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AI explanation
As the name Arthur gained popularity, more people were subconsciously reminded of King Arthur and his legendary Knights of the Round Table. These individuals, feeling a sudden surge of chivalry and valor, decided to channel their inner knights and joust with the air, thereby creating gusts of wind. This unexpected medieval reenactment phenomenon led to a boost in wind power generation in Ukraine, as the country found itself with an abundance of free, renewable energy courtesy of the new-age Arthurs and their noble aspirations. Who would've thought that the winds of change were actually propelled by a wave of Arthurian inspiration?
Model: dalle-3
Prompt: Generate an image that resembles a TIME magazine cover, featuring a playground in Ukraine with wind turbines gracefully spinning in the breeze. Among the playing children, several kids are wearing shirts with the name "Arthur" on them, representing the name's popularity in the country. The focus should be on the natural setting and the playful atmosphere of the children, with the wind turbines blending into the background. 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: Popularity of the first name Arthur and Wind power generated in Ukraine. 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: Cute 2D animation A TIME magazine cover image Minimalism Glitch art Steampunk
ChatGPT: A TIME magazine cover image
Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Popularity of the first name Arthur and Wind power generated in Ukraine together. Keep the description simple.
ChatGPT: A playground in Ukraine is filled with wind turbines spinning gracefully in the breeze. Among the children playing, several kids are wearing shirts with the name "Arthur" on them, indicating the popularity of the name in the country.
Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A TIME magazine cover image ] and depicting this scene: A playground in Ukraine is filled with wind turbines spinning gracefully in the breeze. Among the children playing, several kids are wearing shirts with the name "Arthur" on them, indicating the popularity of the name in the country. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that resembles a TIME magazine cover, featuring a playground in Ukraine with wind turbines gracefully spinning in the breeze. Among the playing children, several kids are wearing shirts with the name "Arthur" on them, representing the name's popularity in the country. The focus should be on the natural setting and the playful atmosphere of the children, with the wind turbines blending into the background.
*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.
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 Popularity of the first name Arthur caused Wind power generated in Ukraine to increase. Make it feel plausible.
AI academic paper
 |
Blowing in the Wind: A Gust of Arthur-nomics in Ukraine's Wind Power Generation The Journal of Renewable Energy Economics and Policy Jan 2024 Reminder: This paper is AI-generated. Not real! |
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.You write titles and abstracts of goofy academic research papers.
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, and add unexpected twists in your writing.
Please draft the title and abstract of an academic research paper presenting the findings of the connection between Popularity of the first name Arthur and Wind power generated in Ukraine. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Make the title punny if you can come up with clever pun. If not, make it sound serious.
Your research team used data from US Social Security Administration and Energy Information Administration to assess this nagging question. You found a correlation coefficient of 0.9654601 and p < 0.01 for 1998 to 2021.
Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)
[[TITLE]]
Lorem ipsum dolor sit
[[ABSTRACT]]
Lorem ipsum dolor sit
ChatGPT:
[[TITLE]]
Blowing in the Wind: A Gust of Arthur-nomics in Ukraine's Wind Power Generation
[[ABSTRACT]]
Pronounced Arthur-nomics looks to delve into the curious, yet elusive, connection between the popularity of the first name “Arthur” and wind power generation in Ukraine. Leveraging the comprehensive data from the US Social Security Administration and the Energy Information Administration, our study uncovers a staggering correlation coefficient of 0.9654601 and a significant p-value of less than 0.01 during the period of 1998 to 2021. While the causation behind this correlation remains a whirlwind of mystery, our findings provide a gust of insight into the peculiar interplay between nomenclature trends and renewable energy production. Indeed, our research breathes new life into the age-old saying – “What’s in a name?” – and suggests that, perhaps, a lot more than meets the eye is blowing in the wind.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.You draft the introduction 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 introduction section of an academic research paper presenting the findings of the connection between Popularity of the first name Arthur and Wind power generated in Ukraine. 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.
Here is the title and abstract of the paper:
[[TITLE]]
Blowing in the Wind: A Gust of Arthur-nomics in Ukraine's Wind Power Generation
[[ABSTRACT]]
Pronounced Arthur-nomics looks to delve into the curious, yet elusive, connection between the popularity of the first name “Arthur” and wind power generation in Ukraine. Leveraging the comprehensive data from the US Social Security Administration and the Energy Information Administration, our study uncovers a staggering correlation coefficient of 0.9654601 and a significant p-value of less than 0.01 during the period of 1998 to 2021. While the causation behind this correlation remains a whirlwind of mystery, our findings provide a gust of insight into the peculiar interplay between nomenclature trends and renewable energy production. Indeed, our research breathes new life into the age-old saying – “What’s in a name?” – and suggests that, perhaps, a lot more than meets the eye is blowing in the wind.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
[[INTRODUCTION]]
The intersection of nomenclature and renewable energy has long been an underexplored territory in the vast landscape of research. While the majority of studies have focused on more conventional variables such as geographic location, climate conditions, and technological advancements, there remains a lingering curiosity about the potential impact of something as seemingly irrelevant as personal names on the generation of wind power. This study aims to unravel the enigmatic relationship between the popularity of the first name "Arthur" and the wind power generated in Ukraine.
It is well established that names hold significant cultural and historical significance, influencing everything from personal identity to societal perceptions. However, the notion that the choice of a name could have implications for a country's renewable energy sector seems, at first glance, as ethereal as the very wind that drives the turbines. Nevertheless, as we shall demonstrate, the winds of statistical analysis have blown us in the direction of uncovering a surprisingly robust connection.
With a treasure trove of data from the US Social Security Administration providing insight into the ebb and flow of the name "Arthur" over time, and the Energy Information Administration offering a gusty account of wind power generation in Ukraine, we set out to apply rigorous statistical methods to probe for any whispers of correlation. What we uncovered was nothing short of a tempestuous revelation.
Our findings indicate a remarkably high correlation coefficient of 0.9654601, approaching near-mythical levels in the realm of statistical associations. Furthermore, the accompanying p-value of less than 0.01 provides compelling evidence against the null hypothesis, leaving us little choice but to confront the existence of a striking relationship. However, as with any tantalizing revelation, caution is warranted in the interpretation of these results.
In the pages that follow, we delve into the wind-swept labyrinth of data analysis, pondering the potential mechanisms behind this unexpected connection. Keeping in mind the wise adage that correlation does not imply causation, we navigate through the zephyrs of speculation while firmly anchored in the bedrock of scientific inquiry. Our quest for understanding propels us through the whirlwinds of statistical significance, as we seek to illuminate the murky depths of this curious correlation.
So, buckle up and prepare to be whisked away on a scholarly journey that promises to unveil the windswept secrets at the intersection of nomenclature and renewable energy. As we embark on this scholarly odyssey, we shall endeavor to chart a course that not only brings clarity to this unforeseen association but also adds a breath of fresh air to the scientific discourse. After all, as researchers, it’s our job to weather the storm of curiosity by harnessing the power of data and statistical reasoning, in the pursuit of unraveling even the most whimsical and unexpected connections.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Popularity of the first name Arthur and Wind power generated in Ukraine. 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]]
Blowing in the Wind: A Gust of Arthur-nomics in Ukraine's Wind Power Generation
[[ABSTRACT]]
Pronounced Arthur-nomics looks to delve into the curious, yet elusive, connection between the popularity of the first name “Arthur” and wind power generation in Ukraine. Leveraging the comprehensive data from the US Social Security Administration and the Energy Information Administration, our study uncovers a staggering correlation coefficient of 0.9654601 and a significant p-value of less than 0.01 during the period of 1998 to 2021. While the causation behind this correlation remains a whirlwind of mystery, our findings provide a gust of insight into the peculiar interplay between nomenclature trends and renewable energy production. Indeed, our research breathes new life into the age-old saying – “What’s in a name?” – and suggests that, perhaps, a lot more than meets the eye is blowing in the wind.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of nomenclature and renewable energy has long been an underexplored territory in the vast landscape of research. While the majority of studies have focused on more conventional variables such as geographic location, climate conditions, and technological advancements, there remains a lingering curiosity about the potential impact of something as seemingly irrelevant as personal names on the generation of wind power. This study aims to unravel the enigmatic relationship between the popularity of the first name "Arthur" and the wind power generated in Ukraine.
It is well established that names hold significant cultural and historical significance, influencing everything from personal identity to societal perceptions. However, the notion that the choice of a name could have implications for a country's renewable energy sector seems, at first glance, as ethereal as the very wind that drives the turbines. Nevertheless, as we shall demonstrate, the winds of statistical analysis have blown us in the direction of uncovering a surprisingly robust connection.
With a treasure trove of data from the US Social Security Administration providing insight into the ebb and flow of the name "Arthur" over time, and the Energy Information Administration offering a gusty account of wind power generation in Ukraine, we set out to apply rigorous statistical methods to probe for any whispers of correlation. What we uncovered was nothing short of a tempestuous revelation.
Our findings indicate a remarkably high correlation coefficient of 0.9654601, approaching near-mythical levels in the realm of statistical associations. Furthermore, the accompanying p-value of less than 0.01 provides compelling evidence against the null hypothesis, leaving us little choice but to confront the existence of a striking relationship. However, as with any tantalizing revelation, caution is warranted in the interpretation of these results.
In the pages that follow, we delve into the wind-swept labyrinth of data analysis, pondering the potential mechanisms behind this unexpected connection. Keeping in mind the wise adage that correlation does not imply causation, we navigate through the zephyrs of speculation while firmly anchored in the bedrock of scientific inquiry. Our quest for understanding propels us through the whirlwinds of statistical significance, as we seek to illuminate the murky depths of this curious correlation.
So, buckle up and prepare to be whisked away on a scholarly journey that promises to unveil the windswept secrets at the intersection of nomenclature and renewable energy. As we embark on this scholarly odyssey, we shall endeavor to chart a course that not only brings clarity to this unforeseen association but also adds a breath of fresh air to the scientific discourse. After all, as researchers, it’s our job to weather the storm of curiosity by harnessing the power of data and statistical reasoning, in the pursuit of unraveling even the most whimsical and unexpected connections.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
The wind, as famously sung by Bob Dylan, indeed carries tales of perplexity and intrigue as it whisks its way through the nooks and crannies of our world. In a similar vein, the inquiry into the association between the popularity of the first name "Arthur" and wind power generation in Ukraine has sent researchers on a gusty odyssey of scholarly exploration. While the topic at hand may appear to be as whimsical as a zephyr, a review of the existing literature unveils a range of unexpected twists and turns, akin to navigating a tempest in uncharted waters.
Smith and Doe (2015) delved into the influence of personal names on societal trends, uncovering fascinating insights into the potential ripple effects of nomenclature shifts. However, little did they know that their explorations would later serve as a mere breeze in comparison to the gale force revelations we are about to unveil. Jones (2017) furthered this line of inquiry by examining the historical trajectories of popular names and their correlation with economic variables, unwittingly laying the groundwork for our current whirlwind investigation into the Arthur-nomics of wind power generation in Ukraine.
Turning now to the broader research landscape, books such as "The Name Book: Over 10,000 Names - Their Meanings, Origins, and Spiritual Significance" by Dorothy Astoria and "Power Generation from Wind Energy: Economics, Politics, and Methods" by R. Hunt and R. Prabhu shed light on the multifaceted dimensions of names and wind power, albeit separately. Yet, it is the unexpected crossover of these seemingly disparate topics that serves as the pivot point for our scholarly endeavor, much like the sudden gust of wind that catches one off guard.
In the realm of fiction, novels with titles like "The Wind in the Willows" by Kenneth Grahame and "The Name of the Wind" by Patrick Rothfuss beckon with a whisper of relevance to our puzzling investigation. While these literary works may not offer concrete empirical evidence, their titles serve as playful reminders of the interconnectedness of wind and nomenclature, even in the realm of imagination.
Moreover, the authors of this study, in the pursuit of scholarly thoroughness, have invested countless hours in tangentially related cinematic pursuits. Movies such as "Gone with the Wind" and "The Wind Rises" have provided a captivating backdrop to our academic deliberations, if only for the fleeting moments of respite they offer amid the whirlwind of data analysis and statistical inquiries.
As we peel back the layers of this blustery conundrum, it becomes increasingly evident that our scholarly voyage is not merely a quest for correlation, but a spirited adventure in the uncharted territory of Arthur-nomics. Buckle up, dear reader, for the winds of academic inquiry are about to carry us to unforeseen heights of statistical revelation and whimsical discovery.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Popularity of the first name Arthur and Wind power generated in Ukraine. 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 US Social Security Administration and Energy Information Administration . You used data from 1998 to 2021
Make up the research methods you don't know. Make them a bit goofy and convoluted.
Here is the title, abstract, and introduction of the paper:
[[TITLE]]
Blowing in the Wind: A Gust of Arthur-nomics in Ukraine's Wind Power Generation
[[ABSTRACT]]
Pronounced Arthur-nomics looks to delve into the curious, yet elusive, connection between the popularity of the first name “Arthur” and wind power generation in Ukraine. Leveraging the comprehensive data from the US Social Security Administration and the Energy Information Administration, our study uncovers a staggering correlation coefficient of 0.9654601 and a significant p-value of less than 0.01 during the period of 1998 to 2021. While the causation behind this correlation remains a whirlwind of mystery, our findings provide a gust of insight into the peculiar interplay between nomenclature trends and renewable energy production. Indeed, our research breathes new life into the age-old saying – “What’s in a name?” – and suggests that, perhaps, a lot more than meets the eye is blowing in the wind.
[[INTRODUCTION]]
The intersection of nomenclature and renewable energy has long been an underexplored territory in the vast landscape of research. While the majority of studies have focused on more conventional variables such as geographic location, climate conditions, and technological advancements, there remains a lingering curiosity about the potential impact of something as seemingly irrelevant as personal names on the generation of wind power. This study aims to unravel the enigmatic relationship between the popularity of the first name "Arthur" and the wind power generated in Ukraine.
It is well established that names hold significant cultural and historical significance, influencing everything from personal identity to societal perceptions. However, the notion that the choice of a name could have implications for a country's renewable energy sector seems, at first glance, as ethereal as the very wind that drives the turbines. Nevertheless, as we shall demonstrate, the winds of statistical analysis have blown us in the direction of uncovering a surprisingly robust connection.
With a treasure trove of data from the US Social Security Administration providing insight into the ebb and flow of the name "Arthur" over time, and the Energy Information Administration offering a gusty account of wind power generation in Ukraine, we set out to apply rigorous statistical methods to probe for any whispers of correlation. What we uncovered was nothing short of a tempestuous revelation.
Our findings indicate a remarkably high correlation coefficient of 0.9654601, approaching near-mythical levels in the realm of statistical associations. Furthermore, the accompanying p-value of less than 0.01 provides compelling evidence against the null hypothesis, leaving us little choice but to confront the existence of a striking relationship. However, as with any tantalizing revelation, caution is warranted in the interpretation of these results.
In the pages that follow, we delve into the wind-swept labyrinth of data analysis, pondering the potential mechanisms behind this unexpected connection. Keeping in mind the wise adage that correlation does not imply causation, we navigate through the zephyrs of speculation while firmly anchored in the bedrock of scientific inquiry. Our quest for understanding propels us through the whirlwinds of statistical significance, as we seek to illuminate the murky depths of this curious correlation.
So, buckle up and prepare to be whisked away on a scholarly journey that promises to unveil the windswept secrets at the intersection of nomenclature and renewable energy. As we embark on this scholarly odyssey, we shall endeavor to chart a course that not only brings clarity to this unforeseen association but also adds a breath of fresh air to the scientific discourse. After all, as researchers, it’s our job to weather the storm of curiosity by harnessing the power of data and statistical reasoning, in the pursuit of unraveling even the most whimsical and unexpected connections.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
In order to untangle the mysterious dance between the popularity of the first name "Arthur" and wind power generation in Ukraine, our research team embarked on a gusty journey of data collection and analysis. The first step in our quest involved obtaining historical records of the frequency of the name "Arthur" from the US Social Security Administration. This veritable treasure trove of nomenclatural data allowed us to track the undulating tides of Arthurian nomenclature from 1998 to 2021. Our dedicated team then set sail upon the vast sea of internet data, casting our virtual nets to capture insights into the generation of wind power in Ukraine, as reported by the venerable Energy Information Administration.
With our datasets secured, we launched into the tempestuous seas of statistical analysis. The first order of business was to calculate the annual popularity of the name "Arthur" and the corresponding wind power generation in Ukraine. We harnessed the elemental power of time series analysis to trace the ebbs and flows of these two distinct yet curiously intertwined variables. As we delved into this intriguing pursuit, we utilized the tools of correlation analysis to ascertain the strength and direction of any potential relationship between the frequency of the name "Arthur" and the wind power generated in Ukraine.
With the calculated correlation coefficient in hand, we then braved the tumultuous waters of hypothesis testing. Our trusty ship of statistical inquiry docked at the port of significance testing, where we waved our flag of p-values to gauge the statistical strength of the observed correlation. Adhering to the time-honored customs of academic research, we maintained a keen eye for lurking confounding variables and potential sources of bias, seeking to navigate through the treacherous currents of spurious correlations and unwarranted causality.
To bolster the robustness of our findings and chart a course towards rigorous conclusions, we also engaged in a spot of sensitivity analysis. This exercise allowed us to gauge the stability of our results across different subsets of the data and weather the storm of potential outliers or aberrant trends. Armed with these methodological bulwarks, we moored our vessel of research at the docks of statistical inference, ready to elucidate the unsuspecting scientific community with the windswept revelations of our findings.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Popularity of the first name Arthur and Wind power generated in Ukraine. 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 US Social Security Administration and Energy Information Administration .
For the time period 1998 to 2021, you found a correlation 0.9654601, r-squared of 0.9321133, 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]]
Blowing in the Wind: A Gust of Arthur-nomics in Ukraine's Wind Power Generation
[[ABSTRACT]]
Pronounced Arthur-nomics looks to delve into the curious, yet elusive, connection between the popularity of the first name “Arthur” and wind power generation in Ukraine. Leveraging the comprehensive data from the US Social Security Administration and the Energy Information Administration, our study uncovers a staggering correlation coefficient of 0.9654601 and a significant p-value of less than 0.01 during the period of 1998 to 2021. While the causation behind this correlation remains a whirlwind of mystery, our findings provide a gust of insight into the peculiar interplay between nomenclature trends and renewable energy production. Indeed, our research breathes new life into the age-old saying – “What’s in a name?” – and suggests that, perhaps, a lot more than meets the eye is blowing in the wind.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intersection of nomenclature and renewable energy has long been an underexplored territory in the vast landscape of research. While the majority of studies have focused on more conventional variables such as geographic location, climate conditions, and technological advancements, there remains a lingering curiosity about the potential impact of something as seemingly irrelevant as personal names on the generation of wind power. This study aims to unravel the enigmatic relationship between the popularity of the first name "Arthur" and the wind power generated in Ukraine.
It is well established that names hold significant cultural and historical significance, influencing everything from personal identity to societal perceptions. However, the notion that the choice of a name could have implications for a country's renewable energy sector seems, at first glance, as ethereal as the very wind that drives the turbines. Nevertheless, as we shall demonstrate, the winds of statistical analysis have blown us in the direction of uncovering a surprisingly robust connection.
With a treasure trove of data from the US Social Security Administration providing insight into the ebb and flow of the name "Arthur" over time, and the Energy Information Administration offering a gusty account of wind power generation in Ukraine, we set out to apply rigorous statistical methods to probe for any whispers of correlation. What we uncovered was nothing short of a tempestuous revelation.
Our findings indicate a remarkably high correlation coefficient of 0.9654601, approaching near-mythical levels in the realm of statistical associations. Furthermore, the accompanying p-value of less than 0.01 provides compelling evidence against the null hypothesis, leaving us little choice but to confront the existence of a striking relationship. However, as with any tantalizing revelation, caution is warranted in the interpretation of these results.
In the pages that follow, we delve into the wind-swept labyrinth of data analysis, pondering the potential mechanisms behind this unexpected connection. Keeping in mind the wise adage that correlation does not imply causation, we navigate through the zephyrs of speculation while firmly anchored in the bedrock of scientific inquiry. Our quest for understanding propels us through the whirlwinds of statistical significance, as we seek to illuminate the murky depths of this curious correlation.
So, buckle up and prepare to be whisked away on a scholarly journey that promises to unveil the windswept secrets at the intersection of nomenclature and renewable energy. As we embark on this scholarly odyssey, we shall endeavor to chart a course that not only brings clarity to this unforeseen association but also adds a breath of fresh air to the scientific discourse. After all, as researchers, it’s our job to weather the storm of curiosity by harnessing the power of data and statistical reasoning, in the pursuit of unraveling even the most whimsical and unexpected connections.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
The statistical analysis of the data revealed a truly remarkable correlation between the popularity of the first name "Arthur" and wind power generation in Ukraine. Over the period from 1998 to 2021, our findings unveiled a correlation coefficient of 0.9654601, indicating a strong positive relationship between these seemingly unrelated variables. This correlation was accompanied by an r-squared value of 0.9321133, suggesting that a substantial proportion of the variability in wind power generation can be explained by the popularity of the name "Arthur." Notably, the p-value was less than 0.01, providing convincing evidence of the significance of this correlation.
In Fig. 1, the scatterplot exhibits a conspicuous linear pattern, showcasing the striking association between the frequency of the name "Arthur" and the wind power generated in Ukraine. The data points align themselves in a manner that is as undeniable as the force of the wind itself, reinforcing the robustness of the observed correlation.
Despite the tantalizing nature of our findings, we must approach these results with the caution befitting a phenomenon as unexpected as this one. While the statistical analysis pointed to a clear relationship, we cannot, with absolute certainty, assert a causal link between the popularity of the name "Arthur" and wind power generation in Ukraine. As researchers, we are mindful of the infamous caution that correlation does not imply causation, and we remain open to the possibility of alternative explanations for this intriguing association.
Nonetheless, the winds of statistical analysis have blown us in a direction that provokes further inquiry. The discovery of such an astonishing correlation prompts us to consider the broader implications of nomenclature in the realm of renewable energy. This unexpected connection stands as a testament to the unyielding surprises that can emerge from the analysis of seemingly disparate variables, reminding us that in the grand tapestry of scientific inquiry, the most unanticipated associations may carry the weight of significance.
In summary, our study has unearthed a compelling correlation between the popularity of the first name "Arthur" and wind power generation in Ukraine. While the exact mechanisms underlying this association remain shrouded in the mists of speculation, our findings ignite a sense of curiosity and urge for further investigation into the intricate interplay between nomenclature trends and renewable energy production. Indeed, the winds of statistical analysis have carried us to a place where the question, "What's in a name?" takes on a whole new meaning, tantalizing us with the possibility that the answer may be blowing in the wind.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Popularity of the first name Arthur and Wind power generated in Ukraine. 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]]
Blowing in the Wind: A Gust of Arthur-nomics in Ukraine's Wind Power Generation
[[ABSTRACT]]
Pronounced Arthur-nomics looks to delve into the curious, yet elusive, connection between the popularity of the first name “Arthur” and wind power generation in Ukraine. Leveraging the comprehensive data from the US Social Security Administration and the Energy Information Administration, our study uncovers a staggering correlation coefficient of 0.9654601 and a significant p-value of less than 0.01 during the period of 1998 to 2021. While the causation behind this correlation remains a whirlwind of mystery, our findings provide a gust of insight into the peculiar interplay between nomenclature trends and renewable energy production. Indeed, our research breathes new life into the age-old saying – “What’s in a name?” – and suggests that, perhaps, a lot more than meets the eye is blowing in the wind.
[[LITERATURE REVIEW]]
The wind, as famously sung by Bob Dylan, indeed carries tales of perplexity and intrigue as it whisks its way through the nooks and crannies of our world. In a similar vein, the inquiry into the association between the popularity of the first name "Arthur" and wind power generation in Ukraine has sent researchers on a gusty odyssey of scholarly exploration. While the topic at hand may appear to be as whimsical as a zephyr, a review of the existing literature unveils a range of unexpected twists and turns, akin to navigating a tempest in uncharted waters.
Smith and Doe (2015) delved into the influence of personal names on societal trends, uncovering fascinating insights into the potential ripple effects of nomenclature shifts. However, little did they know that their explorations would later serve as a mere breeze in comparison to the gale force revelations we are about to unveil. Jones (2017) furthered this line of inquiry by examining the historical trajectories of popular names and their correlation with economic variables, unwittingly laying the groundwork for our current whirlwind investigation into the Arthur-nomics of wind power generation in Ukraine.
Turning now to the broader research landscape, books such as "The Name Book: Over 10,000 Names - Their Meanings, Origins, and Spiritual Significance" by Dorothy Astoria and "Power Generation from Wind Energy: Economics, Politics, and Methods" by R. Hunt and R. Prabhu shed light on the multifaceted dimensions of names and wind power, albeit separately. Yet, it is the unexpected crossover of these seemingly disparate topics that serves as the pivot point for our scholarly endeavor, much like the sudden gust of wind that catches one off guard.
In the realm of fiction, novels with titles like "The Wind in the Willows" by Kenneth Grahame and "The Name of the Wind" by Patrick Rothfuss beckon with a whisper of relevance to our puzzling investigation. While these literary works may not offer concrete empirical evidence, their titles serve as playful reminders of the interconnectedness of wind and nomenclature, even in the realm of imagination.
Moreover, the authors of this study, in the pursuit of scholarly thoroughness, have invested countless hours in tangentially related cinematic pursuits. Movies such as "Gone with the Wind" and "The Wind Rises" have provided a captivating backdrop to our academic deliberations, if only for the fleeting moments of respite they offer amid the whirlwind of data analysis and statistical inquiries.
As we peel back the layers of this blustery conundrum, it becomes increasingly evident that our scholarly voyage is not merely a quest for correlation, but a spirited adventure in the uncharted territory of Arthur-nomics. Buckle up, dear reader, for the winds of academic inquiry are about to carry us to unforeseen heights of statistical revelation and whimsical discovery.
[[RESULTS]]
The statistical analysis of the data revealed a truly remarkable correlation between the popularity of the first name "Arthur" and wind power generation in Ukraine. Over the period from 1998 to 2021, our findings unveiled a correlation coefficient of 0.9654601, indicating a strong positive relationship between these seemingly unrelated variables. This correlation was accompanied by an r-squared value of 0.9321133, suggesting that a substantial proportion of the variability in wind power generation can be explained by the popularity of the name "Arthur." Notably, the p-value was less than 0.01, providing convincing evidence of the significance of this correlation.
In Fig. 1, the scatterplot exhibits a conspicuous linear pattern, showcasing the striking association between the frequency of the name "Arthur" and the wind power generated in Ukraine. The data points align themselves in a manner that is as undeniable as the force of the wind itself, reinforcing the robustness of the observed correlation.
Despite the tantalizing nature of our findings, we must approach these results with the caution befitting a phenomenon as unexpected as this one. While the statistical analysis pointed to a clear relationship, we cannot, with absolute certainty, assert a causal link between the popularity of the name "Arthur" and wind power generation in Ukraine. As researchers, we are mindful of the infamous caution that correlation does not imply causation, and we remain open to the possibility of alternative explanations for this intriguing association.
Nonetheless, the winds of statistical analysis have blown us in a direction that provokes further inquiry. The discovery of such an astonishing correlation prompts us to consider the broader implications of nomenclature in the realm of renewable energy. This unexpected connection stands as a testament to the unyielding surprises that can emerge from the analysis of seemingly disparate variables, reminding us that in the grand tapestry of scientific inquiry, the most unanticipated associations may carry the weight of significance.
In summary, our study has unearthed a compelling correlation between the popularity of the first name "Arthur" and wind power generation in Ukraine. While the exact mechanisms underlying this association remain shrouded in the mists of speculation, our findings ignite a sense of curiosity and urge for further investigation into the intricate interplay between nomenclature trends and renewable energy production. Indeed, the winds of statistical analysis have carried us to a place where the question, "What's in a name?" takes on a whole new meaning, tantalizing us with the possibility that the answer may be blowing in the wind.
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Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
Our study has blown open a realm of investigation that, much like a zephyr, may have seemed whimsical at first glance but has, in fact, revealed a gust of thought-provoking insights. Building on the prior research, which may have appeared as light as a breeze, we have substantiated and indeed elevated the discourse on the correlation between the popularity of the first name "Arthur" and wind power generation in Ukraine.
Our findings, characterized by a correlation coefficient of 0.9654601 and a p-value of less than 0.01, lend substantial empirical support to the hitherto unexplored phenomenon of Arthur-nomics. While the unlikely association between nomenclature trends and renewable energy production may, to some, seem as ephemeral as the wind itself, our robust statistical analysis has placed this correlation on a solid foundation.
Moreover, the literature review, which artfully navigated the tempest in uncharted waters, introduced some unconventional yet intriguing insights. Smith and Doe's exploration of the influence of personal names on societal trends pales in comparison to the gale-force revelations we have unearthed. Similarly, the historical trajectories of popular names explored by Jones unwittingly laid the groundwork for our own whirlwind investigation into the Arthur-nomics of wind power generation in Ukraine.
The unexpected crossover of seemingly disparate topics, akin to the sudden gust of wind that catches one off guard, has come to the fore in our scholarly endeavor. Much like the abrupt change in winds that takes sailers by surprise, our findings have invoked a sense of curiosity and an urge for further investigation.
Despite the caution that correlation does not imply causation, we cannot help but bask in the striking nature of this association, which aligns itself as undeniably as the force of the wind itself. While we remain open to the possibility of alternative explanations, we find ourselves in a whirlwind of curiosity, propelled by the robustness of our observed correlation.
In the grand tapestry of scientific inquiry, our study serves as a poignant reminder that the most unanticipated associations may indeed carry the weight of significance. The winds of statistical analysis have carried us to a place where the question, "What's in a name?" takes on a whole new meaning, tantalizing us with the possibility that the answer may be blowing in the wind.
Indeed, as we continue to navigate the unforeseen heights of statistical revelation and whimsical discovery, our journey through the uncharted territory of Arthur-nomics promises to be a fruitful and enlivening odyssey.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Popularity of the first name Arthur and Wind power generated in Ukraine. 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]]
Blowing in the Wind: A Gust of Arthur-nomics in Ukraine's Wind Power Generation
[[ABSTRACT]]
Pronounced Arthur-nomics looks to delve into the curious, yet elusive, connection between the popularity of the first name “Arthur” and wind power generation in Ukraine. Leveraging the comprehensive data from the US Social Security Administration and the Energy Information Administration, our study uncovers a staggering correlation coefficient of 0.9654601 and a significant p-value of less than 0.01 during the period of 1998 to 2021. While the causation behind this correlation remains a whirlwind of mystery, our findings provide a gust of insight into the peculiar interplay between nomenclature trends and renewable energy production. Indeed, our research breathes new life into the age-old saying – “What’s in a name?” – and suggests that, perhaps, a lot more than meets the eye is blowing in the wind.
[[INTRDUCTION]]
The intersection of nomenclature and renewable energy has long been an underexplored territory in the vast landscape of research. While the majority of studies have focused on more conventional variables such as geographic location, climate conditions, and technological advancements, there remains a lingering curiosity about the potential impact of something as seemingly irrelevant as personal names on the generation of wind power. This study aims to unravel the enigmatic relationship between the popularity of the first name "Arthur" and the wind power generated in Ukraine.
It is well established that names hold significant cultural and historical significance, influencing everything from personal identity to societal perceptions. However, the notion that the choice of a name could have implications for a country's renewable energy sector seems, at first glance, as ethereal as the very wind that drives the turbines. Nevertheless, as we shall demonstrate, the winds of statistical analysis have blown us in the direction of uncovering a surprisingly robust connection.
With a treasure trove of data from the US Social Security Administration providing insight into the ebb and flow of the name "Arthur" over time, and the Energy Information Administration offering a gusty account of wind power generation in Ukraine, we set out to apply rigorous statistical methods to probe for any whispers of correlation. What we uncovered was nothing short of a tempestuous revelation.
Our findings indicate a remarkably high correlation coefficient of 0.9654601, approaching near-mythical levels in the realm of statistical associations. Furthermore, the accompanying p-value of less than 0.01 provides compelling evidence against the null hypothesis, leaving us little choice but to confront the existence of a striking relationship. However, as with any tantalizing revelation, caution is warranted in the interpretation of these results.
In the pages that follow, we delve into the wind-swept labyrinth of data analysis, pondering the potential mechanisms behind this unexpected connection. Keeping in mind the wise adage that correlation does not imply causation, we navigate through the zephyrs of speculation while firmly anchored in the bedrock of scientific inquiry. Our quest for understanding propels us through the whirlwinds of statistical significance, as we seek to illuminate the murky depths of this curious correlation.
So, buckle up and prepare to be whisked away on a scholarly journey that promises to unveil the windswept secrets at the intersection of nomenclature and renewable energy. As we embark on this scholarly odyssey, we shall endeavor to chart a course that not only brings clarity to this unforeseen association but also adds a breath of fresh air to the scientific discourse. After all, as researchers, it’s our job to weather the storm of curiosity by harnessing the power of data and statistical reasoning, in the pursuit of unraveling even the most whimsical and unexpected connections.
[[RESULTS]]
The statistical analysis of the data revealed a truly remarkable correlation between the popularity of the first name "Arthur" and wind power generation in Ukraine. Over the period from 1998 to 2021, our findings unveiled a correlation coefficient of 0.9654601, indicating a strong positive relationship between these seemingly unrelated variables. This correlation was accompanied by an r-squared value of 0.9321133, suggesting that a substantial proportion of the variability in wind power generation can be explained by the popularity of the name "Arthur." Notably, the p-value was less than 0.01, providing convincing evidence of the significance of this correlation.
In Fig. 1, the scatterplot exhibits a conspicuous linear pattern, showcasing the striking association between the frequency of the name "Arthur" and the wind power generated in Ukraine. The data points align themselves in a manner that is as undeniable as the force of the wind itself, reinforcing the robustness of the observed correlation.
Despite the tantalizing nature of our findings, we must approach these results with the caution befitting a phenomenon as unexpected as this one. While the statistical analysis pointed to a clear relationship, we cannot, with absolute certainty, assert a causal link between the popularity of the name "Arthur" and wind power generation in Ukraine. As researchers, we are mindful of the infamous caution that correlation does not imply causation, and we remain open to the possibility of alternative explanations for this intriguing association.
Nonetheless, the winds of statistical analysis have blown us in a direction that provokes further inquiry. The discovery of such an astonishing correlation prompts us to consider the broader implications of nomenclature in the realm of renewable energy. This unexpected connection stands as a testament to the unyielding surprises that can emerge from the analysis of seemingly disparate variables, reminding us that in the grand tapestry of scientific inquiry, the most unanticipated associations may carry the weight of significance.
In summary, our study has unearthed a compelling correlation between the popularity of the first name "Arthur" and wind power generation in Ukraine. While the exact mechanisms underlying this association remain shrouded in the mists of speculation, our findings ignite a sense of curiosity and urge for further investigation into the intricate interplay between nomenclature trends and renewable energy production. Indeed, the winds of statistical analysis have carried us to a place where the question, "What's in a name?" takes on a whole new meaning, tantalizing us with the possibility that the answer may be blowing in the wind.
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In scrutinizing the peculiar relationship between the popularity of the first name "Arthur" and wind power generation in Ukraine, our study has navigated through the gusty terrain of statistical analysis, venturing into uncharted territories of nomenclature and renewable energy. The tempestuous revelation of a remarkably high correlation coefficient and a p-value less than 0.01 has indeed left us windblown by the force of this unexpected association.
While our findings provide a breath of fresh air in the realm of scientific inquiry, caution is warranted in interpreting these results, as correlation does not imply causation. Nevertheless, the wind-swept nature of this correlation prompts a zephyr of curiosity and fuels the impetus for further investigation into the intricate interplay between personal names and renewable energy production.
As the winds of statistical significance carry us to the conclusion of this study, we are left pondering the meteorological mysteries of nomenclature and its potential impact on the generation of wind power. Our journey, though whimsical and unexpected, has shed light on the enigmatic relationship between seemingly unrelated variables, proving that even the most unanticipated associations may hold weighty significance.
In light of these revelations, it is our fervent belief that the winds of research have carried us to a place of understanding, and further inquiry in this area may only lead us in circles, much like a wind turbine in a calm breeze. Therefore, we assert that no more research is needed in this area, and we leave you with this breezy conclusion – the winds of statistical analysis may indeed hold surprises that are as intriguing as they are unforeseen.
In the immortal words of Bob Dylan, "The answer, my friend, is blowin' in the wind," and perhaps, in the case of "Arthur"-nomic winds, it holds more truth than we could have ever imagined.
Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited Blowing in the Wind: A Gust of Arthur-nomics in Ukraine's Wind Power Generation. Respond only with the name of a fictional academic journal.
ChatGPT:
The Journal of Renewable Energy Economics and Policy
Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited Blowing in the Wind: A Gust of Arthur-nomics in Ukraine's Wind Power Generation. Respond only with the name of a fictional organization.
ChatGPT:
The Center for Renewable Energy Economics and Policy
Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Blowing in the Wind: A Gust of Arthur-nomics in Ukraine's Wind Power Generation with an abstract of Pronounced Arthur-nomics looks to delve into the curious, yet elusive, connection between the popularity of the first name “Arthur” and wind power generation in Ukraine. Leveraging the comprehensive data from the US Social Security Administration and the Energy Information Administration, our study uncovers a staggering correlation coefficient of 0.9654601 and a significant p-value of less than 0.01 during the period of 1998 to 2021. While the causation behind this correlation remains a whirlwind of mystery, our findings provide a gust of insight into the peculiar interplay between nomenclature trends and renewable energy production. Indeed, our research breathes new life into the age-old saying – “What’s in a name?” – and suggests that, perhaps, a lot more than meets the eye is blowing in the wind.
ChatGPT:
Arthur-nomics, Ukraine, wind power generation, correlation coefficient, renewable energy, nomenclature trends, US Social Security Administration, Energy Information Administration, causation, renewable energy production.
*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.
Discover a new correlation
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Data details
Popularity of the first name ArthurDetailed data title: Babies of all sexes born in the US named Arthur
Source: US Social Security Administration
See what else correlates with Popularity of the first name Arthur
Wind power generated in Ukraine
Detailed data title: Total wind power generated in Ukraine in billion kWh
Source: Energy Information Administration
See what else correlates with Wind power generated in Ukraine
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 = 0.9321133 (Coefficient of determination)
This means 93.2% of the change in the one variable (i.e., Wind power generated in Ukraine) is predictable based on the change in the other (i.e., Popularity of the first name Arthur) over the 24 years from 1998 through 2021.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.5E-14. 0.0000000000000245123056232563
The p-value is a measure of how probable it is that we would randomly find a result this extreme. 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 0.97 in 2.5E-12% of random cases. Said differently, if you correlated 40,795,835,992,321 random variables You don't actually need 40 trillion variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.
p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.
In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.
Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 23 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 23 because we have two variables measured over a period of 24 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.
[ 0.92, 0.99 ] 95% correlation confidence interval (using the Fisher z-transformation)
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: 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.
| 1998 | 1999 | 2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | |
| Popularity of the first name Arthur (Babies born) | 983 | 874 | 914 | 881 | 844 | 795 | 792 | 830 | 792 | 819 | 862 | 811 | 728 | 893 | 865 | 1030 | 1151 | 1246 | 1349 | 1513 | 1650 | 2190 | 2333 | 2473 |
| Wind power generated in Ukraine (Billion kWh) | 0.003 | 0.003 | 0.006 | 0.016 | 0.022 | 0.031 | 0.033 | 0.038 | 0.035 | 0.045 | 0.045 | 0.043 | 0.051 | 0.09 | 0.288 | 0.639 | 1.13 | 1.084 | 0.954 | 0.983 | 1.188 | 2.02 | 3.271 | 2.833 |
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
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
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. - Confounding variable: 2020 is particularly different from the other years on this graph. Confounding variables (like global pandemics) will cause two variables to look connected when in fact a "sneaky third" variable is influencing both of them behind the scenes.
Try it yourself
You can calculate the values on this page on your own! Try running the Python code to see the calculation results. 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([983,874,914,881,844,795,792,830,792,819,862,811,728,893,865,1030,1151,1246,1349,1513,1650,2190,2333,2473,])
array_2 = np.array([0.003,0.003,0.006,0.016,0.022,0.031,0.033,0.038,0.035,0.045,0.045,0.043,0.051,0.09,0.288,0.639,1.13,1.084,0.954,0.983,1.188,2.02,3.271,2.833,])
array_1_name = "Popularity of the first name Arthur"
array_2_name = "Wind power generated in Ukraine"
# 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. 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
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 Popularity of the first name Arthur
- Line chart for only Wind power generated in Ukraine
- AI-generated correlation image
- The spurious research paper: Blowing in the Wind: A Gust of Arthur-nomics in Ukraine's Wind Power Generation
Hats off to you for rating!
Correlation ID: 1650 · Black Variable ID: 2534 · Red Variable ID: 24063
