Spurious correlation #3,628 · 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 loan interviewers and clerks in Utah and the second variable is Biomass power generated in Australia. The chart goes from 2003 to 2020, 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 loan interviewers and clerks in Utah and the second variable is Biomass power generated in Australia. The chart goes from 2003 to 2020, and the two variables track closely in value over that time.

AI explanation

As more loan interviewers and clerks were hired in Utah, there was a surge in productivity. This productivity led to a surplus of paper, which was then sold to Australia at a great discount. The Australians, known for their resourcefulness, used this paper to create more efficient biomass power systems, ultimately leading to an increase in biomass power generated. So, in a strange and indirect way, Utah's focus on loans ended up fueling Australia's biomass power production!

^{Show image generation prompt} Model: dalle-3
Prompt: [ Cutesy graphics ] An adorable group of loan interviewers and clerks in Utah gather around a cheerful, circular table. They are depicted using biodegradable papers, and stacks of these eco-friendly documents are visible in front of them. The scene shows them engaging in their work happily, with smiles on their faces. In the background, a delightful depiction of a lush forest with friendly animals and vibrant greenery sets the scene for their environmental efforts. Meanwhile, in Australia, a charming biomass power plant is pictured efficiently operating. The plant is represented as a cute, smiling building with large smokestacks emitting heart-shaped clouds of smoke, symbolizing clean energy production. Endearing piles of sustainably sourced wood are shown nearby, featuring little happy faces, as they are being used as fuel. 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 loan interviewers and clerks in Utah and Biomass power generated in Australia. 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: A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s Symbolism Cyberpunk Cutsy graphics
ChatGPT: Cutesy graphics

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 loan interviewers and clerks in Utah and Biomass power generated in Australia, in the following situation: Because loan interviewers and clerks in Utah started using biodegradable paper, reducing deforestation, leading to increased biomass power generation in Australia using sustainably sourced wood.. Keep the description simple.
ChatGPT: In Utah, loan interviewers and clerks gather around a table, with stacks of biodegradable paper in front of them. They are seen busy working, using the eco-friendly paper, which is reducing deforestation. In the background, a picture of a lush forest can be seen. In Australia, a biomass power plant is operating efficiently, with large piles of sustainably sourced wood being used as fuel. The plant is producing a significant amount of clean energy, with smoke rising from the stacks.

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: [ Cutesy graphics ] and depicting this scene: In Utah, loan interviewers and clerks gather around a table, with stacks of biodegradable paper in front of them. They are seen busy working, using the eco-friendly paper, which is reducing deforestation. In the background, a picture of a lush forest can be seen. In Australia, a biomass power plant is operating efficiently, with large piles of sustainably sourced wood being used as fuel. The plant is producing a significant amount of clean energy, with smoke rising from the stacks. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ Cutesy graphics ] An adorable group of loan interviewers and clerks in Utah gather around a cheerful, circular table. They are depicted using biodegradable papers, and stacks of these eco-friendly documents are visible in front of them. The scene shows them engaging in their work happily, with smiles on their faces. In the background, a delightful depiction of a lush forest with friendly animals and vibrant greenery sets the scene for their environmental efforts. Meanwhile, in Australia, a charming biomass power plant is pictured efficiently operating. The plant is represented as a cute, smiling building with large smokestacks emitting heart-shaped clouds of smoke, symbolizing clean energy production. Endearing piles of sustainably sourced wood are shown nearby, featuring little happy faces, as they are being used as fuel.

*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 loan interviewers and clerks in Utah caused Biomass power generated in Australia to increase.

AI academic paper

(Because p < 0.01)

Clowning Clerks: Correlating the Count of Loan Interviewers with the Capacity of Biomass Power

The Journal of Laughter and Biomass Engineering

Jan 2024

^{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 The number of loan interviewers and clerks in Utah and Biomass power generated in Australia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 some movies that you watched that are tangentially related to the topic.

Here is the title and abstract of the paper:
[[TITLE]]
Clowning Clerks: Correlating the Count of Loan Interviewers with the Capacity of Biomass Power

[[ABSTRACT]]
This study delves into the relationship between the number of loan interviewers and clerks in Utah and the biomass power generated in Australia. We utilized data from the Bureau of Labor Statistics and the Energy Information Administration to investigate this rather peculiar connection. With a correlation coefficient of 0.7909577 and a significance level of p < 0.01 for the period spanning from 2003 to 2020, we sought to unravel the enigmatic correlation between these seemingly unrelated variables. Although the premise may seem amusing, our findings underscore a statistically robust association that warrants further examination. The implications of this unexpected correlation are as intriguing as they are comical, challenging conventional wisdom and inviting speculation on potential causative factors. This research ventures into uncharted territories and, with a touch of whimsy, uncovers connections that may leave even the most serious scholars smiling.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The interplay between seemingly disparate factors has long intrigued researchers in various fields. In the realm of economic and environmental studies, uncovering unexpected connections can unearth valuable insights with significant implications. In this study, we explore the curious relationship between the count of loan interviewers and clerks in Utah and the capacity of biomass power generated in Australia. While this unlikely pairing may prompt a smirk or a raised eyebrow, our investigation delves beyond initial amusement to reveal a surprising statistical correlation worthy of serious contemplation.
The mundane work of loan interviewers and clerks in Utah, diligently processing paperwork and crunching numbers, seems a world away from the dynamic energy landscape of Australia. Yet, as we will demonstrate, there may be more to this disconnect than meets the eye. Through rigorous data analysis spanning nearly two decades, we have observed a statistically robust association, prompting us to meander down the uncharted path of unexpected correlations.
Before we embark on this whimsical journey through the land of data and statistics, it is important to underscore the gravity of our findings. Despite the potential for levity in our subject matter, the implications of this correlation extend far beyond mere amusement. As we dissect the numbers and weave together our findings, we invite readers to join us in excavating this curious connection with both inquisitiveness and a healthy dose of humor.
So, fasten your seatbelts and prepare for a scholarly adventure that promises not only surprising revelations but also the occasional chuckle amidst the serious pursuit of knowledge. This paper stands as a testament to the notion that even in the most unlikely pairings, there may lie a kernel of truth waiting to be uncovered.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The search for unexpected correlations and unlikely bedfellows has been a persistent theme in academic inquiry, with researchers constantly unearthing surprising connections in the most unlikely of places. Smith et al. (2010) delved into the relational dynamics of seemingly unrelated variables, providing a framework for understanding the intricacies of statistically robust associations. Building upon this foundation, Doe (2015) further expanded the discourse, emphasizing the need to approach correlations with a blend of skepticism and curiosity.

Turning to the economic realm, Jones et al. (2018) conducted a comprehensive analysis of labor market trends and their impact on energy production. While their focus was not directly aligned with the subject matter at hand, their work laid the groundwork for understanding the interplay between labor dynamics and energy outcomes.

Shifting to more general works that have grappled with the unexpected intersections of disparate elements, "Freakonomics" by Steven D. Levitt and Stephen J. Dubner (2005) stands as a widely acclaimed exploration of unconventional correlations in economics. Similarly, "The Tipping Point" by Malcolm Gladwell (2000) has captivated readers with its exploration of how small changes can lead to significant, and often unexpected, effects.

In the realm of fiction, "The Da Vinci Code" by Dan Brown (2003) and "Angels & Demons" (2000) seem to promise esoteric revelations that might just shed light on our own enigmatic correlation. While the protagonists' quests may not directly align with our scholarly pursuit, the spirit of unraveling mysterious connections resonates with our own pursuit of uncovering the peculiar interplay between loan interviewers and biomass power.

In the cinematic world, "The Secret Life of Walter Mitty" (2013) and "Office Space" (1999) offer reflective, albeit tangential, perspectives on the mundanity of office life and the potential for unexpected adventures amidst tedium. While these films may not directly address the specific juxtaposition of loan interviewers and biomass power, they nonetheless capture the essence of finding humor and intrigue in the seemingly ordinary.

As we immerse ourselves in this scholarly adventure, we welcome the reader to join us in the quest for understanding, with a sprinkle of humor and a spirit of inquiry as we navigate the uncharted waters of correlation and causation.

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 The number of loan interviewers and clerks in Utah and Biomass power generated in Australia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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 2003 to 2020

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]]
Clowning Clerks: Correlating the Count of Loan Interviewers with the Capacity of Biomass Power

[[ABSTRACT]]
This study delves into the relationship between the number of loan interviewers and clerks in Utah and the biomass power generated in Australia. We utilized data from the Bureau of Labor Statistics and the Energy Information Administration to investigate this rather peculiar connection. With a correlation coefficient of 0.7909577 and a significance level of p < 0.01 for the period spanning from 2003 to 2020, we sought to unravel the enigmatic correlation between these seemingly unrelated variables. Although the premise may seem amusing, our findings underscore a statistically robust association that warrants further examination. The implications of this unexpected correlation are as intriguing as they are comical, challenging conventional wisdom and inviting speculation on potential causative factors. This research ventures into uncharted territories and, with a touch of whimsy, uncovers connections that may leave even the most serious scholars smiling.

[[INTRODUCTION]]
The interplay between seemingly disparate factors has long intrigued researchers in various fields. In the realm of economic and environmental studies, uncovering unexpected connections can unearth valuable insights with significant implications. In this study, we explore the curious relationship between the count of loan interviewers and clerks in Utah and the capacity of biomass power generated in Australia. While this unlikely pairing may prompt a smirk or a raised eyebrow, our investigation delves beyond initial amusement to reveal a surprising statistical correlation worthy of serious contemplation.
The mundane work of loan interviewers and clerks in Utah, diligently processing paperwork and crunching numbers, seems a world away from the dynamic energy landscape of Australia. Yet, as we will demonstrate, there may be more to this disconnect than meets the eye. Through rigorous data analysis spanning nearly two decades, we have observed a statistically robust association, prompting us to meander down the uncharted path of unexpected correlations.
Before we embark on this whimsical journey through the land of data and statistics, it is important to underscore the gravity of our findings. Despite the potential for levity in our subject matter, the implications of this correlation extend far beyond mere amusement. As we dissect the numbers and weave together our findings, we invite readers to join us in excavating this curious connection with both inquisitiveness and a healthy dose of humor.
So, fasten your seatbelts and prepare for a scholarly adventure that promises not only surprising revelations but also the occasional chuckle amidst the serious pursuit of knowledge. This paper stands as a testament to the notion that even in the most unlikely pairings, there may lie a kernel of truth waiting to be uncovered.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

In this section, we detail the convoluted and somewhat comical methodology employed to investigate the perplexing relationship between the number of loan interviewers and clerks in Utah and the capacity of biomass power generated in Australia. Our approach, like a clown car, may seem to defy all logic at first glance, but we assure you that beneath the colorful exterior lies a framework of rigor and statistical integrity.

**Data Collection: A Serious Search for Silly Connections**

To begin this whimsical journey, we harnessed the power of the internet, scouring the depths of the worldwide web like intrepid explorers on a quest for hidden treasure. Alas, much like searching for a needle in a haystack, the treasure we sought was buried within the data repositories of the Bureau of Labor Statistics and the Energy Information Administration. We dug through mounds of data, occasionally pausing to ponder the mysteries of the universe and the occasional cat video, until we emerged triumphant with the necessary datasets spanning the years 2003 to 2020.

**Assumptions: Assuming Nothing and Everything**

In the realm of statistical analysis, assumptions are often made with the gravity of a solemn vow. However, in our case, we approached assumptions with the levity of a feather on the breeze. We embraced the assumption of linearity with a knowing wink, recognizing that even the most unexpected relationships can be plotted along a straight line if one squints hard enough. We also cheekily assumed the absence of multicollinearity, hoping that the variables, much like good comedy partners, would not step on each other's punchlines.

**Statistical Analysis: Keeping the Fun in Function**

With our datasets in hand and our assumptions in tow, we embarked on the statistical analysis with a mix of determination and whimsy. Armed with correlation coefficients, p-values, and scatterplots, we navigated the treacherous terrain of data analysis with the agility of a circus acrobat, carefully tiptoeing around deceptive outliers and confounding variables. The core of our analysis rested on uncovering the enigmatic correlation coefficient, striving to unveil a statistically robust association that would confound the skeptics and delight the jesters.

**Sensitivity Analysis: Tickling the Data into Submission**

No scientific investigation would be complete without subjecting the results to a sensitivity analysis, akin to tickling a seemingly serious matter to tease out its true nature. Much like a stand-up comedian testing out new material, we prodded the data with alternate model specifications and diagnostic tests, ensuring that the correlation would stand up to scrutiny even in the face of relentless statistical tickling.

**Robustness Checks: Seeking Resilience in Ridiculousness**

Finally, as a nod to the resilience of truth in the face of absurdity, we subjected our findings to a battery of robustness checks. From bootstrapping to Monte Carlo simulations, we probed and prodded our results, much like poking a rubber chicken to test its durability. Through these checks, we sought to ensure that our research remained steadfast even amidst the playful antics of statistical tomfoolery.

In summary, our methodology, much like a clown at a children's party, seamlessly wove together elements of seriousness and levity, with the ultimate goal of shedding light on a correlation that defies expectation. As we move forward to unveil our results, we invite our esteemed readers to engage in this scholarly adventure with an open mind and perhaps the occasional, well-deserved chuckle.

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 The number of loan interviewers and clerks in Utah and Biomass power generated in Australia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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]]
Clowning Clerks: Correlating the Count of Loan Interviewers with the Capacity of Biomass Power

[[ABSTRACT]]
This study delves into the relationship between the number of loan interviewers and clerks in Utah and the biomass power generated in Australia. We utilized data from the Bureau of Labor Statistics and the Energy Information Administration to investigate this rather peculiar connection. With a correlation coefficient of 0.7909577 and a significance level of p < 0.01 for the period spanning from 2003 to 2020, we sought to unravel the enigmatic correlation between these seemingly unrelated variables. Although the premise may seem amusing, our findings underscore a statistically robust association that warrants further examination. The implications of this unexpected correlation are as intriguing as they are comical, challenging conventional wisdom and inviting speculation on potential causative factors. This research ventures into uncharted territories and, with a touch of whimsy, uncovers connections that may leave even the most serious scholars smiling.

[[LITERATURE REVIEW]]
The search for unexpected correlations and unlikely bedfellows has been a persistent theme in academic inquiry, with researchers constantly unearthing surprising connections in the most unlikely of places. Smith et al. (2010) delved into the relational dynamics of seemingly unrelated variables, providing a framework for understanding the intricacies of statistically robust associations. Building upon this foundation, Doe (2015) further expanded the discourse, emphasizing the need to approach correlations with a blend of skepticism and curiosity.
Turning to the economic realm, Jones et al. (2018) conducted a comprehensive analysis of labor market trends and their impact on energy production. While their focus was not directly aligned with the subject matter at hand, their work laid the groundwork for understanding the interplay between labor dynamics and energy outcomes.
Shifting to more general works that have grappled with the unexpected intersections of disparate elements, "Freakonomics" by Steven D. Levitt and Stephen J. Dubner (2005) stands as a widely acclaimed exploration of unconventional correlations in economics. Similarly, "The Tipping Point" by Malcolm Gladwell (2000) has captivated readers with its exploration of how small changes can lead to significant, and often unexpected, effects.
In the realm of fiction, "The Da Vinci Code" by Dan Brown (2003) and "Angels & Demons" (2000) seem to promise esoteric revelations that might just shed light on our own enigmatic correlation. While the protagonists' quests may not directly align with our scholarly pursuit, the spirit of unraveling mysterious connections resonates with our own pursuit of uncovering the peculiar interplay between loan interviewers and biomass power.
In the cinematic world, "The Secret Life of Walter Mitty" (2013) and "Office Space" (1999) offer reflective, albeit tangential, perspectives on the mundanity of office life and the potential for unexpected adventures amidst tedium. While these films may not directly address the specific juxtaposition of loan interviewers and biomass power, they nonetheless capture the essence of finding humor and intrigue in the seemingly ordinary.
As we immerse ourselves in this scholarly adventure, we welcome the reader to join us in the quest for understanding, with a sprinkle of humor and a spirit of inquiry as we navigate the uncharted waters of correlation and causation.

[[RESULTS]]
The statistical analysis of the relationship between the number of loan interviewers and clerks in Utah and the biomass power generated in Australia yielded some eyebrow-raising results. From 2003 to 2020, we found a strikingly high correlation coefficient of 0.7909577, indicating a strong positive relationship between these seemingly unrelated variables. The r-squared value of 0.6256140 further underscored the robustness of this association, suggesting that approximately 63% of the variability in biomass power generated in Australia can be explained by the count of loan interviewers and clerks in Utah. With a significance level of p < 0.01, we can confidently assert that this connection is not merely a fluke but rather a statistically significant phenomenon.
Fig. 1 presents a scatterplot illustrating the compelling correlation between the count of loan interviewers and clerks in Utah and the capacity of biomass power generated in Australia. The figure vividly portrays the data points clustering along a positively sloped line, affirming the strength of the relationship. While the unexpected nature of this correlation may elicit amusement, it is crucial to recognize the empirical rigor underlying our findings. These results present a conundrum that, while lighthearted on the surface, carries substantial implications for understanding the intricate interplay between economic and environmental factors.
It is worth noting that while our investigation has unveiled this intriguing correlation, the underlying mechanisms driving this relationship remain shrouded in mystery. The whimsical juxtaposition of loan interviewers in Utah with biomass power in Australia reminds us that serendipity often plays a role in the scientific pursuit. This statistical quirk beckons further scrutiny, teasing researchers with the prospect of uncovering the esoteric forces at play.
As unexpected as this correlation may be, it underscores the potential for unearthing meaningful insights in the unlikeliest of places. Our findings serve as a whimsical reminder that in the labyrinth of data and statistics, even the most comical connections may harbor profound revelations, challenging us to approach our investigations with an open mind and perhaps a sense of humor.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of this study converge with prior research that has emphasized the quest for unearthing unexpected correlations and peculiar intersections. The statistically robust association between the number of loan interviewers and clerks in Utah and the biomass power generated in Australia reflects the nuances of seemingly unrelated variables coming together in an intriguing dance. As Smith et al. (2010) and Doe (2015) have expounded, delving into the relational dynamics of unusual connections calls for a blend of skepticism and curiosity, echoing the dual nature of our own inquiry.

An unexpected connection as whimsical as the one between loan interviewers and biomass power could not escape the attention of researchers exploring the labyrinth of correlations. The comical nature of this juxtaposition juxtaposed against the empirical rigor of our findings underscores the vibrant landscape of statistical inquiry, where even the most unconventional associations can yield meaningful insights. Just as Malcolm Gladwell’s "The Tipping Point" (2000) uncovered how small changes lead to significant effects, our research shines a light on the potential for unexpected correlations to defy conventional wisdom and invite speculation on causative factors.

The peculiar correlation uncovered in this study not only aligns with prior calls for a spirit of inquiry and a sprinkle of humor but also echoes the whimsical undercurrents explored in fictional works such as "The Da Vinci Code" by Dan Brown (2003). Just as in Brown’s esoteric quests, our own scholarly pursuit uncovers enigmatic connections that evoke a sense of intrigue and perhaps even a chuckle. The unexpected juxtaposition of loan interviewers and biomass power, while lighthearted on the surface, represents a tantalizing conundrum that demands further scrutiny.

As we reflect on the implications of this study, it becomes apparent that wading into the realm of unexpected correlations offers a rich tapestry of potential discoveries. Just as the cinematic vignettes of "The Secret Life of Walter Mitty" (2013) and "Office Space" (1999) capture the essence of finding humor and intrigue in the seemingly mundane, our own investigation unearths the lighthearted alongside the substantial. The captivating conundrum of loan interviewers dancing with biomass power serves as a whimsical reminder that even in the most serious scholarly pursuits, the allure of unexpected correlations may prompt a smile and, more importantly, a deeper understanding of the intricate interplay between economic and environmental factors.

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 The number of loan interviewers and clerks in Utah and Biomass power generated in Australia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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]]
Clowning Clerks: Correlating the Count of Loan Interviewers with the Capacity of Biomass Power

[[ABSTRACT]]
This study delves into the relationship between the number of loan interviewers and clerks in Utah and the biomass power generated in Australia. We utilized data from the Bureau of Labor Statistics and the Energy Information Administration to investigate this rather peculiar connection. With a correlation coefficient of 0.7909577 and a significance level of p < 0.01 for the period spanning from 2003 to 2020, we sought to unravel the enigmatic correlation between these seemingly unrelated variables. Although the premise may seem amusing, our findings underscore a statistically robust association that warrants further examination. The implications of this unexpected correlation are as intriguing as they are comical, challenging conventional wisdom and inviting speculation on potential causative factors. This research ventures into uncharted territories and, with a touch of whimsy, uncovers connections that may leave even the most serious scholars smiling.

[[INTRDUCTION]]
The interplay between seemingly disparate factors has long intrigued researchers in various fields. In the realm of economic and environmental studies, uncovering unexpected connections can unearth valuable insights with significant implications. In this study, we explore the curious relationship between the count of loan interviewers and clerks in Utah and the capacity of biomass power generated in Australia. While this unlikely pairing may prompt a smirk or a raised eyebrow, our investigation delves beyond initial amusement to reveal a surprising statistical correlation worthy of serious contemplation.
The mundane work of loan interviewers and clerks in Utah, diligently processing paperwork and crunching numbers, seems a world away from the dynamic energy landscape of Australia. Yet, as we will demonstrate, there may be more to this disconnect than meets the eye. Through rigorous data analysis spanning nearly two decades, we have observed a statistically robust association, prompting us to meander down the uncharted path of unexpected correlations.
Before we embark on this whimsical journey through the land of data and statistics, it is important to underscore the gravity of our findings. Despite the potential for levity in our subject matter, the implications of this correlation extend far beyond mere amusement. As we dissect the numbers and weave together our findings, we invite readers to join us in excavating this curious connection with both inquisitiveness and a healthy dose of humor.
So, fasten your seatbelts and prepare for a scholarly adventure that promises not only surprising revelations but also the occasional chuckle amidst the serious pursuit of knowledge. This paper stands as a testament to the notion that even in the most unlikely pairings, there may lie a kernel of truth waiting to be uncovered.

[[RESULTS]]
The statistical analysis of the relationship between the number of loan interviewers and clerks in Utah and the biomass power generated in Australia yielded some eyebrow-raising results. From 2003 to 2020, we found a strikingly high correlation coefficient of 0.7909577, indicating a strong positive relationship between these seemingly unrelated variables. The r-squared value of 0.6256140 further underscored the robustness of this association, suggesting that approximately 63% of the variability in biomass power generated in Australia can be explained by the count of loan interviewers and clerks in Utah. With a significance level of p < 0.01, we can confidently assert that this connection is not merely a fluke but rather a statistically significant phenomenon.
Fig. 1 presents a scatterplot illustrating the compelling correlation between the count of loan interviewers and clerks in Utah and the capacity of biomass power generated in Australia. The figure vividly portrays the data points clustering along a positively sloped line, affirming the strength of the relationship. While the unexpected nature of this correlation may elicit amusement, it is crucial to recognize the empirical rigor underlying our findings. These results present a conundrum that, while lighthearted on the surface, carries substantial implications for understanding the intricate interplay between economic and environmental factors.
It is worth noting that while our investigation has unveiled this intriguing correlation, the underlying mechanisms driving this relationship remain shrouded in mystery. The whimsical juxtaposition of loan interviewers in Utah with biomass power in Australia reminds us that serendipity often plays a role in the scientific pursuit. This statistical quirk beckons further scrutiny, teasing researchers with the prospect of uncovering the esoteric forces at play.
As unexpected as this correlation may be, it underscores the potential for unearthing meaningful insights in the unlikeliest of places. Our findings serve as a whimsical reminder that in the labyrinth of data and statistics, even the most comical connections may harbor profound revelations, challenging us to approach our investigations with an open mind and perhaps a sense of humor.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, the correlation between the number of loan interviewers and clerks in Utah and the capacity of biomass power generated in Australia defies traditional expectations and tickles the scientific imagination. The statistically robust relationship, as evidenced by the high correlation coefficient and r-squared value, beckons researchers to ponder the enigmatic forces at play between these seemingly incongruous variables. While the scatterplot visually captures this unexpected alliance, it also invites a moment of lighthearted reflection on the whimsical nature of statistical surprises.

This study invites us to embrace the unexpected and to approach our scholarly pursuits with both seriousness and a sprightly sense of wonder. As we embrace the eccentricity of this correlation, we also recognize the potential for uncovering meaningful insights in the most improbable linkages. While the origins of this correlation remain shrouded in mystery, the light-hearted nature of this discovery reminds us that even in the data-driven depths of research, a dash of serendipity and amusement may lead to significant revelations.

In the spirit of scholarly exploration, we assert that no further research is needed in this area. The findings of this study stand as a testament to the delightful unpredictability of science and the enduring potential for mirth amid the pursuit of knowledge.

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 Clowning Clerks: Correlating the Count of Loan Interviewers with the Capacity of Biomass Power. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Laughter and Biomass Engineering

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 Clowning Clerks: Correlating the Count of Loan Interviewers with the Capacity of Biomass Power. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Silly 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 Clowning Clerks: Correlating the Count of Loan Interviewers with the Capacity of Biomass Power with an abstract of This study delves into the relationship between the number of loan interviewers and clerks in Utah and the biomass power generated in Australia. We utilized data from the Bureau of Labor Statistics and the Energy Information Administration to investigate this rather peculiar connection. With a correlation coefficient of 0.7909577 and a significance level of p < 0.01 for the period spanning from 2003 to 2020, we sought to unravel the enigmatic correlation between these seemingly unrelated variables. Although the premise may seem amusing, our findings underscore a statistically robust association that warrants further examination. The implications of this unexpected correlation are as intriguing as they are comical, challenging conventional wisdom and inviting speculation on potential causative factors. This research ventures into uncharted territories and, with a touch of whimsy, uncovers connections that may leave even the most serious scholars smiling.

ChatGPT:

loan interviewers, clerks, Utah, biomass power, correlation, Bureau of Labor Statistics, Energy Information Administration, statistical association, unusual correlation, causative factors

*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 loan interviewers and clerks in Utah
Detailed data title: BLS estimate of loan interviewers and clerks in Utah
Source: Bureau of Larbor Statistics
See what else correlates with The number of loan interviewers and clerks in Utah

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

Correlation r = 0.7909577 (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 = 0.6256140 (Coefficient of determination)
This means 62.6% of the change in the one variable (i.e., Biomass power generated in Australia) is predictable based on the change in the other (i.e., The number of loan interviewers and clerks in Utah) over the 18 years from 2003 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 9.3E-5.^Show 0.0000928968816566953000000000
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 0.79 in 0.0093% of random cases. Said differently, if you correlated 10,765 random variables^Note Which I absolutely did.
with the same 17 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 17 because we have two variables measured over a period of 18 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.

[ 0.51, 0.92 ] 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.

	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
The number of loan interviewers and clerks in Utah (Laborers)	1660	1320	4860	3730	4030	3750	3340	2300	2620	2500	3070	3260	3430	2510	2960	3430	3640	3580
Biomass power generated in Australia (Billion kWh)	1.62	1.86	3.83	3.91	3.95	4.596	2.815	2.777	2.102	3.043	3.152	3.511	3.608	3.722	3.501	3.518	3.496	3.352

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([1660,1320,4860,3730,4030,3750,3340,2300,2620,2500,3070,3260,3430,2510,2960,3430,3640,3580,])
array_2 = np.array([1.62,1.86,3.83,3.91,3.95,4.596,2.815,2.777,2.102,3.043,3.152,3.511,3.608,3.722,3.501,3.518,3.496,3.352,])
array_1_name = "The number of loan interviewers and clerks in Utah"
array_2_name = "Biomass power generated in Australia"

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

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

Reuseable content

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

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

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

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

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

Download images for these variables:

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

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

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
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Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only The number of loan interviewers and clerks in Utah
Line chart for only Biomass power generated in Australia
AI-generated correlation image
The spurious research paper: Clowning Clerks: Correlating the Count of Loan Interviewers with the Capacity of Biomass Power

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Correlation ID: 3628 · Black Variable ID: 15112 · Red Variable ID: 23474


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