benefits and environmental impacts. Jones and Doe (2019) explored the challenges and opportunities in electricity generation in Gambia, emphasizing the need for sustainable and affordable energy sources. These serious studies lay the groundwork for our investigation, but hold on to your lab coats because we're about to dive into a sea of literature that is as unpredictable as a game of musical chairs on a rollercoaster.

Turning to non-fiction readings, "Seeds of Change" by Jennifer Allaway delves into the history and future of GMOs, while "The Shock of the Old" by David Edgerton offers a captivating exploration of technological change, which can be as electrifying as discovering a llama with a Ph.D. in quantum physics. Now, let's not forget the riveting world of fiction that could, in some universe, be related to our study. "Electric Eden" by Rob Young may seem like it's about renewable energy, but alas, it's actually a fascinating account of the evolution of folk music. On the other hand, "The Cotton Queen" by Pamela Morsi is a delightful tale of romance and farming, with more drama than a soap opera set in a supermarket.

As we venture further into the literature review, we must acknowledge that scholarly pursuits sometimes lead us down unexpected paths, similar to finding a unicorn riding a unicycle in the grocery store. In our pursuit of knowledge, we even found inspiration from unlikely sources, such as the profound wisdom hidden in ancient cave paintings and the intricate details encoded in CVS receipts. While these may sound like the ramblings of sleep-deprived researchers, rest assured that our journey through the realm of literature has been as enlightening as stumbling upon a disco party in a crypt.

Stay tuned as we unravel the mysteries of the interconnectedness between GMOs in cotton cultivation in Georgia and electricity generation in Gambia. The electrifying revelations that lie ahead will leave you as astonished as a cat discovering the secrets of quantum mechanics.

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 cotton in Georgia and Electricity generation in Gambia. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from USDA and 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]]
"GMOs in Georgia: Generating Gambia's Gigawatts"

[[ABSTRACT]]
In this study, we delved into the seemingly disparate realms of genetically modified organisms (GMOs) in cotton cultivation in Georgia and electricity generation in Gambia. While these two subjects may seem as unrelated as a chicken and a bicycle, our research aimed to unravel the interconnectedness of these activities. Utilizing comprehensive data from the USDA for GMO cotton usage in Georgia and the Energy Information Administration for electricity generation in Gambia, we applied rigorous statistical analysis to uncover any hidden link between the two. Our findings revealed a startling correlation coefficient of 0.9785748 and a p-value of less than 0.01 for the time period of 2000 to 2021. The results point to a significant relationship between the adoption of GMOs in cotton farming in Georgia and the generation of electricity in Gambia. As we dive deeper into this unexpected connection, we explore the implications of this correlation and consider the broader impact on agricultural and energy policies. While it may seem like comparing apples and orangutans, our findings shed light on a previously overlooked relationship that bears potential implications for both the agricultural and energy sectors.

[[INTRODUCTION]]
The use of genetically modified organisms (GMOs) in agriculture has been a topic of much debate, with proponents touting increased yields and pest resistance, while skeptics express concerns about environmental impact and potential health risks. Meanwhile, the generation of electricity in developing nations presents its own set of challenges, from infrastructure limitations to access to affordable and sustainable energy sources. And yet, in the midst of these seemingly divergent spheres, our research has uncovered an unexpected and intriguing connection between GMO cotton cultivation in Georgia and electricity generation in Gambia.
Now, before you go thinking we've been sampling too many GMOs ourselves, let's break down the serious science behind this eyebrow-raising correlation. Our study aims to illuminate a link that may appear as unlikely as finding a Yeti sipping a cup of coffee in your local café. The synergy between these disparate activities may seem as mismatched as wearing a tuxedo to a beach party, but our findings present a compelling case for further exploration.
With one foot firmly planted in the fields of agricultural science and the other tapping away in the realm of energy economics, our research delves into uncharted territory, akin to a daring explorer setting out to discover the fabled city of El Dorado. Armed with data from the United States Department of Agriculture and the Energy Information Administration, we navigated through the labyrinth of statistics, like intrepid treasure hunters, to uncover the hidden relationship between GMO cotton cultivation in the Peach State and the generation of gigawatts in the smiling coast of Africa.
Now, we understand that the idea of GMO cotton and electricity generation coming together might seem as unlikely as a penguin taking up ballet, but hold on to your lab coats, because our findings are no flight of fancy. So, grab your safety goggles, lean in, and let's illuminate the electrifying connection between these seemingly incongruous activities.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To untangle the unexpected web of interconnectedness between the adoption of genetically modified organisms (GMOs) in Georgia's cotton cultivation and electricity generation in Gambia, our research team employed a combination of rigorous statistical analysis and a touch of whimsical curiosity. This methodology, akin to a Sherlock Holmes investigation with a dash of Phileas Fogg's adventurous spirit, aimed to reveal the underlying correlations with an approach as thorough as a cat burglar in a laser maze.

Firstly, we scoured the vast expanse of the internet, traversing the digital landscape like intrepid explorers, to gather data encompassing the years 2000 to 2021. Our primary sources of information included the United States Department of Agriculture (USDA) for GMO cotton usage in Georgia and the Energy Information Administration (EIA) for electricity generation data in Gambia. We cross-referenced these datasets with the diligence of a librarian cataloging ancient tomes, ensuring the accuracy and reliability of the information utilized in our analysis.

To quantify the relationship between GMO cotton adoption in Georgia and electricity generation in Gambia, we employed a variety of statistical tools, such as regression analysis, correlation tests, and time series modeling. These methods, as dependable as an umbrella in a downpour, allowed us to uncover any underlying patterns and associations with the precision of a seasoned detective solving a perplexing case.

Furthermore, in an attempt to inject a bit of lightheartedness into the otherwise serious realm of research methods, we added a sprinkle of creativity to our approach. Think of it as adding a dash of hot sauce to an otherwise bland salad - not necessary, but definitely adds some flavor!

In summary, our methodology blended the steadfastness of traditional statistical analysis with a tinge of unconventional charm, creating a research endeavor that was as unconventional as a penguin at a tea party. With this approach, we aimed to shed light on the surprising connection between GMOs in Georgia and electricity generation in Gambia, and to infuse a touch of whimsy into the often solemn domain of academic research.

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 cotton in Georgia and Electricity generation in Gambia. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

For the time period 2000 to 2021, you found a correlation 0.9785748, r-squared of 0.9576087, 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]]
"GMOs in Georgia: Generating Gambia's Gigawatts"

[[ABSTRACT]]
In this study, we delved into the seemingly disparate realms of genetically modified organisms (GMOs) in cotton cultivation in Georgia and electricity generation in Gambia. While these two subjects may seem as unrelated as a chicken and a bicycle, our research aimed to unravel the interconnectedness of these activities. Utilizing comprehensive data from the USDA for GMO cotton usage in Georgia and the Energy Information Administration for electricity generation in Gambia, we applied rigorous statistical analysis to uncover any hidden link between the two. Our findings revealed a startling correlation coefficient of 0.9785748 and a p-value of less than 0.01 for the time period of 2000 to 2021. The results point to a significant relationship between the adoption of GMOs in cotton farming in Georgia and the generation of electricity in Gambia. As we dive deeper into this unexpected connection, we explore the implications of this correlation and consider the broader impact on agricultural and energy policies. While it may seem like comparing apples and orangutans, our findings shed light on a previously overlooked relationship that bears potential implications for both the agricultural and energy sectors.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The use of genetically modified organisms (GMOs) in agriculture has been a topic of much debate, with proponents touting increased yields and pest resistance, while skeptics express concerns about environmental impact and potential health risks. Meanwhile, the generation of electricity in developing nations presents its own set of challenges, from infrastructure limitations to access to affordable and sustainable energy sources. And yet, in the midst of these seemingly divergent spheres, our research has uncovered an unexpected and intriguing connection between GMO cotton cultivation in Georgia and electricity generation in Gambia.
Now, before you go thinking we've been sampling too many GMOs ourselves, let's break down the serious science behind this eyebrow-raising correlation. Our study aims to illuminate a link that may appear as unlikely as finding a Yeti sipping a cup of coffee in your local café. The synergy between these disparate activities may seem as mismatched as wearing a tuxedo to a beach party, but our findings present a compelling case for further exploration.
With one foot firmly planted in the fields of agricultural science and the other tapping away in the realm of energy economics, our research delves into uncharted territory, akin to a daring explorer setting out to discover the fabled city of El Dorado. Armed with data from the United States Department of Agriculture and the Energy Information Administration, we navigated through the labyrinth of statistics, like intrepid treasure hunters, to uncover the hidden relationship between GMO cotton cultivation in the Peach State and the generation of gigawatts in the smiling coast of Africa.
Now, we understand that the idea of GMO cotton and electricity generation coming together might seem as unlikely as a penguin taking up ballet, but hold on to your lab coats, because our findings are no flight of fancy. So, grab your safety goggles, lean in, and let's illuminate the electrifying connection between these seemingly incongruous activities.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The results of our analysis revealed a strong correlation between the use of genetically modified organisms (GMOs) in cotton cultivation in Georgia and the generation of electricity in Gambia. Our statistical analysis unveiled a correlation coefficient of 0.9785748, with an r-squared value of 0.9576087, and a p-value of less than 0.01 for the time period spanning from 2000 to 2021.

Now, I know what you're thinking - linking GMO cotton to electricity generation in Gambia seems as unlikely as finding a unicorn at a car wash. However, the correlation we unearthed is as solid as a rock. It's as if GMOs and electricity generation have been secret BFFs all along, whispering sweet statistical nothings to each other.

As promised, we present Fig. 1, a scatterplot that visualizes this unexpected bond between GMO use in cotton cultivation in Georgia and electricity generation in Gambia. Just imagine it as a high-voltage love story, with each data point representing a spark of connection between these two seemingly unrelated entities.

In conclusion, our findings suggest that there exists a robust relationship between the adoption of GMOs in cotton farming in Georgia and the production of electricity in Gambia. This correlation opens the door to a host of questions and implications, much like finding a kangaroo hopping around in Antarctica. The implications of this unexpected connection could have far-reaching effects on agricultural and energy policies, much like a game of dominoes – one unexpected move could send ripples throughout the entire system.

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 cotton in Georgia and Electricity generation in Gambia. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 500 tokens.

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

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

[[TITLE]]
"GMOs in Georgia: Generating Gambia's Gigawatts"

[[ABSTRACT]]
In this study, we delved into the seemingly disparate realms of genetically modified organisms (GMOs) in cotton cultivation in Georgia and electricity generation in Gambia. While these two subjects may seem as unrelated as a chicken and a bicycle, our research aimed to unravel the interconnectedness of these activities. Utilizing comprehensive data from the USDA for GMO cotton usage in Georgia and the Energy Information Administration for electricity generation in Gambia, we applied rigorous statistical analysis to uncover any hidden link between the two. Our findings revealed a startling correlation coefficient of 0.9785748 and a p-value of less than 0.01 for the time period of 2000 to 2021. The results point to a significant relationship between the adoption of GMOs in cotton farming in Georgia and the generation of electricity in Gambia. As we dive deeper into this unexpected connection, we explore the implications of this correlation and consider the broader impact on agricultural and energy policies. While it may seem like comparing apples and orangutans, our findings shed light on a previously overlooked relationship that bears potential implications for both the agricultural and energy sectors.

[[LITERATURE REVIEW]]
The current body of literature presents a mix of studies offering insights into genetically modified organisms (GMOs) in agriculture and electricity generation in developing nations. Smith et al. (2018) conducted a comprehensive analysis of GMO adoption rates in cotton cultivation, highlighting the economic benefits and environmental impacts. Jones and Doe (2019) explored the challenges and opportunities in electricity generation in Gambia, emphasizing the need for sustainable and affordable energy sources. These serious studies lay the groundwork for our investigation, but hold on to your lab coats because we're about to dive into a sea of literature that is as unpredictable as a game of musical chairs on a rollercoaster.
Turning to non-fiction readings, "Seeds of Change" by Jennifer Allaway delves into the history and future of GMOs, while "The Shock of the Old" by David Edgerton offers a captivating exploration of technological change, which can be as electrifying as discovering a llama with a Ph.D. in quantum physics. Now, let's not forget the riveting world of fiction that could, in some universe, be related to our study. "Electric Eden" by Rob Young may seem like it's about renewable energy, but alas, it's actually a fascinating account of the evolution of folk music. On the other hand, "The Cotton Queen" by Pamela Morsi is a delightful tale of romance and farming, with more drama than a soap opera set in a supermarket.
As we venture further into the literature review, we must acknowledge that scholarly pursuits sometimes lead us down unexpected paths, similar to finding a unicorn riding a unicycle in the grocery store. In our pursuit of knowledge, we even found inspiration from unlikely sources, such as the profound wisdom hidden in ancient cave paintings and the intricate details encoded in CVS receipts. While these may sound like the ramblings of sleep-deprived researchers, rest assured that our journey through the realm of literature has been as enlightening as stumbling upon a disco party in a crypt.
Stay tuned as we unravel the mysteries of the interconnectedness between GMOs in cotton cultivation in Georgia and electricity generation in Gambia. The electrifying revelations that lie ahead will leave you as astonished as a cat discovering the secrets of quantum mechanics.

[[RESULTS]]
The results of our analysis revealed a strong correlation between the use of genetically modified organisms (GMOs) in cotton cultivation in Georgia and the generation of electricity in Gambia. Our statistical analysis unveiled a correlation coefficient of 0.9785748, with an r-squared value of 0.9576087, and a p-value of less than 0.01 for the time period spanning from 2000 to 2021.
Now, I know what you're thinking - linking GMO cotton to electricity generation in Gambia seems as unlikely as finding a unicorn at a car wash. However, the correlation we unearthed is as solid as a rock. It's as if GMOs and electricity generation have been secret BFFs all along, whispering sweet statistical nothings to each other.
As promised, we present Fig. 1, a scatterplot that visualizes this unexpected bond between GMO use in cotton cultivation in Georgia and electricity generation in Gambia. Just imagine it as a high-voltage love story, with each data point representing a spark of connection between these two seemingly unrelated entities.
In conclusion, our findings suggest that there exists a robust relationship between the adoption of GMOs in cotton farming in Georgia and the production of electricity in Gambia. This correlation opens the door to a host of questions and implications, much like finding a kangaroo hopping around in Antarctica. The implications of this unexpected connection could have far-reaching effects on agricultural and energy policies, much like a game of dominoes – one unexpected move could send ripples throughout the entire system.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our study have left us feeling a bit like Alice in Wonderland, stumbling upon a mad tea party of statistical significance. Our findings not only validate but also amplify the existing body of literature, akin to discovering that the chorus line of elephants in "The Jungle Book" actually had a profound impact on dance theory.

Now, let's address the serious business. Our results corroborate the earlier work of Smith et al. (2018), who emphasized the economic benefits of GMO adoption in cotton cultivation. The strong correlation we uncovered between GMO usage in Georgia and electricity generation in Gambia supports the idea that agricultural advancements can have surprising and far-reaching implications. It's almost as if GMOs and electricity generation had a cosmic connection, like discovering that peanut butter and jelly were meant to be together all along.

Furthermore, our findings align with the concerns raised by Jones and Doe (2019) regarding sustainable and affordable energy sources in Gambia. The unexpectedly robust relationship we identified highlights the potential impact of agricultural practices on energy dynamics—a connection as startling as finding Bigfoot playing a game of Twister with the Loch Ness Monster.

Our study opens the floodgates to a host of questions and implications for both the agricultural and energy sectors. This unexpected correlation could reshape the landscape of policy decisions, sending shockwaves through the very fabric of these industries, akin to a penguin discovering the joys of synchronized swimming.

In summary, our results provide compelling evidence of the interconnectedness between GMO usage in cotton cultivation in Georgia and electricity generation in Gambia. This electrifying revelation leads to a deeper understanding of the intertwined nature of these seemingly disparate fields, much like realizing that Pixar movies all exist in the same universe. The implications of this discovery are as impactful as a sheep in wolf's clothing, challenging conventional wisdom and prompting a reevaluation of agricultural and energy strategies.

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 cotton in Georgia and Electricity generation in Gambia. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"GMOs in Georgia: Generating Gambia's Gigawatts"

[[ABSTRACT]]
In this study, we delved into the seemingly disparate realms of genetically modified organisms (GMOs) in cotton cultivation in Georgia and electricity generation in Gambia. While these two subjects may seem as unrelated as a chicken and a bicycle, our research aimed to unravel the interconnectedness of these activities. Utilizing comprehensive data from the USDA for GMO cotton usage in Georgia and the Energy Information Administration for electricity generation in Gambia, we applied rigorous statistical analysis to uncover any hidden link between the two. Our findings revealed a startling correlation coefficient of 0.9785748 and a p-value of less than 0.01 for the time period of 2000 to 2021. The results point to a significant relationship between the adoption of GMOs in cotton farming in Georgia and the generation of electricity in Gambia. As we dive deeper into this unexpected connection, we explore the implications of this correlation and consider the broader impact on agricultural and energy policies. While it may seem like comparing apples and orangutans, our findings shed light on a previously overlooked relationship that bears potential implications for both the agricultural and energy sectors.

[[INTRDUCTION]]
The use of genetically modified organisms (GMOs) in agriculture has been a topic of much debate, with proponents touting increased yields and pest resistance, while skeptics express concerns about environmental impact and potential health risks. Meanwhile, the generation of electricity in developing nations presents its own set of challenges, from infrastructure limitations to access to affordable and sustainable energy sources. And yet, in the midst of these seemingly divergent spheres, our research has uncovered an unexpected and intriguing connection between GMO cotton cultivation in Georgia and electricity generation in Gambia.
Now, before you go thinking we've been sampling too many GMOs ourselves, let's break down the serious science behind this eyebrow-raising correlation. Our study aims to illuminate a link that may appear as unlikely as finding a Yeti sipping a cup of coffee in your local café. The synergy between these disparate activities may seem as mismatched as wearing a tuxedo to a beach party, but our findings present a compelling case for further exploration.
With one foot firmly planted in the fields of agricultural science and the other tapping away in the realm of energy economics, our research delves into uncharted territory, akin to a daring explorer setting out to discover the fabled city of El Dorado. Armed with data from the United States Department of Agriculture and the Energy Information Administration, we navigated through the labyrinth of statistics, like intrepid treasure hunters, to uncover the hidden relationship between GMO cotton cultivation in the Peach State and the generation of gigawatts in the smiling coast of Africa.
Now, we understand that the idea of GMO cotton and electricity generation coming together might seem as unlikely as a penguin taking up ballet, but hold on to your lab coats, because our findings are no flight of fancy. So, grab your safety goggles, lean in, and let's illuminate the electrifying connection between these seemingly incongruous activities.

[[RESULTS]]
The results of our analysis revealed a strong correlation between the use of genetically modified organisms (GMOs) in cotton cultivation in Georgia and the generation of electricity in Gambia. Our statistical analysis unveiled a correlation coefficient of 0.9785748, with an r-squared value of 0.9576087, and a p-value of less than 0.01 for the time period spanning from 2000 to 2021.
Now, I know what you're thinking - linking GMO cotton to electricity generation in Gambia seems as unlikely as finding a unicorn at a car wash. However, the correlation we unearthed is as solid as a rock. It's as if GMOs and electricity generation have been secret BFFs all along, whispering sweet statistical nothings to each other.
As promised, we present Fig. 1, a scatterplot that visualizes this unexpected bond between GMO use in cotton cultivation in Georgia and electricity generation in Gambia. Just imagine it as a high-voltage love story, with each data point representing a spark of connection between these two seemingly unrelated entities.
In conclusion, our findings suggest that there exists a robust relationship between the adoption of GMOs in cotton farming in Georgia and the production of electricity in Gambia. This correlation opens the door to a host of questions and implications, much like finding a kangaroo hopping around in Antarctica. The implications of this unexpected connection could have far-reaching effects on agricultural and energy policies, much like a game of dominoes – one unexpected move could send ripples throughout the entire system.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has unveiled a shockingly strong correlation between GMO cotton cultivation in Georgia and electricity generation in Gambia, leaving us feeling like we've stumbled upon a unicorn riding a unicycle. The statistical analyses revealed a correlation coefficient that's as close as peanut butter and jelly, with an r-squared value that's tighter than a pair of skinny jeans.

Our findings suggest that there's a mysterious dance happening between these two seemingly divergent activities, akin to an unexpected duet between a banjo and a kazoo. This revelation opens up a Pandora's box of possibilities, much like discovering a polar bear in a tropical rainforest.

Looking forward, the implications of this connection could be as profound as a llama wearing a top hat – it turns heads and raises eyebrows. Policies in both the agricultural and energy sectors may need to tango in response to this newfound relationship, much like a puppy learning to cha-cha.

In the grand scheme of things, our study calls for a bit of a shake-up in how we perceive the relationship between agricultural practices and electricity generation, like finding a penguin teaching a fish how to fly. This correlation, though as unexpected as finding a treasure map in a bottle of ketchup, is as real as it gets.

In summary, it's time to pack up our research gear and call it a day, like Sherlock Holmes solving a case of mistaken identity. No more research is needed in this area, like trying to top the perfect cup of coffee – it's just not achievable.

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 GMOs in Georgia: Generating Gambia's Gigawatts. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Agricultural Advancements

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 GMOs in Georgia: Generating Gambia's Gigawatts. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Agricultural Advancements and Global Energy Solutions (SAAGES)

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 GMOs in Georgia: Generating Gambia's Gigawatts with an abstract of In this study, we delved into the seemingly disparate realms of genetically modified organisms (GMOs) in cotton cultivation in Georgia and electricity generation in Gambia. While these two subjects may seem as unrelated as a chicken and a bicycle, our research aimed to unravel the interconnectedness of these activities. Utilizing comprehensive data from the USDA for GMO cotton usage in Georgia and the Energy Information Administration for electricity generation in Gambia, we applied rigorous statistical analysis to uncover any hidden link between the two. Our findings revealed a startling correlation coefficient of 0.9785748 and a p-value of less than 0.01 for the time period of 2000 to 2021. The results point to a significant relationship between the adoption of GMOs in cotton farming in Georgia and the generation of electricity in Gambia. As we dive deeper into this unexpected connection, we explore the implications of this correlation and consider the broader impact on agricultural and energy policies. While it may seem like comparing apples and orangutans, our findings shed light on a previously overlooked relationship that bears potential implications for both the agricultural and energy sectors.

ChatGPT:

GMOs, Georgia, Gambia, cotton cultivation, electricity generation, USDA, Energy Information Administration, statistical analysis, correlation coefficient, p-value, agriculture, energy policies

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

Electricity generation in Gambia
Detailed data title: Total electricity generation in Gambia, The in billion kWh
Source: Energy Information Administration
See what else correlates with Electricity generation in Gambia

Correlation r = 0.9785748 (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 = 0.9576087 (Coefficient of determination)
This means 95.8% of the change in the one variable (i.e., Electricity generation in Gambia) is predictable based on the change in the other (i.e., GMO use in cotton in Georgia) over the 22 years from 2000 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.4E-15.^Show 0.0000000000000033674347602049
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 3.4E-13% of random cases. Said differently, if you correlated 296,961,952,111,925 random variables^Note You don't actually need 296 trillion 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.

[ 0.95, 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 cotton in Georgia (GMO cotton %)	32	29	30	47	58	55	64	68	73	70	69	72	78	83	92	92	93	91	96	97	94	93
Electricity generation in Gambia (Billion kWh)	0.1105	0.127	0.142	0.158	0.174	0.192	0.204	0.217	0.229	0.2301	0.2333	0.2446	0.2541	0.2481	0.2671	0.2821	0.2981	0.3061	0.3131	0.304835	0.289587	0.307922

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([32,29,30,47,58,55,64,68,73,70,69,72,78,83,92,92,93,91,96,97,94,93,])
array_2 = np.array([0.1105,0.127,0.142,0.158,0.174,0.192,0.204,0.217,0.229,0.2301,0.2333,0.2446,0.2541,0.2481,0.2671,0.2821,0.2981,0.3061,0.3131,0.304835,0.289587,0.307922,])
array_1_name = "GMO use in cotton in Georgia"
array_2_name = "Electricity generation in Gambia"

# 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 cotton in Georgia
Line chart for only Electricity generation in Gambia
AI-generated correlation image
The spurious research paper: GMOs in Georgia: Generating Gambia's Gigawatts

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Correlation ID: 3910 · Black Variable ID: 793 · Red Variable ID: 23678


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