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AI explanation
As Lionel Messi's match count with Argentina decreased, it led to a decrease in the overall happiness of soccer fans around the world. This, in turn, caused a dip in productivity in Kansas, as workers were sneakily watching old Messi highlight reels instead of focusing on proofreading. As a result, the need for proofreaders in Kansas decreased since there was less work to go around. This phenomenon has been dubbed the "Messi Productivity Effect" by baffled economists.Model: dalle-3
Prompt: Create an image of a soccer stadium filled with cheering fans, and Lionel Messi on the field, playing for Argentina. The number on the back of his jersey keeps changing, indicating the increasing match count. Next to the stadium, a line of proofreaders from Kansas are standing, holding stacks of freshly printed Messi jerseys to check for any spelling errors. The more matches Messi plays, the longer the line of proofreaders becomes, reflecting the increasing demand for proofreading his name on the jerseys. Both scenes should be depicted in a vibrant and dynamic style, with a focus on the changing jersey numbers and the growing line of proofreaders. 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: Lionel Messi's match count with Argentina and The number of proofreaders in Kansas. 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: inspired by bauhaus Photorealism A Discovery magazine cover image Glitch art Realistic
ChatGPT: A Discovery magazine cover image
Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Lionel Messi's match count with Argentina and The number of proofreaders in Kansas, in the following situation: The more Messi plays, the more shirts are sold, leading to an increased demand for proofreaders to check the spelling of his name on the back. "Messi's matches are like a spelling bee for Kansas proofreaders.". Keep the description simple.
ChatGPT: A soccer stadium is filled with cheering fans, and Lionel Messi is on the field, playing for Argentina. The number on the back of his jersey keeps changing, indicating the increasing match count. Meanwhile, a line of proofreaders from Kansas are standing next to the stadium, holding stacks of freshly printed Messi jerseys to check for any spelling errors. The more matches Messi plays, the longer the line of proofreaders becomes, reflecting the increasing demand for proofreading his name on the jerseys.
Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A Discovery magazine cover image ] and depicting this scene: A soccer stadium is filled with cheering fans, and Lionel Messi is on the field, playing for Argentina. The number on the back of his jersey keeps changing, indicating the increasing match count. Meanwhile, a line of proofreaders from Kansas are standing next to the stadium, holding stacks of freshly printed Messi jerseys to check for any spelling errors. The more matches Messi plays, the longer the line of proofreaders becomes, reflecting the increasing demand for proofreading his name on the jerseys. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create an image of a soccer stadium filled with cheering fans, and Lionel Messi on the field, playing for Argentina. The number on the back of his jersey keeps changing, indicating the increasing match count. Next to the stadium, a line of proofreaders from Kansas are standing, holding stacks of freshly printed Messi jerseys to check for any spelling errors. The more matches Messi plays, the longer the line of proofreaders becomes, reflecting the increasing demand for proofreading his name on the jerseys. Both scenes should be depicted in a vibrant and dynamic style, with a focus on the changing jersey numbers and the growing line of proofreaders.
*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 a decrease in Lionel Messi's match count with Argentina caused The number of proofreaders in Kansas to decrease.\Make it sound serious until the end.
AI academic paper
Kickin' It with Kansas: Lionel Messi's Match Count with Argentina and the Number of Proofreaders in the Sunflower State The Journal of Sports Analytics and Agricultural Linguistics 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 Lionel Messi's match count with Argentina and The number of proofreaders in Kansas. Make lots of jokes, goofy observations, and puns.
Include a pun in the title.
Your research team used data from Wikipedia and Bureau of Larbor Statistics to assess this nagging question. You found a correlation coefficient of 0.8500820 and p < 0.01 for 2005 to 2020.
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]]
"Kickin' It with Kansas: Lionel Messi's Match Count with Argentina and the Number of Proofreaders in the Sunflower State"
[[ABSTRACT]]
This paper aims to shed some light on the often overlooked relationship between Lionel Messi's exceptional soccer career with the Argentine national team and the rather unexpected factor of the number of proofreaders in the lively state of Kansas. Using data from Wikipedia for Messi's match counts and the Bureau of Labor Statistics for proofreader employment in Kansas, we utilized statistical analysis to uncover the surprising correlation between these seemingly unrelated variables. Our findings reveal a correlation coefficient of 0.8500820 and a p-value less than 0.01 from 2005 to 2020, suggesting a strong and significant association. We hope this study not only adds a touch of humor to the field but also sparks further curiosity and exploration into the whimsical connections that may exist in the world of sports and state employment trends.
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 Lionel Messi's match count with Argentina and The number of proofreaders in Kansas. Make lots of 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]]
"Kickin' It with Kansas: Lionel Messi's Match Count with Argentina and the Number of Proofreaders in the Sunflower State"
[[ABSTRACT]]
This paper aims to shed some light on the often overlooked relationship between Lionel Messi's exceptional soccer career with the Argentine national team and the rather unexpected factor of the number of proofreaders in the lively state of Kansas. Using data from Wikipedia for Messi's match counts and the Bureau of Labor Statistics for proofreader employment in Kansas, we utilized statistical analysis to uncover the surprising correlation between these seemingly unrelated variables. Our findings reveal a correlation coefficient of 0.8500820 and a p-value less than 0.01 from 2005 to 2020, suggesting a strong and significant association. We hope this study not only adds a touch of humor to the field but also sparks further curiosity and exploration into the whimsical connections that may exist in the world of sports and state employment trends.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
[[INTRODUCTION]]
Soccer, or as some of our more globally inclined colleagues may call it, football, has long been a source of fascination and fervor for enthusiasts across the world. However, amidst the spirited chants and dazzling footwork, one cannot help but ponder the less explored avenues that intersect with the realm of sports stardom. This brings us to the enigmatic phenomenon of Lionel Messi's performance with the Argentine national team and its peculiar correlation with the number of proofreaders in the sun-kissed plains of Kansas. While this partnership may seem about as likely as an ostrich taking flight, our inquisitive nature and penchant for the unexpected prompted us to delve into the statistical rabbit hole, in search of insights that may tickle the funny bone and provoke scholarly thought simultaneously.
As we set out on our scholarly escapade, we couldn't help but muse upon the seemingly infinite possibilities that exist within the realms of statistics and research. After all, who would have thought that the number of proofreaders in Kansas and Messi's match count with Argentina might possess a relationship worthy of investigation? As we traverse the landscape of statistical analysis and delve into the world of association and causation, we hope to infuse a dash of whimsy and intrigue into the often staid world of academia.
Armed with the revered data from Wikipedia chronicling Messi's exploits on the pitch and the Bureau of Labor Statistics' insights into the ebbs and flows of proofreader employment in Kansas, we found ourselves brimming with anticipation at the possibility of uncovering a connection as electrifying as a thunderstorm in the Great Plains. Our determination to crack the statistical enigma is propelled not only by scholarly intrigue but also by the joy of unraveling correlations that might rival the twists and turns of a telenovela plot.
Join us on this scholarly romp through the fields of statistics and sports, where the unexpected rendezvous of Lionel Messi and Kansas's proofreaders promises to break stereotypes and add a touch of playfulness to the often sober landscapes of academic inquiry. As we unravel the compelling story behind these seemingly unrelated variables, we hope to not only provoke a chuckle or two but also to kindle a curiosity that transcends the boundaries of conventional research. So, tighten your statistical bootstraps and prepare for a journey that promises a blend of scientific scrutiny and a generous sprinkle of whimsy.
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 Lionel Messi's match count with Argentina and The number of proofreaders in Kansas. Make lots of jokes, goofy observations, and puns.
Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.
Make up the lorem and ipsum part, but make it sound related to the topic at hand.
Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading CVS receipts.
Here is the title and abstract of the paper:
[[TITLE]]
"Kickin' It with Kansas: Lionel Messi's Match Count with Argentina and the Number of Proofreaders in the Sunflower State"
[[ABSTRACT]]
This paper aims to shed some light on the often overlooked relationship between Lionel Messi's exceptional soccer career with the Argentine national team and the rather unexpected factor of the number of proofreaders in the lively state of Kansas. Using data from Wikipedia for Messi's match counts and the Bureau of Labor Statistics for proofreader employment in Kansas, we utilized statistical analysis to uncover the surprising correlation between these seemingly unrelated variables. Our findings reveal a correlation coefficient of 0.8500820 and a p-value less than 0.01 from 2005 to 2020, suggesting a strong and significant association. We hope this study not only adds a touch of humor to the field but also sparks further curiosity and exploration into the whimsical connections that may exist in the world of sports and state employment trends.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Soccer, or as some of our more globally inclined colleagues may call it, football, has long been a source of fascination and fervor for enthusiasts across the world. However, amidst the spirited chants and dazzling footwork, one cannot help but ponder the less explored avenues that intersect with the realm of sports stardom. This brings us to the enigmatic phenomenon of Lionel Messi's performance with the Argentine national team and its peculiar correlation with the number of proofreaders in the sun-kissed plains of Kansas. While this partnership may seem about as likely as an ostrich taking flight, our inquisitive nature and penchant for the unexpected prompted us to delve into the statistical rabbit hole, in search of insights that may tickle the funny bone and provoke scholarly thought simultaneously.
As we set out on our scholarly escapade, we couldn't help but muse upon the seemingly infinite possibilities that exist within the realms of statistics and research. After all, who would have thought that the number of proofreaders in Kansas and Messi's match count with Argentina might possess a relationship worthy of investigation? As we traverse the landscape of statistical analysis and delve into the world of association and causation, we hope to infuse a dash of whimsy and intrigue into the often staid world of academia.
Armed with the revered data from Wikipedia chronicling Messi's exploits on the pitch and the Bureau of Labor Statistics' insights into the ebbs and flows of proofreader employment in Kansas, we found ourselves brimming with anticipation at the possibility of uncovering a connection as electrifying as a thunderstorm in the Great Plains. Our determination to crack the statistical enigma is propelled not only by scholarly intrigue but also by the joy of unraveling correlations that might rival the twists and turns of a telenovela plot.
Join us on this scholarly romp through the fields of statistics and sports, where the unexpected rendezvous of Lionel Messi and Kansas's proofreaders promises to break stereotypes and add a touch of playfulness to the often sober landscapes of academic inquiry. As we unravel the compelling story behind these seemingly unrelated variables, we hope to not only provoke a chuckle or two but also to kindle a curiosity that transcends the boundaries of conventional research. So, tighten your statistical bootstraps and prepare for a journey that promises a blend of scientific scrutiny and a generous sprinkle of whimsy.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
In "Smith et al." the authors find that the number of proofreaders in Kansas has been subject to several economic studies. The relationship between the employment of proofreaders and various external factors has been a subject of interest in labor economics, with a particular focus on the labor market dynamics in the Midwestern region of the United States. However, the literature falls short in exploring the potential connection between the number of proofreaders in the Sunflower State and the illustrious career of Lionel Messi with the Argentine national team.
Doe and Jones have also delved into the dynamics of professional soccer players' careers and their impact on international sports. However, their work is devoid of any mention of the possible influence that the number of proofreaders in Kansas might have on Lionel Messi's match count with Argentina. This glaring gap in the literature prompted us to embark on a unique journey, one that aims to bridge the gap between the mesmerizing world of football and the unsuspecting realm of proofreading employment.
Turning our attention to non-fiction publications related to the theme of unexpected correlations, "Freakonomics" by Steven D. Levitt and Stephen J. Dubner provides insightful narratives about the unconventional relationships that underpin various societal phenomena. While the book doesn't touch upon soccer or proofreading, it sets a compelling backdrop for our endeavor to unearth the whimsical connection between Messi's performances and the proofreading workforce in Kansas.
In a similar vein, "The Tipping Point: How Little Things Can Make a Big Difference" by Malcolm Gladwell serves as a relevant reference for our exploration. Although this bestseller predominantly discusses social epidemics and tipping points, its examination of interconnectedness and unexpected triggers offers a valuable lens through which to view our own investigation. As we strive to illuminate the correlation between Messi's match count with Argentina and the number of proofreaders in Kansas, we draw inspiration from Gladwell's insightful revelations about seemingly inconsequential factors that can catalyze significant changes.
Venturing into the realm of fiction, "The Alchemist" by Paulo Coelho, and "The Hitchhiker's Guide to the Galaxy" by Douglas Adams, while not directly related to soccer or employment figures, embody the spirit of embarking on an unconventional quest. As the protagonists of these novels traverse whimsical landscapes and encounter unexpected companions, we similarly find ourselves navigating the uncharted territories of statistical inquiry, seeking an elusive thread that ties together two seemingly disparate entities.
Delving into the more unconventional sources, we perused the contents of grocery lists, airline safety manuals, and even the fine print on amusement park tickets in a bid to uncover any hidden clues or cryptic messages that might shed light on the relationship between Lionel Messi's match count with Argentina and the number of proofreaders in Kansas. Alas, our efforts yielded little more than amusement and the realization that statistical revelations are not to be found in the mundane minutiae of everyday life.
With this motley collection of sources and a hint of whimsy, we set out to unravel the peculiar correlation that lies at the intersection of world-renowned soccer prowess and the unassuming workforce of Kansas's proofreaders. As we present our findings, we invite you to join us in embracing the unexpected and indulging in the delightful absurdity that often accompanies scholarly pursuits.
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 Lionel Messi's match count with Argentina and The number of proofreaders in Kansas. Make lots of 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 Wikipedia and Bureau of Larbor Statistics . You used data from 2005 to 2020
Make up the research methods you don't know. Make them a bit goofy and convoluted.
Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"Kickin' It with Kansas: Lionel Messi's Match Count with Argentina and the Number of Proofreaders in the Sunflower State"
[[ABSTRACT]]
This paper aims to shed some light on the often overlooked relationship between Lionel Messi's exceptional soccer career with the Argentine national team and the rather unexpected factor of the number of proofreaders in the lively state of Kansas. Using data from Wikipedia for Messi's match counts and the Bureau of Labor Statistics for proofreader employment in Kansas, we utilized statistical analysis to uncover the surprising correlation between these seemingly unrelated variables. Our findings reveal a correlation coefficient of 0.8500820 and a p-value less than 0.01 from 2005 to 2020, suggesting a strong and significant association. We hope this study not only adds a touch of humor to the field but also sparks further curiosity and exploration into the whimsical connections that may exist in the world of sports and state employment trends.
[[INTRODUCTION]]
Soccer, or as some of our more globally inclined colleagues may call it, football, has long been a source of fascination and fervor for enthusiasts across the world. However, amidst the spirited chants and dazzling footwork, one cannot help but ponder the less explored avenues that intersect with the realm of sports stardom. This brings us to the enigmatic phenomenon of Lionel Messi's performance with the Argentine national team and its peculiar correlation with the number of proofreaders in the sun-kissed plains of Kansas. While this partnership may seem about as likely as an ostrich taking flight, our inquisitive nature and penchant for the unexpected prompted us to delve into the statistical rabbit hole, in search of insights that may tickle the funny bone and provoke scholarly thought simultaneously.
As we set out on our scholarly escapade, we couldn't help but muse upon the seemingly infinite possibilities that exist within the realms of statistics and research. After all, who would have thought that the number of proofreaders in Kansas and Messi's match count with Argentina might possess a relationship worthy of investigation? As we traverse the landscape of statistical analysis and delve into the world of association and causation, we hope to infuse a dash of whimsy and intrigue into the often staid world of academia.
Armed with the revered data from Wikipedia chronicling Messi's exploits on the pitch and the Bureau of Labor Statistics' insights into the ebbs and flows of proofreader employment in Kansas, we found ourselves brimming with anticipation at the possibility of uncovering a connection as electrifying as a thunderstorm in the Great Plains. Our determination to crack the statistical enigma is propelled not only by scholarly intrigue but also by the joy of unraveling correlations that might rival the twists and turns of a telenovela plot.
Join us on this scholarly romp through the fields of statistics and sports, where the unexpected rendezvous of Lionel Messi and Kansas's proofreaders promises to break stereotypes and add a touch of playfulness to the often sober landscapes of academic inquiry. As we unravel the compelling story behind these seemingly unrelated variables, we hope to not only provoke a chuckle or two but also to kindle a curiosity that transcends the boundaries of conventional research. So, tighten your statistical bootstraps and prepare for a journey that promises a blend of scientific scrutiny and a generous sprinkle of whimsy.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
To uncover the mysterious connections between Lionel Messi's match count with the Argentine national team and the number of proofreaders in the heartland of America, we embarked on a research journey filled with as many twists and turns as a penalty shootout. Our methodologies were as varied and entertaining as a collection of sports bloopers and as meticulously crafted as a well-worded punchline.
First, we gathered data from Wikipedia to capture the mesmerizing saga of Messi's appearances in the blue and white stripes of Argentina, from the early 2000s to the present day. With each click and scroll through the digital annals of soccer lore, our team marveled at the reliability and completeness of the information, much like a perfectly executed bicycle kick. Although some may question the scholarly merit of data extracted from a community-edited website, we held firm in our belief that Wikipedia can be as dependable as a seasoned goalkeeper when it comes to capturing Messi's on-field exploits.
For the complement to Messi's soccer prowess, we turned our attention to the captivating world of proofreaders in Kansas. Utilizing the Bureau of Labor Statistics as our guide, we navigated through the statistical terrain of employment figures, taking care to avoid the quagmires of omitted data and statistical bias. Much like the intricate dance between teammates on the field, our meticulous handling of employment statistics ensured that every data point had its chance to shine under the bright lights of scrutiny.
With our data in hand, we donned our statistical jerseys and flexed our analytical muscles to assess the relationship between Messi's match count and the number of diligent proofreaders in Kansas. Employing the formidable arsenal of correlation analysis, we sought to uncover any hidden links that may be lurking beneath the surface, like a well-timed nutmeg that leaves the opposition bewildered.
Our statistical foray culminated in the revelation of a correlation coefficient of 0.8500820 and a p-value less than 0.01, indicating a robust and significant association between these seemingly disparate variables. The strength of this relationship elicited reactions from our research team ranging from befuddlement reminiscent of a controversial offside call to sheer jubilation akin to a last-minute game-winning goal.
In the spirit of disclosure, we acknowledge the limitations of our approach, recognizing that our analysis represents just one chapter in the unfolding narrative of statistical exploration. Nevertheless, the vigor and enthusiasm with which we undertook this endeavor reflect our unwavering commitment to unearthing unexpected connections, even if they seem as improbable as a goal from the halfway line.
As we bid adieu to the methodologies that brought us here, we extend an invitation to fellow researchers and sports aficionados to join us in this joyous romp through the sometimes whimsical, always thought-provoking realm of statistical inquiry. For in the blend of soccer sagas and state employment trends, there exists a rich tapestry of insight and wonder waiting to be explored.
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 Lionel Messi's match count with Argentina and The number of proofreaders in Kansas. Make lots of 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 Wikipedia and Bureau of Larbor Statistics .
For the time period 2005 to 2020, you found a correlation 0.8500820, r-squared of 0.7226394, 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]]
"Kickin' It with Kansas: Lionel Messi's Match Count with Argentina and the Number of Proofreaders in the Sunflower State"
[[ABSTRACT]]
This paper aims to shed some light on the often overlooked relationship between Lionel Messi's exceptional soccer career with the Argentine national team and the rather unexpected factor of the number of proofreaders in the lively state of Kansas. Using data from Wikipedia for Messi's match counts and the Bureau of Labor Statistics for proofreader employment in Kansas, we utilized statistical analysis to uncover the surprising correlation between these seemingly unrelated variables. Our findings reveal a correlation coefficient of 0.8500820 and a p-value less than 0.01 from 2005 to 2020, suggesting a strong and significant association. We hope this study not only adds a touch of humor to the field but also sparks further curiosity and exploration into the whimsical connections that may exist in the world of sports and state employment trends.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
Soccer, or as some of our more globally inclined colleagues may call it, football, has long been a source of fascination and fervor for enthusiasts across the world. However, amidst the spirited chants and dazzling footwork, one cannot help but ponder the less explored avenues that intersect with the realm of sports stardom. This brings us to the enigmatic phenomenon of Lionel Messi's performance with the Argentine national team and its peculiar correlation with the number of proofreaders in the sun-kissed plains of Kansas. While this partnership may seem about as likely as an ostrich taking flight, our inquisitive nature and penchant for the unexpected prompted us to delve into the statistical rabbit hole, in search of insights that may tickle the funny bone and provoke scholarly thought simultaneously.
As we set out on our scholarly escapade, we couldn't help but muse upon the seemingly infinite possibilities that exist within the realms of statistics and research. After all, who would have thought that the number of proofreaders in Kansas and Messi's match count with Argentina might possess a relationship worthy of investigation? As we traverse the landscape of statistical analysis and delve into the world of association and causation, we hope to infuse a dash of whimsy and intrigue into the often staid world of academia.
Armed with the revered data from Wikipedia chronicling Messi's exploits on the pitch and the Bureau of Labor Statistics' insights into the ebbs and flows of proofreader employment in Kansas, we found ourselves brimming with anticipation at the possibility of uncovering a connection as electrifying as a thunderstorm in the Great Plains. Our determination to crack the statistical enigma is propelled not only by scholarly intrigue but also by the joy of unraveling correlations that might rival the twists and turns of a telenovela plot.
Join us on this scholarly romp through the fields of statistics and sports, where the unexpected rendezvous of Lionel Messi and Kansas's proofreaders promises to break stereotypes and add a touch of playfulness to the often sober landscapes of academic inquiry. As we unravel the compelling story behind these seemingly unrelated variables, we hope to not only provoke a chuckle or two but also to kindle a curiosity that transcends the boundaries of conventional research. So, tighten your statistical bootstraps and prepare for a journey that promises a blend of scientific scrutiny and a generous sprinkle of whimsy.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
Our research endeavor into the whimsical world of soccer superstardom and state employment trends has unearthed a rather unexpected and, dare we say, entertaining association between Lionel Messi's match count with Argentina and the number of proofreaders in the sun-drenched plains of Kansas. With bated breath and statistical fervor, we crunched the numbers and emerged with a correlation coefficient of 0.8500820, an r-squared of 0.7226394, and a p-value less than 0.01 for the period spanning 2005 to 2020. This prodigious p-value suggests a remarkably robust and significant correlation between these seemingly unrelated variables, leaving us nodding our heads in both amusement and scholarly wonder.
We present the evidence of this fanciful association in the form of a scatterplot (Fig. 1), which portrays the strikingly strong correlation between Lionel Messi's match count with Argentina and the number of proofreaders in Kansas. As we gaze upon the plot, we are reminded of the unpredictable nature of statistical exploration, where the unlikeliest of duos can dance their way into the annals of correlation.
The magnitude of this correlation coefficient beckons us to consider the possibility of a causal relationship between Messi's soccer endeavors and the state of proofreading in Kansas. Could it be that the fervor surrounding Messi's matches somehow influences the employment trends of proofreaders in the heartland of America? Perhaps proofreaders find themselves so engrossed in Messi's on-field exploits that they inadvertently overlook a typo or two in the midst of their enthusiasm. While we jest, the statistical significance of our findings prompts us to contemplate the underlying mechanisms at play, invoking a sense of scientific curiosity interwoven with a hearty chuckle.
In conclusion, our research has demonstrated a compelling and statistically significant correlation between Lionel Messi's match count with Argentina and the number of proofreaders in Kansas. This finding not only tickles our intellectual fancy but also invites scholars and enthusiasts alike to delve into the eccentric and amusing connections that may lie concealed within the vast tapestry of sports and state employment. With this discovery, we hope to inject a jolt of levity into the world of statistical inquiry, while propelling the scholarly community into uncharted territories of correlation and causation.
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 Lionel Messi's match count with Argentina and The number of proofreaders in Kansas. Make lots of 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]]
"Kickin' It with Kansas: Lionel Messi's Match Count with Argentina and the Number of Proofreaders in the Sunflower State"
[[ABSTRACT]]
This paper aims to shed some light on the often overlooked relationship between Lionel Messi's exceptional soccer career with the Argentine national team and the rather unexpected factor of the number of proofreaders in the lively state of Kansas. Using data from Wikipedia for Messi's match counts and the Bureau of Labor Statistics for proofreader employment in Kansas, we utilized statistical analysis to uncover the surprising correlation between these seemingly unrelated variables. Our findings reveal a correlation coefficient of 0.8500820 and a p-value less than 0.01 from 2005 to 2020, suggesting a strong and significant association. We hope this study not only adds a touch of humor to the field but also sparks further curiosity and exploration into the whimsical connections that may exist in the world of sports and state employment trends.
[[LITERATURE REVIEW]]
In "Smith et al." the authors find that the number of proofreaders in Kansas has been subject to several economic studies. The relationship between the employment of proofreaders and various external factors has been a subject of interest in labor economics, with a particular focus on the labor market dynamics in the Midwestern region of the United States. However, the literature falls short in exploring the potential connection between the number of proofreaders in the Sunflower State and the illustrious career of Lionel Messi with the Argentine national team.
Doe and Jones have also delved into the dynamics of professional soccer players' careers and their impact on international sports. However, their work is devoid of any mention of the possible influence that the number of proofreaders in Kansas might have on Lionel Messi's match count with Argentina. This glaring gap in the literature prompted us to embark on a unique journey, one that aims to bridge the gap between the mesmerizing world of football and the unsuspecting realm of proofreading employment.
Turning our attention to non-fiction publications related to the theme of unexpected correlations, "Freakonomics" by Steven D. Levitt and Stephen J. Dubner provides insightful narratives about the unconventional relationships that underpin various societal phenomena. While the book doesn't touch upon soccer or proofreading, it sets a compelling backdrop for our endeavor to unearth the whimsical connection between Messi's performances and the proofreading workforce in Kansas.
In a similar vein, "The Tipping Point: How Little Things Can Make a Big Difference" by Malcolm Gladwell serves as a relevant reference for our exploration. Although this bestseller predominantly discusses social epidemics and tipping points, its examination of interconnectedness and unexpected triggers offers a valuable lens through which to view our own investigation. As we strive to illuminate the correlation between Messi's match count with Argentina and the number of proofreaders in Kansas, we draw inspiration from Gladwell's insightful revelations about seemingly inconsequential factors that can catalyze significant changes.
Venturing into the realm of fiction, "The Alchemist" by Paulo Coelho, and "The Hitchhiker's Guide to the Galaxy" by Douglas Adams, while not directly related to soccer or employment figures, embody the spirit of embarking on an unconventional quest. As the protagonists of these novels traverse whimsical landscapes and encounter unexpected companions, we similarly find ourselves navigating the uncharted territories of statistical inquiry, seeking an elusive thread that ties together two seemingly disparate entities.
Delving into the more unconventional sources, we perused the contents of grocery lists, airline safety manuals, and even the fine print on amusement park tickets in a bid to uncover any hidden clues or cryptic messages that might shed light on the relationship between Lionel Messi's match count with Argentina and the number of proofreaders in Kansas. Alas, our efforts yielded little more than amusement and the realization that statistical revelations are not to be found in the mundane minutiae of everyday life.
With this motley collection of sources and a hint of whimsy, we set out to unravel the peculiar correlation that lies at the intersection of world-renowned soccer prowess and the unassuming workforce of Kansas's proofreaders. As we present our findings, we invite you to join us in embracing the unexpected and indulging in the delightful absurdity that often accompanies scholarly pursuits.
[[RESULTS]]
Our research endeavor into the whimsical world of soccer superstardom and state employment trends has unearthed a rather unexpected and, dare we say, entertaining association between Lionel Messi's match count with Argentina and the number of proofreaders in the sun-drenched plains of Kansas. With bated breath and statistical fervor, we crunched the numbers and emerged with a correlation coefficient of 0.8500820, an r-squared of 0.7226394, and a p-value less than 0.01 for the period spanning 2005 to 2020. This prodigious p-value suggests a remarkably robust and significant correlation between these seemingly unrelated variables, leaving us nodding our heads in both amusement and scholarly wonder.
We present the evidence of this fanciful association in the form of a scatterplot (Fig. 1), which portrays the strikingly strong correlation between Lionel Messi's match count with Argentina and the number of proofreaders in Kansas. As we gaze upon the plot, we are reminded of the unpredictable nature of statistical exploration, where the unlikeliest of duos can dance their way into the annals of correlation.
The magnitude of this correlation coefficient beckons us to consider the possibility of a causal relationship between Messi's soccer endeavors and the state of proofreading in Kansas. Could it be that the fervor surrounding Messi's matches somehow influences the employment trends of proofreaders in the heartland of America? Perhaps proofreaders find themselves so engrossed in Messi's on-field exploits that they inadvertently overlook a typo or two in the midst of their enthusiasm. While we jest, the statistical significance of our findings prompts us to contemplate the underlying mechanisms at play, invoking a sense of scientific curiosity interwoven with a hearty chuckle.
In conclusion, our research has demonstrated a compelling and statistically significant correlation between Lionel Messi's match count with Argentina and the number of proofreaders in Kansas. This finding not only tickles our intellectual fancy but also invites scholars and enthusiasts alike to delve into the eccentric and amusing connections that may lie concealed within the vast tapestry of sports and state employment. With this discovery, we hope to inject a jolt of levity into the world of statistical inquiry, while propelling the scholarly community into uncharted territories of correlation and causation.
-------
Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
Our investigation into the relationship between Lionel Messi's match count with Argentina and the number of proofreaders in the illustrious state of Kansas has yielded captivating results that not only provoke scholarly contemplation but also elicit a chuckle or two. The bond we have unearthed between Messi's on-field prowess and the bustling world of proofreading employment in the heartland of America lends itself to a delightful dance of correlation, prompting us to ponder the possible mechanisms that underpin this whimsical relationship.
The rather unexpected yet statistically robust correlation coefficient of 0.8500820 that we have uncovered aligns with the earlier noted research by Smith et al., who diligently examined the labor market dynamics in Kansas. While their work may not have explicitly delved into the enthralling influence of Messi's match count on proofreader employment, our findings corroborate the notion that external factors indeed play a substantial role in shaping labor trends. Moreover, our results echo the sentiments expressed in "The Tipping Point" by Malcolm Gladwell, as the unexpected interconnectedness between Messi's performances and the number of proofreaders in Kansas serves as a testament to the impact of seemingly inconsequential factors that can trigger significant changes. As we traverse this peculiar landscape of statistical inquiry, we find ourselves echoing the sentiment of Coelho's "The Alchemist" and Adams' "The Hitchhiker's Guide to the Galaxy," embracing the unpredictable nature of scholarly exploration with a twinkle in our eyes and a touch of whimsy in our hearts.
The strength of the correlation coefficient invites playful speculation about the potential causative dynamics at play—could Messi's captivating displays on the soccer pitch inadvertently cast a spell on the diligent proofreaders of Kansas, leading them to dedicate themselves to their work with renewed fervor? While this notion may belong in the realm of jest, our results call for a deeper introspection into the mechanisms that link the mesmerizing world of soccer with the industrious spirit of proofreading. It is in this light-hearted spirit that we aim to unleash a wave of curiosity and amusement within the scholarly community, inviting fellow researchers to follow us down the delightful rabbit hole of whimsical correlations.
In closing, the findings of our study not only underscore the unlikely connection between Lionel Messi's match count with Argentina and the number of proofreaders in Kansas but also beckon us to celebrate the joyous dance of statistics and the enchanting mysteries that await within its intricate steps. As we bid adieu to this discussion, we leave behind a trail of statistical merriment, inviting scholars and enthusiasts to join us in celebrating the splendid absurdity that often accompanies the pursuit of knowledge.
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 Lionel Messi's match count with Argentina and The number of proofreaders in Kansas. Make lots of 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]]
"Kickin' It with Kansas: Lionel Messi's Match Count with Argentina and the Number of Proofreaders in the Sunflower State"
[[ABSTRACT]]
This paper aims to shed some light on the often overlooked relationship between Lionel Messi's exceptional soccer career with the Argentine national team and the rather unexpected factor of the number of proofreaders in the lively state of Kansas. Using data from Wikipedia for Messi's match counts and the Bureau of Labor Statistics for proofreader employment in Kansas, we utilized statistical analysis to uncover the surprising correlation between these seemingly unrelated variables. Our findings reveal a correlation coefficient of 0.8500820 and a p-value less than 0.01 from 2005 to 2020, suggesting a strong and significant association. We hope this study not only adds a touch of humor to the field but also sparks further curiosity and exploration into the whimsical connections that may exist in the world of sports and state employment trends.
[[INTRDUCTION]]
Soccer, or as some of our more globally inclined colleagues may call it, football, has long been a source of fascination and fervor for enthusiasts across the world. However, amidst the spirited chants and dazzling footwork, one cannot help but ponder the less explored avenues that intersect with the realm of sports stardom. This brings us to the enigmatic phenomenon of Lionel Messi's performance with the Argentine national team and its peculiar correlation with the number of proofreaders in the sun-kissed plains of Kansas. While this partnership may seem about as likely as an ostrich taking flight, our inquisitive nature and penchant for the unexpected prompted us to delve into the statistical rabbit hole, in search of insights that may tickle the funny bone and provoke scholarly thought simultaneously.
As we set out on our scholarly escapade, we couldn't help but muse upon the seemingly infinite possibilities that exist within the realms of statistics and research. After all, who would have thought that the number of proofreaders in Kansas and Messi's match count with Argentina might possess a relationship worthy of investigation? As we traverse the landscape of statistical analysis and delve into the world of association and causation, we hope to infuse a dash of whimsy and intrigue into the often staid world of academia.
Armed with the revered data from Wikipedia chronicling Messi's exploits on the pitch and the Bureau of Labor Statistics' insights into the ebbs and flows of proofreader employment in Kansas, we found ourselves brimming with anticipation at the possibility of uncovering a connection as electrifying as a thunderstorm in the Great Plains. Our determination to crack the statistical enigma is propelled not only by scholarly intrigue but also by the joy of unraveling correlations that might rival the twists and turns of a telenovela plot.
Join us on this scholarly romp through the fields of statistics and sports, where the unexpected rendezvous of Lionel Messi and Kansas's proofreaders promises to break stereotypes and add a touch of playfulness to the often sober landscapes of academic inquiry. As we unravel the compelling story behind these seemingly unrelated variables, we hope to not only provoke a chuckle or two but also to kindle a curiosity that transcends the boundaries of conventional research. So, tighten your statistical bootstraps and prepare for a journey that promises a blend of scientific scrutiny and a generous sprinkle of whimsy.
[[RESULTS]]
Our research endeavor into the whimsical world of soccer superstardom and state employment trends has unearthed a rather unexpected and, dare we say, entertaining association between Lionel Messi's match count with Argentina and the number of proofreaders in the sun-drenched plains of Kansas. With bated breath and statistical fervor, we crunched the numbers and emerged with a correlation coefficient of 0.8500820, an r-squared of 0.7226394, and a p-value less than 0.01 for the period spanning 2005 to 2020. This prodigious p-value suggests a remarkably robust and significant correlation between these seemingly unrelated variables, leaving us nodding our heads in both amusement and scholarly wonder.
We present the evidence of this fanciful association in the form of a scatterplot (Fig. 1), which portrays the strikingly strong correlation between Lionel Messi's match count with Argentina and the number of proofreaders in Kansas. As we gaze upon the plot, we are reminded of the unpredictable nature of statistical exploration, where the unlikeliest of duos can dance their way into the annals of correlation.
The magnitude of this correlation coefficient beckons us to consider the possibility of a causal relationship between Messi's soccer endeavors and the state of proofreading in Kansas. Could it be that the fervor surrounding Messi's matches somehow influences the employment trends of proofreaders in the heartland of America? Perhaps proofreaders find themselves so engrossed in Messi's on-field exploits that they inadvertently overlook a typo or two in the midst of their enthusiasm. While we jest, the statistical significance of our findings prompts us to contemplate the underlying mechanisms at play, invoking a sense of scientific curiosity interwoven with a hearty chuckle.
In conclusion, our research has demonstrated a compelling and statistically significant correlation between Lionel Messi's match count with Argentina and the number of proofreaders in Kansas. This finding not only tickles our intellectual fancy but also invites scholars and enthusiasts alike to delve into the eccentric and amusing connections that may lie concealed within the vast tapestry of sports and state employment. With this discovery, we hope to inject a jolt of levity into the world of statistical inquiry, while propelling the scholarly community into uncharted territories of correlation and causation.
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In this study, we have uncovered a correlation between Lionel Messi's match count with Argentina and the number of proofreaders in Kansas that is as surprising as finding a soccer ball in a haystack. The statistical significance of this association not only raises eyebrows but also prompts us to consider the whimsical ways in which sports superstardom and state employment trends may intertwine.
As we reflect on the robust correlation coefficient and the tantalizing p-value, we can't help but envision a scenario where the fervor surrounding Messi's performances somehow influences the meticulousness of Kansas's proofreaders, leading them to dot their i's and cross their t's with renewed vigor. While the idea of Messi's goals directly impacting the state of proofreading may seem as far-fetched as a kangaroo playing chess, the data paints a compelling picture that tickles our analytical sensibilities and our funny bones simultaneously.
The scatterplot depicting this correlation serves as a delightful reminder that in the realm of statistical exploration, the unlikeliest of pairs can waltz into mathematical harmony, much like peanut butter and jelly or macaroni and cheese. This finding not only showcases the unexpected connections that statistics can unveil but also adds a touch of amusement to the often serious milieu of academic research.
In conclusion, our study not only sheds light on the potential influence of soccer stardom on proofreading practices but also serves as a testament to the delightful surprises that statistical analysis can unearth. As we close the chapter on this research, we assert with utmost confidence that no further investigation is needed in this area. After all, who would dare to challenge the statistical magic of Messi and Kansas?
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 Kickin' It with Kansas: Lionel Messi's Match Count with Argentina and the Number of Proofreaders in the Sunflower State. Respond only with the name of a fictional academic journal.
ChatGPT:
The Journal of Sports Analytics and Agricultural Linguistics
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 Kickin' It with Kansas: Lionel Messi's Match Count with Argentina and the Number of Proofreaders in the Sunflower State. Respond only with the name of a fictional organization.
ChatGPT:
The Institute for Midwestern Sports Analytics
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 Kickin' It with Kansas: Lionel Messi's Match Count with Argentina and the Number of Proofreaders in the Sunflower State with an abstract of This paper aims to shed some light on the often overlooked relationship between Lionel Messi's exceptional soccer career with the Argentine national team and the rather unexpected factor of the number of proofreaders in the lively state of Kansas. Using data from Wikipedia for Messi's match counts and the Bureau of Labor Statistics for proofreader employment in Kansas, we utilized statistical analysis to uncover the surprising correlation between these seemingly unrelated variables. Our findings reveal a correlation coefficient of 0.8500820 and a p-value less than 0.01 from 2005 to 2020, suggesting a strong and significant association. We hope this study not only adds a touch of humor to the field but also sparks further curiosity and exploration into the whimsical connections that may exist in the world of sports and state employment trends.
ChatGPT:
Lionel Messi, Argentina national team, soccer career, proofreaders, Kansas, Wikipedia, Bureau of Labor Statistics, statistical analysis, correlation coefficient, p-value, 2005-2020, sports, state employment trends
*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.
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Data details
Lionel Messi's match count with ArgentinaDetailed data title: Lionel Messi's Argentina match count
Source: Wikipedia
See what else correlates with Lionel Messi's match count with Argentina
The number of proofreaders in Kansas
Detailed data title: BLS estimate of proofreaders and copy markers in Kansas
Source: Bureau of Larbor Statistics
See what else correlates with The number of proofreaders in Kansas
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 = 0.7226394 (Coefficient of determination)
This means 72.3% of the change in the one variable (i.e., The number of proofreaders in Kansas) is predictable based on the change in the other (i.e., Lionel Messi's match count with Argentina) over the 16 years from 2005 through 2020.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.04E-5. 0.0000304129497672655220000000
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 0.85 in 0.00304% of random cases. Said differently, if you correlated 32,881 random variables You don't actually need 32 thousand variables to find a correlation like this one. 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.
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 15 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 15 because we have two variables measured over a period of 16 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, 0.95 ] 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.
2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | |
Lionel Messi's match count with Argentina (Match played) | 5 | 7 | 14 | 8 | 10 | 10 | 13 | 9 | 7 | 8 | 4 | 8 | 4 | 4 | 5 | 1 |
The number of proofreaders in Kansas (Laborers) | 70 | 260 | 270 | 200 | 240 | 230 | 270 | 210 | 180 | 170 | 90 | 80 | 70 | 40 | 50 | 50 |
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.
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([5,7,14,8,10,10,13,9,7,8,4,8,4,4,5,1,])
array_2 = np.array([70,260,270,200,240,230,270,210,180,170,90,80,70,40,50,50,])
array_1_name = "Lionel Messi's match count with Argentina"
array_2_name = "The number of proofreaders in Kansas"
# 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)
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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.
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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 Lionel Messi's match count with Argentina
- Line chart for only The number of proofreaders in Kansas
- AI-generated correlation image
- The spurious research paper: Kickin' It with Kansas: Lionel Messi's Match Count with Argentina and the Number of Proofreaders in the Sunflower State
Your rating is much appreciated!
Correlation ID: 2453 · Black Variable ID: 73 · Red Variable ID: 8280