Spurious correlation #3,500 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the second variable is Wind power generated in Kosovo. The chart goes from 2010 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 The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the second variable is Wind power generated in Kosovo. The chart goes from 2010 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The increased presence of metal and plastic workers in South Carolina led to a surge in their creative energy. This, in turn, resulted in the spontaneous formation of a 'forging tornado' – a rare meteorological phenomenon characterized by the whirring and shaping of wind currents into powerful, turbine-like vortexes. These forging tornadoes inadvertently bolstered the wind power infrastructure in Kosovo, effectively 'boosting' their energy production.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image that showcases the contrast between a bustling factory floor in South Carolina and a landscape in Kosovo dotted with towering wind turbines. The factory floor should be filled with forging machine operators meticulously crafting metal and plastic parts, surrounded by whirring machines that create a symphony of bustling productivity. The landscape in Kosovo should depict the towering wind turbines, slicing effortlessly through the air, generating clean, sustainable energy. Capture the duality of increased production in South Carolina fueling the demand for wind turbines, and Kosovo accelerating its wind power generation to propel into a leading position in the renewable energy market. 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: The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and Wind power generated in Kosovo. 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: Oragami An image that looks like it is a scene from A Disney Movie a funny image you would see on reddit a meme A Discovery magazine cover image
ChatGPT: A Discovery magazine cover image

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 The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and Wind power generated in Kosovo, in the following situation: Because more forging machine operators in South Carolina means more metal and plastic parts produced, which in turn increases the global demand for wind turbines. Kosovo responds by ramping up wind power generation to meet this demand and gain a competitive edge in the renewable energy market.. Keep the description simple.
ChatGPT: In South Carolina, a bustling factory floor is filled with forging machine operators meticulously crafting metal and plastic parts. The whirring machines create a symphony of bustling productivity. Meanwhile, in Kosovo, the landscape is dotted with towering wind turbines, their blades slicing effortlessly through the air, generating clean, sustainable energy. The increased production in South Carolina fuels the demand for wind turbines, driving Kosovo to accelerate its wind power generation, propelling them into a leading position in the renewable energy market.

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 Discovery magazine cover image ] and depicting this scene: In South Carolina, a bustling factory floor is filled with forging machine operators meticulously crafting metal and plastic parts. The whirring machines create a symphony of bustling productivity. Meanwhile, in Kosovo, the landscape is dotted with towering wind turbines, their blades slicing effortlessly through the air, generating clean, sustainable energy. The increased production in South Carolina fuels the demand for wind turbines, driving Kosovo to accelerate its wind power generation, propelling them into a leading position in the renewable energy market. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that showcases the contrast between a bustling factory floor in South Carolina and a landscape in Kosovo dotted with towering wind turbines. The factory floor should be filled with forging machine operators meticulously crafting metal and plastic parts, surrounded by whirring machines that create a symphony of bustling productivity. The landscape in Kosovo should depict the towering wind turbines, slicing effortlessly through the air, generating clean, sustainable energy. Capture the duality of increased production in South Carolina fueling the demand for wind turbines, and Kosovo accelerating its wind power generation to propel into a leading position in the renewable energy market.

*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 The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina caused Wind power generated in Kosovo to increase.\Make it sound serious until the end.

AI academic paper

(Because p < 0.01)

Blowing in the Wind: A Forged Connection Between Machine Setters in South Carolina and Wind Power in Kosovo

Journal of Transcontinental Technology Transfer

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and Wind power generated in Kosovo. 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 name a few real TV shows that sound like they might be relevant to the topic that you watched as research.

Here is the title and abstract of the paper:
[[TITLE]]
Blowing in the Wind: A Forged Connection Between Machine Setters in South Carolina and Wind Power in Kosovo

[[ABSTRACT]]
This study explores the unsuspecting link between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the generation of wind power in Kosovo. Through the meticulous analysis of data from the Bureau of Labor Statistics and the Energy Information Administration, our research team uncovered a surprisingly strong correlation. With a correlation coefficient of 0.9636845 and p < 0.01, our findings suggest a compelling relationship that defies conventional wisdom. Despite the miles of distance and disparate industries, it seems that the movements of machines in South Carolina have been sending a subtle, yet influential breeze to the wind turbines of Kosovo. Our results shed light on the interconnectedness of seemingly unrelated sectors and emphasize the need for further investigation into the whimsical ways of the world.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The field of labor statistics and energy generation often conjures up images of stodgy spreadsheets and monotonous machinery. However, our research endeavors to infuse a breath of fresh air into this seemingly mundane realm by exploring an unlikely connection between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the wind power generated in Kosovo. Like a gentle zephyr swirling through the data, our study reveals a surprising correlation that challenges traditional assumptions and introduces a whimsical twist to the world of labor and energy.
While the idea of forging machines in South Carolina somehow influencing the wind power in Kosovo might sound as plausible as an indoor rain dance, our rigorous analysis of the Bureau of Labor Statistics and the Energy Information Administration data has unveiled a remarkably robust relationship. Like a clandestine game of "telephone," where one whispers into the ear of the next, the movements of machines in South Carolina seem to harbor the ability to conjure up a breeze that reaches the distant turbines in Kosovo. It's almost as if the industrial processes are engaging in a clandestine tango, choreographing an elaborate dance of energy and labor across continents.
As we delve into the labyrinth of statistical analyses, we cannot help but appreciate the quirky and often downright capricious connections that emerge from the depths of empirical data. It's as if the universe has a mischievous sense of humor, weaving together threads of causation and correlation in the most unexpected of places. As researchers, we are tasked with unraveling these enigmatic phenomena, peeling back the layers of complexity to reveal the underlying mechanisms at play.
In the following sections, we will dissect our findings with the precision of a skilled artisan, teasing out the implications and potential explanations for this unorthodox relationship. Our hope is that this investigation will not only provide insight into the peculiar interactions between seemingly disparate sectors but also inspire further curiosity and exploration into the playful interplay of labor and energy on a global scale.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

Numerous studies have sought to unravel the enigmatic web of connections between diverse industries and far-flung geographical regions, yet few have ventured into the whimsical realm that our research embarks upon. Smith (2015) examined the labor dynamics in South Carolina, shedding light on the nuanced interplay of forging machine setters and operators. Meanwhile, Doe (2017) delved into the intricate intricacies of wind power generation in Kosovo, extolling the virtues of renewable energy in a rapidly evolving global landscape. These serious and scholarly investigations provided the groundwork for our current exploration of the unexpected liaison between the two previously unrelated domains.

Turning our attention to the broader literature, "The Art and Science of Metalworking" by Jones (2018) offers a comprehensive overview of the metalworking industry, delving into the subtleties of machine operation and the dexterity required of metal and plastic workers. In a similar vein, "The Power of Wind: Harnessing Nature's Breath" by Renewable Energy Society (2019) provides a thorough analysis of wind power generation, examining the technical, economic, and environmental implications of this burgeoning field. These texts serve as invaluable resources for grounding our investigation within the larger context of labor dynamics and energy production.

Beyond the factual expositions of non-fiction literature, we cannot overlook the potential insights hidden within the pages of fictional works that may surprisingly shed light on the peculiarity of our findings. As such, the classic novel "Gone with the Wind" by Margaret Mitchell presents a captivating tale set against the backdrop of the American Civil War, with winds of change shaping the fates of its characters. Similarly, the whimsical "The Wind in the Willows" by Kenneth Grahame transports readers to a charming world where the capricious winds play a role in the lives of anthropomorphic animals. While these novels may seem far removed from the empirical world of labor and energy, their poetic exploration of wind's effects hints at the underlying interconnectedness that our research endeavors to uncover.

The quest for understanding the unexpected relationship between forging machine operators in South Carolina and wind power in Kosovo also led us to explore the realm of popular culture. TV shows such as "How It's Made" and "Renewable Energy Today" provided a glimpse into the everyday lives of laborers and the intricacies of renewable energy technologies. While these programs are designed for entertainment, their portrayal of industrial processes and energy production offered valuable insights into the underlying mechanisms that may contribute to the unorthodox correlation uncovered in our research.

In the verdant landscape of literature and popular culture, diverse narratives intertwine, offering glimpses of resonance with our own investigation. As we traverse the terrain of academic discourse, we must not overlook the potential for unexpected revelations and serendipitous parallels that may emerge from the most unlikely sources.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and Wind power generated in Kosovo. 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 Bureau of Larbor Statistics and Energy Information Administration . You used data from 2010 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]]
Blowing in the Wind: A Forged Connection Between Machine Setters in South Carolina and Wind Power in Kosovo

[[ABSTRACT]]
This study explores the unsuspecting link between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the generation of wind power in Kosovo. Through the meticulous analysis of data from the Bureau of Labor Statistics and the Energy Information Administration, our research team uncovered a surprisingly strong correlation. With a correlation coefficient of 0.9636845 and p < 0.01, our findings suggest a compelling relationship that defies conventional wisdom. Despite the miles of distance and disparate industries, it seems that the movements of machines in South Carolina have been sending a subtle, yet influential breeze to the wind turbines of Kosovo. Our results shed light on the interconnectedness of seemingly unrelated sectors and emphasize the need for further investigation into the whimsical ways of the world.

[[INTRODUCTION]]
The field of labor statistics and energy generation often conjures up images of stodgy spreadsheets and monotonous machinery. However, our research endeavors to infuse a breath of fresh air into this seemingly mundane realm by exploring an unlikely connection between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the wind power generated in Kosovo. Like a gentle zephyr swirling through the data, our study reveals a surprising correlation that challenges traditional assumptions and introduces a whimsical twist to the world of labor and energy.
While the idea of forging machines in South Carolina somehow influencing the wind power in Kosovo might sound as plausible as an indoor rain dance, our rigorous analysis of the Bureau of Labor Statistics and the Energy Information Administration data has unveiled a remarkably robust relationship. Like a clandestine game of "telephone," where one whispers into the ear of the next, the movements of machines in South Carolina seem to harbor the ability to conjure up a breeze that reaches the distant turbines in Kosovo. It's almost as if the industrial processes are engaging in a clandestine tango, choreographing an elaborate dance of energy and labor across continents.
As we delve into the labyrinth of statistical analyses, we cannot help but appreciate the quirky and often downright capricious connections that emerge from the depths of empirical data. It's as if the universe has a mischievous sense of humor, weaving together threads of causation and correlation in the most unexpected of places. As researchers, we are tasked with unraveling these enigmatic phenomena, peeling back the layers of complexity to reveal the underlying mechanisms at play.
In the following sections, we will dissect our findings with the precision of a skilled artisan, teasing out the implications and potential explanations for this unorthodox relationship. Our hope is that this investigation will not only provide insight into the peculiar interactions between seemingly disparate sectors but also inspire further curiosity and exploration into the playful interplay of labor and energy on a global scale.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To unravel the mysterious connection between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the wind power generated in Kosovo, a multi-faceted research approach was adopted. The data collection process resembled a treasure hunt, with the research team scouring the depths of the internet to unearth relevant statistics, primarily drawing from the Bureau of Labor Statistics and the Energy Information Administration. Snorkeling through the sea of available data, the team meticulously curated information spanning from 2010 to 2021, piecing together a comprehensive dataset that would serve as the bedrock of our analysis.

The initial step involved gathering detailed employment figures for forging machine setters, operators, and tenders, metal and plastic in South Carolina. We combed through employment reports, perhaps turning into data sleuths, to trace the ebbs and flows of personnel within this niche sector. The statistics, like pieces of a jigsaw puzzle, were carefully arranged to construct a panoramic view of the workforce dynamics in the forging industry.

Simultaneously, the winds of inquiry led us to delve into the world of wind power generation in Kosovo. The Energy Information Administration became our virtual compass, guiding our exploration of wind energy production in this Eastern European nation. Measuring the gusts of energy output, we harnessed the statistical zephyrs to paint a comprehensive picture of Kosovo's wind power landscape.

With these two distinct sets of data in hand, we navigated the twisting paths of statistical analysis like intrepid explorers, employing various quantitative techniques to uncover patterns and relationships. The tidal waves of empirical data demanded careful scrutiny, and like diligent surfers, we rode the waves of correlation and causation, never losing sight of our quest for insight.

The next phase of the methodology involved employing advanced statistical tools to untangle the intricate web of associations between the workforce composition in South Carolina and the wind power generated in Kosovo. Through the wizardry of regression analyses, we probed beneath the surface of the data, seeking to extract hidden currents of influence and impact.

Furthermore, a time-series analysis was performed to capture the temporal dynamics of both forging machine employment in South Carolina and wind power generation in Kosovo. This allowed us to trace the evolving patterns and trends, akin to observing the graceful dance of two disparate yet interconnected phenomena across the years.

Lastly, to ensure the robustness of our findings, sensitivity analyses were conducted, testing the stability of the relationship under varying conditions and assumptions. Much like stress-testing a bridge, these analyses served as a validation mechanism, fortifying the integrity of our results and bolstering the reliability of our conclusions.

In summary, our methodology navigated the tumultuous seas of data collection, statistical analysis, and validation with the tenacity of intrepid sailors, ultimately unveiling the remarkable correlation between forging machine employment in South Carolina and wind power generation in Kosovo.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and Wind power generated in Kosovo. 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]]
Blowing in the Wind: A Forged Connection Between Machine Setters in South Carolina and Wind Power in Kosovo

[[ABSTRACT]]
This study explores the unsuspecting link between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the generation of wind power in Kosovo. Through the meticulous analysis of data from the Bureau of Labor Statistics and the Energy Information Administration, our research team uncovered a surprisingly strong correlation. With a correlation coefficient of 0.9636845 and p < 0.01, our findings suggest a compelling relationship that defies conventional wisdom. Despite the miles of distance and disparate industries, it seems that the movements of machines in South Carolina have been sending a subtle, yet influential breeze to the wind turbines of Kosovo. Our results shed light on the interconnectedness of seemingly unrelated sectors and emphasize the need for further investigation into the whimsical ways of the world.

[[LITERATURE REVIEW]]
Numerous studies have sought to unravel the enigmatic web of connections between diverse industries and far-flung geographical regions, yet few have ventured into the whimsical realm that our research embarks upon. Smith (2015) examined the labor dynamics in South Carolina, shedding light on the nuanced interplay of forging machine setters and operators. Meanwhile, Doe (2017) delved into the intricate intricacies of wind power generation in Kosovo, extolling the virtues of renewable energy in a rapidly evolving global landscape. These serious and scholarly investigations provided the groundwork for our current exploration of the unexpected liaison between the two previously unrelated domains.
Turning our attention to the broader literature, "The Art and Science of Metalworking" by Jones (2018) offers a comprehensive overview of the metalworking industry, delving into the subtleties of machine operation and the dexterity required of metal and plastic workers. In a similar vein, "The Power of Wind: Harnessing Nature's Breath" by Renewable Energy Society (2019) provides a thorough analysis of wind power generation, examining the technical, economic, and environmental implications of this burgeoning field. These texts serve as invaluable resources for grounding our investigation within the larger context of labor dynamics and energy production.
Beyond the factual expositions of non-fiction literature, we cannot overlook the potential insights hidden within the pages of fictional works that may surprisingly shed light on the peculiarity of our findings. As such, the classic novel "Gone with the Wind" by Margaret Mitchell presents a captivating tale set against the backdrop of the American Civil War, with winds of change shaping the fates of its characters. Similarly, the whimsical "The Wind in the Willows" by Kenneth Grahame transports readers to a charming world where the capricious winds play a role in the lives of anthropomorphic animals. While these novels may seem far removed from the empirical world of labor and energy, their poetic exploration of wind's effects hints at the underlying interconnectedness that our research endeavors to uncover.
The quest for understanding the unexpected relationship between forging machine operators in South Carolina and wind power in Kosovo also led us to explore the realm of popular culture. TV shows such as "How It's Made" and "Renewable Energy Today" provided a glimpse into the everyday lives of laborers and the intricacies of renewable energy technologies. While these programs are designed for entertainment, their portrayal of industrial processes and energy production offered valuable insights into the underlying mechanisms that may contribute to the unorthodox correlation uncovered in our research.
In the verdant landscape of literature and popular culture, diverse narratives intertwine, offering glimpses of resonance with our own investigation. As we traverse the terrain of academic discourse, we must not overlook the potential for unexpected revelations and serendipitous parallels that may emerge from the most unlikely sources.

[[RESULTS]]
The results of our analysis revealed a remarkably strong correlation between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the wind power generated in Kosovo. Over the time period from 2010 to 2021, a correlation coefficient of 0.9636845 was observed, with an r-squared of 0.9286878 and a p-value of less than 0.01. This statistical significance indicates that the relationship between these seemingly unrelated variables is not due to mere chance, but rather suggests a genuine association between the two.
Figure 1 depicts a scatterplot graphing the connection between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the wind power generated in Kosovo. The graph vividly illustrates the tight clustering of data points, indicative of the strong correlation uncovered in our analysis. It's as though the forging machines in South Carolina are surreptitiously whispering to the wind turbines in Kosovo, creating an unexpected symphony of industrial movements and renewable energy generation.
While one might initially dismiss such a connection as ludicrous, the robust statistical evidence cannot be overlooked. It seems the intricacies of labor and the ethereal movements of wind are more intertwined than one might have previously imagined. It is as if the labor force in South Carolina, unaware of its influence, has been casting a benevolent industrial breeze across the miles to aid the wind turbines in Kosovo.
This unorthodox relationship between the manufacturing sector in one location and energy generation in another challenges traditional assumptions and emphasizes the need for further investigation into the whimsical interplay of different sectors on a global scale. Indeed, the winds of correlation have blown us into uncharted territory, where the unexpected connections between labor and energy beckon for more exploration and a thoughtful reconsideration of conventional boundaries.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of our study have unveiled an intriguing association between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the wind power generated in Kosovo. While this connection may initially appear to be as outlandish as a person attempting to catch the wind in a net, our results provide compelling evidence for the existence of this improbable relationship.

Drawing from the literature review, we first acknowledge the unexpected sources of inspiration that guided our investigation. The mention of the novel "Gone with the Wind" and "The Wind in the Willows" initially seemed like whimsical diversions from the academic discourse. However, our results have unexpectedly lent credence to the notion that these literary works, which place wind at the center of their narratives, may hold deeper insights into the intricate interplay of forces that transcend geographical and industrial boundaries.

The robust correlation coefficient of 0.9636845 and p < 0.01 observed in our study lends support to the prior research by Smith (2015) and Doe (2017), who separately delved into the labor dynamics in South Carolina and wind power generation in Kosovo. Our findings suggest that the movements of machines in South Carolina exert a significant influence on the wind power generated in Kosovo, underscoring a connection that defies conventional expectations. It appears that the labor force in South Carolina, through the deft orchestration of forging machine operations, has inadvertently become a maestro of the wind, creating a harmonious symphony of manufacturing prowess and renewable energy initiative.

The implications of this unexpected association extend beyond the domain of industrial and energy sectors, challenging our preconceived notions of the boundaries that demarcate distinct areas of influence. It is as though the winds of correlation have beckoned us to reconsider the conventional divisions between industries and geographic regions, inviting us to embrace a more holistic perspective that recognizes the subtle yet potent influence of one sector on another.

In conclusion, our research has brought to light the peculiar yet compelling relationship between the forging machine operators in South Carolina and wind power generation in Kosovo. While the winds of correlation may at first seem capricious, our results underscore the need to further explore the interconnectedness of seemingly disparate domains, inspiring a more whimsical and open-minded approach to understanding the multifaceted interactions that shape our world.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and Wind power generated in Kosovo. 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]]
Blowing in the Wind: A Forged Connection Between Machine Setters in South Carolina and Wind Power in Kosovo

[[ABSTRACT]]
This study explores the unsuspecting link between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the generation of wind power in Kosovo. Through the meticulous analysis of data from the Bureau of Labor Statistics and the Energy Information Administration, our research team uncovered a surprisingly strong correlation. With a correlation coefficient of 0.9636845 and p < 0.01, our findings suggest a compelling relationship that defies conventional wisdom. Despite the miles of distance and disparate industries, it seems that the movements of machines in South Carolina have been sending a subtle, yet influential breeze to the wind turbines of Kosovo. Our results shed light on the interconnectedness of seemingly unrelated sectors and emphasize the need for further investigation into the whimsical ways of the world.

[[INTRDUCTION]]
The field of labor statistics and energy generation often conjures up images of stodgy spreadsheets and monotonous machinery. However, our research endeavors to infuse a breath of fresh air into this seemingly mundane realm by exploring an unlikely connection between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the wind power generated in Kosovo. Like a gentle zephyr swirling through the data, our study reveals a surprising correlation that challenges traditional assumptions and introduces a whimsical twist to the world of labor and energy.
While the idea of forging machines in South Carolina somehow influencing the wind power in Kosovo might sound as plausible as an indoor rain dance, our rigorous analysis of the Bureau of Labor Statistics and the Energy Information Administration data has unveiled a remarkably robust relationship. Like a clandestine game of "telephone," where one whispers into the ear of the next, the movements of machines in South Carolina seem to harbor the ability to conjure up a breeze that reaches the distant turbines in Kosovo. It's almost as if the industrial processes are engaging in a clandestine tango, choreographing an elaborate dance of energy and labor across continents.
As we delve into the labyrinth of statistical analyses, we cannot help but appreciate the quirky and often downright capricious connections that emerge from the depths of empirical data. It's as if the universe has a mischievous sense of humor, weaving together threads of causation and correlation in the most unexpected of places. As researchers, we are tasked with unraveling these enigmatic phenomena, peeling back the layers of complexity to reveal the underlying mechanisms at play.
In the following sections, we will dissect our findings with the precision of a skilled artisan, teasing out the implications and potential explanations for this unorthodox relationship. Our hope is that this investigation will not only provide insight into the peculiar interactions between seemingly disparate sectors but also inspire further curiosity and exploration into the playful interplay of labor and energy on a global scale.

[[RESULTS]]
The results of our analysis revealed a remarkably strong correlation between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the wind power generated in Kosovo. Over the time period from 2010 to 2021, a correlation coefficient of 0.9636845 was observed, with an r-squared of 0.9286878 and a p-value of less than 0.01. This statistical significance indicates that the relationship between these seemingly unrelated variables is not due to mere chance, but rather suggests a genuine association between the two.
Figure 1 depicts a scatterplot graphing the connection between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the wind power generated in Kosovo. The graph vividly illustrates the tight clustering of data points, indicative of the strong correlation uncovered in our analysis. It's as though the forging machines in South Carolina are surreptitiously whispering to the wind turbines in Kosovo, creating an unexpected symphony of industrial movements and renewable energy generation.
While one might initially dismiss such a connection as ludicrous, the robust statistical evidence cannot be overlooked. It seems the intricacies of labor and the ethereal movements of wind are more intertwined than one might have previously imagined. It is as if the labor force in South Carolina, unaware of its influence, has been casting a benevolent industrial breeze across the miles to aid the wind turbines in Kosovo.
This unorthodox relationship between the manufacturing sector in one location and energy generation in another challenges traditional assumptions and emphasizes the need for further investigation into the whimsical interplay of different sectors on a global scale. Indeed, the winds of correlation have blown us into uncharted territory, where the unexpected connections between labor and energy beckon for more exploration and a thoughtful reconsideration of conventional boundaries.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has brought to light the unexpected yet compelling connection between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the wind power generated in Kosovo. The robust correlation coefficient of 0.9636845 has left us winded with its strength, akin to a gust of statistical significance that cannot be brushed aside. It's as if the forging machines in South Carolina have been engaged in an international dance of influence, twirling their laborious movements across the seas to stir up the wind turbines in Kosovo.

The implications of this unorthodox relationship are both fascinating and, dare I say, breezy. It seems that the world of labor and energy is not as rigid and static as we once thought but is rather imbued with a whimsical dynamism, much like a waltz between two distant partners. As we contemplate the subtle yet profound impact of machinery movements in one corner of the globe on the renewable energy generation in another, we are compelled to reconsider the boundaries and barriers that we impose on the interconnectedness of industrial sectors.

Our findings beckon for a shift in perspective, inviting researchers to take a refreshing breath of curiosity and delve deeper into the playful interplay of seemingly unrelated domains. We are reminded of the mischievous nature of statistical relationships and the unpredictable capers that data can lead us on. It's as if the universe itself is a wily puppeteer, orchestrating a symphony of cause and effect that transcends the confines of conventional wisdom.

In light of these revelatory findings, we assert, with a lighthearted twinkle in our eyes, that no further research may be needed in this area. For the winds of statistical significance have blown us to a firm and unequivocal conclusion, leaving no room for doubt or hesitation. It seems that the zephyrs of correlation have carried our research to a serendipitous destination, where the playful tango of industrial movements and renewable energy generation has pirouetted into the spotlight, demanding our attention and rekindling our sense of wonder.

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 Blowing in the Wind: A Forged Connection Between Machine Setters in South Carolina and Wind Power in Kosovo. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Transcontinental Technology Transfer

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 Blowing in the Wind: A Forged Connection Between Machine Setters in South Carolina and Wind Power in Kosovo. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Cross-Cultural Wind Integration Studies

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Blowing in the Wind: A Forged Connection Between Machine Setters in South Carolina and Wind Power in Kosovo with an abstract of This study explores the unsuspecting link between the number of forging machine setters, operators, and tenders, metal and plastic in South Carolina and the generation of wind power in Kosovo. Through the meticulous analysis of data from the Bureau of Labor Statistics and the Energy Information Administration, our research team uncovered a surprisingly strong correlation. With a correlation coefficient of 0.9636845 and p < 0.01, our findings suggest a compelling relationship that defies conventional wisdom. Despite the miles of distance and disparate industries, it seems that the movements of machines in South Carolina have been sending a subtle, yet influential breeze to the wind turbines of Kosovo. Our results shed light on the interconnectedness of seemingly unrelated sectors and emphasize the need for further investigation into the whimsical ways of the world.

ChatGPT:

forging machine setters, forging machine operators, forging machine tenders, metal and plastics industry, South Carolina labor statistics, wind power generation, Kosovo wind power, correlation coefficient, Bureau of Labor Statistics data, Energy Information Administration data, interconnectedness of industries, unconventional relationships, industrial influence, global interconnectedness, sector correlation

*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.

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Data details

The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina
Detailed data title: BLS estimate of forging machine setters, operators, and tenders, metal and plastic in South Carolina
Source: Bureau of Larbor Statistics
See what else correlates with The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina

Wind power generated in Kosovo
Detailed data title: Total wind power generated in Kosovo in billion kWh
Source: Energy Information Administration
See what else correlates with Wind power generated in Kosovo

Correlation r = 0.9636845 (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 = 0.9286878 (Coefficient of determination)
This means 92.9% of the change in the one variable (i.e., Wind power generated in Kosovo) is predictable based on the change in the other (i.e., The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina) over the 12 years from 2010 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 4.7E-7.^Show 0.0000004679962557733768400000
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 0.96 in 4.7E-5% of random cases. Said differently, if you correlated 2,136,769 random variables^Note You don't actually need 2 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 11 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 11 because we have two variables measured over a period of 12 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.

[ 0.87, 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.

	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina (Laborers)	450	370	290	220	190	240	240	300	290	1210	1140	1250
Wind power generated in Kosovo (Billion kWh)	0.001	0.003	0.003	0.003	0.003	0.003	0.003	0.003	0.03	0.091	0.09	0.09

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([450,370,290,220,190,240,240,300,290,1210,1140,1250,])
array_2 = np.array([0.001,0.003,0.003,0.003,0.003,0.003,0.003,0.003,0.03,0.091,0.09,0.09,])
array_1_name = "The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina"
array_2_name = "Wind power generated in Kosovo"

# 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
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Line chart for only The number of forging machine setters, operators, and tenders, metal and plastic in South Carolina
Line chart for only Wind power generated in Kosovo
AI-generated correlation image
The spurious research paper: Blowing in the Wind: A Forged Connection Between Machine Setters in South Carolina and Wind Power in Kosovo

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Correlation ID: 3500 · Black Variable ID: 14152 · Red Variable ID: 23775


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