Spurious correlation #3,089 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Total private high school enrollment in the United States and the second variable is Kobe Bryant's total free throw count in NBA regular season. The chart goes from 1997 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 Total private high school enrollment in the United States and the second variable is Kobe Bryant's total free throw count in NBA regular season. The chart goes from 1997 to 2014, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The more high school students there are, the greater the demand for basketball hoops, leading to an increase in the number of available basketball hoops across the country. With more opportunities to practice, Kobe Bryant was able to perfect his free throw technique and score more points in NBA regular season games.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a bustling gymnasium at a private high school in the United States, where students are seen flocking to the basketball court, eagerly practicing their free throws. The gymnasium, inspired by Brutalist architecture, features imposing concrete pillars and bold geometric shapes. The students, driven by the enthusiasm for the sport of basketball, are depicted in motion, capturing the dynamic energy of the scene. At the center of the court, Kobe Bryant, with a determined look on his face, is diligently perfecting his free throw technique, setting a shining example for the aspiring players. The image showcases the link between the increase in private high school enrollment and Kobe Bryant's total free throw count in NBA regular seasons, conveying a vibrant and bustling atmosphere. 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: Total private high school enrollment in the United States and Kobe Bryant's total free throw count in NBA regular season. 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: Cyberpunk Brutalism inspired by bauhaus Psychedelic Vaporwave
ChatGPT: Brutalism

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 Total private high school enrollment in the United States and Kobe Bryant's total free throw count in NBA regular season, in the following situation: Because as private high school enrollment increases, more students have access to basketball courts, leading to an influx of young athletes eager to emulate their favorite players. This heightened interest in basketball results in more kids practicing free throws, ultimately inspiring Kobe Bryant to set an example by honing his own free throw skills to perfection. Thus, the increase in total private high school enrollment directly correlates with Kobe Bryant's total free throw count in NBA regular seasons.. Keep the description simple.
ChatGPT: In a bustling gymnasium at a private high school in the United States, students flock to the basketball court, eagerly practicing their free throws. As the school's enrollment continues to rise, more young athletes are drawn to the sport, inspired by the likes of Kobe Bryant. In the midst of this newfound enthusiasm, Kobe Bryant, with a determined look on his face, is seen diligently perfecting his free throw technique, setting a shining example for the aspiring players. The link between the increase in private high school enrollment and Kobe Bryant's total free throw count in NBA regular seasons is clear in this vibrant and bustling scene.

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: [ Brutalism ] and depicting this scene: In a bustling gymnasium at a private high school in the United States, students flock to the basketball court, eagerly practicing their free throws. As the school's enrollment continues to rise, more young athletes are drawn to the sport, inspired by the likes of Kobe Bryant. In the midst of this newfound enthusiasm, Kobe Bryant, with a determined look on his face, is seen diligently perfecting his free throw technique, setting a shining example for the aspiring players. The link between the increase in private high school enrollment and Kobe Bryant's total free throw count in NBA regular seasons is clear in this vibrant and bustling scene. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a bustling gymnasium at a private high school in the United States, where students are seen flocking to the basketball court, eagerly practicing their free throws. The gymnasium, inspired by Brutalist architecture, features imposing concrete pillars and bold geometric shapes. The students, driven by the enthusiasm for the sport of basketball, are depicted in motion, capturing the dynamic energy of the scene. At the center of the court, Kobe Bryant, with a determined look on his face, is diligently perfecting his free throw technique, setting a shining example for the aspiring players. The image showcases the link between the increase in private high school enrollment and Kobe Bryant's total free throw count in NBA regular seasons, conveying a vibrant and bustling atmosphere.

*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 Total private high school enrollment in the United States positively influenced Kobe Bryant's total free throw count in NBA regular season.

AI academic paper

(Because p < 0.01)

The Ties between Total private high school enrollment in the United States and Total free throw count of the Tenacious Kobe Bryant in NBA regular seasons: A Tantalizing Tidbit

The Journal of Quirky Quantitative Research

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 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 Total private high school enrollment in the United States and Kobe Bryant's total free throw count in NBA regular season. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading the backs of shampoo bottles.

Here is the title and abstract of the paper:
[[TITLE]]
The Ties between Total private high school enrollment in the United States and Total free throw count of the Tenacious Kobe Bryant in NBA regular seasons: A Tantalizing Tidbit

[[ABSTRACT]]
The relationship between seemingly unrelated phenomena often perplexes researchers, leading them down peculiar paths of inquiry. In this study, we delve into the connection between the total private high school enrollment in the United States and the free throw count of the tenacious Kobe Bryant during NBA regular seasons. Evidently, the similarity in numbers is as intriguing as it is amusing, much like a dad joke that just won't quit.

Drawing from data provided by Statista and the NBA, we meticulously examined the statistics from 1997 to 2014. Our analysis revealed a remarkably strong correlation coefficient of 0.8630521 with a p-value of less than 0.01. If this correlation were any stronger, it would have its own gravitational pull, much like the magnetic allure of a dad joke at a family gathering.

The implications of these findings extend beyond mere statistical quirks. They provoke contemplation of the subtle interplay between seemingly disparate elements, akin to the unexpected punchline of a well-crafted dad joke. As we present our results, we invite fellow researchers to join us in this whimsical exploration of the unanticipated connections that unfold in the realm of data analysis. After all, every data point has a story to tell, much like a good old dad joke.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The interplay between seemingly unrelated variables has long been a source of fascination for researchers across disciplines. The intricate dance of data can lead us down unexpected paths, much like a dad joke that catches us off guard and elicits a groan and a chuckle in equal measure. In this study, we embark on a whimsical journey into the connection between two unusual bedfellows: the total private high school enrollment in the United States and the free throw count of the tenacious Kobe Bryant during NBA regular seasons.
Why did the statistician go to art school? Because he wanted to learn how to draw a correlation! Much like this classic dad joke, our exploration of the connection between high school enrollment and free throw counts promises both amusement and insight.
For this peculiar investigation, we turned to data from Statista and the NBA, covering the years 1997 to 2014. Our mission was clear: to uncover any hint of a relationship between these disparate variables and, in the process, unravel a mystery as puzzling as the punchline of an elaborate dad joke.
The initial stages of our analysis yielded a surprising revelation. The correlation coefficient between total private high school enrollment and Kobe Bryant's free throw count stood at a remarkable 0.8630521, with a p-value of less than 0.01. This strong correlation had us scratching our heads in disbelief, much like the reaction to a particularly groan-worthy dad joke at a family dinner.
It's often said that correlation does not imply causation, but in the case of our findings, the connection between these two phenomena seemed too tantalizing to ignore. Our study digs into the implications of this unexpected relationship, prompting contemplation of the intricate ways in which seemingly disparate elements can intertwine, not unlike the clever twist in a well-crafted dad joke.
Join us as we unravel this enigmatic entanglement, for in the realm of data analysis, every unexpected correlation holds the potential to spark joy and insight, akin to the delivery of a perfectly timed dad joke.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

To our surprise and amusement, our foray into the peculiar connection between total private high school enrollment in the United States and the free throw count of the tenacious Kobe Bryant during NBA regular seasons unearthed a curious blend of research and quirkiness, much like stumbling upon a dad joke in a scholarly journal.

Scholars such as Smith and Doe have extensively examined the factors contributing to fluctuations in private high school enrollment, highlighting socioeconomic influences, academic reputation, and demographic shifts. Similarly, Jones delved into the nuances of free throw performance in professional basketball, emphasizing the psychological and physical aspects that impact a player's success from the foul line.

In "The High School Enrollment Dilemma" and "The Psychology of Free Throws," the authors articulate the multifaceted dynamics at play within these realms, offering valuable insights into the intricate nature of our study's focal points. However, little did they know, we were about to take this scholarly exploration into the domain of unlikely correlations, much like a dad joke takes an unexpected turn and catches everyone off guard.

Turning to non-fiction works with tangential relevance, "Freakonomics" and "The Tipping Point" also stand as exemplars of investigations into surprising links and unforeseen relationships within diverse phenomena. These scholarly works, much like a well-structured dad joke, capture the imagination and incite curiosity about the hidden connections lying beneath the surface of seemingly disparate entities.

Adding a dash of whimsy, we looked to fictional narratives such as "The Da Vinci Code" and "Murder on the Orient Express" for inspiration, drawing parallels between the intricate web of clues in a literary mystery and the unexpected connections we sought to uncover in our statistical analysis. These works, much like a witty one-liner from a dad joke enthusiast, stimulated our own quest to unravel the enigmatic tie between high school enrollment and free throw counts.

As we sought a comprehensive understanding of this unconventional correlation, we cast our net wide, even venturing into uncharted territories. In an unconventional approach to literature review, we drew inspiration from diverse sources including the backs of cereal boxes, shampoo bottles, and fortune cookies, because sometimes, inspiration strikes in the most unexpected places - much like the setup to a dad joke that catches you off guard in the middle of a serious conversation.

In this lighthearted search for scholarly wisdom, we remained committed to unearthing the most unexpected and delightfully bizarre connections, for in the realm of quirky correlations, every unexpected source could hold the key to unlocking the mysteries that lie beneath the surface, not unlike the payoff of a well-crafted dad joke that catches you by surprise and leaves you with a lingering smile.

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 Total private high school enrollment in the United States and Kobe Bryant's total free throw count in NBA regular season. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Your research team collected data from all across the internet, but mostly just used information from Statista and NBA . You used data from 1997 to 2014

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

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
The Ties between Total private high school enrollment in the United States and Total free throw count of the Tenacious Kobe Bryant in NBA regular seasons: A Tantalizing Tidbit

[[ABSTRACT]]
The relationship between seemingly unrelated phenomena often perplexes researchers, leading them down peculiar paths of inquiry. In this study, we delve into the connection between the total private high school enrollment in the United States and the free throw count of the tenacious Kobe Bryant during NBA regular seasons. Evidently, the similarity in numbers is as intriguing as it is amusing, much like a dad joke that just won't quit.

Drawing from data provided by Statista and the NBA, we meticulously examined the statistics from 1997 to 2014. Our analysis revealed a remarkably strong correlation coefficient of 0.8630521 with a p-value of less than 0.01. If this correlation were any stronger, it would have its own gravitational pull, much like the magnetic allure of a dad joke at a family gathering.

The implications of these findings extend beyond mere statistical quirks. They provoke contemplation of the subtle interplay between seemingly disparate elements, akin to the unexpected punchline of a well-crafted dad joke. As we present our results, we invite fellow researchers to join us in this whimsical exploration of the unanticipated connections that unfold in the realm of data analysis. After all, every data point has a story to tell, much like a good old dad joke.

[[INTRODUCTION]]
The interplay between seemingly unrelated variables has long been a source of fascination for researchers across disciplines. The intricate dance of data can lead us down unexpected paths, much like a dad joke that catches us off guard and elicits a groan and a chuckle in equal measure. In this study, we embark on a whimsical journey into the connection between two unusual bedfellows: the total private high school enrollment in the United States and the free throw count of the tenacious Kobe Bryant during NBA regular seasons.
Why did the statistician go to art school? Because he wanted to learn how to draw a correlation! Much like this classic dad joke, our exploration of the connection between high school enrollment and free throw counts promises both amusement and insight.
For this peculiar investigation, we turned to data from Statista and the NBA, covering the years 1997 to 2014. Our mission was clear: to uncover any hint of a relationship between these disparate variables and, in the process, unravel a mystery as puzzling as the punchline of an elaborate dad joke.
The initial stages of our analysis yielded a surprising revelation. The correlation coefficient between total private high school enrollment and Kobe Bryant's free throw count stood at a remarkable 0.8630521, with a p-value of less than 0.01. This strong correlation had us scratching our heads in disbelief, much like the reaction to a particularly groan-worthy dad joke at a family dinner.
It's often said that correlation does not imply causation, but in the case of our findings, the connection between these two phenomena seemed too tantalizing to ignore. Our study digs into the implications of this unexpected relationship, prompting contemplation of the intricate ways in which seemingly disparate elements can intertwine, not unlike the clever twist in a well-crafted dad joke.
Join us as we unravel this enigmatic entanglement, for in the realm of data analysis, every unexpected correlation holds the potential to spark joy and insight, akin to the delivery of a perfectly timed dad joke.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To unravel the enigmatic connection between total private high school enrollment in the United States and Kobe Bryant's total free throw count in NBA regular seasons, we employed a methodological approach that was as meticulous as it was light-hearted. Our research team embarked on a quest for data that rivaled the determination of a dad searching for the perfect golf swing, scouring sources such as Statista and the NBA for the years spanning 1997 to 2014. We chose these years to capture a significant portion of Kobe Bryant's illustrious basketball career and provide a robust representation of high school enrollment trends.

With the tenacity of a basketball player striving for a perfect free throw, we meticulously collected data on total private high school enrollment in the United States and Kobe Bryant's free throw count for each NBA regular season during the specified timeframe. Our team left no stone unturned in this pursuit, ensuring that our data mining efforts were as comprehensive as the repertoire of dad jokes at a family reunion.

In order to ensure the trustworthiness of our data, we cross-referenced multiple sources to verify the accuracy and completeness of the statistics. Like a vigilant guardian of statistical integrity, we scrutinized each data point with the precision of a dad meticulously arranging the barbecue grill, striving for the perfect distribution of correlations and causations.

Next, we employed a range of statistical analyses to tease out any potential relationship between the two variables under investigation. It was a journey filled with as much suspense as a setup to a classic dad joke, each statistical test paving the way for an unexpected punchline in the form of insightful findings.

The statistical techniques applied included correlation analysis, regression analysis, and time series analysis, each chosen with the deliberation of a seasoned punsmith selecting the perfect wordplay for a dad joke. By employing this array of methods, we sought to illuminate any underlying patterns and interactions between high school enrollment and free throw counts, much like a skilled comedian revealing the subtle nuances of a well-crafted jest.

Finally, we conducted sensitivity analyses and robustness checks to ensure that our findings held up under varying conditions. This additional step was akin to refining the delivery of a dad joke, testing different audiences and settings to gauge the universal appeal of our research findings.

With these methodological considerations in place, we approached the investigation with the precision of a seasoned statistician and the humor of a dad at a comedy club, blending rigor and levity to unravel the tantalizing tidbit that lies at the intersection of high school enrollment and NBA greatness.

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 Total private high school enrollment in the United States and Kobe Bryant's total free throw count in NBA regular season. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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 Ties between Total private high school enrollment in the United States and Total free throw count of the Tenacious Kobe Bryant in NBA regular seasons: A Tantalizing Tidbit

[[ABSTRACT]]
The relationship between seemingly unrelated phenomena often perplexes researchers, leading them down peculiar paths of inquiry. In this study, we delve into the connection between the total private high school enrollment in the United States and the free throw count of the tenacious Kobe Bryant during NBA regular seasons. Evidently, the similarity in numbers is as intriguing as it is amusing, much like a dad joke that just won't quit.

Drawing from data provided by Statista and the NBA, we meticulously examined the statistics from 1997 to 2014. Our analysis revealed a remarkably strong correlation coefficient of 0.8630521 with a p-value of less than 0.01. If this correlation were any stronger, it would have its own gravitational pull, much like the magnetic allure of a dad joke at a family gathering.

The implications of these findings extend beyond mere statistical quirks. They provoke contemplation of the subtle interplay between seemingly disparate elements, akin to the unexpected punchline of a well-crafted dad joke. As we present our results, we invite fellow researchers to join us in this whimsical exploration of the unanticipated connections that unfold in the realm of data analysis. After all, every data point has a story to tell, much like a good old dad joke.

[[LITERATURE REVIEW]]
To our surprise and amusement, our foray into the peculiar connection between total private high school enrollment in the United States and the free throw count of the tenacious Kobe Bryant during NBA regular seasons unearthed a curious blend of research and quirkiness, much like stumbling upon a dad joke in a scholarly journal.
Scholars such as Smith and Doe have extensively examined the factors contributing to fluctuations in private high school enrollment, highlighting socioeconomic influences, academic reputation, and demographic shifts. Similarly, Jones delved into the nuances of free throw performance in professional basketball, emphasizing the psychological and physical aspects that impact a player's success from the foul line.
In "The High School Enrollment Dilemma" and "The Psychology of Free Throws," the authors articulate the multifaceted dynamics at play within these realms, offering valuable insights into the intricate nature of our study's focal points. However, little did they know, we were about to take this scholarly exploration into the domain of unlikely correlations, much like a dad joke takes an unexpected turn and catches everyone off guard.
Turning to non-fiction works with tangential relevance, "Freakonomics" and "The Tipping Point" also stand as exemplars of investigations into surprising links and unforeseen relationships within diverse phenomena. These scholarly works, much like a well-structured dad joke, capture the imagination and incite curiosity about the hidden connections lying beneath the surface of seemingly disparate entities.
Adding a dash of whimsy, we looked to fictional narratives such as "The Da Vinci Code" and "Murder on the Orient Express" for inspiration, drawing parallels between the intricate web of clues in a literary mystery and the unexpected connections we sought to uncover in our statistical analysis. These works, much like a witty one-liner from a dad joke enthusiast, stimulated our own quest to unravel the enigmatic tie between high school enrollment and free throw counts.
As we sought a comprehensive understanding of this unconventional correlation, we cast our net wide, even venturing into uncharted territories. In an unconventional approach to literature review, we drew inspiration from diverse sources including the backs of cereal boxes, shampoo bottles, and fortune cookies, because sometimes, inspiration strikes in the most unexpected places - much like the setup to a dad joke that catches you off guard in the middle of a serious conversation.
In this lighthearted search for scholarly wisdom, we remained committed to unearthing the most unexpected and delightfully bizarre connections, for in the realm of quirky correlations, every unexpected source could hold the key to unlocking the mysteries that lie beneath the surface, not unlike the payoff of a well-crafted dad joke that catches you by surprise and leaves you with a lingering smile.

[[RESULTS]]
The analysis of the data from 1997 to 2014 revealed a strong correlation coefficient of 0.8630521 between total private high school enrollment in the United States and Kobe Bryant's total free throw count in NBA regular seasons. This correlation coefficient, often denoted as "r," suggests a highly positive association between these two variables. It's almost as if the high school enrollment numbers were shouting "Kobe!" every time they saw the free throw count, much like an enthusiastic fan at a basketball game.
The r-squared value of 0.7448590 further indicates that approximately 74.49% of the variability in Kobe Bryant's free throw count can be explained by the variation in total private high school enrollment. This strong association warrants closer scrutiny, akin to the anticipation of a dad joke's punchline – you know it's coming, but it still manages to surprise you.
The calculated p-value, being less than 0.01, provides compelling evidence against the null hypothesis of no correlation between these variables. In statistical terms, this means that the likelihood of observing such a strong relationship by random chance is less than 1%. This result is so statistically significant that it would make a dad joke aficionado burst into laughter, much like a well-timed pun at a comedy club.
As illustrated in Fig. 1, the scatterplot visually demonstrates the tight relationship between total private high school enrollment and Kobe Bryant's free throw count. The data points are clustered closely around a clear trendline, resembling the organized chaos of a well-constructed dad joke – seemingly disparate elements coming together in perfect harmony.
Overall, these findings significantly underscore the unexpected yet compelling link between these two seemingly unrelated variables, much like the delightful surprise of a dad joke that catches you off guard and leaves you pondering its unexpected wit. These results open the door to further exploration and contemplation of the delightful and often mysterious connections that emerge from the analysis of diverse datasets. Just like a good old dad joke, this correlation is both amusing and conducive to profound reflection.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our study have brought to light a remarkably robust correlation between total private high school enrollment in the United States and Kobe Bryant's free throw count in NBA regular seasons. This finding not only validates the unexpected connection proposed in our literature review but also underscores the undeniable relationship between these seemingly unrelated variables. It’s almost as if these numbers were performing a perfectly timed alley-oop, making us wonder if there's a hidden playbook that explains this peculiar linkage. Just like a dad joke can catch you off guard, this correlation has turned heads and sparked a playful curiosity among researchers and enthusiasts alike.

Our results align with and extend the work of previous scholars who have probed into the intricate dynamics of high school enrollment and individual sports performance. In doing so, our study has shed light on the resilience of this correlation, much like how a good dad joke can stand the test of time, eliciting laughter time and time again despite its predictability.

These findings invite contemplation of the nuanced interplay between social and athletic spheres. We have effectively unraveled a statistical puzzle so unexpected and intriguing that it rivals the clever quirk of a carefully constructed dad joke. By weaving together these disparate threads of inquiry and delving into the realm of whimsical data connections, we have ushered in a new chapter of scholarly discourse – one that emphasizes the delight of uncovering unexpected correlations, much like a well-timed dad joke that catches everyone by surprise.

More importantly, our results call for a reevaluation of the conventional boundaries within data analysis and statistical investigations. Just as a dad joke can transcend the ordinary and incite laughter in the most unexpected settings, our study challenges researchers to look beyond the obvious and embrace the delightful unpredictability that underpins the world of statistical relationships. It's as if our findings are whispering to us, signaling the importance of embracing the unexpected with the same zeal as one embraces a classic dad joke at a family gathering.

In conclusion, the connection between total private high school enrollment in the United States and Kobe Bryant's free throw count in NBA regular seasons stands as a testament to the delightful surprises that await those who venture into the realm of statistical analysis. Let's be honest, much like a dad joke, this correlation may not change the world, but it certainly adds a touch of whimsy to the otherwise serious business of academic research. And perhaps, in the spirit of a good dad joke, our findings will inspire others to approach their own research with a dash of humor and a willingness to embrace the unexpected.

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 Total private high school enrollment in the United States and Kobe Bryant's total free throw count in NBA regular season. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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 Ties between Total private high school enrollment in the United States and Total free throw count of the Tenacious Kobe Bryant in NBA regular seasons: A Tantalizing Tidbit

[[ABSTRACT]]
The relationship between seemingly unrelated phenomena often perplexes researchers, leading them down peculiar paths of inquiry. In this study, we delve into the connection between the total private high school enrollment in the United States and the free throw count of the tenacious Kobe Bryant during NBA regular seasons. Evidently, the similarity in numbers is as intriguing as it is amusing, much like a dad joke that just won't quit.

Drawing from data provided by Statista and the NBA, we meticulously examined the statistics from 1997 to 2014. Our analysis revealed a remarkably strong correlation coefficient of 0.8630521 with a p-value of less than 0.01. If this correlation were any stronger, it would have its own gravitational pull, much like the magnetic allure of a dad joke at a family gathering.

The implications of these findings extend beyond mere statistical quirks. They provoke contemplation of the subtle interplay between seemingly disparate elements, akin to the unexpected punchline of a well-crafted dad joke. As we present our results, we invite fellow researchers to join us in this whimsical exploration of the unanticipated connections that unfold in the realm of data analysis. After all, every data point has a story to tell, much like a good old dad joke.

[[INTRDUCTION]]
The interplay between seemingly unrelated variables has long been a source of fascination for researchers across disciplines. The intricate dance of data can lead us down unexpected paths, much like a dad joke that catches us off guard and elicits a groan and a chuckle in equal measure. In this study, we embark on a whimsical journey into the connection between two unusual bedfellows: the total private high school enrollment in the United States and the free throw count of the tenacious Kobe Bryant during NBA regular seasons.
Why did the statistician go to art school? Because he wanted to learn how to draw a correlation! Much like this classic dad joke, our exploration of the connection between high school enrollment and free throw counts promises both amusement and insight.
For this peculiar investigation, we turned to data from Statista and the NBA, covering the years 1997 to 2014. Our mission was clear: to uncover any hint of a relationship between these disparate variables and, in the process, unravel a mystery as puzzling as the punchline of an elaborate dad joke.
The initial stages of our analysis yielded a surprising revelation. The correlation coefficient between total private high school enrollment and Kobe Bryant's free throw count stood at a remarkable 0.8630521, with a p-value of less than 0.01. This strong correlation had us scratching our heads in disbelief, much like the reaction to a particularly groan-worthy dad joke at a family dinner.
It's often said that correlation does not imply causation, but in the case of our findings, the connection between these two phenomena seemed too tantalizing to ignore. Our study digs into the implications of this unexpected relationship, prompting contemplation of the intricate ways in which seemingly disparate elements can intertwine, not unlike the clever twist in a well-crafted dad joke.
Join us as we unravel this enigmatic entanglement, for in the realm of data analysis, every unexpected correlation holds the potential to spark joy and insight, akin to the delivery of a perfectly timed dad joke.

[[RESULTS]]
The analysis of the data from 1997 to 2014 revealed a strong correlation coefficient of 0.8630521 between total private high school enrollment in the United States and Kobe Bryant's total free throw count in NBA regular seasons. This correlation coefficient, often denoted as "r," suggests a highly positive association between these two variables. It's almost as if the high school enrollment numbers were shouting "Kobe!" every time they saw the free throw count, much like an enthusiastic fan at a basketball game.
The r-squared value of 0.7448590 further indicates that approximately 74.49% of the variability in Kobe Bryant's free throw count can be explained by the variation in total private high school enrollment. This strong association warrants closer scrutiny, akin to the anticipation of a dad joke's punchline – you know it's coming, but it still manages to surprise you.
The calculated p-value, being less than 0.01, provides compelling evidence against the null hypothesis of no correlation between these variables. In statistical terms, this means that the likelihood of observing such a strong relationship by random chance is less than 1%. This result is so statistically significant that it would make a dad joke aficionado burst into laughter, much like a well-timed pun at a comedy club.
As illustrated in Fig. 1, the scatterplot visually demonstrates the tight relationship between total private high school enrollment and Kobe Bryant's free throw count. The data points are clustered closely around a clear trendline, resembling the organized chaos of a well-constructed dad joke – seemingly disparate elements coming together in perfect harmony.
Overall, these findings significantly underscore the unexpected yet compelling link between these two seemingly unrelated variables, much like the delightful surprise of a dad joke that catches you off guard and leaves you pondering its unexpected wit. These results open the door to further exploration and contemplation of the delightful and often mysterious connections that emerge from the analysis of diverse datasets. Just like a good old dad joke, this correlation is both amusing and conducive to profound reflection.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation into the relationship between total private high school enrollment in the United States and Kobe Bryant's free throw count in NBA regular seasons has revealed a compelling correlation that rivals the seamless synchronization of a well-rehearsed dad joke. With a correlation coefficient of 0.8630521 and a p-value of less than 0.01, this bond between high school enrollment and free throw counts is as undeniable as a dad's penchant for puns at the dinner table.

The r-squared value of 0.7448590 further emphasizes the substantial influence of total private high school enrollment on Kobe Bryant's free throw count, much like the way a dad's melodramatic storytelling captivates a family gathering.

The visual depiction of this relationship in the scatterplot is as clear-cut as a classic dad joke, weaving seemingly unrelated elements into a coherent narrative with a punchline that leaves us nodding in agreement, feeling surprised, or laughing out loud.

Thus, we can confidently state that the connection between these variables is more than a statistical anomaly – it's a delightful entanglement that tickles the funny bone and piques the curiosity of researchers and dad joke enthusiasts alike.

In light of these findings, we assert that further research in this area is as unnecessary as explaining a dad joke to the one relative who never seems to get it. It's clear that this correlation stands as a playful reminder of the unexpected and delightful connections that data analysis can uncover. Just like a dad joke, it may seem quirky and nonsensical at first, but upon closer examination, it reveals layers of nuance and insight that brighten our academic journey. Therefore, we assert with dad-like confidence – no further investigation is needed in this whimsical realm of statistical exploration!

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 Ties between Total private high school enrollment in the United States and Total free throw count of the Tenacious Kobe Bryant in NBA regular seasons: A Tantalizing Tidbit. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Quirky Quantitative Research

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 Ties between Total private high school enrollment in the United States and Total free throw count of the Tenacious Kobe Bryant in NBA regular seasons: A Tantalizing Tidbit. Respond only with the name of a fictional organization.

ChatGPT:

The National Institute for Intergalactic Statistics and Sports Analysis

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 Ties between Total private high school enrollment in the United States and Total free throw count of the Tenacious Kobe Bryant in NBA regular seasons: A Tantalizing Tidbit with an abstract of The relationship between seemingly unrelated phenomena often perplexes researchers, leading them down peculiar paths of inquiry. In this study, we delve into the connection between the total private high school enrollment in the United States and the free throw count of the tenacious Kobe Bryant during NBA regular seasons. Evidently, the similarity in numbers is as intriguing as it is amusing, much like a dad joke that just won't quit.
Drawing from data provided by Statista and the NBA, we meticulously examined the statistics from 1997 to 2014. Our analysis revealed a remarkably strong correlation coefficient of 0.8630521 with a p-value of less than 0.01. If this correlation were any stronger, it would have its own gravitational pull, much like the magnetic allure of a dad joke at a family gathering.
The implications of these findings extend beyond mere statistical quirks. They provoke contemplation of the subtle interplay between seemingly disparate elements, akin to the unexpected punchline of a well-crafted dad joke. As we present our results, we invite fellow researchers to join us in this whimsical exploration of the unanticipated connections that unfold in the realm of data analysis. After all, every data point has a story to tell, much like a good old dad joke.

ChatGPT:

"private high school enrollment United States", "Kobe Bryant NBA free throw count", "correlation between high school enrollment and free throws", "Statista high school enrollment data", "NBA free throws statistics", "correlation coefficient analysis", "unrelated phenomena correlation", "whimsical data analysis", "unexpected connections in data"

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

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Data details

Total private high school enrollment in the United States
Source: Statista
See what else correlates with Total private high school enrollment in the United States

Kobe Bryant's total free throw count in NBA regular season
Source: NBA
See what else correlates with Kobe Bryant's total free throw count in NBA regular season

Correlation r = 0.8630521 (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 = 0.7448590 (Coefficient of determination)
This means 74.5% of the change in the one variable (i.e., Kobe Bryant's total free throw count in NBA regular season) is predictable based on the change in the other (i.e., Total private high school enrollment in the United States) over the 18 years from 1997 through 2014.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 4.01E-6.^Show 0.0000040120049551273024000000
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 0.86 in 0.000401% of random cases. Said differently, if you correlated 249,252 random variables^Note You don't actually need 249 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 17 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 17 because we have two variables measured over a period of 18 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.

[ 0.66, 0.95 ] 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.

	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014
Total private high school enrollment in the United States (High school enrollment)	1185	1212	1229	1264	1297	1306	1311	1331	1349	1360	1364	1342	1309	1299	1291	1275	1235	1197
Kobe Bryant's total free throw count in NBA regular season (Free throws)	136	363	245	331	475	488	601	454	542	696	667	623	483	439	483	381	525	18

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.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

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([1185,1212,1229,1264,1297,1306,1311,1331,1349,1360,1364,1342,1309,1299,1291,1275,1235,1197,])
array_2 = np.array([136,363,245,331,475,488,601,454,542,696,667,623,483,439,483,381,525,18,])
array_1_name = "Total private high school enrollment in the United States"
array_2_name = "Kobe Bryant's total free throw count in NBA regular season"

# 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 Total private high school enrollment in the United States
Line chart for only Kobe Bryant's total free throw count in NBA regular season
AI-generated correlation image
The spurious research paper: The Ties between Total private high school enrollment in the United States and Total free throw count of the Tenacious Kobe Bryant in NBA regular seasons: A Tantalizing Tidbit

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Correlation ID: 3089 · Black Variable ID: 380 · Red Variable ID: 318


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