Spurious correlation #4,888 · 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 Kathy and the second variable is Air pollution in Hartford, Connecticut. The chart goes from 1980 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 Kathy and the second variable is Air pollution in Hartford, Connecticut. The chart goes from 1980 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the popularity of the name Kathy waned, so did the number of personalized "Kathy" novelty license plates. This led to a decrease in production at the local plate manufacturing plant, ultimately reducing the overall carbon emissions in Hartford. It turns out, when it comes to air quality, Kathy really does make a difference!

^{Show image generation prompt} Model: dalle-3
Prompt: "Generate an image of a meme with the following scene: In a small factory in Hartford, Connecticut, a stack of "Kathy" personalized license plates sits untouched on a shelf, while the production line slows down. Outside, the air is noticeably cleaner, with fewer emissions billowing from the factory's smokestack. As the popularity of the name Kathy wanes, so does the production at the plant, leading to a positive impact on the air quality in Hartford. The meme should portray the contrast between the stagnant Kathy license plates and the cleaner air, conveying the unexpected positive environmental impact of the declining popularity of the name." 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 Kathy and Air pollution in Hartford, Connecticut. 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: Steampunk 3D Pixar Style Vaporwave a meme a meme
ChatGPT: a meme

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 Kathy and Air pollution in Hartford, Connecticut, in the following situation: As the popularity of the name Kathy waned, so did the number of personalized "Kathy" novelty license plates. This led to a decrease in production at the local plate manufacturing plant, ultimately reducing the overall carbon emissions in Hartford. It turns out, when it comes to air quality, Kathy really does make a difference!. Keep the description simple.
ChatGPT: In a small factory in Hartford, Connecticut, a stack of "Kathy" personalized license plates sat untouched on a shelf, while the production line slowed down. Outside, the air was noticeably cleaner, with fewer emissions billowing from the factory's smokestack. As the popularity of the name Kathy waned, so did the production at the plant, leading to a positive impact on the air quality in Hartford.

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 meme ] and depicting this scene: In a small factory in Hartford, Connecticut, a stack of "Kathy" personalized license plates sat untouched on a shelf, while the production line slowed down. Outside, the air was noticeably cleaner, with fewer emissions billowing from the factory's smokestack. As the popularity of the name Kathy waned, so did the production at the plant, leading to a positive impact on the air quality in Hartford. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: "Generate an image of a meme with the following scene: In a small factory in Hartford, Connecticut, a stack of "Kathy" personalized license plates sits untouched on a shelf, while the production line slows down. Outside, the air is noticeably cleaner, with fewer emissions billowing from the factory's smokestack. As the popularity of the name Kathy wanes, so does the production at the plant, leading to a positive impact on the air quality in Hartford. The meme should portray the contrast between the stagnant Kathy license plates and the cleaner air, conveying the unexpected positive environmental impact of the declining popularity of the name."

*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how a decrease in Popularity of the first name Kathy caused Air pollution in Hartford, Connecticut to decrease. Make it feel plausible.

AI academic paper

(Because p < 0.01)

The Relationship Between the Popularity of the First Name Kathy and Air Quality in Hartford: A Statistical Odyssey

The Journal of Irreverent Interdisciplinary Research

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Popularity of the first name Kathy and Air pollution in Hartford, Connecticut. 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 Relationship Between the Popularity of the First Name Kathy and Air Quality in Hartford: A Statistical Odyssey

[[ABSTRACT]]
This empirical study delves into the curious correlation between the prevalence of the first name "Kathy" and the ambient air quality in the urban enclave of Hartford, Connecticut. Leveraging data spanning from 1980 to 2022 sourced from the US Social Security Administration and the Environmental Protection Agency, our research team has unearthed a staggering correlation coefficient of 0.9000747 and an astoundingly low p-value of less than 0.01. This robust statistical relationship persists despite the vast difference in the nature of the two datasets, leaving us in a state of bemusement about the whimsical interplay of nomenclature and atmospheric pollution. The findings of this study not only underscore the surprising influence of personal names on environmental phenomena but also open the door to a future where urban planning considerations may need to account for the ebb and flow of appellations in the population.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The correlation between seemingly unrelated phenomena has long captivated researchers, prompting investigations into unusual pairings such as cheese consumption and bedsheet color preferences. In this vein, our study embarks on an exploration of the intriguing and, some might say, puzzling connection between the popularity of the first name "Kathy" and the air quality in Hartford, Connecticut. While the average passerby might not readily perceive a connection between these two disparate factors, our initial analysis has revealed a statistical relationship that is as surprising as finding a vegan in a butcher shop.
As any seasoned researcher knows, delving into uncharted territory requires an arsenal of data and a keen eye for patterns. Leveraging a wealth of information from the US Social Security Administration, we tracked the frequency of the name "Kathy" in the Hartford area from 1980 to 2022. Concurrently, we scoured data from the Environmental Protection Agency to gauge the levels of air pollution in the same time frame. The confluence of these datasets unveiled a correlation coefficient that could knock the socks off even the most stoic statistician, standing firm at a whopping 0.9000747. With a p-value that would make even the toughest peer reviewer nod in solemn approval, clocking in at less than 0.01, the statistical robustness of our findings is enough to make a researcher utter "Eureka!" - or, at the very least, raise an eyebrow in bemused disbelief.
One might be forgiven for dismissing this correlation as a mere statistical fluke, a chance alignment of the stars in the data firmament. However, as we peeled back the layers of this curious onion, the relationship between the prevalence of the name "Kathy" and the atmospheric composition in Hartford remained as solid as a rock in a thunderstorm. The persistence of this association, against all odds, left our research team in a state of introspective bewilderment, pondering the whimsical hand of fate in the very air we breathe.
Our findings not only raise eyebrows but also beckon us to consider the profound implications of appellation on the environment. If ubiquitous monikers like "Kathy" wield such influence over atmospheric phenomena, one can't help but wonder what other surprises lie in the vast expanse of the statistical cosmos. As we venture deeper into this statistical odyssey, we invite the reader to suspend disbelief and join us on this quirky journey through the intersection of nomenclature and atmospheric dynamics in the urban landscape. After all, in the realm of research, one never knows what peculiar discoveries await around the next statistical bend.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The underlying premise of our investigation - that there exists a relationship between the prevalence of the first name "Kathy" and air quality in Hartford, Connecticut - might strike some as peculiar, akin to the notion of finding a diamond in a haystack. However, as we dive into the sea of existing research, we uncover a surprising tapestry of scholarly work that sheds light on similar, if not equally quirky, associations.

Smith and Doe (2015) examined the unexpected link between the popularity of given names and ecological phenomena. Their study, aptly titled "The Name Game: An Exploration of Moniker Manifestation in Environmental Settings," delved into the correlation between names like "Sunny" and sunshine levels, and "Rose" and flower blooming patterns. While their research focused on a broader spectrum of names, it paved the way for our exploration of the specific impact of "Kathy" on air pollution levels.

Expanding our horizons beyond the realm of academic literature, non-fiction works such as "The Hidden Life of Trees" by Peter Wohlleben and "Breath: The New Science of a Lost Art" by James Nestor offer intriguing insights into the interconnectedness of human activities and the environment. While neither explicitly touches on the influence of names on atmospheric dynamics, the fundamental premise of ecological interdependence resonates with our study, albeit in a more metaphorical sense.

On the fictional front, literary connoisseurs might raise an eyebrow at the mention of "The Name of the Wind" by Patrick Rothfuss and "Cloud Atlas" by David Mitchell in this context. While these masterpieces of literary fiction may not directly address the correlation between a name and environmental conditions, their thematic exploration of interconnectedness and the ripple effects of individual actions serves as a whimsical analogy to our statistical findings. After all, in the world of fiction as in reality, the butterfly effect can manifest in the most unforeseen and delightful ways.

In our quest to uncover the hidden nuances of this peculiar correlation, we navigated uncharted territories, even venturing into the realms of children's entertainment. Shows such as "Captain Planet and the Planeteers" and "The Magic School Bus" embodied the spirit of environmental consciousness, albeit with a level of whimsy and levity that serves as a delightful parallel to the unexpected twists and turns in our statistical journey. While not scholarly in the traditional sense, these whimsical forays into the world of animated educational programming provided a refreshing perspective on the intricate dance between human influence and the natural world.

As we stand at the confluence of scholarly research, non-fiction musings, fiction narratives, and children's edutainment, the sheer breadth of our literature review underscores the multidimensional nature of our investigation. By weaving together the serious and the whimsical, the expected and the unexpected, we lay the foundation for a statistical odyssey that is as intellectually stimulating as it is tinged with a hint of playfulness.

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 Popularity of the first name Kathy and Air pollution in Hartford, Connecticut. 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 US Social Security Administration and Environmental Protection Agency . You used data from 1980 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 Relationship Between the Popularity of the First Name Kathy and Air Quality in Hartford: A Statistical Odyssey

[[ABSTRACT]]
This empirical study delves into the curious correlation between the prevalence of the first name "Kathy" and the ambient air quality in the urban enclave of Hartford, Connecticut. Leveraging data spanning from 1980 to 2022 sourced from the US Social Security Administration and the Environmental Protection Agency, our research team has unearthed a staggering correlation coefficient of 0.9000747 and an astoundingly low p-value of less than 0.01. This robust statistical relationship persists despite the vast difference in the nature of the two datasets, leaving us in a state of bemusement about the whimsical interplay of nomenclature and atmospheric pollution. The findings of this study not only underscore the surprising influence of personal names on environmental phenomena but also open the door to a future where urban planning considerations may need to account for the ebb and flow of appellations in the population.

[[INTRODUCTION]]
The correlation between seemingly unrelated phenomena has long captivated researchers, prompting investigations into unusual pairings such as cheese consumption and bedsheet color preferences. In this vein, our study embarks on an exploration of the intriguing and, some might say, puzzling connection between the popularity of the first name "Kathy" and the air quality in Hartford, Connecticut. While the average passerby might not readily perceive a connection between these two disparate factors, our initial analysis has revealed a statistical relationship that is as surprising as finding a vegan in a butcher shop.
As any seasoned researcher knows, delving into uncharted territory requires an arsenal of data and a keen eye for patterns. Leveraging a wealth of information from the US Social Security Administration, we tracked the frequency of the name "Kathy" in the Hartford area from 1980 to 2022. Concurrently, we scoured data from the Environmental Protection Agency to gauge the levels of air pollution in the same time frame. The confluence of these datasets unveiled a correlation coefficient that could knock the socks off even the most stoic statistician, standing firm at a whopping 0.9000747. With a p-value that would make even the toughest peer reviewer nod in solemn approval, clocking in at less than 0.01, the statistical robustness of our findings is enough to make a researcher utter "Eureka!" - or, at the very least, raise an eyebrow in bemused disbelief.
One might be forgiven for dismissing this correlation as a mere statistical fluke, a chance alignment of the stars in the data firmament. However, as we peeled back the layers of this curious onion, the relationship between the prevalence of the name "Kathy" and the atmospheric composition in Hartford remained as solid as a rock in a thunderstorm. The persistence of this association, against all odds, left our research team in a state of introspective bewilderment, pondering the whimsical hand of fate in the very air we breathe.
Our findings not only raise eyebrows but also beckon us to consider the profound implications of appellation on the environment. If ubiquitous monikers like "Kathy" wield such influence over atmospheric phenomena, one can't help but wonder what other surprises lie in the vast expanse of the statistical cosmos. As we venture deeper into this statistical odyssey, we invite the reader to suspend disbelief and join us on this quirky journey through the intersection of nomenclature and atmospheric dynamics in the urban landscape. After all, in the realm of research, one never knows what peculiar discoveries await around the next statistical bend.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To elucidate the enigmatic connection between the frequency of the name "Kathy" and atmospheric pollution in Hartford, Connecticut, our research team undertook a methodological odyssey replete with data collection, statistical analyses, and a touch of whimsy.

Data Collection:
The first step in our expedition involved combing through the archives of the US Social Security Administration to extract the frequency of occurrences of the name "Kathy" in the Hartford area from 1980 to 2022. Embracing the sheer magnitude of the digital expanse, our researchers traversed through enough data to make even the hardiest of information superhighways shudder with delight.

Simultaneously, we charted a course through the labyrinthine corridors of the Environmental Protection Agency's data repositories, harvesting information on air quality metrics in Hartford during the same time period. Our intrepid researchers navigated through a deluge of chemical compounds, particulate matter, and atmospheric indices to capture the essence of Hartford's atmospheric composition.

Statistical Analysis:
With both datasets securely ensconced in our analytical arsenal, we summoned the spirits of statistical prowess to unveil any hidden link between the eponymous "Kathy" and the atmospheric tableau of Hartford. Employing a veritable menagerie of statistical methods, including but not limited to correlation analysis, regression modeling, and multivariate wizardry, we sought to disentangle the web of connections that lay dormant amidst the data.

Weaving through the statistical underbrush, we uncovered a correlation coefficient that stood tall and proud at 0.9000747, akin to a beacon of statistical significance in a sea of probability. Furthermore, the p-value, that elusive arbiter of statistical credibility, whispered sweet nothings in our ears with a tantalizing value of less than 0.01, bestowing upon our findings the stamp of empirical legitimacy.

Interdisciplinary Considerations:
As we navigated the complex terrain of statistical inference, our research team remained acutely aware of the multidisciplinary implications of our findings. The curious nexus between personal nomenclature and environmental phenomena beckons us to consider the intertwining of sociocultural dynamics, demographical shifts, and atmospheric chemistry in our interpretations. Indeed, this intersection of statistical investigation and interdisciplinary musings led our team to contemplate the profound influence of individual monikers on the very air we respire, a notion that, while initially whimsical, bears the weight of empirical substantiation.

Overall, our eight-fold path through the thickets of data collection, statistical modeling, and multidisciplinary pondering has illuminated a correlation of such robustness and statistical flair that we find ourselves at the precipice of a whimsical epiphany - that the name "Kathy" may very well hold the key to unlocking the secrets of Hartford's atmospheric mysteries.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between Popularity of the first name Kathy and Air pollution in Hartford, Connecticut. 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 Relationship Between the Popularity of the First Name Kathy and Air Quality in Hartford: A Statistical Odyssey

[[ABSTRACT]]
This empirical study delves into the curious correlation between the prevalence of the first name "Kathy" and the ambient air quality in the urban enclave of Hartford, Connecticut. Leveraging data spanning from 1980 to 2022 sourced from the US Social Security Administration and the Environmental Protection Agency, our research team has unearthed a staggering correlation coefficient of 0.9000747 and an astoundingly low p-value of less than 0.01. This robust statistical relationship persists despite the vast difference in the nature of the two datasets, leaving us in a state of bemusement about the whimsical interplay of nomenclature and atmospheric pollution. The findings of this study not only underscore the surprising influence of personal names on environmental phenomena but also open the door to a future where urban planning considerations may need to account for the ebb and flow of appellations in the population.

[[LITERATURE REVIEW]]
The underlying premise of our investigation - that there exists a relationship between the prevalence of the first name "Kathy" and air quality in Hartford, Connecticut - might strike some as peculiar, akin to the notion of finding a diamond in a haystack. However, as we dive into the sea of existing research, we uncover a surprising tapestry of scholarly work that sheds light on similar, if not equally quirky, associations.
Smith and Doe (2015) examined the unexpected link between the popularity of given names and ecological phenomena. Their study, aptly titled "The Name Game: An Exploration of Moniker Manifestation in Environmental Settings," delved into the correlation between names like "Sunny" and sunshine levels, and "Rose" and flower blooming patterns. While their research focused on a broader spectrum of names, it paved the way for our exploration of the specific impact of "Kathy" on air pollution levels.
Expanding our horizons beyond the realm of academic literature, non-fiction works such as "The Hidden Life of Trees" by Peter Wohlleben and "Breath: The New Science of a Lost Art" by James Nestor offer intriguing insights into the interconnectedness of human activities and the environment. While neither explicitly touches on the influence of names on atmospheric dynamics, the fundamental premise of ecological interdependence resonates with our study, albeit in a more metaphorical sense.
On the fictional front, literary connoisseurs might raise an eyebrow at the mention of "The Name of the Wind" by Patrick Rothfuss and "Cloud Atlas" by David Mitchell in this context. While these masterpieces of literary fiction may not directly address the correlation between a name and environmental conditions, their thematic exploration of interconnectedness and the ripple effects of individual actions serves as a whimsical analogy to our statistical findings. After all, in the world of fiction as in reality, the butterfly effect can manifest in the most unforeseen and delightful ways.
In our quest to uncover the hidden nuances of this peculiar correlation, we navigated uncharted territories, even venturing into the realms of children's entertainment. Shows such as "Captain Planet and the Planeteers" and "The Magic School Bus" embodied the spirit of environmental consciousness, albeit with a level of whimsy and levity that serves as a delightful parallel to the unexpected twists and turns in our statistical journey. While not scholarly in the traditional sense, these whimsical forays into the world of animated educational programming provided a refreshing perspective on the intricate dance between human influence and the natural world.
As we stand at the confluence of scholarly research, non-fiction musings, fiction narratives, and children's edutainment, the sheer breadth of our literature review underscores the multidimensional nature of our investigation. By weaving together the serious and the whimsical, the expected and the unexpected, we lay the foundation for a statistical odyssey that is as intellectually stimulating as it is tinged with a hint of playfulness.

[[RESULTS]]
The crux of our findings lies in the striking correlation coefficient of 0.9000747, which suggests a compelling relationship between the prevalence of the name "Kathy" and the ambient air quality in Hartford, Connecticut. This coefficient implies a robust connection, standing as firm as a coffee table in a college dorm room.
Furthermore, the coefficient of determination (r-squared) of 0.8101344 indicates that approximately 81% of the variability in air pollution levels in Hartford can be explained by the prevalence of the name "Kathy." In other words, the popularity of this name appears to be exerting a considerable influence on the atmospheric composition, leaving us to wonder if there's a hidden breeze of "Kathy-ness" wafting through the air.
The p-value of less than 0.01 provides further support for the statistical significance of this association, essentially telling us that the likelihood of this correlation occurring by chance is about as probable as finding a needle in a haystack while blindfolded. This p-value invites us to consider the possibility that there may be more than mere happenstance at play in this curious dance of names and air quality.
Our visual representation of this correlation in Figure 1 depicts a scatterplot that unmistakably showcases the confluence of these two variables. The strong clustering of data points serves as a graphic testament to the unexpected relationship between the frequency of the name "Kathy" and the air pollution levels in Hartford. It's a sight to behold, akin to witnessing a harmonious duet between a saxophone and a vacuum cleaner - unexpected, yet strangely compelling.
In sum, our results present a compelling case for the association between the prevalence of the name "Kathy" and the ambient air quality in Hartford, Connecticut. This unexpected correlation not only adds a touch of whimsy to the world of empirical research but also prompts us to ponder the subtle yet profound ways in which personal names may intertwine with environmental phenomena. As we continue to unpack the implications of this statistical oddity, we are reminded that in the realm of research, even the most unexpected connections can lead to enlightening discoveries.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The striking correlation between the prevalence of the name "Kathy" and air quality in Hartford, Connecticut, presents a conundrum as beguiling as a riddle wrapped in an enigma surrounded by whimsy. Our findings not only corroborate the prior research that revealed unconventional associations between names and environmental phenomena but also elevate this phenomenon to a level of statistical significance that demands earnest contemplation, much like the sudden appearance of a flock of flamingos in a city park.

Hearkening back to the study by Smith and Doe (2015), which probed the relationship between names and ecological patterns, our results amplify the peculiar nature of these connections. Much like spotting a unicorn in a herd of horses, the robust correlation coefficient and infinitesimal p-value affirm the substantial impact of the name "Kathy" on the atmospheric fabric of Hartford. These findings stand as a testament to the unforeseen influence that personal appellations can wield over environmental dynamics, akin to a whispered incantation that influences the natural order of the world around us.

Turning to the non-fiction works by Wohlleben and Nestor, which expound on the interdependence of human activities and the environment, our study amplifies the tangible manifestation of this interconnectedness, albeit in the captivating realm of nomenclature. The statistical rigor of our findings illuminates the intricate symbiosis between personal names and atmospheric conditions, evoking a sense of wonder akin to stumbling upon a treasure trove hidden in plain sight.

In parallel, the thematic exploration of interconnectedness in fictional masterpieces such as "The Name of the Wind" and "Cloud Atlas" finds resonance in our statistical revelations, akin to uncovering a secret passage in a labyrinthine mansion. Just as these literary narratives unravel the far-reaching repercussions of individual actions, our study unveils the pervasive influence of the name "Kathy" on the air quality of Hartford, conjuring a sense of intrigue as tantalizing as a cliffhanger in a thrilling novel.

Even our unexpected foray into children's educational programming, with its whimsical portrayal of environmental awareness, finds an unlikely parallel in our statistical escapade. The lighthearted resonance between the spirited ethos of these shows and the unforeseen correlation we've unveiled infuses our empirical endeavor with a touch of jocularity, much like discovering a rainbow amidst a sudden summer shower.

In summary, our study not only upholds the findings of prior research that hinted at the whimsical nature of associations between personal names and environmental variables but also magnifies the intrigue, offering a statistical odyssey that harmonizes empirical rigor with a dash of whimsy. As we continue to plumb the depths of this enigmatic correlation, we are reminded that amidst the rigidity of statistical analysis, the unexpected can lend a touch of playfulness to our scholarly pursuits, enriching the fabric of academic inquiry with a measure of wonder and delight.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Popularity of the first name Kathy and Air pollution in Hartford, Connecticut. 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 Relationship Between the Popularity of the First Name Kathy and Air Quality in Hartford: A Statistical Odyssey

[[ABSTRACT]]
This empirical study delves into the curious correlation between the prevalence of the first name "Kathy" and the ambient air quality in the urban enclave of Hartford, Connecticut. Leveraging data spanning from 1980 to 2022 sourced from the US Social Security Administration and the Environmental Protection Agency, our research team has unearthed a staggering correlation coefficient of 0.9000747 and an astoundingly low p-value of less than 0.01. This robust statistical relationship persists despite the vast difference in the nature of the two datasets, leaving us in a state of bemusement about the whimsical interplay of nomenclature and atmospheric pollution. The findings of this study not only underscore the surprising influence of personal names on environmental phenomena but also open the door to a future where urban planning considerations may need to account for the ebb and flow of appellations in the population.

[[INTRDUCTION]]
The correlation between seemingly unrelated phenomena has long captivated researchers, prompting investigations into unusual pairings such as cheese consumption and bedsheet color preferences. In this vein, our study embarks on an exploration of the intriguing and, some might say, puzzling connection between the popularity of the first name "Kathy" and the air quality in Hartford, Connecticut. While the average passerby might not readily perceive a connection between these two disparate factors, our initial analysis has revealed a statistical relationship that is as surprising as finding a vegan in a butcher shop.
As any seasoned researcher knows, delving into uncharted territory requires an arsenal of data and a keen eye for patterns. Leveraging a wealth of information from the US Social Security Administration, we tracked the frequency of the name "Kathy" in the Hartford area from 1980 to 2022. Concurrently, we scoured data from the Environmental Protection Agency to gauge the levels of air pollution in the same time frame. The confluence of these datasets unveiled a correlation coefficient that could knock the socks off even the most stoic statistician, standing firm at a whopping 0.9000747. With a p-value that would make even the toughest peer reviewer nod in solemn approval, clocking in at less than 0.01, the statistical robustness of our findings is enough to make a researcher utter "Eureka!" - or, at the very least, raise an eyebrow in bemused disbelief.
One might be forgiven for dismissing this correlation as a mere statistical fluke, a chance alignment of the stars in the data firmament. However, as we peeled back the layers of this curious onion, the relationship between the prevalence of the name "Kathy" and the atmospheric composition in Hartford remained as solid as a rock in a thunderstorm. The persistence of this association, against all odds, left our research team in a state of introspective bewilderment, pondering the whimsical hand of fate in the very air we breathe.
Our findings not only raise eyebrows but also beckon us to consider the profound implications of appellation on the environment. If ubiquitous monikers like "Kathy" wield such influence over atmospheric phenomena, one can't help but wonder what other surprises lie in the vast expanse of the statistical cosmos. As we venture deeper into this statistical odyssey, we invite the reader to suspend disbelief and join us on this quirky journey through the intersection of nomenclature and atmospheric dynamics in the urban landscape. After all, in the realm of research, one never knows what peculiar discoveries await around the next statistical bend.

[[RESULTS]]
The crux of our findings lies in the striking correlation coefficient of 0.9000747, which suggests a compelling relationship between the prevalence of the name "Kathy" and the ambient air quality in Hartford, Connecticut. This coefficient implies a robust connection, standing as firm as a coffee table in a college dorm room.
Furthermore, the coefficient of determination (r-squared) of 0.8101344 indicates that approximately 81% of the variability in air pollution levels in Hartford can be explained by the prevalence of the name "Kathy." In other words, the popularity of this name appears to be exerting a considerable influence on the atmospheric composition, leaving us to wonder if there's a hidden breeze of "Kathy-ness" wafting through the air.
The p-value of less than 0.01 provides further support for the statistical significance of this association, essentially telling us that the likelihood of this correlation occurring by chance is about as probable as finding a needle in a haystack while blindfolded. This p-value invites us to consider the possibility that there may be more than mere happenstance at play in this curious dance of names and air quality.
Our visual representation of this correlation in Figure 1 depicts a scatterplot that unmistakably showcases the confluence of these two variables. The strong clustering of data points serves as a graphic testament to the unexpected relationship between the frequency of the name "Kathy" and the air pollution levels in Hartford. It's a sight to behold, akin to witnessing a harmonious duet between a saxophone and a vacuum cleaner - unexpected, yet strangely compelling.
In sum, our results present a compelling case for the association between the prevalence of the name "Kathy" and the ambient air quality in Hartford, Connecticut. This unexpected correlation not only adds a touch of whimsy to the world of empirical research but also prompts us to ponder the subtle yet profound ways in which personal names may intertwine with environmental phenomena. As we continue to unpack the implications of this statistical oddity, we are reminded that in the realm of research, even the most unexpected connections can lead to enlightening discoveries.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has uncovered a noteworthy and, dare we say, titillating correlation between the prevalence of the name "Kathy" and the air quality in Hartford, Connecticut. The statistical relationship is as solid as a rock – or perhaps we should say as solid as the stoicism of a librarian during a book sale. With a correlation coefficient that would make even the most steadfast skeptics raise an intrigued eyebrow, our findings beckon us to consider the whimsical interplay of nomenclature and atmospheric dynamics in the urban landscape. It appears that the winds of change in the environmental arena may carry more than just pollutants; there might be a subtle blend of "Kathy-ness" wafting through the air as well.

Our statistical odyssey has not only revealed an unexpected connection but has also opened a door to a fascinating frontier where the ebb and flow of personal names may sway environmental phenomena. As we wrap up this investigation, we can't help but wonder if the air in Hartford is whispering the name "Kathy" amidst its chemical concoction, like a playful secret shared between old friends.

Furthermore, it is clear that no more research is needed in this area. We have plumbed the depths of this statistical odyssey and emerged with findings that would make even the most statuesque of researchers crack a smile. It's as though we've stumbled upon a pot of statistical gold at the end of a nomenclatural rainbow, leaving us with the realization that sometimes the most improbable connections can lead to intriguing revelations. Thus, we confidently assert that the saga of "Kathy" and air quality is a closed book - or perhaps, more fittingly, a closed statistical spreadsheet.

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 Relationship Between the Popularity of the First Name Kathy and Air Quality in Hartford: A Statistical Odyssey. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Irreverent Interdisciplinary Research

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited The Relationship Between the Popularity of the First Name Kathy and Air Quality in Hartford: A Statistical Odyssey. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Quirky Social Science Research

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 Relationship Between the Popularity of the First Name Kathy and Air Quality in Hartford: A Statistical Odyssey with an abstract of This empirical study delves into the curious correlation between the prevalence of the first name "Kathy" and the ambient air quality in the urban enclave of Hartford, Connecticut. Leveraging data spanning from 1980 to 2022 sourced from the US Social Security Administration and the Environmental Protection Agency, our research team has unearthed a staggering correlation coefficient of 0.9000747 and an astoundingly low p-value of less than 0.01. This robust statistical relationship persists despite the vast difference in the nature of the two datasets, leaving us in a state of bemusement about the whimsical interplay of nomenclature and atmospheric pollution. The findings of this study not only underscore the surprising influence of personal names on environmental phenomena but also open the door to a future where urban planning considerations may need to account for the ebb and flow of appellations in the population.

ChatGPT:

Kathy, first name popularity, air quality, Hartford, Connecticut, statistical correlation, US Social Security Administration data, Environmental Protection Agency data, correlation coefficient, urban planning, environmental phenomena, personal names, urban enclave, atmospheric pollution, nomenclature, statistical relationship, empirical study, research team, statistical odyssey, urban planning considerations, population dynamics.

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

Air pollution in Hartford, Connecticut
Detailed data title: Percentage of days 'unhealthy' or worse air quality in Hartford-West Hartford-East Hartford, CT
Source: Environmental Protection Agency
See what else correlates with Air pollution in Hartford, Connecticut

Correlation r = 0.9000747 (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 = 0.8101344 (Coefficient of determination)
This means 81% of the change in the one variable (i.e., Air pollution in Hartford, Connecticut) is predictable based on the change in the other (i.e., Popularity of the first name Kathy) over the 43 years from 1980 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.2E-16.^Show 0.0000000000000002210203087463
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 0.9 in 2.2E-14% of random cases. Said differently, if you correlated 4,524,471,102,553,107 random variables^Note You don't actually need 4 quadrillion 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 42 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 42 because we have two variables measured over a period of 43 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.

[ 0.82, 0.94 ] 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.

	1980	1981	1982	1983	1984	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 Kathy (Babies born)	1017	956	887	770	675	617	571	517	527	469	477	472	474	444	454	409	445	389	386	361	379	343	362	307	283	286	293	294	268	204	190	171	175	139	142	126	159	110	91	76	83	72	75
Air pollution in Hartford, Connecticut (Bad air quality days)	14.4809	7.94521	11.5068	11.2329	7.9235	5.47945	2.46575	5.47945	10.1093	4.65753	3.56164	5.47945	3.82514	3.56164	4.93151	3.56164	1.38504	4.38356	2.73973	4.65753	1.36612	4.38356	6.0274	1.91781	0.819672	2.73973	1.64384	2.73973	0.819672	0	0.547945	1.09589	0.819672	0.821918	0.273973	0	0.546448	0.273973	0	0.273973	0	0.273973	0

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.
Outlandish outliers: There are "outliers" in this data.^Note In concept, "outlier" just means "way different than the rest of your dataset." When calculating a correlation like this, they are particularly impactful because a single outlier can substantially increase your correlation.

For the purposes of this project, I counted a point as an outlier if it the residual was two standard deviations from the mean.

(This bullet point only shows up in the details page on charts that do, in fact, have outliers.)
They stand out on the scatterplot above: notice the dots that are far away from any other dots. I intentionally mishandeled outliers, which makes the correlation look extra strong.

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([1017,956,887,770,675,617,571,517,527,469,477,472,474,444,454,409,445,389,386,361,379,343,362,307,283,286,293,294,268,204,190,171,175,139,142,126,159,110,91,76,83,72,75,])
array_2 = np.array([14.4809,7.94521,11.5068,11.2329,7.9235,5.47945,2.46575,5.47945,10.1093,4.65753,3.56164,5.47945,3.82514,3.56164,4.93151,3.56164,1.38504,4.38356,2.73973,4.65753,1.36612,4.38356,6.0274,1.91781,0.819672,2.73973,1.64384,2.73973,0.819672,0,0.547945,1.09589,0.819672,0.821918,0.273973,0,0.546448,0.273973,0,0.273973,0,0.273973,0,])
array_1_name = "Popularity of the first name Kathy"
array_2_name = "Air pollution in Hartford, Connecticut"

# 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 Kathy
Line chart for only Air pollution in Hartford, Connecticut
AI-generated correlation image
The spurious research paper: The Relationship Between the Popularity of the First Name Kathy and Air Quality in Hartford: A Statistical Odyssey

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Correlation ID: 4888 · Black Variable ID: 2972 · Red Variable ID: 20714


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