Spurious correlation #2,969 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Burglaries in Ohio and the second variable is The number of bill collectors in Ohio. The chart goes from 2003 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 Burglaries in Ohio and the second variable is The number of bill collectors in Ohio. The chart goes from 2003 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The decrease in burglaries meant that there were fewer things for bill collectors to hound people about, leading to a decrease in their numbers in Ohio. After all, there's only so much you can do when the opportunity to repossess someone's stolen goods dwindles.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a serene Ohio neighborhood, with traditional woodblock painting aesthetics. Depict former burglars, now neatly dressed in business attire, offering empathetic support to individuals in debt. Show the transformation of these individuals from criminals to understanding community members, with a peaceful atmosphere and a sense of positive change in the neighborhood. 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: Burglaries in Ohio and The number of bill collectors in Ohio. 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: Magic realism Woodblock painting Magic realism Woodblock painting Charcoal
ChatGPT: Woodblock painting

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 Burglaries in Ohio and The number of bill collectors in Ohio, in the following situation: As burglaries in Ohio decreased, burglars found other careers, leading to a decline in the number of bill collectors as former burglars empathized with those in debt and stopped harassing them.. Keep the description simple.
ChatGPT: In a quiet Ohio neighborhood, the number of burglaries declined. Former burglars, now wearing business attire, were seen offering support and understanding to individuals in debt. As a result, the number of bill collectors in Ohio also diminished, as these former criminals transitioned to more empathetic careers.

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: [ Woodblock painting ] and depicting this scene: In a quiet Ohio neighborhood, the number of burglaries declined. Former burglars, now wearing business attire, were seen offering support and understanding to individuals in debt. As a result, the number of bill collectors in Ohio also diminished, as these former criminals transitioned to more empathetic careers. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a serene Ohio neighborhood, with traditional woodblock painting aesthetics. Depict former burglars, now neatly dressed in business attire, offering empathetic support to individuals in debt. Show the transformation of these individuals from criminals to understanding community members, with a peaceful atmosphere and a sense of positive change in the neighborhood.

*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 Burglaries in Ohio caused The number of bill collectors in Ohio to decrease. Make it feel plausible.

AI academic paper

(Because p < 0.01)

The Debt Collector's Dilemma: Exploring the Correlation Between Burglaries and Bill Collectors in Ohio

The Journal of Financial Criminology

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 Burglaries in Ohio and The number of bill collectors in Ohio. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 you might move on to cartoons and children's shows that you watched for research.

Here is the title and abstract of the paper:
[[TITLE]]
"The Debt Collector's Dilemma: Exploring the Correlation Between Burglaries and Bill Collectors in Ohio"

[[ABSTRACT]]
This paper delves into the intriguing connection between the prevalence of burglaries and the number of bill collectors in the state of Ohio. Using data sourced from the FBI Criminal Justice Information Services and the Bureau of Labor Statistics for the time period spanning 2003 to 2022, our research team has discovered a remarkably high correlation coefficient of 0.9497645 with a significance level of p < 0.01 between the two variables. The findings of this research shed light on the intricate interplay between societal factors and economic pressures, beckoning further investigation into the nuances of this unexpected relationship.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
In the realm of criminology and economics, researchers have long been captivated by the multitude of factors that influence criminal behavior and economic phenomena. From socioeconomic disparities to legislative reforms, the interconnected web of variables continues to unravel with each investigation. Our current study aims to uncover the often overlooked relationship between burglaries and the presence of bill collectors in the state of Ohio. This unlikely pairing of crime and finance paints a compelling picture of the potential interdependence of seemingly disparate societal elements.
With an eye on rigor and robust statistical analyses, we embarked on a journey to scrutinize the trends in both burglary rates and the number of bill collectors in the Buckeye State. Much like a detective unraveling a complex mystery, our research team meticulously gathered data from authoritative sources such as the FBI Criminal Justice Information Services and the Bureau of Labor Statistics. The resulting dataset spanning nearly two decades provided fertile ground for our inquiries.
It is undeniably intriguing that burglaries, emblematic of criminal activity, may exhibit a relationship with the workforce responsible for recovering debts. Our initial hypothesis was met with skepticism by some colleagues, who curiously quipped that forced entry might be a confrontational strategy for addressing unpaid debts. Of course, we regard such jests with the levity they deserve, yet the unexpected juxtaposition of these two variables cannot be ignored.
As we unravel the threads of this exploration, we invite our fellow scholars to join us in peering beneath the surface of this correlation. The implications of our findings extend beyond mere statistical curiosities, pointing towards the intricate dance of economic pressures and societal behaviors. We urge readers to approach this investigation with an open mind and perhaps a hint of whimsy, for the fantastical may lie just beyond the mundane. With that, we invite you to embark on this peculiar journey into the heart of the Debt Collector's Dilemma.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "Smith et al. (2010)," the authors find a significant positive correlation between burglaries and economic hardship in urban areas. Similarly, "Doe and Johnson (2015)" suggest that financial distress is a driving force behind property crimes, such as burglary. These studies underscore the established link between economic factors and criminal activities.

Moving beyond the conventional research, we turn our attention to a less orthodox source of insight. In "Debt Collection Dynamics: From Dusk Till Dawn," the authors delve into the nocturnal operations of bill collectors and their encounters with debtors. While the book offers a firsthand account of the challenges in debt recovery, it also sheds some light on the enigmatic world of late-night escapades, albeit in a less traditional scholarly manner.

Furthermore, "The Art of the Heist: A Psychological Analysis of Thieves" presents a captivating narrative of the motivations and behaviors of professional burglars. Through a psychological lens, the authors make a compelling case for the intricate dance between financial pressures and criminal proclivities – a perspective that resonates with the focus of our investigation.

Taking an even quirkier turn, we draw inspiration from the animated realm of "Scooby-Doo" and its episodic encounters with elusive criminals. While the cartoon series may seem like an unlikely source of scholarly inspiration, the group's knack for unraveling mysteries and unmasking culprits offers a whimsical parallel to our pursuit of unraveling the connection between crime and financial distress.

In a similar vein, "Inspector Gadget" and his comical escapades in foiling the plans of villains provide a lighthearted lens through which to consider the interplay of law enforcement and criminal endeavors. While not a conventional scholarly reference, the character's bumbling yet ultimately successful exploits provide a humorous backdrop to our exploration of the intricate relationship between burglaries and bill collectors.

As we transition from the conventional to the unconventional, it becomes abundantly clear that the pursuit of knowledge knows no bounds, and perhaps a touch of whimsy and humor can illuminate paths less trodden in the scholarly landscape.

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 Burglaries in Ohio and The number of bill collectors in Ohio. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from FBI Criminal Justice Information Services and Bureau of Larbor Statistics . You used data from 2003 to 2022

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 Debt Collector's Dilemma: Exploring the Correlation Between Burglaries and Bill Collectors in Ohio"

[[ABSTRACT]]
This paper delves into the intriguing connection between the prevalence of burglaries and the number of bill collectors in the state of Ohio. Using data sourced from the FBI Criminal Justice Information Services and the Bureau of Labor Statistics for the time period spanning 2003 to 2022, our research team has discovered a remarkably high correlation coefficient of 0.9497645 with a significance level of p < 0.01 between the two variables. The findings of this research shed light on the intricate interplay between societal factors and economic pressures, beckoning further investigation into the nuances of this unexpected relationship.

[[INTRODUCTION]]
In the realm of criminology and economics, researchers have long been captivated by the multitude of factors that influence criminal behavior and economic phenomena. From socioeconomic disparities to legislative reforms, the interconnected web of variables continues to unravel with each investigation. Our current study aims to uncover the often overlooked relationship between burglaries and the presence of bill collectors in the state of Ohio. This unlikely pairing of crime and finance paints a compelling picture of the potential interdependence of seemingly disparate societal elements.
With an eye on rigor and robust statistical analyses, we embarked on a journey to scrutinize the trends in both burglary rates and the number of bill collectors in the Buckeye State. Much like a detective unraveling a complex mystery, our research team meticulously gathered data from authoritative sources such as the FBI Criminal Justice Information Services and the Bureau of Labor Statistics. The resulting dataset spanning nearly two decades provided fertile ground for our inquiries.
It is undeniably intriguing that burglaries, emblematic of criminal activity, may exhibit a relationship with the workforce responsible for recovering debts. Our initial hypothesis was met with skepticism by some colleagues, who curiously quipped that forced entry might be a confrontational strategy for addressing unpaid debts. Of course, we regard such jests with the levity they deserve, yet the unexpected juxtaposition of these two variables cannot be ignored.
As we unravel the threads of this exploration, we invite our fellow scholars to join us in peering beneath the surface of this correlation. The implications of our findings extend beyond mere statistical curiosities, pointing towards the intricate dance of economic pressures and societal behaviors. We urge readers to approach this investigation with an open mind and perhaps a hint of whimsy, for the fantastical may lie just beyond the mundane. With that, we invite you to embark on this peculiar journey into the heart of the Debt Collector's Dilemma.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

In an endeavor to untangle the enigmatic connection between the incidence of burglaries and the presence of bill collectors in Ohio, we employed a meticulously crafted methodology that balanced methodical rigor with a dash of whimsy. Our data collection process drew primarily from the FBI Criminal Justice Information Services (CJIS) and the Bureau of Labor Statistics (BLS).

To begin, we harnessed the digital prowess of the internet to peruse through the labyrinthine corridors of online repositories, gracefully skirting dubious websites and navigating the vast expanse of credible data sources. Our eyes were keenly fixed on the CJIS and BLS datasets, akin to intrepid explorers traversing uncharted territories in pursuit of statistical treasures.

The sampled data spanned the period from 2003 to 2022, capturing the ebb and flow of both burglary rates and the populace of bill collectors in the state of Ohio. We then gently coaxed the information from these repositories, treating it with the tender care befitting delicate statistical artifacts. The data, once liberated from the confines of spreadsheets and databases, gleamed invitingly in its purity, ready to divulge its secrets to our intrepid research team.

With a discerning eye, we sifted through the trove of numerical enigmas, discerning patterns and anomalies with the precision of a detective deciphering cryptic clues. We meticulously applied statistical techniques, such as correlation analyses and regression modeling, to unravel the intricate dance between the frequency of burglaries and the labor force dedicated to debt recovery.

Our approach veered away from the pedestrian, embracing the unpredictability of statistical exploration with a fervor reminiscent of a gambler at the roulette wheel. We ensured that our methodology was a tapestry woven from the threads of tradition and the sparks of unorthodox introspection, a marriage of scholarly prudence and creative audacity.

The resulting analysis presented a compelling narrative, a tale spun from the intricate interactions of societal dynamics and economic exigencies. As we laid bare the peculiar correlation between burglaries and the cohort of bill collectors, we found ourselves on the precipice of a revelation both surprising and tantalizing, akin to unearthing a long-lost artifact in the annals of statistical inquiry. Thus, armed with our concoction of empirical rigor and enigmatic exploration, we invite readers to accompany us into the labyrinthine heart of our methodological odyssey.

In summary, our methodology fused the conventional with the unconventional, twirling through the arcane mazes of data with a dance of empirical inquiry and intellectual buoyancy, ultimately yielding insights that transcend the bounds of mere statistical fascination.

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 Burglaries in Ohio and The number of bill collectors in Ohio. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
"The Debt Collector's Dilemma: Exploring the Correlation Between Burglaries and Bill Collectors in Ohio"

[[ABSTRACT]]
This paper delves into the intriguing connection between the prevalence of burglaries and the number of bill collectors in the state of Ohio. Using data sourced from the FBI Criminal Justice Information Services and the Bureau of Labor Statistics for the time period spanning 2003 to 2022, our research team has discovered a remarkably high correlation coefficient of 0.9497645 with a significance level of p < 0.01 between the two variables. The findings of this research shed light on the intricate interplay between societal factors and economic pressures, beckoning further investigation into the nuances of this unexpected relationship.

[[LITERATURE REVIEW]]
In "Smith et al. (2010)," the authors find a significant positive correlation between burglaries and economic hardship in urban areas. Similarly, "Doe and Johnson (2015)" suggest that financial distress is a driving force behind property crimes, such as burglary. These studies underscore the established link between economic factors and criminal activities.
Moving beyond the conventional research, we turn our attention to a less orthodox source of insight. In "Debt Collection Dynamics: From Dusk Till Dawn," the authors delve into the nocturnal operations of bill collectors and their encounters with debtors. While the book offers a firsthand account of the challenges in debt recovery, it also sheds some light on the enigmatic world of late-night escapades, albeit in a less traditional scholarly manner.
Furthermore, "The Art of the Heist: A Psychological Analysis of Thieves" presents a captivating narrative of the motivations and behaviors of professional burglars. Through a psychological lens, the authors make a compelling case for the intricate dance between financial pressures and criminal proclivities – a perspective that resonates with the focus of our investigation.
Taking an even quirkier turn, we draw inspiration from the animated realm of "Scooby-Doo" and its episodic encounters with elusive criminals. While the cartoon series may seem like an unlikely source of scholarly inspiration, the group's knack for unraveling mysteries and unmasking culprits offers a whimsical parallel to our pursuit of unraveling the connection between crime and financial distress.
In a similar vein, "Inspector Gadget" and his comical escapades in foiling the plans of villains provide a lighthearted lens through which to consider the interplay of law enforcement and criminal endeavors. While not a conventional scholarly reference, the character's bumbling yet ultimately successful exploits provide a humorous backdrop to our exploration of the intricate relationship between burglaries and bill collectors.
As we transition from the conventional to the unconventional, it becomes abundantly clear that the pursuit of knowledge knows no bounds, and perhaps a touch of whimsy and humor can illuminate paths less trodden in the scholarly landscape.

[[RESULTS]]
Our inquiry into the peculiar connection between burglaries and the number of bill collectors in Ohio has yielded some eyebrow-raising results. Upon conducting rigorous data analysis, we uncovered a strikingly high correlation coefficient of 0.9497645 and a robust R-squared value of 0.9020526, which is to say that these two variables seem to be performing an intricate dance, complete with twirls and dips, spanning the time period from 2003 to 2022. The p-value of less than 0.01 further accentuates the whimsical nature of this unexpected relationship, as if the statistical gods themselves are nudging us in the ribs and whispering, "Isn't this curious?"
Fig. 1 displays the scatterplot, illustrating the undeniable link between the prevalence of bill collectors and the occurrence of burglaries in Ohio. One cannot help but marvel at the almost poetic embrace of these two seemingly incongruent elements, akin to a waltz at the intersection of finance and felonies.
The results of this investigation surprise and beckon further exploration, similar to finding a clownfish in a bowl of alphabet soup – unexpected, yet undeniably intriguing. This unexpected link between debt collectors and burglaries challenges our traditional understanding of crime and economic forces, inviting us to peer into the depths of this enigmatic correlation.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of this study align with previous research that has underscored the influence of economic pressures on criminal activities, akin to a musical performance where the sway of societal factors and financial constraints orchestrates a harmonious dance. The significant positive correlation between burglaries and the number of bill collectors in Ohio echoes previous studies that have shone a spotlight on the intricate relationship between financial adversity and property crimes. While seemingly incongruent at first glance, these two elements, much like a pair of mismatched socks that surprisingly complement each other, entwine in a compelling embrace that captivates the imagination.

The unexpected connection uncovered in this study challenges our conventional understanding of crime and finance, akin to stumbling upon a pineapple in a grocery aisle stocked with turnips – an unexpected juxtaposition that defies initial expectations. The whimsical nature of this relationship, illustrated by the high correlation coefficient and robust R-squared value, beckons further inquiry and contemplation, akin to finding a four-leaf clover in a field of statistical significance. The plot thickens, much like a hearty stew simmering over the academic hearth, as we consider the implications of this unforeseen association for policy-making and societal interventions.

While the literature review ventured into unorthodox territories – from nocturnal escapades of bill collectors to the whimsical realm of animated sleuths – these seemingly unconventional sources of insight reverberate with a resonance that challenges the boundaries of traditional scholarly exploration. The quirky inspirations of "Scooby-Doo" and "Inspector Gadget," much like a lively pair of dancing shoes that infuse the solemn dance floor with an unexpected rhythm, invite us to embrace a lighthearted perspective on the enigmatic connection between crime and financial dynamics.

In closing, the results of this investigation serve as a compelling testament to the unanticipated turns that scholarly inquiry can take, akin to stumbling upon a treasure map in a thesis on economic theory – unexpected, yet undeniably alluring. The tantalizing implications of this study prompt a reevaluation of our understanding of criminal motivations and economic influences, much like a magician's unexpected reveal at the end of a seemingly mundane performance. This unorthodox relationship between burglaries and bill collectors in Ohio has the potential to inspire further research and perhaps even a dash of whimsy in our scholarly pursuits.

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 Burglaries in Ohio and The number of bill collectors in Ohio. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
"The Debt Collector's Dilemma: Exploring the Correlation Between Burglaries and Bill Collectors in Ohio"

[[ABSTRACT]]
This paper delves into the intriguing connection between the prevalence of burglaries and the number of bill collectors in the state of Ohio. Using data sourced from the FBI Criminal Justice Information Services and the Bureau of Labor Statistics for the time period spanning 2003 to 2022, our research team has discovered a remarkably high correlation coefficient of 0.9497645 with a significance level of p < 0.01 between the two variables. The findings of this research shed light on the intricate interplay between societal factors and economic pressures, beckoning further investigation into the nuances of this unexpected relationship.

[[INTRDUCTION]]
In the realm of criminology and economics, researchers have long been captivated by the multitude of factors that influence criminal behavior and economic phenomena. From socioeconomic disparities to legislative reforms, the interconnected web of variables continues to unravel with each investigation. Our current study aims to uncover the often overlooked relationship between burglaries and the presence of bill collectors in the state of Ohio. This unlikely pairing of crime and finance paints a compelling picture of the potential interdependence of seemingly disparate societal elements.
With an eye on rigor and robust statistical analyses, we embarked on a journey to scrutinize the trends in both burglary rates and the number of bill collectors in the Buckeye State. Much like a detective unraveling a complex mystery, our research team meticulously gathered data from authoritative sources such as the FBI Criminal Justice Information Services and the Bureau of Labor Statistics. The resulting dataset spanning nearly two decades provided fertile ground for our inquiries.
It is undeniably intriguing that burglaries, emblematic of criminal activity, may exhibit a relationship with the workforce responsible for recovering debts. Our initial hypothesis was met with skepticism by some colleagues, who curiously quipped that forced entry might be a confrontational strategy for addressing unpaid debts. Of course, we regard such jests with the levity they deserve, yet the unexpected juxtaposition of these two variables cannot be ignored.
As we unravel the threads of this exploration, we invite our fellow scholars to join us in peering beneath the surface of this correlation. The implications of our findings extend beyond mere statistical curiosities, pointing towards the intricate dance of economic pressures and societal behaviors. We urge readers to approach this investigation with an open mind and perhaps a hint of whimsy, for the fantastical may lie just beyond the mundane. With that, we invite you to embark on this peculiar journey into the heart of the Debt Collector's Dilemma.

[[RESULTS]]
Our inquiry into the peculiar connection between burglaries and the number of bill collectors in Ohio has yielded some eyebrow-raising results. Upon conducting rigorous data analysis, we uncovered a strikingly high correlation coefficient of 0.9497645 and a robust R-squared value of 0.9020526, which is to say that these two variables seem to be performing an intricate dance, complete with twirls and dips, spanning the time period from 2003 to 2022. The p-value of less than 0.01 further accentuates the whimsical nature of this unexpected relationship, as if the statistical gods themselves are nudging us in the ribs and whispering, "Isn't this curious?"
Fig. 1 displays the scatterplot, illustrating the undeniable link between the prevalence of bill collectors and the occurrence of burglaries in Ohio. One cannot help but marvel at the almost poetic embrace of these two seemingly incongruent elements, akin to a waltz at the intersection of finance and felonies.
The results of this investigation surprise and beckon further exploration, similar to finding a clownfish in a bowl of alphabet soup – unexpected, yet undeniably intriguing. This unexpected link between debt collectors and burglaries challenges our traditional understanding of crime and economic forces, inviting us to peer into the depths of this enigmatic correlation.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has illuminated the enigmatic relationship between burglaries and the number of bill collectors in Ohio, revealing a correlation so strong, it's as if they were two peas in a pod, or perhaps in this case, two thieves in a heist. The statistical tango between these variables calls to mind a whimsical waltz, with burglary rates and debt collection figures spinning around each other in a dance as old as time, or at least as old as our dataset from 2003 to 2022.

The unexpected nature of this correlation can be likened to stumbling upon a misplaced ladle in a cutlery drawer – an unusual discovery, but one that piques our curiosity nonetheless. With a correlation coefficient of 0.9497645, it's as if these two variables are sharing a secret handshake behind the back of conventional wisdom, whispering, "We're in cahoots, but don't let the societal norms find out!"

This interplay between criminal activity and financial pressures raises questions that may not have easy answers, much like trying to decide between dessert or cheese at the end of a hedonistic meal – both enticing options, yet each with its own set of consequences.

As we close the chapter on this intriguing investigation, we assert with confidence that no further research is needed in this area, as we've surely hit the jackpot in uncovering this unforeseen connection. It seems that in the dance of societal phenomena, burglary rates and bill collectors have found themselves as the unlikeliest of dance partners, twirling through the annals of Ohio's economic and criminal landscape.

In the grand symphony of academic pursuits, our findings add a quirky melody to the ongoing exploration of the intricate interplay between societal elements and economic forces. It's as if in our pursuit of scholarly knowledge, we stumbled upon a jester's hat in the hallowed halls of academia – a lighthearted addition to the weighty matters at hand.

In the words of the venerable Sherlock Holmes, "The game is afoot," and indeed, it is afoot in the most unexpected of arenas – the intersection of debt collection and burglary rates in the heart of Ohio.

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 Debt Collector's Dilemma: Exploring the Correlation Between Burglaries and Bill Collectors in Ohio. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Financial Criminology

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 Debt Collector's Dilemma: Exploring the Correlation Between Burglaries and Bill Collectors in Ohio. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Economic Mobility and Social Justice

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 Debt Collector's Dilemma: Exploring the Correlation Between Burglaries and Bill Collectors in Ohio with an abstract of This paper delves into the intriguing connection between the prevalence of burglaries and the number of bill collectors in the state of Ohio. Using data sourced from the FBI Criminal Justice Information Services and the Bureau of Labor Statistics for the time period spanning 2003 to 2022, our research team has discovered a remarkably high correlation coefficient of 0.9497645 with a significance level of p < 0.01 between the two variables. The findings of this research shed light on the intricate interplay between societal factors and economic pressures, beckoning further investigation into the nuances of this unexpected relationship.

ChatGPT:

burglaries, bill collectors, Ohio, correlation, FBI Criminal Justice Information Services, Bureau of Labor Statistics, societal factors, economic pressures

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

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Data details

Burglaries in Ohio
Detailed data title: The burglary rate per 100,000 residents in Ohio
Source: FBI Criminal Justice Information Services
See what else correlates with Burglaries in Ohio

The number of bill collectors in Ohio
Detailed data title: BLS estimate of bill and account collectors in Ohio
Source: Bureau of Larbor Statistics
See what else correlates with The number of bill collectors in Ohio

Correlation r = 0.9497645 (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 = 0.9020526 (Coefficient of determination)
This means 90.2% of the change in the one variable (i.e., The number of bill collectors in Ohio) is predictable based on the change in the other (i.e., Burglaries in Ohio) over the 20 years from 2003 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.6E-10.^Show 0.0000000001611629651711173000
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 0.95 in 1.6E-8% of random cases. Said differently, if you correlated 6,204,899,488 random variables^Note You don't actually need 6 billion 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 19 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 19 because we have two variables measured over a period of 20 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.

[ 0.87, 0.98 ] 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.

	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Burglaries in Ohio (Burglary rate)	831.3	842.9	873.4	920.4	863.8	891.8	901.1	928.5	978.3	911.6	787.2	685.2	603.1	577.2	493.1	423.6	378.2	318.8	272.4	255.3
The number of bill collectors in Ohio (Laborers)	18970	20160	19380	18990	16660	16020	17430	17700	19390	16440	16650	15920	14690	13640	12900	11800	9890	8250	8150	8020

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([831.3,842.9,873.4,920.4,863.8,891.8,901.1,928.5,978.3,911.6,787.2,685.2,603.1,577.2,493.1,423.6,378.2,318.8,272.4,255.3,])
array_2 = np.array([18970,20160,19380,18990,16660,16020,17430,17700,19390,16440,16650,15920,14690,13640,12900,11800,9890,8250,8150,8020,])
array_1_name = "Burglaries in Ohio"
array_2_name = "The number of bill collectors in Ohio"

# 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 Burglaries in Ohio
Line chart for only The number of bill collectors in Ohio
AI-generated correlation image
The spurious research paper: The Debt Collector's Dilemma: Exploring the Correlation Between Burglaries and Bill Collectors in Ohio

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Correlation ID: 2969 · Black Variable ID: 20113 · Red Variable ID: 12918


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