Spurious correlation #1,998 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is NCAA Men's Skiing champion's points and the second variable is Visitors to SeaWorld California. The chart goes from 2007 to 2021, 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 NCAA Men's Skiing champion's points and the second variable is Visitors to SeaWorld California. The chart goes from 2007 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the skiers shredded the slopes, they unknowingly created a wave of interest in aquatic prowess, leading to a surge in SeaWorld California visitors. It seems their stellar performances were simply too fin-tastic to resist, proving that when it comes to attracting crowds, these champions really know how to make a splash! Remember correlation does not equal causation!

^{Show image generation prompt} Model: dalle-3
Prompt: [ Cutsy graphics ] A bright and cheerful illustration of NCAA Men's Skiing champions, relaxed and jovial, chatting amongst themselves as they stroll by the lively dolphin exhibit at SeaWorld California. Meanwhile, a group of delighted visitors watches in awe as the graceful dolphins perform their aquatic acrobatics, adding to the festive atmosphere at the park. The sunny, vibrant scene captures the joyful interaction between the athletes, the majestic dolphins, and the admiring crowd. 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: NCAA Men's Skiing champion's points and Visitors to SeaWorld California. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: A black-and-white newspaper illustration Oragami Concept art Parallax art Cutsy graphics
ChatGPT: Cutsy graphics

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both NCAA Men's Skiing champion's points and Visitors to SeaWorld California together. Keep the description simple.
ChatGPT: On a sunny day at SeaWorld California, a group of NCAA Men's Skiing champions are enjoying a break off the slopes, discussing their impressive points total for the season as they walk by the dolphin exhibit. Nearby, a crowd of visitors marvel at the graceful movements of the dolphins, adding to the festive atmosphere at the park.

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: [ Cutsy graphics ] and depicting this scene: On a sunny day at SeaWorld California, a group of NCAA Men's Skiing champions are enjoying a break off the slopes, discussing their impressive points total for the season as they walk by the dolphin exhibit. Nearby, a crowd of visitors marvel at the graceful movements of the dolphins, adding to the festive atmosphere at the park. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ Cutsy graphics ] A bright and cheerful illustration of NCAA Men's Skiing champions, relaxed and jovial, chatting amongst themselves as they stroll by the lively dolphin exhibit at SeaWorld California. Meanwhile, a group of delighted visitors watches in awe as the graceful dolphins perform their aquatic acrobatics, adding to the festive atmosphere at the park. The sunny, vibrant scene captures the joyful interaction between the athletes, the majestic dolphins, and the admiring crowd.

*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 NCAA Men's Skiing champion's points positively influenced Visitors to SeaWorld California. Include one good pun.

AI academic paper

(Because p < 0.01)

Ski Me to SeaWorld: Unraveling the Surprising Link Between NCAA Men's Skiing Champions' Points and Visitors to SeaWorld California

Journal of Sports Tourism and Animal Behavior

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 goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between NCAA Men's Skiing champion's points and Visitors to SeaWorld California. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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 NCAA and TEA . You used data from 2007 to 2021

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
Ski Me to SeaWorld: Unraveling the Surprising Link Between NCAA Men's Skiing Champions' Points and Visitors to SeaWorld California

[[ABSTRACT]]
In the realm of sports and entertainment, it's often thought that what happens on the ski slopes stays on the ski slopes, while the fascinating world of marine life at SeaWorld California remains a world unto itself. However, our research sought to bridge these seemingly disparate domains and shed light on the unexpected relationship between NCAA men's skiing champion's points and visitor attendance at SeaWorld California. By using data from the NCAA and The Economic Analysis (TEA) park attendance report, we delved into this peculiar correlation, uncovering a coefficient of 0.9159078 with a level of significance at p < 0.01 for the years spanning from 2007 to 2021.

Our findings not only raise eyebrows but also provide a new perspective on the interplay between athletic achievement and leisure activities. Who would have guessed that the thrill of the slopes could somehow translate into an uptick in orca admiration? It seems that when it comes to sports and marine life, the connection runs deeper than anticipated – perhaps even as deep as the ocean itself!

So, the next time you witness a breathtaking slalom run or a heart-stopping ski jump, remember that those exhilarating moments might just be contributing to the joyous cheers at SeaWorld – it's a real ski-to-sea experience!

[[INTRODUCTION]]
Sporting events and amusement parks are like polar opposites - one involves intense physical competition, while the other revolves around carefree leisure and joy. However, as researchers, it is our duty to explore uncharted territories and uncover unexpected connections, even if it means skiing through unchartered snow to find them. Our study aims to do just that, delving into the puzzling correlation between NCAA Men's Skiing champion's points and the number of visitors to SeaWorld California. If statistical analysis were a ski slope, then we are attempting to navigate the black diamond runs of correlation to unveil the hidden link between these seemingly distinct domains.
As the saying goes, "why did the statistician go to SeaWorld? To visit the whale of p-values!" Our pursuit of this peculiar relationship began with a simple question: could the thrilling victories and astonishing feats on the ski slopes have any bearing on the leisure choices of marine life enthusiasts visiting SeaWorld California? It may sound like a punchline, but our findings have revealed a surprising coefficient that would make even the most serious statistician do a double take.
We embarked on this journey armed with data from the NCAA, where athletes braved icy terrains and reached new heights in competitive skiing, not unlike researchers scaling the treacherous terrain of correlation and causation. The Economic Analysis (TEA) park attendance report was our next stop, where we delved into the depths of visitor numbers to unravel the mysteries of the marine world. Like intrepid explorers, we combed through the data from 2007 to 2021, charting a course through the sea of numbers, ensuring our analysis was anchored in rigorous statistical methods, and not just "reely" loose conjecture.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

It's time to hit the slopes and dive into the methodological framework that propelled us toward unraveling the enigmatic connection between NCAA Men's Skiing champion's points and the number of visitors to SeaWorld California. Our research team adopted a meticulous approach, combining cunning statistical maneuvers with a touch of whimsical curiosity in true academic fashion.

Firstly, we harnessed the power of web scraping to gather data from various reputable sources, diligently sifting through the digital snowdrifts of the internet. We focused our efforts on extracting detailed information regarding NCAA Men's Skiing championship points from official records, ensuring that our dataset sparkled as brightly as freshly fallen powder. It was like snowboarding through the cyber wilderness – exhilarating and occasionally bumpy, but always full of unexpected discoveries. After all, when it comes to research, you never know when you'll stumble upon a hidden statistical gem or a cheeky pun just waiting to be plucked.

Upon securing the skiing championship data, we ventured into the digital depths once more, this time setting our sights on the treasure trove of visitor attendance figures at SeaWorld California. Through the wonders of digital archiving and data mining, we navigated the virtual waves, collecting attendance data from 2007 to 2021 with the precision of a seasoned marine explorer. It was the research equivalent of surfing the net, but instead of catching virtual waves, we were riding the tumultuous seas of statistics and amusement park metrics. A bit, shall we say, "sea-riously" exhilarating.

Once we had corralled these disparate datasets like expert skiers navigating a slalom course, it was time to wave our statistical wands and conjure the magic of analysis. We donned our metaphorical lab coats and embarked on a journey through the windswept landscape of correlation coefficients, leveraging the vaunted power of Pearson's r to measure the strength and direction of the relationship between NCAA Men's Skiing champion's points and SeaWorld California visitors. It was like crafting an intricate snowflake – each statistic meticulously carved to reveal the delicate patterns beneath the surface. And, just like a snowflake, our findings promised to be utterly unique and bound to melt a few hearts.

In addition to our statistical forays, we employed a series of robust checks and controls to ensure the integrity and reliability of our analysis. We leveraged outlier detection methods to sift through the data and separate the statistical moguls from the modest slopes, ensuring that our findings were built on a foundation of solid, unadulterated information and not on any "slippery slope" of dubious data. After all, in the world of research, we prefer our data as pure as the Alpine snow and as reliable as a well-waxed pair of skis.

With our methods finely tuned and our data in hand, we embarked on a voyage of exploration and discovery, eager to uncover the surprising relationship that lay hidden between ski slopes and marine marvels. It was a thrilling ride, navigating the peaks and valleys of data analysis with the spirit of intrepid explorers, pushing the boundaries of academic inquiry and perhaps picking up a few new ski buddies along the way. As they say, "why did the statistician bring skis to the amusement park? For the correlation coefficient rides, of course!"

Stay tuned for our upcoming adventures in data interpretation and results, where we'll unveil the intriguing findings that emerged from this unprecedented blend of athletic prowess and aquatic admiration.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between NCAA Men's Skiing champion's points and Visitors to SeaWorld California. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
Ski Me to SeaWorld: Unraveling the Surprising Link Between NCAA Men's Skiing Champions' Points and Visitors to SeaWorld California

[[ABSTRACT]]
In the realm of sports and entertainment, it's often thought that what happens on the ski slopes stays on the ski slopes, while the fascinating world of marine life at SeaWorld California remains a world unto itself. However, our research sought to bridge these seemingly disparate domains and shed light on the unexpected relationship between NCAA men's skiing champion's points and visitor attendance at SeaWorld California. By using data from the NCAA and The Economic Analysis (TEA) park attendance report, we delved into this peculiar correlation, uncovering a coefficient of 0.9159078 with a level of significance at p < 0.01 for the years spanning from 2007 to 2021.

Our findings not only raise eyebrows but also provide a new perspective on the interplay between athletic achievement and leisure activities. Who would have guessed that the thrill of the slopes could somehow translate into an uptick in orca admiration? It seems that when it comes to sports and marine life, the connection runs deeper than anticipated – perhaps even as deep as the ocean itself!

So, the next time you witness a breathtaking slalom run or a heart-stopping ski jump, remember that those exhilarating moments might just be contributing to the joyous cheers at SeaWorld – it's a real ski-to-sea experience!

[[LITERATURE REVIEW]]
The connection between NCAA Men's Skiing champion's points and visitor attendance at SeaWorld California may seem like a stretch - pun intended - but our research has unearthed a surprising body of literature that provides insight into this unexpected relationship. In "Slope Stats Quarterly," Smith and Doe analyze the performance of NCAA men's skiing champions and their impact on local tourism, shedding light on the potential influence of sporting events on leisure activities. Similarly, Jones and Johnson, in "Cold Pursuits and Hot Spots," delve into the sports tourism phenomenon, examining the ripple effects of sporting victories on tourist behaviors. These serious scholars set the stage for our investigation into the intersection of sports achievement and marine park visitation.
However, as we wade through the scholarly waters, it's important to recognize the broader cultural and societal context that may influence such correlations. In "The Economics of Fun," economist Emma Moneybags highlights the intricacies of consumer behavior in the realm of leisure and entertainment, providing a framework for understanding the factors that drive individuals to seek out recreational experiences. On the fiction front, novels such as "The Skiing Whale Whisperer" and "Orca Olympics: A Tale of Two Worlds" offer imaginative narratives that, while not rooted in empirical research, capture the essence of the ski-to-sea connection we are exploring.
As we ventured further into the literature, it became apparent that even unconventional sources can offer valuable insights. While not traditionally considered scholarly, the back of shampoo bottles – yes, you read that right – provide a surprising amount of information about marine life and outdoor activities. Who knew that a simple morning shower could double as data collection for our research? But it's all par for the course in unraveling the enigmatic relationship between skiing prowess and SeaWorld visits. After all, when it comes to making waves in research, sometimes you have to dive into unexpected pools of knowledge.

[[RESULTS]]
The correlation coefficient between NCAA Men's Skiing champion's points and visitor attendance at SeaWorld California yielded a surprising value of 0.9159078, indicating a remarkably strong positive relationship between these two variables. In simpler terms, it's like discovering that the thrill of speeding down a ski slope is somehow linked to an increase in dolphin sightings at SeaWorld – a wave of unexpected connectedness that leaves us "fin"tastically surprised.
The coefficient of determination (r-squared) also proved to be quite robust at 0.8388871, suggesting that a substantial 83.9% of the variation in SeaWorld California's visitor attendance can be explained by changes in NCAA Men's Skiing champion's points. This means that every time a skier scores big on the slopes, there's an overwhelming likelihood of a surge in attendance at the marine park. It's as if every triumphant slalom run sends a wave of excitement rippling through SeaWorld – talk about making a splash in the statistical pool!
The statistical significance further reinforced the strength of this relationship, with a level of significance at p < 0.01. In other words, the probability of this extraordinary association occurring by chance is less than 1 in 100, making it a highly unlikely statistical fluke. It's safe to say that this surprising interplay between skiing and marine life is no mere statistical "manta-ray."
The correlation between the NCAA Men's Skiing champion's points and SeaWorld California's visitor attendance is visually represented in Figure 1. This scatterplot depicts a clear, upward-trending pattern, mirroring the cohesive bond unearthed by our analysis. If data visualization were a ski jump, this graph would be soaring through the statistical slopes, representing a thrilling leap from the expected into the unanticipated realm of sports and marine attraction.
Who would have thought that the exhilaration of skiing victory and the delight of marine life could be so closely intertwined? It's a statistical revelation that certainly "makes waves" in the attitudes toward sports and leisure activities. It turns out that a love for skiing and a passion for marine life can go hand-in-fin after all - a surprising twist that leaves us "whale-y" impressed!

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our results have indeed confirmed and extended the prior research that hinted at a connection between NCAA Men's Skiing champion's points and visitor attendance at SeaWorld California. The robust correlation coefficient of 0.9159078 not only aligns with the findings by Smith and Doe in "Slope Stats Quarterly" but also surpasses expectations, much like a skier defying gravity on the slopes. This substantial correlation suggests that the thrill of skiing success may serve as an unexpected magnet for marine park enthusiasts, creating a symbiotic relationship that's the perfect blend of slope prowess and sea wow-ness.

It seems that our foray into the intersection of athletic achievement and leisure activities has turned previous assumptions upside-down – much like an expertly executed mogul turn. Our research not only supports the notion put forth by Jones and Johnson in "Cold Pursuits and Hot Spots" but also elevates it to new heights, akin to a ski jumper reaching the apex of their flight. The statistical "manta-ray" we've uncovered solidifies the idea that sporting triumphs can send ripples of influence far beyond the competition arena, shaping the recreational choices of enthusiasts and casual spectators alike.

We can't help but revel in the irony that while the intriguing influence of skiing victories on tourist behaviors might seem like a slippery slope of a hypothesis, our data has firmly grounded it in statistical reality, much like a well-gripped ski binding. It's a reminder that in the world of research, truth can indeed be stranger than fiction - or in this case, even stranger than the shampoo bottle trivia we stumbled upon in our literature review. Because when it comes to unraveling the enigmatic relationships in our world, sometimes the most unexpected sources hold the most surprising nuggets of truth.

The visually captivating upward-trending pattern depicted in Figure 1 not only reinforces the strength of our findings but also serves as a visual reminder that in the world of statistical analysis, what may initially seem like a "slippery slope" of a relationship can, in fact, turn out to be an exhilarating ski jump of statistical significance. It's a fitting metaphor for the unexpected connections we've unveiled, where the excitement of one domain cascades into the delight of another, creating a seamless blend of ski-thrill and sea-chill.

Our research yields an illuminating revelation - that the enthralling achievements on the ski slopes and the captivating allure of marine life are not disparate realms but interconnected spheres that feed off each other's exhilaration. It's a statistical insight that's as awe-inspiring as a stunning ski jump and as harmonious as a synchronized orca performance. Our findings not only "snowball" the existing literature but also pave the way for further exploration of the intricate web of connections between sports, leisure, and unexpected correlations. After all, whether skiing or statistical analysis, it's clear that incredible connections can be made when we're just willing to "slope" down and take a closer look!

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between NCAA Men's Skiing champion's points and Visitors to SeaWorld California. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
Ski Me to SeaWorld: Unraveling the Surprising Link Between NCAA Men's Skiing Champions' Points and Visitors to SeaWorld California

[[ABSTRACT]]
In the realm of sports and entertainment, it's often thought that what happens on the ski slopes stays on the ski slopes, while the fascinating world of marine life at SeaWorld California remains a world unto itself. However, our research sought to bridge these seemingly disparate domains and shed light on the unexpected relationship between NCAA men's skiing champion's points and visitor attendance at SeaWorld California. By using data from the NCAA and The Economic Analysis (TEA) park attendance report, we delved into this peculiar correlation, uncovering a coefficient of 0.9159078 with a level of significance at p < 0.01 for the years spanning from 2007 to 2021.

Our findings not only raise eyebrows but also provide a new perspective on the interplay between athletic achievement and leisure activities. Who would have guessed that the thrill of the slopes could somehow translate into an uptick in orca admiration? It seems that when it comes to sports and marine life, the connection runs deeper than anticipated – perhaps even as deep as the ocean itself!

So, the next time you witness a breathtaking slalom run or a heart-stopping ski jump, remember that those exhilarating moments might just be contributing to the joyous cheers at SeaWorld – it's a real ski-to-sea experience!

[[INTRDUCTION]]
Sporting events and amusement parks are like polar opposites - one involves intense physical competition, while the other revolves around carefree leisure and joy. However, as researchers, it is our duty to explore uncharted territories and uncover unexpected connections, even if it means skiing through unchartered snow to find them. Our study aims to do just that, delving into the puzzling correlation between NCAA Men's Skiing champion's points and the number of visitors to SeaWorld California. If statistical analysis were a ski slope, then we are attempting to navigate the black diamond runs of correlation to unveil the hidden link between these seemingly distinct domains.
As the saying goes, "why did the statistician go to SeaWorld? To visit the whale of p-values!" Our pursuit of this peculiar relationship began with a simple question: could the thrilling victories and astonishing feats on the ski slopes have any bearing on the leisure choices of marine life enthusiasts visiting SeaWorld California? It may sound like a punchline, but our findings have revealed a surprising coefficient that would make even the most serious statistician do a double take.
We embarked on this journey armed with data from the NCAA, where athletes braved icy terrains and reached new heights in competitive skiing, not unlike researchers scaling the treacherous terrain of correlation and causation. The Economic Analysis (TEA) park attendance report was our next stop, where we delved into the depths of visitor numbers to unravel the mysteries of the marine world. Like intrepid explorers, we combed through the data from 2007 to 2021, charting a course through the sea of numbers, ensuring our analysis was anchored in rigorous statistical methods, and not just "reely" loose conjecture.

[[RESULTS]]
The correlation coefficient between NCAA Men's Skiing champion's points and visitor attendance at SeaWorld California yielded a surprising value of 0.9159078, indicating a remarkably strong positive relationship between these two variables. In simpler terms, it's like discovering that the thrill of speeding down a ski slope is somehow linked to an increase in dolphin sightings at SeaWorld – a wave of unexpected connectedness that leaves us "fin"tastically surprised.
The coefficient of determination (r-squared) also proved to be quite robust at 0.8388871, suggesting that a substantial 83.9% of the variation in SeaWorld California's visitor attendance can be explained by changes in NCAA Men's Skiing champion's points. This means that every time a skier scores big on the slopes, there's an overwhelming likelihood of a surge in attendance at the marine park. It's as if every triumphant slalom run sends a wave of excitement rippling through SeaWorld – talk about making a splash in the statistical pool!
The statistical significance further reinforced the strength of this relationship, with a level of significance at p < 0.01. In other words, the probability of this extraordinary association occurring by chance is less than 1 in 100, making it a highly unlikely statistical fluke. It's safe to say that this surprising interplay between skiing and marine life is no mere statistical "manta-ray."
The correlation between the NCAA Men's Skiing champion's points and SeaWorld California's visitor attendance is visually represented in Figure 1. This scatterplot depicts a clear, upward-trending pattern, mirroring the cohesive bond unearthed by our analysis. If data visualization were a ski jump, this graph would be soaring through the statistical slopes, representing a thrilling leap from the expected into the unanticipated realm of sports and marine attraction.
Who would have thought that the exhilaration of skiing victory and the delight of marine life could be so closely intertwined? It's a statistical revelation that certainly "makes waves" in the attitudes toward sports and leisure activities. It turns out that a love for skiing and a passion for marine life can go hand-in-fin after all - a surprising twist that leaves us "whale-y" impressed!

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has unveiled an astonishingly strong positive relationship between NCAA Men's Skiing champion's points and visitor attendance at SeaWorld California, akin to discovering a remarkable bond between two seemingly unrelated entities – like finding out dolphins are secretly huge fans of downhill skiing. Our findings defy conventional wisdom and showcase the unexpected interconnectedness within the realms of competitive skiing and marine park leisure, shedding light on a correlation that's as striking as a polar bear in a snowstorm.

The robustness of the correlation coefficient and the coefficient of determination not only reinforces the strength of this peculiar connection but also underscores the statistical significance of our findings. It's as if the statistical gods themselves are saying, "Yes, this is real – not just some statistical fluke, folks!" It's a statistical slam dunk that leaves us feeling like we've just hit the slopes and scored a perfect 10 from the judges.

Our scatterplot graph stands as a visual testament to this surprising relationship, painting a vivid picture of the synchronized rise in both NCAA Men's Skiing champion's points and SeaWorld California's visitor attendance. It's like watching a beautiful choreography unfold before our very eyes, except instead of dancers, we have data points gracefully waltzing across the plot, demonstrating the unexpected harmony between snowy pursuits and marine marvels.

So, based on our findings, it's safe to say that the success of NCAA Men's Skiing champions creates a ripple effect that resonates all the way to SeaWorld California. It's like a heartwarming story of triumph on the slopes leading to cheers and applause amidst the majestic sea creatures – a tale that would undoubtedly make even the most hardened statistician shed a tear of joy.

In light of these compelling results, we assert that further research in this area is simply unnecessary. After all, when the statistical slopes bring us such delightful surprises, why go seeking more correlations? It seems that for now, we've hit the sweet spot – or should we say the "slope" spot - where skiing champions and marine park enthusiasts intersect, creating a statistical marvel that's as awe-inspiring as witnessing a killer whale leap into the air.

No more research needed, folks - we've hit the jackpot in this quirky world of statistical connections. It's time to call it a day and bask in the glow of this unexpected, yet marvelous, statistical amusement.

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 Ski Me to SeaWorld: Unraveling the Surprising Link Between NCAA Men's Skiing Champions' Points and Visitors to SeaWorld California. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Sports Tourism and Animal Behavior

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 Ski Me to SeaWorld: Unraveling the Surprising Link Between NCAA Men's Skiing Champions' Points and Visitors to SeaWorld California. Respond only with the name of a fictional organization.

ChatGPT:

The Alpine Sports and Marine Life Research Institute

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 Ski Me to SeaWorld: Unraveling the Surprising Link Between NCAA Men's Skiing Champions' Points and Visitors to SeaWorld California with an abstract of In the realm of sports and entertainment, it's often thought that what happens on the ski slopes stays on the ski slopes, while the fascinating world of marine life at SeaWorld California remains a world unto itself. However, our research sought to bridge these seemingly disparate domains and shed light on the unexpected relationship between NCAA men's skiing champion's points and visitor attendance at SeaWorld California. By using data from the NCAA and The Economic Analysis (TEA) park attendance report, we delved into this peculiar correlation, uncovering a coefficient of 0.9159078 with a level of significance at p < 0.01 for the years spanning from 2007 to 2021.
Our findings not only raise eyebrows but also provide a new perspective on the interplay between athletic achievement and leisure activities. Who would have guessed that the thrill of the slopes could somehow translate into an uptick in orca admiration? It seems that when it comes to sports and marine life, the connection runs deeper than anticipated – perhaps even as deep as the ocean itself!
So, the next time you witness a breathtaking slalom run or a heart-stopping ski jump, remember that those exhilarating moments might just be contributing to the joyous cheers at SeaWorld – it's a real ski-to-sea experience!

ChatGPT:

NCAA men's skiing champion, SeaWorld California, TEA park attendance report, NCAA skiing champions' points, visitor attendance correlation, sports and leisure activities, ski slope impact on marine life, athletic achievement and leisure activities, ski-to-sea experience, surprising link between sports and marine life

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

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Data details

NCAA Men's Skiing champion's points
Source: NCAA
See what else correlates with NCAA Men's Skiing champion's points

Visitors to SeaWorld California
Detailed data title: SeaWorld California visitor count
Source: TEA
See what else correlates with Visitors to SeaWorld California

Correlation r = 0.9159078 (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 = 0.8388871 (Coefficient of determination)
This means 83.9% of the change in the one variable (i.e., Visitors to SeaWorld California) is predictable based on the change in the other (i.e., NCAA Men's Skiing champion's points) over the 15 years from 2007 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.6E-6.^Show 0.0000016432558236520518000000
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 0.92 in 0.00016% of random cases. Said differently, if you correlated 608,548 random variables^Note You don't actually need 608 thousand variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.

In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 14 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 14 because we have two variables measured over a period of 15 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.

[ 0.76, 0.97 ] 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.

	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
NCAA Men's Skiing champion's points (Points)	698	649.6	659	785.5	831	832	708	556	505	567.5	541.5	603	530.5	0	554
Visitors to SeaWorld California (Visitors)	4.26	4.147	4.2	3.8	4.294	4.444	4.311	3.794	3.528	3.528	3.1	3.723	3.731	1.139	2.8

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.
Confounding variable: 2020 is particularly different from the other years on this graph. Confounding variables (like global pandemics) will cause two variables to look connected when in fact a "sneaky third" variable is influencing both of them behind the scenes.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. ^{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([698,649.6,659,785.5,831,832,708,556,505,567.5,541.5,603,530.5,0,554,])
array_2 = np.array([4.26,4.147,4.2,3.8,4.294,4.444,4.311,3.794,3.528,3.528,3.1,3.723,3.731,1.139,2.8,])
array_1_name = "NCAA Men's Skiing champion's points"
array_2_name = "Visitors to SeaWorld California"

# 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 NCAA Men's Skiing champion's points
Line chart for only Visitors to SeaWorld California
AI-generated correlation image
The spurious research paper: Ski Me to SeaWorld: Unraveling the Surprising Link Between NCAA Men's Skiing Champions' Points and Visitors to SeaWorld California

View another random correlation

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Correlation ID: 1998 · Black Variable ID: 241 · Red Variable ID: 524


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