Spurious correlation #1,993 · View random

AI explanation

As the number of social workers in West Virginia increased, so did their passion for helping others. This outpouring of warmth and care created a metaphorical ripple effect, eventually leading to a surge in the production of heartwarming news stories. These uplifting tales were then used as inspiration in Ghana to develop more efficient kerosene production methods, ultimately leading to an increase in kerosene usage as people were drawn to the cozy and heartening glow of the improved kerosene lamps.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image showing a serene Ghanaian village at night. Families are gathered around a warm kerosene lamp on a table, illuminating their joyful faces. In the background, kindly depict social workers from West Virginia engaging with the community, exuding an atmosphere of support and camaraderie. Let the scene impart a sense of warmth, togetherness, and mutual respect. 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 social workers in West Virginia and Kerosene used in Ghana. 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: Parallax art Realistic Art nouveau Surrealism Cutsy art illustration
ChatGPT: Realistic

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 social workers in West Virginia and Kerosene used in Ghana together. Keep the description simple.
ChatGPT: In a small village in Ghana, families gather around a warm kerosene lamp on a table. In the backdrop, several social workers from West Virginia are visiting the village, engaging with the community and providing support.

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: [ Realistic ] and depicting this scene: In a small village in Ghana, families gather around a warm kerosene lamp on a table. In the backdrop, several social workers from West Virginia are visiting the village, engaging with the community and providing support. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image showing a serene Ghanaian village at night. Families are gathered around a warm kerosene lamp on a table, illuminating their joyful faces. In the background, kindly depict social workers from West Virginia engaging with the community, exuding an atmosphere of support and camaraderie. Let the scene impart a sense of warmth, togetherness, and mutual respect.

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

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how increases in The number of social workers in West Virginia positively influenced Kerosene used in Ghana.

AI academic paper

(Because p < 0.01)

The Social Work-Clean Energy Connection: An Unlikely Pairing

The Journal of Sustainable Systems and Social Solutions

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 social workers in West Virginia and Kerosene used in Ghana. 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 devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading the backs of shampoo bottles.

Here is the title and abstract of the paper:
[[TITLE]]
The Social Work-Clean Energy Connection: An Unlikely Pairing

[[ABSTRACT]]
This research aims to investigate the seemingly bizarre connection between the number of social workers in West Virginia and the consumption of kerosene in Ghana. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, our research team conducted a rigorous analysis covering the period from 2003 to 2021. Surprisingly, we discovered a significant correlation coefficient of 0.8322550 and a p-value of less than 0.01, indicating a robust relationship between these seemingly disparate variables. While initially met with skepticism, our findings suggest that there is an uncanny link between the deployment of social workers in one location and the consumption of kerosene in another. We propose that further investigation into the underlying mechanisms driving this unexpected relationship could provide valuable insights for both social work and energy policy.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The quest for knowledge has led researchers down many unexpected paths, from the depths of the ocean to the far reaches of outer space. In the realm of social sciences, the pursuit of understanding human behavior often uncovers peculiar and seemingly improbable connections. The intertwining of variables in statistical analysis can reveal surprising relationships, much like stumbling upon a hidden pathway in a labyrinth.
The study at hand delves into the enigmatic association between the number of social workers in West Virginia and the consumption of kerosene in Ghana. At first glance, one may question the rationale behind examining these two seemingly unrelated entities. However, as researchers, we are often called to venture into uncharted territory, embracing the unconventional and the unexpected. After all, who would have thought that the humble bean sprout would provide a key to unlocking a mystery in molecular biology, or that the study of fruit flies would yield insights into genetics?
Employing a comprehensive dataset sourced from the Bureau of Labor Statistics and the Energy Information Administration, our research team embarked on a rigorous exploration of the relationship between social work deployment and kerosene consumption. With ardent dedication to statistical methods and quantitative analysis, we meticulously combed through the data spanning nearly two decades. Amidst the sea of numbers and coefficients, patterns began to emerge, beckoning us to unravel the tangled web of connections.
As we navigated through the labyrinth of regression analyses and correlation coefficients, we were met with a surprising revelation - a significant correlation coefficient of 0.8322550 and a p-value of less than 0.01. This unanticipated discovery sent ripples through the realm of social science research, beckoning us to ponder the mysterious dance between social workers in one corner of the world and the flickering flames of kerosene lamps in another.
This study challenges us to expand our conceptual horizons, compelling us to consider the intricate interplay between seemingly disparate realms. As we embark on this scholarly journey, we invite fellow researchers and inquisitive minds to join us in unraveling the riddles that lie at the intersection of social work and clean energy. After all, in the labyrinth of research, one may stumble upon unexpected treasures buried beneath a mound of data. Let us venture forth, armed with curiosity and statistical rigor, as we seek to shed light on this unlikely pairing.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The study at hand dives into the unexplored territory of the relationship between the number of social workers in West Virginia and the consumption of kerosene in Ghana. While this seemingly bizarre connection may prompt raised eyebrows and skeptical glances, the journey into the literature reveals a weaving tapestry of unexpected correlations and improbable pairings.

Smith (2015) investigates the deployment of social workers in various regions of the United States and their impact on local community well-being. The findings of this study elucidate the pivotal role played by social workers in addressing socio-economic disparities and fostering a sense of community cohesion, albeit within the confines of the United States. Meanwhile, Doe (2018) meticulously examines the patterns of kerosene consumption in developing nations, shedding light on the factors influencing energy choices in off-grid communities. The juxtaposition of these two strands of literature sets the stage for the enigmatic puzzle that lies at the heart of this research endeavor.

Turning to the realm of non-fiction literature, the works of authors such as "The Grid: The Fraying Wires Between Americans and Our Energy Future" by Gretchen Bakke and "Social Work: A Very Short Introduction" by Sally Holland offer insightful perspectives on the intricate dynamics of energy systems and the role of social work in addressing societal challenges. Additionally, exploring the fictional domain, novels such as "Light a Penny Candle" by Maeve Binchy and "The Alchemist" by Paulo Coelho beckon the reader into the realm of illumination and discovery, resonating with the quest to unravel the mysteries that lie at the intersection of social work and clean energy.

Furthermore, as the research team delved deeper into the literature, a serendipitous encounter with unconventional sources unveiled unexpected insights. The back covers of shampoo bottles, with their cryptic promises of silky, lustrous hair, offered an unlikely yet strangely captivating glimpse into the realm of consumer behavior and decision-making processes. While these sources may appear unrelated to the scholarly pursuit at hand, they underscore the notion that knowledge may spring from the most unanticipated of sources, much like a hidden pathway in a labyrinth.

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 social workers in West Virginia and Kerosene used in Ghana. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Larbor Statistics and Energy Information Administration . You used data from 2003 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]]
The Social Work-Clean Energy Connection: An Unlikely Pairing

[[ABSTRACT]]
This research aims to investigate the seemingly bizarre connection between the number of social workers in West Virginia and the consumption of kerosene in Ghana. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, our research team conducted a rigorous analysis covering the period from 2003 to 2021. Surprisingly, we discovered a significant correlation coefficient of 0.8322550 and a p-value of less than 0.01, indicating a robust relationship between these seemingly disparate variables. While initially met with skepticism, our findings suggest that there is an uncanny link between the deployment of social workers in one location and the consumption of kerosene in another. We propose that further investigation into the underlying mechanisms driving this unexpected relationship could provide valuable insights for both social work and energy policy.

[[INTRODUCTION]]
The quest for knowledge has led researchers down many unexpected paths, from the depths of the ocean to the far reaches of outer space. In the realm of social sciences, the pursuit of understanding human behavior often uncovers peculiar and seemingly improbable connections. The intertwining of variables in statistical analysis can reveal surprising relationships, much like stumbling upon a hidden pathway in a labyrinth.
The study at hand delves into the enigmatic association between the number of social workers in West Virginia and the consumption of kerosene in Ghana. At first glance, one may question the rationale behind examining these two seemingly unrelated entities. However, as researchers, we are often called to venture into uncharted territory, embracing the unconventional and the unexpected. After all, who would have thought that the humble bean sprout would provide a key to unlocking a mystery in molecular biology, or that the study of fruit flies would yield insights into genetics?
Employing a comprehensive dataset sourced from the Bureau of Labor Statistics and the Energy Information Administration, our research team embarked on a rigorous exploration of the relationship between social work deployment and kerosene consumption. With ardent dedication to statistical methods and quantitative analysis, we meticulously combed through the data spanning nearly two decades. Amidst the sea of numbers and coefficients, patterns began to emerge, beckoning us to unravel the tangled web of connections.
As we navigated through the labyrinth of regression analyses and correlation coefficients, we were met with a surprising revelation - a significant correlation coefficient of 0.8322550 and a p-value of less than 0.01. This unanticipated discovery sent ripples through the realm of social science research, beckoning us to ponder the mysterious dance between social workers in one corner of the world and the flickering flames of kerosene lamps in another.
This study challenges us to expand our conceptual horizons, compelling us to consider the intricate interplay between seemingly disparate realms. As we embark on this scholarly journey, we invite fellow researchers and inquisitive minds to join us in unraveling the riddles that lie at the intersection of social work and clean energy. After all, in the labyrinth of research, one may stumble upon unexpected treasures buried beneath a mound of data. Let us venture forth, armed with curiosity and statistical rigor, as we seek to shed light on this unlikely pairing.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The first step in our research endeavor involved the meticulous curation of data pertaining to the number of employed social workers in West Virginia and the consumption of kerosene in Ghana. This information was primarily sourced from the Bureau of Labor Statistics and the Energy Information Administration, two bastions of numerical insight and statistical revelation. To ensure comprehensiveness, we scoured the data from the years 2003 to 2021, weaving a tapestry of information that spanned nearly two decades. Like intrepid explorers traversing uncharted terrain, we ventured through the digital landscapes of labor statistics and energy consumption trends, assembling a mosaic of numbers and figures that would form the bedrock of our analysis.

Data Preprocessing:
With our treasure trove of data in hand, the next stage involved careful preprocessing to cleanse the data of any blemishes or anomalies. This process included the meticulous removal of outliers, invalid entries, and any statistical gremlins that may have sneaked their way into our dataset. This careful curation of information, akin to tending a delicate garden of numerical flora, ensured the integrity and reliability of our data, laying the groundwork for robust analysis and interpretation.

Statistical Analysis:
Armed with a pristine dataset, we delved into the realm of statistical analysis with fervor and precision. Employing advanced analytical techniques such as regression analysis and correlation studies, we sought to unravel the hidden threads that bound social work deployment in West Virginia to the consumption of kerosene in Ghana. With each coefficient and p-value, we peeled back the layers of complexity, uncovering the enigmatic dance of variables that underpinned this curious relationship. It was in the crucible of statistical analysis that the true nature of this unlikely pairing began to emerge, much like the unveiling of a cosmic phenomenon through the lens of a telescope.

Ethical Considerations:
While our pursuit of knowledge led us down unorthodox paths, we remained steadfast in our commitment to ethical research practices. All data utilized in this study were aggregated and anonymized, safeguarding the privacy and confidentiality of individuals represented in the datasets. Furthermore, our analysis adhered to the principles of academic integrity and transparency, ensuring that our findings were grounded in rigorous methodology and scholarly rigor.

Limitations:
It is incumbent upon us to acknowledge the limitations of our study. While our research uncovered a compelling correlation between the number of social workers in West Virginia and kerosene consumption in Ghana, the nature of causality remains enigmatic as ever. Additionally, external factors and unobserved variables may influence the dynamics of this relationship. As we navigate these intellectual crossroads, we recognize the need for further exploration and nuanced investigation to illuminate the intricacies of this peculiar association.

In conclusion, the journey through the labyrinth of data analysis and statistical inference has yielded unexpected revelations, posing new questions and opening avenues for scholarly inquiry. The methodology employed in this study stands as a testament to the rigorous pursuit of empirical truth and the unyielding spirit of exploration.

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 results section of an academic research paper. You speak in the past tense (you found...).
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the results section of an academic research paper presenting the findings of the connection between The number of social workers in West Virginia and Kerosene used in Ghana. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

For the time period 2003 to 2021, you found a correlation 0.8322550, r-squared of 0.6926484, and p < 0.01.

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

Here is the title and abstract of the paper:
[[TITLE]]
The Social Work-Clean Energy Connection: An Unlikely Pairing

[[ABSTRACT]]
This research aims to investigate the seemingly bizarre connection between the number of social workers in West Virginia and the consumption of kerosene in Ghana. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, our research team conducted a rigorous analysis covering the period from 2003 to 2021. Surprisingly, we discovered a significant correlation coefficient of 0.8322550 and a p-value of less than 0.01, indicating a robust relationship between these seemingly disparate variables. While initially met with skepticism, our findings suggest that there is an uncanny link between the deployment of social workers in one location and the consumption of kerosene in another. We propose that further investigation into the underlying mechanisms driving this unexpected relationship could provide valuable insights for both social work and energy policy.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The quest for knowledge has led researchers down many unexpected paths, from the depths of the ocean to the far reaches of outer space. In the realm of social sciences, the pursuit of understanding human behavior often uncovers peculiar and seemingly improbable connections. The intertwining of variables in statistical analysis can reveal surprising relationships, much like stumbling upon a hidden pathway in a labyrinth.
The study at hand delves into the enigmatic association between the number of social workers in West Virginia and the consumption of kerosene in Ghana. At first glance, one may question the rationale behind examining these two seemingly unrelated entities. However, as researchers, we are often called to venture into uncharted territory, embracing the unconventional and the unexpected. After all, who would have thought that the humble bean sprout would provide a key to unlocking a mystery in molecular biology, or that the study of fruit flies would yield insights into genetics?
Employing a comprehensive dataset sourced from the Bureau of Labor Statistics and the Energy Information Administration, our research team embarked on a rigorous exploration of the relationship between social work deployment and kerosene consumption. With ardent dedication to statistical methods and quantitative analysis, we meticulously combed through the data spanning nearly two decades. Amidst the sea of numbers and coefficients, patterns began to emerge, beckoning us to unravel the tangled web of connections.
As we navigated through the labyrinth of regression analyses and correlation coefficients, we were met with a surprising revelation - a significant correlation coefficient of 0.8322550 and a p-value of less than 0.01. This unanticipated discovery sent ripples through the realm of social science research, beckoning us to ponder the mysterious dance between social workers in one corner of the world and the flickering flames of kerosene lamps in another.
This study challenges us to expand our conceptual horizons, compelling us to consider the intricate interplay between seemingly disparate realms. As we embark on this scholarly journey, we invite fellow researchers and inquisitive minds to join us in unraveling the riddles that lie at the intersection of social work and clean energy. After all, in the labyrinth of research, one may stumble upon unexpected treasures buried beneath a mound of data. Let us venture forth, armed with curiosity and statistical rigor, as we seek to shed light on this unlikely pairing.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

A thorough analysis of the data revealed a strong positive correlation between the number of social workers in West Virginia and the consumption of kerosene in Ghana over the period from 2003 to 2021. The correlation coefficient of 0.8322550 suggests a robust relationship between these seemingly unrelated variables, akin to finding a connection between the speed of a tortoise and the global consumption of ice cream.

Furthermore, the coefficient of determination, indicated by an r-squared value of 0.6926484, implies that approximately 69.26% of the variation in kerosene consumption in Ghana can be explained by the number of social workers in West Virginia. This level of explanatory power rivals the predictability of a morning routine or the inevitability of encountering a statistical p-value in research discussions.

The statistical significance of the correlation was confirmed, with a p-value of less than 0.01, reinforcing the strength of the relationship between these two disparate variables. This p-value would even impress the most discerning of statistical connoisseurs, akin to uncovering a rare artifact in a world of digital abundance.

The scatterplot (Fig. 1) visually encapsulates the striking correlation between the number of social workers in West Virginia and the consumption of kerosene in Ghana, providing a graphical representation of the unexpected bond between these two entities. It's as if the plot itself exudes the sentiment "who would have thought?" in its depiction of this improbable connection.

These findings challenge conventional wisdom and beckon the research community to further explore the mechanisms underlying this remarkable link. Ultimately, this study sheds light on the unanticipated interplay between social work and clean energy, inviting further scholarly inquiry and perhaps the occasional bemused chuckle from the academic community.

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 social workers in West Virginia and Kerosene used in Ghana. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

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

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

[[TITLE]]
The Social Work-Clean Energy Connection: An Unlikely Pairing

[[ABSTRACT]]
This research aims to investigate the seemingly bizarre connection between the number of social workers in West Virginia and the consumption of kerosene in Ghana. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, our research team conducted a rigorous analysis covering the period from 2003 to 2021. Surprisingly, we discovered a significant correlation coefficient of 0.8322550 and a p-value of less than 0.01, indicating a robust relationship between these seemingly disparate variables. While initially met with skepticism, our findings suggest that there is an uncanny link between the deployment of social workers in one location and the consumption of kerosene in another. We propose that further investigation into the underlying mechanisms driving this unexpected relationship could provide valuable insights for both social work and energy policy.

[[LITERATURE REVIEW]]
The study at hand dives into the unexplored territory of the relationship between the number of social workers in West Virginia and the consumption of kerosene in Ghana. While this seemingly bizarre connection may prompt raised eyebrows and skeptical glances, the journey into the literature reveals a weaving tapestry of unexpected correlations and improbable pairings.
Smith (2015) investigates the deployment of social workers in various regions of the United States and their impact on local community well-being. The findings of this study elucidate the pivotal role played by social workers in addressing socio-economic disparities and fostering a sense of community cohesion, albeit within the confines of the United States. Meanwhile, Doe (2018) meticulously examines the patterns of kerosene consumption in developing nations, shedding light on the factors influencing energy choices in off-grid communities. The juxtaposition of these two strands of literature sets the stage for the enigmatic puzzle that lies at the heart of this research endeavor.
Turning to the realm of non-fiction literature, the works of authors such as "The Grid: The Fraying Wires Between Americans and Our Energy Future" by Gretchen Bakke and "Social Work: A Very Short Introduction" by Sally Holland offer insightful perspectives on the intricate dynamics of energy systems and the role of social work in addressing societal challenges. Additionally, exploring the fictional domain, novels such as "Light a Penny Candle" by Maeve Binchy and "The Alchemist" by Paulo Coelho beckon the reader into the realm of illumination and discovery, resonating with the quest to unravel the mysteries that lie at the intersection of social work and clean energy.
Furthermore, as the research team delved deeper into the literature, a serendipitous encounter with unconventional sources unveiled unexpected insights. The back covers of shampoo bottles, with their cryptic promises of silky, lustrous hair, offered an unlikely yet strangely captivating glimpse into the realm of consumer behavior and decision-making processes. While these sources may appear unrelated to the scholarly pursuit at hand, they underscore the notion that knowledge may spring from the most unanticipated of sources, much like a hidden pathway in a labyrinth.

[[RESULTS]]
A thorough analysis of the data revealed a strong positive correlation between the number of social workers in West Virginia and the consumption of kerosene in Ghana over the period from 2003 to 2021. The correlation coefficient of 0.8322550 suggests a robust relationship between these seemingly unrelated variables, akin to finding a connection between the speed of a tortoise and the global consumption of ice cream.
Furthermore, the coefficient of determination, indicated by an r-squared value of 0.6926484, implies that approximately 69.26% of the variation in kerosene consumption in Ghana can be explained by the number of social workers in West Virginia. This level of explanatory power rivals the predictability of a morning routine or the inevitability of encountering a statistical p-value in research discussions.
The statistical significance of the correlation was confirmed, with a p-value of less than 0.01, reinforcing the strength of the relationship between these two disparate variables. This p-value would even impress the most discerning of statistical connoisseurs, akin to uncovering a rare artifact in a world of digital abundance.
The scatterplot (Fig. 1) visually encapsulates the striking correlation between the number of social workers in West Virginia and the consumption of kerosene in Ghana, providing a graphical representation of the unexpected bond between these two entities. It's as if the plot itself exudes the sentiment "who would have thought?" in its depiction of this improbable connection.
These findings challenge conventional wisdom and beckon the research community to further explore the mechanisms underlying this remarkable link. Ultimately, this study sheds light on the unanticipated interplay between social work and clean energy, inviting further scholarly inquiry and perhaps the occasional bemused chuckle from the academic community.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our investigation into the relationship between the number of social workers in West Virginia and the consumption of kerosene in Ghana have provided compelling evidence of an unexpected and robust association. This unorthodox coupling of variables, reminiscent of a mismatched pair in a Shakespearean comedy, defied initial skepticism and has surfaced as a noteworthy nexus that warrants further examination.

The discovery of a significant correlation coefficient of 0.8322550, akin to stumbling upon a hidden treasure in the labyrinth of data analysis, aligns with prior research highlighting the influence of social workers on community well-being. Smith's (2015) work on the impact of social workers in local communities offers a pertinent parallel, underscoring the profound role played by these professionals. Our findings suggest that the deployment of social workers can reverberate far beyond the borders of the regions they serve, apparently extending their reach to affect energy consumption in distant locales.

Similarly, Doe's (2018) exploration of kerosene consumption in developing nations echoes the themes uncovered in our study. The nuanced investigation into the determinants of energy choices resonates with our unanticipated linkage between the number of social workers and kerosene consumption. This linking of seemingly disparate areas of inquiry mirrors the surprising connections one might make while meandering through the labyrinth of a library or discovering an unexpected link between two seemingly unrelated pages of a novel.

In a nod to the literary world, the unconventional engagement with the back covers of shampoo bottles, reminiscent of a digression into an unexpected avenue of inquiry, sheds light on the notion that knowledge may indeed emerge from the most unlikely of sources. This resonates with the revelatory nature of our unanticipated findings, urging the scholarly community to embrace the serendipitous and recognize that a chance encounter with an unconventional source may yield unexpected insights, much like stumbling across an unexpected pun in the dense prose of an academic paper.

The statistical robustness of our findings, evidenced by the visually striking scatterplot and reinforced by the p-value of less than 0.01, provides unwavering support for the significance of this liaison. The predictability and strength of this relationship rival the classic "predictability" of cliché research discussions and compel the academic community to marvel at the unexpected connections that can emerge from the troves of data analysis and scholarly inquiry.

In conclusion, our study underscores the improbable yet remarkable connection between social work and clean energy, inviting further exploration and discovery in an arena where unexpected pairings may offer valuable insights, much like finding a diamond in the rough of scientific inquiry.

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 social workers in West Virginia and Kerosene used in Ghana. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
The Social Work-Clean Energy Connection: An Unlikely Pairing

[[ABSTRACT]]
This research aims to investigate the seemingly bizarre connection between the number of social workers in West Virginia and the consumption of kerosene in Ghana. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, our research team conducted a rigorous analysis covering the period from 2003 to 2021. Surprisingly, we discovered a significant correlation coefficient of 0.8322550 and a p-value of less than 0.01, indicating a robust relationship between these seemingly disparate variables. While initially met with skepticism, our findings suggest that there is an uncanny link between the deployment of social workers in one location and the consumption of kerosene in another. We propose that further investigation into the underlying mechanisms driving this unexpected relationship could provide valuable insights for both social work and energy policy.

[[INTRDUCTION]]
The quest for knowledge has led researchers down many unexpected paths, from the depths of the ocean to the far reaches of outer space. In the realm of social sciences, the pursuit of understanding human behavior often uncovers peculiar and seemingly improbable connections. The intertwining of variables in statistical analysis can reveal surprising relationships, much like stumbling upon a hidden pathway in a labyrinth.
The study at hand delves into the enigmatic association between the number of social workers in West Virginia and the consumption of kerosene in Ghana. At first glance, one may question the rationale behind examining these two seemingly unrelated entities. However, as researchers, we are often called to venture into uncharted territory, embracing the unconventional and the unexpected. After all, who would have thought that the humble bean sprout would provide a key to unlocking a mystery in molecular biology, or that the study of fruit flies would yield insights into genetics?
Employing a comprehensive dataset sourced from the Bureau of Labor Statistics and the Energy Information Administration, our research team embarked on a rigorous exploration of the relationship between social work deployment and kerosene consumption. With ardent dedication to statistical methods and quantitative analysis, we meticulously combed through the data spanning nearly two decades. Amidst the sea of numbers and coefficients, patterns began to emerge, beckoning us to unravel the tangled web of connections.
As we navigated through the labyrinth of regression analyses and correlation coefficients, we were met with a surprising revelation - a significant correlation coefficient of 0.8322550 and a p-value of less than 0.01. This unanticipated discovery sent ripples through the realm of social science research, beckoning us to ponder the mysterious dance between social workers in one corner of the world and the flickering flames of kerosene lamps in another.
This study challenges us to expand our conceptual horizons, compelling us to consider the intricate interplay between seemingly disparate realms. As we embark on this scholarly journey, we invite fellow researchers and inquisitive minds to join us in unraveling the riddles that lie at the intersection of social work and clean energy. After all, in the labyrinth of research, one may stumble upon unexpected treasures buried beneath a mound of data. Let us venture forth, armed with curiosity and statistical rigor, as we seek to shed light on this unlikely pairing.

[[RESULTS]]
A thorough analysis of the data revealed a strong positive correlation between the number of social workers in West Virginia and the consumption of kerosene in Ghana over the period from 2003 to 2021. The correlation coefficient of 0.8322550 suggests a robust relationship between these seemingly unrelated variables, akin to finding a connection between the speed of a tortoise and the global consumption of ice cream.
Furthermore, the coefficient of determination, indicated by an r-squared value of 0.6926484, implies that approximately 69.26% of the variation in kerosene consumption in Ghana can be explained by the number of social workers in West Virginia. This level of explanatory power rivals the predictability of a morning routine or the inevitability of encountering a statistical p-value in research discussions.
The statistical significance of the correlation was confirmed, with a p-value of less than 0.01, reinforcing the strength of the relationship between these two disparate variables. This p-value would even impress the most discerning of statistical connoisseurs, akin to uncovering a rare artifact in a world of digital abundance.
The scatterplot (Fig. 1) visually encapsulates the striking correlation between the number of social workers in West Virginia and the consumption of kerosene in Ghana, providing a graphical representation of the unexpected bond between these two entities. It's as if the plot itself exudes the sentiment "who would have thought?" in its depiction of this improbable connection.
These findings challenge conventional wisdom and beckon the research community to further explore the mechanisms underlying this remarkable link. Ultimately, this study sheds light on the unanticipated interplay between social work and clean energy, inviting further scholarly inquiry and perhaps the occasional bemused chuckle from the academic community.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation has unveiled an intriguing link between the number of social workers in West Virginia and the consumption of kerosene in Ghana. While at first glance, this association may seem as unlikely as finding a statistical anomaly in a field of quantitative research, our findings present a compelling case for further exploration.

The substantial correlation coefficient of 0.8322550 and the impressively low p-value of less than 0.01 have demonstrated a strong relationship between these seemingly incongruous variables. This result is as surprising as coming across a statistically significant result on the first try or finding a unicorn in a forest of data.

The coefficient of determination, indicated by an r-squared value of 0.6926484, further underscores the remarkable explanatory power of the deployment of social workers in West Virginia on kerosene consumption in Ghana. This level of predictability rivals the certainty of encountering a correlation in a research paper or the ubiquity of coffee in academic settings.

The scatterplot (Fig. 1) visually encapsulates the striking correlation, serving as a graphic representation of this unexpected alliance. It's as unexpected as finding a correlation between the number of library science majors and the global market price of avocados. This peculiar relationship challenges conventional wisdom and invites further investigation into the underlying mechanisms, providing a glimmer of amusement in the otherwise serious landscape of academic inquiry.

Based on our findings, we assert with confidence that no further research on this specific connection is warranted. The likelihood of stumbling upon an even more improbable link between disparate variables is as rare as finding a statistical outlier in a sea of data. Further studies would be akin to searching for a needle in a haystack made entirely of statistical significance.

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 The Social Work-Clean Energy Connection: An Unlikely Pairing. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Sustainable Systems and Social Solutions

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 The Social Work-Clean Energy Connection: An Unlikely Pairing. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Sustainable Social Solutions

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 The Social Work-Clean Energy Connection: An Unlikely Pairing with an abstract of This research aims to investigate the seemingly bizarre connection between the number of social workers in West Virginia and the consumption of kerosene in Ghana. Utilizing data from the Bureau of Labor Statistics and the Energy Information Administration, our research team conducted a rigorous analysis covering the period from 2003 to 2021. Surprisingly, we discovered a significant correlation coefficient of 0.8322550 and a p-value of less than 0.01, indicating a robust relationship between these seemingly disparate variables. While initially met with skepticism, our findings suggest that there is an uncanny link between the deployment of social workers in one location and the consumption of kerosene in another. We propose that further investigation into the underlying mechanisms driving this unexpected relationship could provide valuable insights for both social work and energy policy.

ChatGPT:

social work, clean energy, correlation analysis, Bureau of Labor Statistics, Energy Information Administration, West Virginia, Ghana, kerosene consumption, social workers, energy policy, unexpected relationship

*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 social workers in West Virginia
Detailed data title: BLS estimate of child, family, and school social workers in West Virginia
Source: Bureau of Larbor Statistics
See what else correlates with The number of social workers in West Virginia

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

Correlation r = 0.8322550 (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 = 0.6926484 (Coefficient of determination)
This means 69.3% of the change in the one variable (i.e., Kerosene used in Ghana) is predictable based on the change in the other (i.e., The number of social workers in West Virginia) over the 19 years from 2003 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 9.9E-6.^Show 0.0000098927339000307480000000
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 0.83 in 0.00099% of random cases. Said differently, if you correlated 101,084 random variables^Note You don't actually need 101 thousand 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 18 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 18 because we have two variables measured over a period of 19 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.

[ 0.61, 0.93 ] 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	2021
The number of social workers in West Virginia (Laborers)	2590	2350	2340	2730	2690	5240	3380	2450	2360	2040	1860	1630	1290	1030	960	1350	620	2020	2180
Kerosene used in Ghana (Million Barrels/Day)	2.32959	2.34434	1.86367	1.37658	2.58373	3.56932	1.88485	1.60953	1.31304	0.97153	0.592986	1.18597	1	0.543151	0.0434521	0.716959	0.260712	0.760411	0.10863

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([2590,2350,2340,2730,2690,5240,3380,2450,2360,2040,1860,1630,1290,1030,960,1350,620,2020,2180,])
array_2 = np.array([2.32959,2.34434,1.86367,1.37658,2.58373,3.56932,1.88485,1.60953,1.31304,0.97153,0.592986,1.18597,1,0.543151,0.0434521,0.716959,0.260712,0.760411,0.10863,])
array_1_name = "The number of social workers in West Virginia"
array_2_name = "Kerosene used in Ghana"

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

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

Reuseable content

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

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

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

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

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

Download images for these variables:

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

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

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only The number of social workers in West Virginia
Line chart for only Kerosene used in Ghana
AI-generated correlation image
The spurious research paper: The Social Work-Clean Energy Connection: An Unlikely Pairing

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Correlation ID: 1993 · Black Variable ID: 15935 · Red Variable ID: 24465


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