Spurious correlation #2,437 · 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 Kali and the second variable is Robberies in South Dakota. 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 Kali and the second variable is Robberies in South Dakota. The chart goes from 1985 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

Every time someone named Kali entered the state, they couldn't help but steal the spotlight, and apparently a few other things while they were at it. It was a case of irresistible charm and undeniable magnetism leading to a rise in notoriety, both for the name and for the crime statistics in South Dakota. It seems Kali wasn't just a popular name; it was also a criminal mastermind in the making, making off with more than just hearts.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate a scene that captures the rebellious spirit of a group of young girls named Kali, confidently riding motorcycles through a small South Dakota town. In the style of a 1960s comic book illustration, use muted colors and a dotted halftone pattern to depict the girls in matching leather jackets, proudly displaying their popularity as they cause chaos and take cows along the dusty roads. Emphasize the girls' camaraderie and the dynamic motion of their motorcycles within the simplicity of the visual depiction. 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 Kali and Robberies in South Dakota. 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: Parallax art Cute cartoon Concept art A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s Oragami
ChatGPT: A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s

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 Kali and Robberies in South Dakota, in the following situation: As more parents named their daughters Kali, a surge of young girls with the same name formed a notorious gang in South Dakota. Their motto? "Kali's Angels, causing chaos and taking your cows.". Keep the description simple.
ChatGPT: In a small town in South Dakota, a group of young girls with the first name Kali, dressed in matching leather jackets and riding motorcycles, proudly display their popularity by causing chaos and taking cows along the dusty roads.

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: [ A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s ] and depicting this scene: In a small town in South Dakota, a group of young girls with the first name Kali, dressed in matching leather jackets and riding motorcycles, proudly display their popularity by causing chaos and taking cows along the dusty roads. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate a scene that captures the rebellious spirit of a group of young girls named Kali, confidently riding motorcycles through a small South Dakota town. In the style of a 1960s comic book illustration, use muted colors and a dotted halftone pattern to depict the girls in matching leather jackets, proudly displaying their popularity as they cause chaos and take cows along the dusty roads. Emphasize the girls' camaraderie and the dynamic motion of their motorcycles within the simplicity of the visual depiction.

*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 an increase in Popularity of the first name Kali caused Robberies in South Dakota to increase.

AI academic paper

(Because p < 0.01)

The Kali Robbery Connection: A Steal of a Correlation

The Journal of Mirthful Misconnections

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Popularity of the first name Kali and Robberies in South Dakota. Make lots of jokes, goofy observations, and puns.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
The Kali Robbery Connection: A Steal of a Correlation

[[ABSTRACT]]
This study examines the potential relationship between the popularity of the first name Kali and the occurrence of robberies in South Dakota. Utilizing data from the US Social Security Administration and the FBI Criminal Justice Information Services, we conducted a thorough analysis spanning from 1985 to 2022. Our findings revealed a surprisingly strong correlation coefficient of 0.7614401 and a statistically significant p-value of less than 0.01, indicating a connection that is anything but a theft of imagination. This research sheds light on the unexpected influence of first names on criminal activity and paves the way for further investigations into the stranger-than-fiction world of nomenclature and nefarious deeds.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The relationship between first names and criminal behavior has long been a topic of fascination, but never has it been so delightfully peculiar as in the case of the first name Kali and its alleged link to robberies in the charming state of South Dakota. It's as though the mischievous hands of fate reached into the annals of nomenclature and orchestrated a captivating symphony of statistical intrigue. The very idea that the popularity of a given moniker could somehow influence illicit activities is a notion as tantalizing as a forbidden fruit hanging just within reach.
While the thought of a name wielding such sway over criminal behavior may seem far-fetched, it is precisely this implausibility that draws us in, much like the plot of a compelling crime novel. And so, armed with data from the US Social Security Administration and the FBI Criminal Justice Information Services, we embarked on a Sherlockian quest to discern whether the popularity of the name Kali bore any relationship to the occurrence of robberies in the land of Mount Rushmore.
In the following pages, we unveil the results of our investigation, unraveling a correlation that is both perplexing and peculiarly persuasive. Perhaps, after perusing our findings, you will find yourself not only pondering the peculiarity of human behavior, but also the potential power of a name to nudge individuals toward nefarious deeds. Indeed, the world of nomenclature may not be as innocent as we once thought, and the intertwined paths of nomenclature and criminality may prove to be a labyrinthine terrain of twists and turns.
So, dear reader, fasten your seatbelts and prepare to be whisked away on a wild ride through the unexpected nexus of nomenclature, naughtiness, and numerals. It's a saga of statistics and storytelling that may just leave you asking: what's in a name? In the case of Kali and robberies in South Dakota, the answer may be more than meets the eye.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The potential influence of first names on criminal activity has been a subject of both scholarly inquiry and popular imagination. A study by Smith et al. (2010) delved into the relationship between first names and criminal behavior, examining a diverse array of monikers and their potential associations with unlawful deeds. Similarly, Doe and Jones (2015) provided a comprehensive analysis of the linguistic and sociological factors that may underpin the curious connection between names and nefarious activities. These scholarly contributions have laid the groundwork for the exploration of the unexpected nexus between nomenclature and naughtiness.

In "The Name Effect" by Patel (2018), the author delves into the subtle yet profound impact of names on various aspects of human behavior, including but not limited to academic performance, professional success, and even, one might speculate, criminal proclivities. Another compelling exploration of the societal implications of names can be found in "Naming and Nefariousness" by Singh (2017), which elucidates the intricate dance between nomenclature and misdeeds.

Turning to works of fiction that may shed light on the potential influence of names on criminal activities, we encounter "The Adventures of Sherlock Holmes" by Sir Arthur Conan Doyle, a classic literary work filled with mysteries and intrigues. While not explicitly focused on the relationship between names and criminal behavior, the adventures of the astute detective offer a captivating portrayal of the enigmatic forces that shape human actions, including the potential influence of nomenclature.

In the realm of television and children's programming, the animated series "Scooby-Doo, Where Are You!" presents an intriguing parallel to our inquiry. As the intrepid gang solves mysteries with aplomb, one cannot help but consider the impact of the names of both the characters and the cases they encounter. Similarly, the animated show "Inspector Gadget" features a bumbling yet endearing detective whose name, though not central to the narrative, may hold unforeseen connections to the mischievous exploits he faces.

In sum, while scholarly investigations have paved the way for our exploration, works of fiction and animated entertainment offer delightfully imaginative lenses through which to view the potential intersection of nomenclature and criminal activities. As we embark on our study of the Kali robbery connection, let us remain open to the unexpected twists and turns that lie ahead, much like the plot of a captivating tale.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the 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 Popularity of the first name Kali and Robberies in South Dakota. Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from US Social Security Administration and FBI Criminal Justice Information Services . You used data from 1985 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 Kali Robbery Connection: A Steal of a Correlation

[[ABSTRACT]]
This study examines the potential relationship between the popularity of the first name Kali and the occurrence of robberies in South Dakota. Utilizing data from the US Social Security Administration and the FBI Criminal Justice Information Services, we conducted a thorough analysis spanning from 1985 to 2022. Our findings revealed a surprisingly strong correlation coefficient of 0.7614401 and a statistically significant p-value of less than 0.01, indicating a connection that is anything but a theft of imagination. This research sheds light on the unexpected influence of first names on criminal activity and paves the way for further investigations into the stranger-than-fiction world of nomenclature and nefarious deeds.

[[INTRODUCTION]]
The relationship between first names and criminal behavior has long been a topic of fascination, but never has it been so delightfully peculiar as in the case of the first name Kali and its alleged link to robberies in the charming state of South Dakota. It's as though the mischievous hands of fate reached into the annals of nomenclature and orchestrated a captivating symphony of statistical intrigue. The very idea that the popularity of a given moniker could somehow influence illicit activities is a notion as tantalizing as a forbidden fruit hanging just within reach.
While the thought of a name wielding such sway over criminal behavior may seem far-fetched, it is precisely this implausibility that draws us in, much like the plot of a compelling crime novel. And so, armed with data from the US Social Security Administration and the FBI Criminal Justice Information Services, we embarked on a Sherlockian quest to discern whether the popularity of the name Kali bore any relationship to the occurrence of robberies in the land of Mount Rushmore.
In the following pages, we unveil the results of our investigation, unraveling a correlation that is both perplexing and peculiarly persuasive. Perhaps, after perusing our findings, you will find yourself not only pondering the peculiarity of human behavior, but also the potential power of a name to nudge individuals toward nefarious deeds. Indeed, the world of nomenclature may not be as innocent as we once thought, and the intertwined paths of nomenclature and criminality may prove to be a labyrinthine terrain of twists and turns.
So, dear reader, fasten your seatbelts and prepare to be whisked away on a wild ride through the unexpected nexus of nomenclature, naughtiness, and numerals. It's a saga of statistics and storytelling that may just leave you asking: what's in a name? In the case of Kali and robberies in South Dakota, the answer may be more than meets the eye.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To investigate the suspected connection between the popularity of the first name Kali and the incidence of robberies in South Dakota, a hodgepodge of data collection and analysis methods was concocted, akin to a concoction from a mad scientist's lab.

The first task at hand involved obtaining historical data on the popularity of the name Kali from the US Social Security Administration. This necessitated trawling through digitized archives and navigating the digital catacombs of name popularity statistics. The data on the frequency of the name Kali from 1985 to 2022 was then meticulously harvested, much like a vintner harvesting grapes for the finest vintage.

Simultaneously, the occurrence of robberies in South Dakota over the same time period was gleaned from the FBI Criminal Justice Information Services. This process was not unlike embarking on a scavenger hunt through a labyrinthine maze of crime data, with each twist and turn revealing a new piece of the puzzle.

With the datasets in hand, a pseudo-scientific incantation known as statistical analysis was applied to unravel any potential connections between the popularity of the name Kali and the prevalence of robberies in South Dakota. The correlation coefficient was calculated with the precision of a watchmaker crafting a timepiece, and a hypothesis test was performed to ascertain the statistical significance of any observed relationship.

Furthermore, a series of supplementary analyses were conducted to explore potential confounding variables and to ensure that the findings were not merely a mirage on the statistical horizon. Sensitivity analyses were carried out to probe the robustness of the results, akin to stress testing a rickety bridge to ascertain its structural integrity.

In summary, the approach taken in this study combined the data wrangling prowess of a digital archaeologist, the statistical acumen of a number-crunching savant, and the untold tenacity of a relentless sleuth, all in pursuit of unraveling the enigmatic nexus between the name Kali and the shenanigans of robbers in the heartland of South Dakota.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between Popularity of the first name Kali and Robberies in South Dakota. 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]]
The Kali Robbery Connection: A Steal of a Correlation

[[ABSTRACT]]
This study examines the potential relationship between the popularity of the first name Kali and the occurrence of robberies in South Dakota. Utilizing data from the US Social Security Administration and the FBI Criminal Justice Information Services, we conducted a thorough analysis spanning from 1985 to 2022. Our findings revealed a surprisingly strong correlation coefficient of 0.7614401 and a statistically significant p-value of less than 0.01, indicating a connection that is anything but a theft of imagination. This research sheds light on the unexpected influence of first names on criminal activity and paves the way for further investigations into the stranger-than-fiction world of nomenclature and nefarious deeds.

[[LITERATURE REVIEW]]
The potential influence of first names on criminal activity has been a subject of both scholarly inquiry and popular imagination. A study by Smith et al. (2010) delved into the relationship between first names and criminal behavior, examining a diverse array of monikers and their potential associations with unlawful deeds. Similarly, Doe and Jones (2015) provided a comprehensive analysis of the linguistic and sociological factors that may underpin the curious connection between names and nefarious activities. These scholarly contributions have laid the groundwork for the exploration of the unexpected nexus between nomenclature and naughtiness.
In "The Name Effect" by Patel (2018), the author delves into the subtle yet profound impact of names on various aspects of human behavior, including but not limited to academic performance, professional success, and even, one might speculate, criminal proclivities. Another compelling exploration of the societal implications of names can be found in "Naming and Nefariousness" by Singh (2017), which elucidates the intricate dance between nomenclature and misdeeds.
Turning to works of fiction that may shed light on the potential influence of names on criminal activities, we encounter "The Adventures of Sherlock Holmes" by Sir Arthur Conan Doyle, a classic literary work filled with mysteries and intrigues. While not explicitly focused on the relationship between names and criminal behavior, the adventures of the astute detective offer a captivating portrayal of the enigmatic forces that shape human actions, including the potential influence of nomenclature.
In the realm of television and children's programming, the animated series "Scooby-Doo, Where Are You!" presents an intriguing parallel to our inquiry. As the intrepid gang solves mysteries with aplomb, one cannot help but consider the impact of the names of both the characters and the cases they encounter. Similarly, the animated show "Inspector Gadget" features a bumbling yet endearing detective whose name, though not central to the narrative, may hold unforeseen connections to the mischievous exploits he faces.
In sum, while scholarly investigations have paved the way for our exploration, works of fiction and animated entertainment offer delightfully imaginative lenses through which to view the potential intersection of nomenclature and criminal activities. As we embark on our study of the Kali robbery connection, let us remain open to the unexpected twists and turns that lie ahead, much like the plot of a captivating tale.

[[RESULTS]]
The results of our analysis revealed a correlation coefficient (r) of 0.7614401 between the popularity of the first name Kali and the incidence of robberies in South Dakota over the period from 1985 to 2022. This moderately high positive correlation suggests a potential relationship between the two variables. The r-squared value of 0.5797910 indicates that approximately 58% of the variability in robberies can be explained by the popularity of the name Kali, further bolstering the validity of this unexpectedly intriguing association.
The p-value of less than 0.01 provides strong evidence against the null hypothesis and supports the conclusion that there is indeed a statistically significant link between the frequency of the name Kali and the occurrence of robberies in the picturesque state of South Dakota. This finding is as remarkable as stumbling upon a hidden treasure amidst the vast expanse of statistical data.
In Figure 1, the scatterplot vividly illustrates the robust correlation between the popularity of the first name Kali and the prevalence of robberies in South Dakota, serving as a visual testament to the intrigue and delight of our investigation.
These results challenge conventional wisdom and prompt us to reconsider the potential influence of names on criminal behavior. The relationship between nomenclature and criminality is a fascinating area of inquiry that continues to yield unexpected discoveries, making this study a true steal of a correlation.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our investigation undeniably support and extend the prior research that has delved into the curious link between nomenclature and criminal activities. The noteworthy correlation coefficient of 0.7614401 we uncovered between the popularity of the first name Kali and the incidence of robberies in South Dakota echoes the findings of Smith et al. (2010) and Doe and Jones (2015), who laid the groundwork for our own exploration. It appears that the influence of names on criminal behavior is not to be dismissed as mere fiction but rather an unexpectedly compelling avenue of study.

The scholarly contributions of Patel (2018) and Singh (2017) have espoused the profound impact of names on various aspects of human behavior. Our findings add a new chapter to this narrative, revealing the significant role that the name Kali may play in shaping criminal activities in the picturesque state of South Dakota. Indeed, our results vividly illustrate the unexpected twists and turns that lie in the realm of nomenclature and nefarious deeds, much like the plot of a captivating tale. As we continue to unravel the enigmatic forces that shape human actions, we may find that the influence of names extends further than we could have imagined, much like uncovering a hidden treasure amidst the vast expanse of statistical data.

While our study may initially elicit incredulous amusement akin to the whimsical adventures of "Scooby-Doo, Where Are You!" and "Inspector Gadget," the robustness of the correlation discovered prompts us to take this matter with the seriousness it deserves. The p-value of less than 0.01 provides strong evidence against the null hypothesis, offering support for the conclusion that the association between the frequency of the name Kali and the occurrence of robberies in South Dakota is not to be dismissed as mere happenstance.

In conclusion, our findings lend credence to the notion that the relationship between nomenclature and criminality is a fascinating area of inquiry that continues to yield unexpected discoveries. This study, with its palpably strong correlation, is indeed a true steal of a correlation, as remarkable as stumbling upon a hidden treasure amidst the vast expanse of statistical data.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Popularity of the first name Kali and Robberies in South Dakota. 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]]
The Kali Robbery Connection: A Steal of a Correlation

[[ABSTRACT]]
This study examines the potential relationship between the popularity of the first name Kali and the occurrence of robberies in South Dakota. Utilizing data from the US Social Security Administration and the FBI Criminal Justice Information Services, we conducted a thorough analysis spanning from 1985 to 2022. Our findings revealed a surprisingly strong correlation coefficient of 0.7614401 and a statistically significant p-value of less than 0.01, indicating a connection that is anything but a theft of imagination. This research sheds light on the unexpected influence of first names on criminal activity and paves the way for further investigations into the stranger-than-fiction world of nomenclature and nefarious deeds.

[[INTRDUCTION]]
The relationship between first names and criminal behavior has long been a topic of fascination, but never has it been so delightfully peculiar as in the case of the first name Kali and its alleged link to robberies in the charming state of South Dakota. It's as though the mischievous hands of fate reached into the annals of nomenclature and orchestrated a captivating symphony of statistical intrigue. The very idea that the popularity of a given moniker could somehow influence illicit activities is a notion as tantalizing as a forbidden fruit hanging just within reach.
While the thought of a name wielding such sway over criminal behavior may seem far-fetched, it is precisely this implausibility that draws us in, much like the plot of a compelling crime novel. And so, armed with data from the US Social Security Administration and the FBI Criminal Justice Information Services, we embarked on a Sherlockian quest to discern whether the popularity of the name Kali bore any relationship to the occurrence of robberies in the land of Mount Rushmore.
In the following pages, we unveil the results of our investigation, unraveling a correlation that is both perplexing and peculiarly persuasive. Perhaps, after perusing our findings, you will find yourself not only pondering the peculiarity of human behavior, but also the potential power of a name to nudge individuals toward nefarious deeds. Indeed, the world of nomenclature may not be as innocent as we once thought, and the intertwined paths of nomenclature and criminality may prove to be a labyrinthine terrain of twists and turns.
So, dear reader, fasten your seatbelts and prepare to be whisked away on a wild ride through the unexpected nexus of nomenclature, naughtiness, and numerals. It's a saga of statistics and storytelling that may just leave you asking: what's in a name? In the case of Kali and robberies in South Dakota, the answer may be more than meets the eye.

[[RESULTS]]
The results of our analysis revealed a correlation coefficient (r) of 0.7614401 between the popularity of the first name Kali and the incidence of robberies in South Dakota over the period from 1985 to 2022. This moderately high positive correlation suggests a potential relationship between the two variables. The r-squared value of 0.5797910 indicates that approximately 58% of the variability in robberies can be explained by the popularity of the name Kali, further bolstering the validity of this unexpectedly intriguing association.
The p-value of less than 0.01 provides strong evidence against the null hypothesis and supports the conclusion that there is indeed a statistically significant link between the frequency of the name Kali and the occurrence of robberies in the picturesque state of South Dakota. This finding is as remarkable as stumbling upon a hidden treasure amidst the vast expanse of statistical data.
In Figure 1, the scatterplot vividly illustrates the robust correlation between the popularity of the first name Kali and the prevalence of robberies in South Dakota, serving as a visual testament to the intrigue and delight of our investigation.
These results challenge conventional wisdom and prompt us to reconsider the potential influence of names on criminal behavior. The relationship between nomenclature and criminality is a fascinating area of inquiry that continues to yield unexpected discoveries, making this study a true steal of a correlation.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation into the connection between the popularity of the first name Kali and the occurrence of robberies in South Dakota has unearthed a correlation that is as intriguing as a locked-room mystery. The statistically significant relationship we uncovered between nomenclature and nefarious deeds is reminiscent of a plot twist in a classic whodunit, leaving us both bemused and beguiled by the unexpected power of a name.

The moderately high positive correlation coefficient of 0.7614401 has proven to be a veritable gem in the treasure trove of statistical revelations, shining as brightly as a sheriff's badge in the sunlit prairies of South Dakota. This finding challenges preconceived notions and nudges us to reevaluate the potential impact of names on criminal activity, piquing the interest of criminologists and linguists alike in a manner akin to a gripping cliffhanger in an enthralling crime novel.

Our study has not only shed light on the curious interplay between nomenclature and illicit behavior but also opened the door to a realm of research that is as lively and unpredictable as a high-stakes game of cat and mouse. As we bid adieu to this curious chapter of inquiry, we are left with a sense of wonder at the labyrinthine connections that underpin human behavior, much like a detective marveling at the intricate web of clues in a case.

In light of these findings, we assert that further research in this area is as unnecessary as a getaway car with a full tank of gas and no destination in sight. With a correlation coefficient as conspicuous as a bandit in a black-and-white striped shirt, the compelling case of the Kali robbery connection has been laid to rest, leaving us with a whodunit for the ages. After all, in the world of academia, some mysteries are best left unsolved.

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 Kali Robbery Connection: A Steal of a Correlation. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Mirthful Misconnections

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 Kali Robbery Connection: A Steal of a Correlation. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Wacky Statistical Analysis

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled The Kali Robbery Connection: A Steal of a Correlation with an abstract of This study examines the potential relationship between the popularity of the first name Kali and the occurrence of robberies in South Dakota. Utilizing data from the US Social Security Administration and the FBI Criminal Justice Information Services, we conducted a thorough analysis spanning from 1985 to 2022. Our findings revealed a surprisingly strong correlation coefficient of 0.7614401 and a statistically significant p-value of less than 0.01, indicating a connection that is anything but a theft of imagination. This research sheds light on the unexpected influence of first names on criminal activity and paves the way for further investigations into the stranger-than-fiction world of nomenclature and nefarious deeds.

ChatGPT:

Kali name popularity, Kali first name, Kali correlation with robbery, Robbery occurrence in South Dakota, Kali name and crime correlation, Social Security Administration Kali name data, FBI Criminal Justice Information Services robbery data, Influence of first names on criminal activity

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

Robberies in South Dakota
Detailed data title: The robbery rate per 100,000 residents in South Dakota
Source: FBI Criminal Justice Information Services
See what else correlates with Robberies in South Dakota

Correlation r = 0.7614401 (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 = 0.5797910 (Coefficient of determination)
This means 58% of the change in the one variable (i.e., Robberies in South Dakota) is predictable based on the change in the other (i.e., Popularity of the first name Kali) over the 38 years from 1985 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.8E-8.^Show 0.0000000284167093802040480000
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 0.76 in 2.8E-6% of random cases. Said differently, if you correlated 35,190,563 random variables^Note You don't actually need 35 million 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 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.

[ 0.58, 0.87 ] 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 Kali (Babies born)	338	367	357	355	472	564	572	639	610	668	763	861	813	694	776	678	611	508	512	545	562	560	682	630	616	559	608	765	893	1282	1215	1120	1126	1236	1352	1223	1164	1249
Robberies in South Dakota (Robbery rate)	17.1	16.2	12.3	12.2	11.7	12.4	18.8	16.9	15	18.7	25.9	18.9	23.3	20.2	14	17.4	13.6	15.4	13.6	14.5	18.7	20.2	14.8	16.7	14.4	18.9	20.3	18.8	18.5	23.3	25.1	31.8	30.2	25.3	21.4	30.7	19.6	25.3

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([338,367,357,355,472,564,572,639,610,668,763,861,813,694,776,678,611,508,512,545,562,560,682,630,616,559,608,765,893,1282,1215,1120,1126,1236,1352,1223,1164,1249,])
array_2 = np.array([17.1,16.2,12.3,12.2,11.7,12.4,18.8,16.9,15,18.7,25.9,18.9,23.3,20.2,14,17.4,13.6,15.4,13.6,14.5,18.7,20.2,14.8,16.7,14.4,18.9,20.3,18.8,18.5,23.3,25.1,31.8,30.2,25.3,21.4,30.7,19.6,25.3,])
array_1_name = "Popularity of the first name Kali"
array_2_name = "Robberies in South Dakota"

# 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 Kali
Line chart for only Robberies in South Dakota
AI-generated correlation image
The spurious research paper: The Kali Robbery Connection: A Steal of a Correlation

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Correlation ID: 2437 · Black Variable ID: 2608 · Red Variable ID: 20217


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