Spurious correlation #3,651 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Justin and the second variable is Motor vehicle thefts in Maine. The chart goes from 1985 to 2022, 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 Popularity of the first name Justin and the second variable is Motor vehicle thefts in Maine. The chart goes from 1985 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the name Justin fell out of favor, there were fewer individuals to defend their vehicle with the iconic "Justin Case" anti-theft device. This decline in protection led to a decrease in motor vehicle thefts in Maine as the Justin Case proved to be a major deterrent. Remember, when it comes to protecting your car, it's always better to be safe than Justin Case!

^{Show image generation prompt} Model: dalle-3
Prompt: Create an image that resembles a scene from a Disney movie where a serene, picturesque street in Maine is depicted. Show a row of parked cars adorned with charming details, suggesting a quiet and peaceful neighborhood. One of the cars should prominently display a license plate with the name "Justin," emphasizing the theme of fading popularity. The overall atmosphere should convey a sense of nostalgia and tranquility, reminiscent of a classic Disney setting. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

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

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: Popularity of the first name Justin and Motor vehicle thefts in Maine. 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: Psychedelic Pop art Symbolism 3D Pixar Style An image that looks like it is a scene from A Disney Movie
ChatGPT: An image that looks like it is a scene from A Disney Movie

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Popularity of the first name Justin and Motor vehicle thefts in Maine, in the following situation: Because as the name Justin fell out of favor, fewer people felt inspired to blast "Justin Bieber" music in their cars, dissuading thieves in Maine.. Keep the description simple.
ChatGPT: On a quiet street in Maine, a line of parked cars sits undisturbed. One car with the name "Justin" on its license plate stays safe from theft, as the absence of blaring "Justin Bieber" music indicates the declining popularity of the name.

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: [ An image that looks like it is a scene from A Disney Movie ] and depicting this scene: On a quiet street in Maine, a line of parked cars sits undisturbed. One car with the name "Justin" on its license plate stays safe from theft, as the absence of blaring "Justin Bieber" music indicates the declining popularity of the name. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create an image that resembles a scene from a Disney movie where a serene, picturesque street in Maine is depicted. Show a row of parked cars adorned with charming details, suggesting a quiet and peaceful neighborhood. One of the cars should prominently display a license plate with the name "Justin," emphasizing the theme of fading popularity. The overall atmosphere should convey a sense of nostalgia and tranquility, reminiscent of a classic Disney setting.

*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 a decrease in Popularity of the first name Justin caused Motor vehicle thefts in Maine to decrease.\Make it sound serious until the end.

AI academic paper

(Because p < 0.01)

Justin Time: The Surprising Link Between the Popularity of a Name and Motor Vehicle Thefts in Maine

The Journal of Quirky Sociological Studies

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 Popularity of the first name Justin and Motor vehicle thefts in Maine. Make lots of jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
"Justin Time: The Surprising Link Between the Popularity of a Name and Motor Vehicle Thefts in Maine"

[[ABSTRACT]]
In this paper, we delve into the unexpected connection between the popularity of the first name "Justin" and the occurrence of motor vehicle thefts in the state of Maine. Drawing on data from the US Social Security Administration and the FBI Criminal Justice Information Services spanning the years 1985 to 2022, our research team conducted a thorough analysis to determine if there is a tangible link between these two seemingly disparate factors. Much to our surprise, we uncovered a correlation coefficient of 0.9858415 and a p-value of less than 0.01, suggesting a remarkably strong association. Our findings not only shed light on this unusual correlation but also provoke intriguing questions about the impact of names on criminal behavior. This research offers a lighthearted perspective on a complex societal issue, reminding us that sometimes, the most unexpected connections can lead to valuable insights.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
When one thinks of names with potential behavioral effects, one might conjure images of biblical figures or perhaps modern celebrities influencing trends in fashion or entertainment. However, one name that may not immediately spring to mind in this context is "Justin." While historically associated with righteousness and fairness, our research has uncovered a more unexpected connection between this name and a criminal activity that has wheeled its way into the spotlight – motor vehicle thefts in the state of Maine.
As we delve into this quirky and eyebrow-raising association, it's worth pondering the sheer number of Justins out there – the cool Justins, the nerdy Justins, and even the Justins who insist they were "cool before it was cool." But could the collective presence of these individuals contribute to a spike in motor vehicle theft activity? This is the question that has driven our investigation, prompting us to take a deep dive into statistical analysis and sociological interpretation.
With countless researchers fixating on serious and weighty subjects, we decided to take a road less traveled, steering our academic inquiry into the realm of whimsy. Yes, we admit that the title of our paper may prompt a few bemused chuckles, but behind this lighthearted facade lies a serious commitment to unearthing the unexpected, challenging preconceptions, and wrangling data to reveal patterns where none might be expected.
As we navigate the twists and turns of this peculiar correlation, we will not only uncover the statistical evidence but also intersperse our findings with puns, quips, and the occasional witty observation. After all, what is academia if not an opportunity to inject a little levity into the mire of rigorous analysis?
So, fasten your seatbelts, dear reader, as we embark on this whimsical journey to explore the surprising connection between a name and a crime, reminding us that life – and research – can indeed be full of delightful surprises.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The literature on the connection between individuals' names and their potential impact on societal phenomena is an intriguing and multidisciplinary field of study. While often overlooked in more somber academic pursuits, the influence of nomenclature on various aspects of human behavior has been a subject of fascination for scholars across disciplines ranging from sociology to psychology. In the realm of criminology, the potential correlation between the popularity of certain names and criminal activities has been a topic of interest, albeit one frequently met with raised eyebrows and skeptical smirks.

Smith et al. (2010) conducted a pioneering study examining the relationship between the frequency of given names and criminal involvement, uncovering modest correlations in certain urban centers. Meanwhile, Doe and Johnson (2015) ventured into the labyrinth of longitudinal data to probe the curious interplay between monikers and illicit behaviors, unearthing patterns that appeared to defy conventional wisdom. Jones (2018) further explored the nuances of this connection, threading together the tales of names and criminality with a finesse that elicited both scholarly admiration and bemusement.

However, as we swerve into the peculiar nexus of "Justin" and motor vehicle thefts in Maine, we find ourselves venturing beyond the trodden paths of traditional scholarship. What awaits us is a merry mix of statistical analyses, chuckle-inducing anecdotes, and the occasional whimsical diversion. It is with this buoyant spirit that we now embark on our literature review journey, delving into sources that may not have graced the bookshelves of conventional criminological libraries.

Turning from the dry to the quirky, we shift our focus to non-fiction works that have offered peculiar insights into the unexpected. "Freakonomics" by Steven D. Levitt and Stephen J. Dubner (2005) may not delve specifically into the connection between names and crime in Maine, but its unorthodox exploration of societal phenomena invites us to ponder the unforeseen ramifications of seemingly unrelated factors. In a similarly offbeat vein, "The Tipping Point" by Malcolm Gladwell (2000) prompts us to consider the tipping factors that could contribute to the proliferation of criminal activities linked to specific names – an idea that is at once compelling and eccentric.

As we spiral into the realm of fiction, we encounter an intriguing array of literary creations that, while not directly addressing our topic, offer a whimsical lens through which to view our research. From the enigmatic "The Name of the Wind" by Patrick Rothfuss to the tantalizing "The Girl with the Dragon Tattoo" by Stieg Larsson, these works of fiction beckon us to consider the symbolic weight of names and their potential impact on the grand stage of human affairs.

In a departure from traditional scholarly citations, we also draw from the well of social media musings, where astute observers have raised eyebrow-raising conjectures about the uncanny connection between "Justin" and motor vehicle thefts. A tweet from @DriveSafeMaine speculating, "Is it just coincidence that whenever a 'Justin' moves to town, the number of car thefts seems to rise? 🚗🔒 #NameGame #CrimeSpree" serves as a noteworthy example, embodying the levity and curiosity that infuse our research endeavor.

As we navigate this whimsical landscape of literature and social media conjectures, we aim to blend rigorous analysis with moments of lighthearted musing. Our scholarly pursuit of the unexpected is underpinned by a commitment to unraveling the peculiar connections that permeate the fabric of society, reminding us that the world of research can indeed be an adventure brimming with delightful whimsy.

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 Popularity of the first name Justin and Motor vehicle thefts in Maine. Make lots of jokes, goofy observations, and puns.

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]]
"Justin Time: The Surprising Link Between the Popularity of a Name and Motor Vehicle Thefts in Maine"

[[ABSTRACT]]
In this paper, we delve into the unexpected connection between the popularity of the first name "Justin" and the occurrence of motor vehicle thefts in the state of Maine. Drawing on data from the US Social Security Administration and the FBI Criminal Justice Information Services spanning the years 1985 to 2022, our research team conducted a thorough analysis to determine if there is a tangible link between these two seemingly disparate factors. Much to our surprise, we uncovered a correlation coefficient of 0.9858415 and a p-value of less than 0.01, suggesting a remarkably strong association. Our findings not only shed light on this unusual correlation but also provoke intriguing questions about the impact of names on criminal behavior. This research offers a lighthearted perspective on a complex societal issue, reminding us that sometimes, the most unexpected connections can lead to valuable insights.

[[LITERATURE REVIEW]]
The literature on the connection between individuals' names and their potential impact on societal phenomena is an intriguing and multidisciplinary field of study. While often overlooked in more somber academic pursuits, the influence of nomenclature on various aspects of human behavior has been a subject of fascination for scholars across disciplines ranging from sociology to psychology. In the realm of criminology, the potential correlation between the popularity of certain names and criminal activities has been a topic of interest, albeit one frequently met with raised eyebrows and skeptical smirks.
Smith et al. (2010) conducted a pioneering study examining the relationship between the frequency of given names and criminal involvement, uncovering modest correlations in certain urban centers. Meanwhile, Doe and Johnson (2015) ventured into the labyrinth of longitudinal data to probe the curious interplay between monikers and illicit behaviors, unearthing patterns that appeared to defy conventional wisdom. Jones (2018) further explored the nuances of this connection, threading together the tales of names and criminality with a finesse that elicited both scholarly admiration and bemusement.
However, as we swerve into the peculiar nexus of "Justin" and motor vehicle thefts in Maine, we find ourselves venturing beyond the trodden paths of traditional scholarship. What awaits us is a merry mix of statistical analyses, chuckle-inducing anecdotes, and the occasional whimsical diversion. It is with this buoyant spirit that we now embark on our literature review journey, delving into sources that may not have graced the bookshelves of conventional criminological libraries.
Turning from the dry to the quirky, we shift our focus to non-fiction works that have offered peculiar insights into the unexpected. "Freakonomics" by Steven D. Levitt and Stephen J. Dubner (2005) may not delve specifically into the connection between names and crime in Maine, but its unorthodox exploration of societal phenomena invites us to ponder the unforeseen ramifications of seemingly unrelated factors. In a similarly offbeat vein, "The Tipping Point" by Malcolm Gladwell (2000) prompts us to consider the tipping factors that could contribute to the proliferation of criminal activities linked to specific names – an idea that is at once compelling and eccentric.
As we spiral into the realm of fiction, we encounter an intriguing array of literary creations that, while not directly addressing our topic, offer a whimsical lens through which to view our research. From the enigmatic "The Name of the Wind" by Patrick Rothfuss to the tantalizing "The Girl with the Dragon Tattoo" by Stieg Larsson, these works of fiction beckon us to consider the symbolic weight of names and their potential impact on the grand stage of human affairs.
In a departure from traditional scholarly citations, we also draw from the well of social media musings, where astute observers have raised eyebrow-raising conjectures about the uncanny connection between "Justin" and motor vehicle thefts. A tweet from @DriveSafeMaine speculating, "Is it just coincidence that whenever a 'Justin' moves to town, the number of car thefts seems to rise? 🚗🔒 #NameGame #CrimeSpree" serves as a noteworthy example, embodying the levity and curiosity that infuse our research endeavor.
As we navigate this whimsical landscape of literature and social media conjectures, we aim to blend rigorous analysis with moments of lighthearted musing. Our scholarly pursuit of the unexpected is underpinned by a commitment to unraveling the peculiar connections that permeate the fabric of society, reminding us that the world of research can indeed be an adventure brimming with delightful whimsy.

[[RESULTS]]
Upon conducting our rigorous analysis, we were nothing short of astonished by the robust correlation we uncovered between the popularity of the first name "Justin" and the occurrence of motor vehicle thefts in the state of Maine. Our research revealed a correlation coefficient of 0.9858415, suggesting an exceptionally strong relationship between these two seemingly unrelated variables. Furthermore, the coefficient of determination (r-squared) of 0.9718834 indicated that a substantial 97.18% of the variability in motor vehicle thefts can be explained by the popularity of the name "Justin." In addition, the p-value of less than 0.01 provided strong evidence against the null hypothesis, further bolstering the credibility of our findings.
As depicted in the scatterplot (Fig. 1), the relationship between the popularity of the name "Justin" and motor vehicle thefts in Maine is strikingly linear, with a clear upward trend that leaves little room for doubt. The correlation is so pronounced that one might be tempted to coin a new term: "auto-Justin theft" to encapsulate this peculiar phenomenon.
The implications of these findings are as unexpected as they are intriguing. It appears that as the popularity of the name "Justin" has waxed and waned over the years, so too have the rates of motor vehicle thefts in Maine – a link that elicits a quizzical, if not bemused, response.
In the annals of criminology and sociological research, it is not often that a connection of this nature emerges, prompting both scholarly contemplation and the occasional chuckle. The veracity of this correlation encourages us to contemplate the subtle yet apparently influential tendrils that names – yes, names – can extend into the fabric of society, weaving their influence in ways that defy conventional wisdom and ignite the imagination.
While the findings of this study may prompt raised eyebrows and incredulous grins, we must resist the temptation to dismiss this correlation as mere happenstance. Instead, we encourage our esteemed colleagues to join us in pondering the greater riddles of human behavior, embracing the unexpected paths that research may unveil, and, most importantly, celebrating the curious charm of academic inquiry. After all, in the realm of scholarly pursuits, it's not every day that one encounters a correlation as catchy as "Justin and auto-Justin theft."

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The robust correlation we have uncovered between the popularity of the first name "Justin" and the incidence of motor vehicle thefts in Maine is nothing short of astonishing. Our findings not only confirm, but also amplify, the quirky connections alluded to in prior literature. The works of Smith et al. (2010), Doe and Johnson (2015), and Jones (2018) provided the initial stepping stones into the realm of names and criminality, offering glimpses of the unexpected interplay between nomenclature and illicit activities. While these studies may have been met with skeptical smirks, our results resoundingly affirm the premise that there might be something more than mere coincidence at play in the curious realm of "auto-Justin theft."

Our research adds a touch of whimsy to the scholarly landscape, underscoring the profound implications of seemingly nonchalant variables. The correlation coefficient of 0.9858415 and the r-squared value of 0.9718834 furnish compelling evidence in support of the hypothesis that the vicissitudes in the popularity of the name "Justin" are indeed significantly linked to fluctuations in motor vehicle thefts in Maine. Dare we say, our work evokes a reinterpretation of Maine's state motto from "Dirigo" to "Car-Justin-Time" – a tongue-in-cheek nod to the uncanny association we have unearthed.

As we wade through these unexpected waters, it becomes increasingly evident that the impact of names on societal phenomena goes beyond a mere play on words. The resounding strength of our findings prompts us to consider the peculiar influence that names can wield in shaping human behavior, underscoring that the world of criminology may have more quirks than meets the eye. It appears that sometimes, the most unconventional theories hold water – or in this case, the oil and grease of a car engine.

Nevertheless, it is paramount to approach our findings with a healthy dose of scholarly mirth. While the strength of the correlation may elicit a chuckle or two, it is crucial to recognize the broader implications of our discovery. Our results beckon us to reconsider the significance of seemingly innocuous details in understanding complex societal dynamics, reminding us that even the most unassuming variables can harbor unforeseen connections. Therefore, we urge the scholarly community to embark on this amusing yet illuminating journey with us, as we navigate the winding roads of academic inquiry – and perhaps indulge in the occasional pit stop for a name-based pun or two.

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 Popularity of the first name Justin and Motor vehicle thefts in Maine. Make lots of jokes, goofy observations, and puns.

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]]
"Justin Time: The Surprising Link Between the Popularity of a Name and Motor Vehicle Thefts in Maine"

[[ABSTRACT]]
In this paper, we delve into the unexpected connection between the popularity of the first name "Justin" and the occurrence of motor vehicle thefts in the state of Maine. Drawing on data from the US Social Security Administration and the FBI Criminal Justice Information Services spanning the years 1985 to 2022, our research team conducted a thorough analysis to determine if there is a tangible link between these two seemingly disparate factors. Much to our surprise, we uncovered a correlation coefficient of 0.9858415 and a p-value of less than 0.01, suggesting a remarkably strong association. Our findings not only shed light on this unusual correlation but also provoke intriguing questions about the impact of names on criminal behavior. This research offers a lighthearted perspective on a complex societal issue, reminding us that sometimes, the most unexpected connections can lead to valuable insights.

[[INTRDUCTION]]
When one thinks of names with potential behavioral effects, one might conjure images of biblical figures or perhaps modern celebrities influencing trends in fashion or entertainment. However, one name that may not immediately spring to mind in this context is "Justin." While historically associated with righteousness and fairness, our research has uncovered a more unexpected connection between this name and a criminal activity that has wheeled its way into the spotlight – motor vehicle thefts in the state of Maine.
As we delve into this quirky and eyebrow-raising association, it's worth pondering the sheer number of Justins out there – the cool Justins, the nerdy Justins, and even the Justins who insist they were "cool before it was cool." But could the collective presence of these individuals contribute to a spike in motor vehicle theft activity? This is the question that has driven our investigation, prompting us to take a deep dive into statistical analysis and sociological interpretation.
With countless researchers fixating on serious and weighty subjects, we decided to take a road less traveled, steering our academic inquiry into the realm of whimsy. Yes, we admit that the title of our paper may prompt a few bemused chuckles, but behind this lighthearted facade lies a serious commitment to unearthing the unexpected, challenging preconceptions, and wrangling data to reveal patterns where none might be expected.
As we navigate the twists and turns of this peculiar correlation, we will not only uncover the statistical evidence but also intersperse our findings with puns, quips, and the occasional witty observation. After all, what is academia if not an opportunity to inject a little levity into the mire of rigorous analysis?
So, fasten your seatbelts, dear reader, as we embark on this whimsical journey to explore the surprising connection between a name and a crime, reminding us that life – and research – can indeed be full of delightful surprises.

[[RESULTS]]
Upon conducting our rigorous analysis, we were nothing short of astonished by the robust correlation we uncovered between the popularity of the first name "Justin" and the occurrence of motor vehicle thefts in the state of Maine. Our research revealed a correlation coefficient of 0.9858415, suggesting an exceptionally strong relationship between these two seemingly unrelated variables. Furthermore, the coefficient of determination (r-squared) of 0.9718834 indicated that a substantial 97.18% of the variability in motor vehicle thefts can be explained by the popularity of the name "Justin." In addition, the p-value of less than 0.01 provided strong evidence against the null hypothesis, further bolstering the credibility of our findings.
As depicted in the scatterplot (Fig. 1), the relationship between the popularity of the name "Justin" and motor vehicle thefts in Maine is strikingly linear, with a clear upward trend that leaves little room for doubt. The correlation is so pronounced that one might be tempted to coin a new term: "auto-Justin theft" to encapsulate this peculiar phenomenon.
The implications of these findings are as unexpected as they are intriguing. It appears that as the popularity of the name "Justin" has waxed and waned over the years, so too have the rates of motor vehicle thefts in Maine – a link that elicits a quizzical, if not bemused, response.
In the annals of criminology and sociological research, it is not often that a connection of this nature emerges, prompting both scholarly contemplation and the occasional chuckle. The veracity of this correlation encourages us to contemplate the subtle yet apparently influential tendrils that names – yes, names – can extend into the fabric of society, weaving their influence in ways that defy conventional wisdom and ignite the imagination.
While the findings of this study may prompt raised eyebrows and incredulous grins, we must resist the temptation to dismiss this correlation as mere happenstance. Instead, we encourage our esteemed colleagues to join us in pondering the greater riddles of human behavior, embracing the unexpected paths that research may unveil, and, most importantly, celebrating the curious charm of academic inquiry. After all, in the realm of scholarly pursuits, it's not every day that one encounters a correlation as catchy as "Justin and auto-Justin theft."

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our foray into the enigmatic world of "Justin Time" has yielded surprising results that not only pique the curiosity but also tickle the funny bone. The robust correlation between the popularity of the first name "Justin" and motor vehicle thefts in Maine has left us all revved up and ready to hit the road with our puns and quirky observations.

It's as if the name "Justin" has taken on a life of its own, steering the wheels of criminal activity in Maine. Who would have thought that behind every stolen car, there may lurk a "Justin" with a penchant for automotive adventures? The implications of our findings stretch far and wide, prompting us to contemplate the impact of nomenclature on the highways and byways of societal behavior.

We'd be remiss not to mention that this correlation is more than just a flash in the pan or a fleeting fender bender of statistical significance. It has ignited a spark of curiosity that reminds us that in the realm of academia, the road less traveled may lead to unexpected treasures – or in this case, correlations. It's a reminder that rigorous analysis need not always be a somber affair; in fact, our "Justin Time" journey exemplifies the whimsical charm that research can hold.

So, as we wrap up this delightfully quirky study, it's time to hit the brakes and assert that further research in the domain of "Justin and auto-Justin theft" is as unnecessary as a car alarm in a windstorm. We have thoroughly explored this correlation, leaving no stone unturned, and now it's time for the academic community to embrace these findings with open arms and perhaps a chuckle or two.

In the grand scheme of scholarly pursuits, our "Justin Time" escapade stands as a testament to the endless surprises that await those who dare to delve into the most unconventional avenues of inquiry. As the curtains close on this eccentric journey, we bid adieu to "Justin" and his unexpected dalliance with motor vehicle thefts, leaving behind a trail of statistical evidence and a dash of academic whimsy.

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 Justin Time: The Surprising Link Between the Popularity of a Name and Motor Vehicle Thefts in Maine. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Quirky Sociological Studies

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 Justin Time: The Surprising Link Between the Popularity of a Name and Motor Vehicle Thefts in Maine. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Social Dynamics and Alarming Coincidences

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 Justin Time: The Surprising Link Between the Popularity of a Name and Motor Vehicle Thefts in Maine with an abstract of In this paper, we delve into the unexpected connection between the popularity of the first name "Justin" and the occurrence of motor vehicle thefts in the state of Maine. Drawing on data from the US Social Security Administration and the FBI Criminal Justice Information Services spanning the years 1985 to 2022, our research team conducted a thorough analysis to determine if there is a tangible link between these two seemingly disparate factors. Much to our surprise, we uncovered a correlation coefficient of 0.9858415 and a p-value of less than 0.01, suggesting a remarkably strong association. Our findings not only shed light on this unusual correlation but also provoke intriguing questions about the impact of names on criminal behavior. This research offers a lighthearted perspective on a complex societal issue, reminding us that sometimes, the most unexpected connections can lead to valuable insights.

ChatGPT:

"Justin popularity motor vehicle theft Maine"
"Name popularity crime correlation"
"First name impact on criminal behavior"
"US Social Security Administration data analysis"
"Link between names and criminal activity"
"Maine motor vehicle theft statistics by year"
"FBI Criminal Justice Information Services theft correlation"
"Unusual correlations in societal behavior"

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

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Data details

Popularity of the first name Justin
Detailed data title: Babies of all sexes born in the US named Justin
Source: US Social Security Administration
See what else correlates with Popularity of the first name Justin

Motor vehicle thefts in Maine
Detailed data title: The motor vehicle theft rate per 100,000 residents in Maine
Source: FBI Criminal Justice Information Services
See what else correlates with Motor vehicle thefts in Maine

Correlation r = 0.9858415 (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 = 0.9718834 (Coefficient of determination)
This means 97.2% of the change in the one variable (i.e., Motor vehicle thefts in Maine) is predictable based on the change in the other (i.e., Popularity of the first name Justin) over the 38 years from 1985 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.^Show 0.0000000000000000000000000000
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 0.99 in 0% of random cases. Said differently, if you correlated
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inf random variables^Note
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You don't actually need
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INF variables to find a correlation like this one.
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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.
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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 37 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 37 because we have two variables measured over a period of 38 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.

[ 0.97, 0.99 ] 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.

	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Popularity of the first name Justin (Babies born)	25718	26879	31747	35297	33047	30786	26899	23706	21615	21004	21659	20065	18780	18021	18328	17832	16268	14938	13683	11929	10535	9987	10061	9384	8644	7862	7133	5919	4882	4034	3725	3570	3149	2801	2545	2307	2159	2067
Motor vehicle thefts in Maine (Motor Vehicle Theft rate)	165.1	164.2	173.4	204.7	183.2	176.6	163.3	144	134.4	143.2	134.8	142.9	132.2	121.3	135.2	103.7	130.1	110.4	111.2	99.1	102.3	101.9	96.9	89.6	77.5	74.5	81.1	74.6	68.6	60.2	61.2	58.3	59.4	58	53.9	63.8	65.1	70.9

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.

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([25718,26879,31747,35297,33047,30786,26899,23706,21615,21004,21659,20065,18780,18021,18328,17832,16268,14938,13683,11929,10535,9987,10061,9384,8644,7862,7133,5919,4882,4034,3725,3570,3149,2801,2545,2307,2159,2067,])
array_2 = np.array([165.1,164.2,173.4,204.7,183.2,176.6,163.3,144,134.4,143.2,134.8,142.9,132.2,121.3,135.2,103.7,130.1,110.4,111.2,99.1,102.3,101.9,96.9,89.6,77.5,74.5,81.1,74.6,68.6,60.2,61.2,58.3,59.4,58,53.9,63.8,65.1,70.9,])
array_1_name = "Popularity of the first name Justin"
array_2_name = "Motor vehicle thefts in Maine"

# 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 Popularity of the first name Justin
Line chart for only Motor vehicle thefts in Maine
AI-generated correlation image
The spurious research paper: Justin Time: The Surprising Link Between the Popularity of a Name and Motor Vehicle Thefts in Maine

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Correlation ID: 3651 · Black Variable ID: 1999 · Red Variable ID: 20138


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