Spurious correlation #3,797 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of merchandise displayers and window trimmers in District of Columbia and the second variable is Global shipwrecks . The chart goes from 2006 to 2014, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of merchandise displayers and window trimmers in District of Columbia and the second variable is Global shipwrecks . The chart goes from 2006 to 2014, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the number of merchandise displayers and window trimmers in District of Columbia increased, more and more ships passing by were getting distracted by the fabulous displays, leading to an increase in global shipwrecks. It seems like the 'retail tide' was just too strong for them to resist!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of the bustling port of District of Columbia, with vibrant merchandise displayers and window trimmers creating a dazzling array of colors and patterns. As passing ship captains navigate the harbor, they are captivated by the mesmerizing displays, leading to an increase in shipwrecks. Capture the chaos and beauty of the port, illustrating the clash between the alluring displays and the dangerous distractions they pose to the passing vessels. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: The number of merchandise displayers and window trimmers in District of Columbia and Global shipwrecks . 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: Pop art a funny image you would see on reddit Concept art Whimsical illustration Data cyber art
ChatGPT: Data cyber art

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both The number of merchandise displayers and window trimmers in District of Columbia and Global shipwrecks , in the following situation: The increase in merchandise displayers and window trimmers in District of Columbia has led to distracted ships captains admiring the colorful displays as they pass by, resulting in a higher number of shipwrecks.. Keep the description simple.
ChatGPT: In the busy port of District of Columbia, there are more merchandise displayers and window trimmers than ever before. The displays are so eye-catching that passing ship captains are often distracted, gazing at the colorful window designs and merchandise arrangements. Unfortunately, this has led to an increase in shipwrecks as the captains struggle to navigate their vessels safely through the bustling harbor.

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: [ Data cyber art ] and depicting this scene: In the busy port of District of Columbia, there are more merchandise displayers and window trimmers than ever before. The displays are so eye-catching that passing ship captains are often distracted, gazing at the colorful window designs and merchandise arrangements. Unfortunately, this has led to an increase in shipwrecks as the captains struggle to navigate their vessels safely through the bustling harbor. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of the bustling port of District of Columbia, with vibrant merchandise displayers and window trimmers creating a dazzling array of colors and patterns. As passing ship captains navigate the harbor, they are captivated by the mesmerizing displays, leading to an increase in shipwrecks. Capture the chaos and beauty of the port, illustrating the clash between the alluring displays and the dangerous distractions they pose to the passing vessels.

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

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

AI academic paper

(Because p < 0.01)

The Trade of Trimmers and Troubles at Sea: A Tale of Two Tides

The Journal of Nautical Nonsense and Maritime Mischief

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between The number of merchandise displayers and window trimmers in District of Columbia and Global shipwrecks . Make lots of jokes, goofy observations, and puns.

Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.

Make up the lorem and ipsum part, but make it sound related to the topic at hand.

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name some cartoons and childrens' shows that you watched that are related to the topic.

Here is the title and abstract of the paper:
[[TITLE]]
The Trade of Trimmers and Troubles at Sea: A Tale of Two Tides

[[ABSTRACT]]
This paper presents an investigation into the curious correlation between the number of merchandise displayers and window trimmers in the District of Columbia and global shipwrecks. Using data from the Bureau of Labor Statistics and Wikipedia, our research team sought to illuminate this enigmatic connection. Strangely enough, our analysis yielded a correlation coefficient of 0.8020442 and p < 0.01 for the years 2006 to 2014, indicating a statistically significant relationship between these seemingly disparate variables. We delve into the implications of these findings, pondering whether the meticulous attention paid to storefront aesthetics might inadvertently lead to maritime mishaps. This study sheds light on an unexpected linkage between commerce and calamity, provoking both amusement and contemplation within the research community.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
The endeavor to unravel the perplexing connection between the number of merchandise displayers and window trimmers in the District of Columbia and global shipwrecks has led our research team down an intriguing and, dare I say, riveting path. While most researchers may set sail in search of correlations between more conventional variables, our voyage into this uncharted territory has yielded unexpected and, some might say, slightly whimsical findings.
As we delved into the seemingly incongruous pairing of storefront aesthetics and maritime mayhem, we couldn't help but marvel at the statistical relationship that emerged from our analysis. With a correlation coefficient of 0.8020442 and a p-value of less than 0.01, our results left us pondering whether there is more to the art of window trimming than meets the eye. The uncanny dance between these variables has prompted us to seek answers in the most unlikely of places.
This study, poised at the intersection of commerce and catastrophe, serves as a reminder of the serendipitous nature of research. While some may see only the choppy seas of data analysis, we find ourselves navigating the intricate currents of correlation, buoyed by the buoyancy of statistical significance.
So, join us on this whimsical and slightly absurd expedition, as we unfurl the sails of curiosity and set our course toward a better understanding of the capricious tides that bind commerce and calamity. Our findings may invite disbelief, perhaps even amusement, but we assure you, dear reader, that this is a tale worth telling.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

To begin our examination of the curious correlation between the number of merchandise displayers and window trimmers in the District of Columbia and global shipwrecks, we turn to the serious and esteemed works that have laid the foundation for our current inquiry. Smith and Doe (2005) explore the urban dynamics of storefront aesthetics and its potential impact on maritime activities. Jones (2010) delves into the demographics of window trimmers and their potential role in shaping economic forces at sea. These foundational works offer valuable insights into the interplay of commerce and calamity, setting the stage for our own whimsical yet rigorous exploration.

As we sail further into the choppy waters of our investigation, we encounter an array of non-fiction sources that shed light on the intricate relationship between our seemingly disparate variables. "The Art of Display: Enhancing Visual Merchandising" (Stevens, 2013) offers insights into the psychology of storefront presentation and its ripple effects on unsuspecting sailors navigating the high seas. In "Trimming Troubles: A Comprehensive Study of Window Trimming in Urban Landscapes" (Robinson, 2016), the author examines the potential repercussions of neglecting window aesthetics on global maritime mishaps. These scholarly works, while earnest in their approach, provide a touch of whimsy and perhaps a hint of nautical folly to our exploration.

Turning to slightly more unexpected sources, we uncover a treasure trove of literature that, while not directly related to our topic, offers a whimsical parallel to our investigation. "Shipwrecks and Scones: A Culinary Odyssey" (Baker, 2018) may not chart the waters of commerce and window adornment, but its delightful title resonates with the maritime misfortunes that have piqued our curiosity. Similarly, "The Merchant's Misery: Tales of Retail Woe" (Garcia, 2014) captures the essence of our investigation in a more metaphorical sense, weaving a narrative of commerce and catastrophe that mirrors our own academic odyssey.

In the realm of fiction, we encounter works that playfully dance on the edge of relevance to our study. Jules Verne's "Twenty Thousand Leagues Under the Sea" offers a fantastical portrayal of maritime adventure that, while not grounded in empirical research, captures the imaginative spirit of our own expedition. Similarly, Patrick O'Brian's "Master and Commander" series, though focused on naval warfare rather than window adornment, provides a captivating glimpse into the tempestuous world of the high seas.

Finally, in a more lighthearted turn, we draw inspiration from the whimsical world of children's cartoons and television shows. Classics such as "SpongeBob SquarePants" and "The Little Mermaid" beckon us to contemplate the enigmatic interplay of commerce and calamity in the context of oceanic narratives. With their colorful characters and nautical escapades, these childhood favorites provide a playful backdrop to our scholarly endeavors, reminding us not to take ourselves too seriously as we navigate the curious currents of correlation.

As we embark on this scholarly odyssey, we invite our fellow researchers to join us in embracing the whimsical allure of our investigation. While the topic at hand may seem quirky and improbable, we assure our colleagues that beneath the veneer of levity lies a robust and intellectually rigorous pursuit of knowledge. Let us set sail together, not merely to discover correlations, but to revel in the delight of academic inquiry as we navigate the capricious tides that bind commerce and calamity.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between The number of merchandise displayers and window trimmers in District of Columbia and Global shipwrecks . 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]]
The Trade of Trimmers and Troubles at Sea: A Tale of Two Tides

[[ABSTRACT]]
This paper presents an investigation into the curious correlation between the number of merchandise displayers and window trimmers in the District of Columbia and global shipwrecks. Using data from the Bureau of Labor Statistics and Wikipedia, our research team sought to illuminate this enigmatic connection. Strangely enough, our analysis yielded a correlation coefficient of 0.8020442 and p < 0.01 for the years 2006 to 2014, indicating a statistically significant relationship between these seemingly disparate variables. We delve into the implications of these findings, pondering whether the meticulous attention paid to storefront aesthetics might inadvertently lead to maritime mishaps. This study sheds light on an unexpected linkage between commerce and calamity, provoking both amusement and contemplation within the research community.

[[LITERATURE REVIEW]]
To begin our examination of the curious correlation between the number of merchandise displayers and window trimmers in the District of Columbia and global shipwrecks, we turn to the serious and esteemed works that have laid the foundation for our current inquiry. Smith and Doe (2005) explore the urban dynamics of storefront aesthetics and its potential impact on maritime activities. Jones (2010) delves into the demographics of window trimmers and their potential role in shaping economic forces at sea. These foundational works offer valuable insights into the interplay of commerce and calamity, setting the stage for our own whimsical yet rigorous exploration.
As we sail further into the choppy waters of our investigation, we encounter an array of non-fiction sources that shed light on the intricate relationship between our seemingly disparate variables. "The Art of Display: Enhancing Visual Merchandising" (Stevens, 2013) offers insights into the psychology of storefront presentation and its ripple effects on unsuspecting sailors navigating the high seas. In "Trimming Troubles: A Comprehensive Study of Window Trimming in Urban Landscapes" (Robinson, 2016), the author examines the potential repercussions of neglecting window aesthetics on global maritime mishaps. These scholarly works, while earnest in their approach, provide a touch of whimsy and perhaps a hint of nautical folly to our exploration.
Turning to slightly more unexpected sources, we uncover a treasure trove of literature that, while not directly related to our topic, offers a whimsical parallel to our investigation. "Shipwrecks and Scones: A Culinary Odyssey" (Baker, 2018) may not chart the waters of commerce and window adornment, but its delightful title resonates with the maritime misfortunes that have piqued our curiosity. Similarly, "The Merchant's Misery: Tales of Retail Woe" (Garcia, 2014) captures the essence of our investigation in a more metaphorical sense, weaving a narrative of commerce and catastrophe that mirrors our own academic odyssey.
In the realm of fiction, we encounter works that playfully dance on the edge of relevance to our study. Jules Verne's "Twenty Thousand Leagues Under the Sea" offers a fantastical portrayal of maritime adventure that, while not grounded in empirical research, captures the imaginative spirit of our own expedition. Similarly, Patrick O'Brian's "Master and Commander" series, though focused on naval warfare rather than window adornment, provides a captivating glimpse into the tempestuous world of the high seas.
Finally, in a more lighthearted turn, we draw inspiration from the whimsical world of children's cartoons and television shows. Classics such as "SpongeBob SquarePants" and "The Little Mermaid" beckon us to contemplate the enigmatic interplay of commerce and calamity in the context of oceanic narratives. With their colorful characters and nautical escapades, these childhood favorites provide a playful backdrop to our scholarly endeavors, reminding us not to take ourselves too seriously as we navigate the curious currents of correlation.
As we embark on this scholarly odyssey, we invite our fellow researchers to join us in embracing the whimsical allure of our investigation. While the topic at hand may seem quirky and improbable, we assure our colleagues that beneath the veneer of levity lies a robust and intellectually rigorous pursuit of knowledge. Let us set sail together, not merely to discover correlations, but to revel in the delight of academic inquiry as we navigate the capricious tides that bind commerce and calamity.

[[RESULTS]]
In our quest to untangle the curious correlation between the number of merchandise displayers and window trimmers in the District of Columbia and global shipwrecks, we were confronted with a remarkable discovery. A statistically significant correlation coefficient of 0.8020442 and an r-squared of 0.6432749 for the time period 2006 to 2014 left us reeling with astonishment. The p-value of less than 0.01, akin to an elusive treasure buried deep within the realm of statistical significance, further underscored the strength of this relationship.
Even more intriguing, our exploration culminated in the creation of a scatterplot (Fig. 1) that vividly illustrates the robust correlation between these two seemingly unrelated variables. This visual representation of our findings invites the imagination to set sail on a fanciful journey through the tempestuous waters of correlation and causation, ultimately mooring at the harbor of statistical significance.
The unexpected nature of our discovery invokes a sense of whimsy, beckoning us to contemplate the fantastic possibility that the meticulous arrangement of storefront merchandise and window adornments in the District of Columbia may hold the key to unraveling the mysteries of marine misfortune on a global scale. As we reflect on the implications of these findings, we are reminded of the capricious nature of research, where the tides of inquiry can lead us to the most unlikely shores of correlation and causation.
In summary, our foray into this uncharted territory has not only highlighted the enchanting dance between commerce and calamity but has also engendered a sense of wonder within the research community. The implications of this study transcend the mundane boundaries of statistical analysis, inviting scholars to ponder the whimsical threads that intertwine the world of commerce and the tempestuous seas of maritime mishaps.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The pursuit of scholarly inquiry often leads us to unexpected shores, where the enigmatic interplay of seemingly unrelated variables beckons us to unravel the whimsical mysteries that lurk beneath the surface of statistical analysis. Our findings, which reveal a statistically significant correlation between the number of merchandise displayers and window trimmers in the District of Columbia and global shipwrecks, have cast a playful light on the serious business of research.

Harking back to the whimsical wisdom of Smith and Doe (2005) and Jones (2010), we find our results in alignment with their earnest endeavors to navigate the complex currents of commerce and calamity. The substantial correlation coefficient and r-squared value affirm the robustness of this unexpected relationship, rivalling the most captivating tales of maritime adventure. The winds of statistical significance have blown us into uncharted waters, where the landscape is dotted with scatterplots that serve as the compass for our intellectual odyssey.

Revisiting the lighthearted parallels drawn in "The Art of Display: Enhancing Visual Merchandising" (Stevens, 2013) and "Trimming Troubles: A Comprehensive Study of Window Trimming in Urban Landscapes" (Robinson, 2016), we find that our findings not only corroborate but also breathe new life into their contemplations. The whimsical allure of our inquiry invites us to ponder whether the meticulous artistry of storefront presentation might, in fact, hold the power to sway the fickle fortunes of seafaring vessels across the globe.

While our findings might seem plucked from the pages of a nautical novel, they beckon us to contemplate the uncharted depths of correlation and causation, reminding us that truth can often be stranger than fiction. In the spirit of academic inquiry, let us hoist the sails of statistical rigor and navigate the capricious tides that bind commerce and calamity, unearthing the treasures of knowledge that lie submerged in the curious seas of correlation.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between The number of merchandise displayers and window trimmers in District of Columbia and Global shipwrecks . 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]]
The Trade of Trimmers and Troubles at Sea: A Tale of Two Tides

[[ABSTRACT]]
This paper presents an investigation into the curious correlation between the number of merchandise displayers and window trimmers in the District of Columbia and global shipwrecks. Using data from the Bureau of Labor Statistics and Wikipedia, our research team sought to illuminate this enigmatic connection. Strangely enough, our analysis yielded a correlation coefficient of 0.8020442 and p < 0.01 for the years 2006 to 2014, indicating a statistically significant relationship between these seemingly disparate variables. We delve into the implications of these findings, pondering whether the meticulous attention paid to storefront aesthetics might inadvertently lead to maritime mishaps. This study sheds light on an unexpected linkage between commerce and calamity, provoking both amusement and contemplation within the research community.

[[INTRDUCTION]]
INTRODUCTION
The endeavor to unravel the perplexing connection between the number of merchandise displayers and window trimmers in the District of Columbia and global shipwrecks has led our research team down an intriguing and, dare I say, riveting path. While most researchers may set sail in search of correlations between more conventional variables, our voyage into this uncharted territory has yielded unexpected and, some might say, slightly whimsical findings.
As we delved into the seemingly incongruous pairing of storefront aesthetics and maritime mayhem, we couldn't help but marvel at the statistical relationship that emerged from our analysis. With a correlation coefficient of 0.8020442 and a p-value of less than 0.01, our results left us pondering whether there is more to the art of window trimming than meets the eye. The uncanny dance between these variables has prompted us to seek answers in the most unlikely of places.
This study, poised at the intersection of commerce and catastrophe, serves as a reminder of the serendipitous nature of research. While some may see only the choppy seas of data analysis, we find ourselves navigating the intricate currents of correlation, buoyed by the buoyancy of statistical significance.
So, join us on this whimsical and slightly absurd expedition, as we unfurl the sails of curiosity and set our course toward a better understanding of the capricious tides that bind commerce and calamity. Our findings may invite disbelief, perhaps even amusement, but we assure you, dear reader, that this is a tale worth telling.

[[RESULTS]]
In our quest to untangle the curious correlation between the number of merchandise displayers and window trimmers in the District of Columbia and global shipwrecks, we were confronted with a remarkable discovery. A statistically significant correlation coefficient of 0.8020442 and an r-squared of 0.6432749 for the time period 2006 to 2014 left us reeling with astonishment. The p-value of less than 0.01, akin to an elusive treasure buried deep within the realm of statistical significance, further underscored the strength of this relationship.
Even more intriguing, our exploration culminated in the creation of a scatterplot (Fig. 1) that vividly illustrates the robust correlation between these two seemingly unrelated variables. This visual representation of our findings invites the imagination to set sail on a fanciful journey through the tempestuous waters of correlation and causation, ultimately mooring at the harbor of statistical significance.
The unexpected nature of our discovery invokes a sense of whimsy, beckoning us to contemplate the fantastic possibility that the meticulous arrangement of storefront merchandise and window adornments in the District of Columbia may hold the key to unraveling the mysteries of marine misfortune on a global scale. As we reflect on the implications of these findings, we are reminded of the capricious nature of research, where the tides of inquiry can lead us to the most unlikely shores of correlation and causation.
In summary, our foray into this uncharted territory has not only highlighted the enchanting dance between commerce and calamity but has also engendered a sense of wonder within the research community. The implications of this study transcend the mundane boundaries of statistical analysis, inviting scholars to ponder the whimsical threads that intertwine the world of commerce and the tempestuous seas of maritime mishaps.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

As we bring our investigation to a close, we are left with a shipload of thoughts swirling in our heads. The statistically significant correlation between the number of merchandise displayers and window trimmers in the District of Columbia and global shipwrecks has left us feeling like we've stumbled upon a treasure trove of absurdity. It's as if the meticulous arrangement of items in store windows has a siren's call, luring in unsuspecting ships to their doom.

Our findings, while eyebrow-raising, prompt the equally eyebrow-raising question: could the craftsmanship of window trimmers be wielding an unseen force upon the high seas? Perhaps a well-trimmed window creates a hypnotic effect akin to the mesmerizing song of the Sirens, drawing vessels into treacherous waters.

This study offers a whimsical and fanciful reflection on the intersections of commerce and calamity. It's as if the winds of correlation have blown us off course into the realm of the unexpected and improbable. Who would have thought that the glistening displays of consumer goods and the artful decoration of storefront windows could wield such power over the fate of ships at sea?

In conclusion, our research sheds light on a correlation that defies the bounds of convention. With our sails fully unfurled, we urge our fellow researchers to embrace the whimsy of statistics and to ponder the unlikely connections that underpin the world around us. As we dock our research vessel for now, we assert that no further voyages into this captivating realm are necessary.

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited The Trade of Trimmers and Troubles at Sea: A Tale of Two Tides. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Nautical Nonsense and Maritime Mischief

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited The Trade of Trimmers and Troubles at Sea: A Tale of Two Tides. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Nautical Investigations and Maritime Studies

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled The Trade of Trimmers and Troubles at Sea: A Tale of Two Tides with an abstract of This paper presents an investigation into the curious correlation between the number of merchandise displayers and window trimmers in the District of Columbia and global shipwrecks. Using data from the Bureau of Labor Statistics and Wikipedia, our research team sought to illuminate this enigmatic connection. Strangely enough, our analysis yielded a correlation coefficient of 0.8020442 and p < 0.01 for the years 2006 to 2014, indicating a statistically significant relationship between these seemingly disparate variables. We delve into the implications of these findings, pondering whether the meticulous attention paid to storefront aesthetics might inadvertently lead to maritime mishaps. This study sheds light on an unexpected linkage between commerce and calamity, provoking both amusement and contemplation within the research community.

ChatGPT:

merchandise displayers, window trimmers, District of Columbia, global shipwrecks, correlation, Bureau of Labor Statistics, Wikipedia, correlation coefficient, statistical significance, storefront aesthetics, maritime mishaps, commerce and calamity, research community, unexpected linkage

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

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Data details

The number of merchandise displayers and window trimmers in District of Columbia
Detailed data title: BLS estimate of merchandise displayers and window trimmers in District of Columbia
Source: Bureau of Larbor Statistics
See what else correlates with The number of merchandise displayers and window trimmers in District of Columbia

Global shipwrecks
Detailed data title: Ships sunk, foundered, grounded, or otherwise lost during the year
Source: Wikipedia
See what else correlates with Global shipwrecks

Correlation r = 0.8020442 (Pearson correlation coefficient)
Correlation is a measure of how much the variables move together. If it is 0.99, when one goes up the other goes up. If it is 0.02, the connection is very weak or non-existent. If it is -0.99, then when one goes up the other goes down. If it is 1.00, you probably messed up your correlation function.

r² = 0.6432749 (Coefficient of determination)
This means 64.3% of the change in the one variable (i.e., Global shipwrecks ) is predictable based on the change in the other (i.e., The number of merchandise displayers and window trimmers in District of Columbia) over the 9 years from 2006 through 2014.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00931.^Show 0.0093077672675272170000000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.8 in 0.931% of random cases. Said differently, if you correlated 107 random variables^Note Which I absolutely did.
with the same 8 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 8 because we have two variables measured over a period of 9 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.

[ 0.3, 0.96 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: ^Note If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	2006	2007	2008	2009	2010	2011	2012	2013	2014
The number of merchandise displayers and window trimmers in District of Columbia (Laborers)	40	50	60	110	190	180	130	90	60
Global shipwrecks (Wrecks)	13	33	31	55	71	123	111	70	52

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.
Very low n: There are not many data points included in this analysis. Even if the p-value is high, we should be suspicious of using so few datapoints in a correlation.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. ^{Show the steps to do this.} Step 1: Download and install Python on your computer.

Step 2: Open a plaintext editor like Notepad and paste the code below into it.

Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

Step 7: Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."

# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([40,50,60,110,190,180,130,90,60,])
array_2 = np.array([13,33,31,55,71,123,111,70,52,])
array_1_name = "The number of merchandise displayers and window trimmers in District of Columbia"
array_2_name = "Global shipwrecks "

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

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

Reuseable content

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

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

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

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

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

Download images for these variables:

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

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

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only The number of merchandise displayers and window trimmers in District of Columbia
Line chart for only Global shipwrecks
AI-generated correlation image
The spurious research paper: The Trade of Trimmers and Troubles at Sea: A Tale of Two Tides

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Correlation ID: 3797 · Black Variable ID: 17662 · Red Variable ID: 504


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