Spurious correlation #5,318 · 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 Odalys and the second variable is Air pollution in Prescott, Arizona. The chart goes from 1981 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 Odalys and the second variable is Air pollution in Prescott, Arizona. The chart goes from 1981 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

It turns out that all the Odalyses in Prescott were fans of releasing their debut mixtape, "Ozone Overload," and couldn't resist dropping some sick air pollutants into the atmosphere. It was a real smoggy situation, but at least they were a breath of fresh air in the music scene!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a low poly representation of a park in Prescott, Arizona, with an ethereal hazy fog hanging in the air. In the center of the park, several Odalyses stand proudly, holding their debut mixtape, "Ozone Overload," while their music releases colorful air pollutants into the atmosphere. The low poly style should emphasize the geometric shapes and sharp edges, creating a visually striking representation of the scene. 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 Odalys and Air pollution in Prescott, Arizona. 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: Scalar art Cubism Low poly a Polaroid photo High-tech
ChatGPT: Low poly

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 Odalys and Air pollution in Prescott, Arizona, in the following situation: It turns out that all the Odalyses in Prescott were fans of releasing their debut mixtape, "Ozone Overload," and couldn't resist dropping some sick air pollutants into the atmosphere. It was a real smoggy situation, but at least they were a breath of fresh air in the music scene!. Keep the description simple.
ChatGPT: In Prescott, Arizona, a group of Odalyses gather in a park, surrounded by a hazy fog. They proudly display their debut mixtape, "Ozone Overload," as they release air pollutants into the atmosphere. Despite the smog, their music brings a breath of fresh air to the local music scene.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ Low poly ] and depicting this scene: In Prescott, Arizona, a group of Odalyses gather in a park, surrounded by a hazy fog. They proudly display their debut mixtape, "Ozone Overload," as they release air pollutants into the atmosphere. Despite the smog, their music brings a breath of fresh air to the local music scene. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a low poly representation of a park in Prescott, Arizona, with an ethereal hazy fog hanging in the air. In the center of the park, several Odalyses stand proudly, holding their debut mixtape, "Ozone Overload," while their music releases colorful air pollutants into the atmosphere. The low poly style should emphasize the geometric shapes and sharp edges, creating a visually striking representation of the scene.

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

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

AI academic paper

(Because p < 0.01)

The Curious Case of Odalys: A Study of Air in Prescott Skies

The Journal of Atmospheric Anomalies

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 Odalys and Air pollution in Prescott, Arizona. 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 cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
The Curious Case of Odalys: A Study of Air in Prescott Skies

[[ABSTRACT]]
This research paper seeks to examine the association between the popularity of the first name Odalys and air pollution in Prescott, Arizona. Leveraging data from the US Social Security Administration and the Environmental Protection Agency, our research team undertook a comprehensive analysis spanning the years 1981 to 2022. The study revealed a striking correlation coefficient of 0.8624824, indicating a robust statistical relationship between the naming trends and ambient air quality. Additionally, with p < 0.01, the significance of this connection cannot be disregarded. Our findings may prompt a reevaluation of the impact of nomenclature on environmental conditions, offering an unexpected avenue for future interdisciplinary investigation. This paper serves as a testament to the remarkable, if not peculiar, relationships that can be unearthed through the fusion of seemingly unrelated datasets.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of nomenclature and environmental factors has long been a subject of fascination and speculation, often relegated to the realms of folklore or the musings of eccentric observers. However, the etymology of names and their potential influence on the surrounding environment has, until recently, largely evaded the scrutiny of systematic inquiry. The present study seeks to shed light on this enigmatic juncture by exploring the connection between the prevalence of the first name Odalys and the state of air quality in Prescott, Arizona.
The ethereal nature of names, often bestowed upon individuals without consideration of their potential repercussions on atmospheric conditions, has relegated such inquiries to the periphery of scholarly discourse. However, as we embark on this expedition into the peculiar realm of nomenclature, it is imperative to maintain a spirit of open-minded inquiry and embrace the unexpected turns that may arise.
The nominal choice of Odalys, a name imbued with its own historical and cultural resonance, intertwines with the intangible currents of ambient air quality in ways that demand attention. As we navigate this unexplored terrain, it is crucial to approach our findings with a modicum of skepticism and a healthy dose of curiosity, lest we be swept away by the whimsical whims of fate and nomenclature.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The existing body of literature on the intersection of nomenclature and environmental influences has thus far been characterized by a paucity of empirical investigations, lending an air of enigma to this peculiar research domain. However, recent forays into interdisciplinary inquiry have illuminated hitherto uncharted connections, conveying a sense of intrigue that beckons further exploration.

In "Smith et al.," the authors find a tepid link between naming trends and localized atmospheric conditions, akin to the perfunctory stirrings of a zephyr. Building upon this foundational work, the investigation by "Doe and colleagues" introduces a nuanced consideration of region-specific naming proclivities and their ostensible effects on air quality, evoking a semblance of intrigue.

Venturing beyond the conventional purview of empirical research, "Jones et al." embark upon an unconventional odyssey into the realm of nomenclature and its purported reverberations on environmental realms, hinting at the clandestine interplay of nomenclature and atmospheric phenomena that remains shrouded in mystery.

Expanding the scope to include broader sociocultural implications, the compendium "The Names We Give" posits nuanced reflections on the metaphorical ethos of nomenclature and its potential ripples through the fabric of environmental conditions. Conversely, the treatise "Essence of Odalys" proffers an in-depth exploration of the historical and cultural connotations embedded within the namesake of interest, paving the way for a multifaceted interrogation of the nomenclatural enigma.

Delving further into the annals of literary exegesis, the speculative fiction novel "Winds of Naming" invokes a whimsical narrative that intertwines the intrinsic vibrancy of names with the ineffable currents of the atmosphere, blurring the boundaries between the quotidian and the surreal. In a similar vein, the enigmatic tome "The Airborne Odyssey of Odalys" weaves a tapestry of fantastical realms wherein the eponymous name assumes an ethereal significance, echoing the arcane harmonies of the ambient atmosphere.

In the digital realm, social media discourse has provided an unanticipated wellspring of anecdotal revelations and ephemeral ponderings concerning the purported interplay between the prevalence of the name Odalys and the atmospheric milieu in Prescott, Arizona. Proffering an informal yet intriguing lens into the popular consciousness, such online forums offer a tantalizing glimpse into the collective musings that have permeated the cultural milieu.

Whilst the scholastic landscape may have afforded only sparse insights into this curious nexus thus far, the burgeoning interest and emerging strands of inquiry collectively impel a concerted effort towards unraveling the enigmatic intertwinement of nomenclature and atmospheric phenomena.

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 Odalys and Air pollution in Prescott, Arizona. 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 Curious Case of Odalys: A Study of Air in Prescott Skies

[[ABSTRACT]]
This research paper seeks to examine the association between the popularity of the first name Odalys and air pollution in Prescott, Arizona. Leveraging data from the US Social Security Administration and the Environmental Protection Agency, our research team undertook a comprehensive analysis spanning the years 1981 to 2022. The study revealed a striking correlation coefficient of 0.8624824, indicating a robust statistical relationship between the naming trends and ambient air quality. Additionally, with p < 0.01, the significance of this connection cannot be disregarded. Our findings may prompt a reevaluation of the impact of nomenclature on environmental conditions, offering an unexpected avenue for future interdisciplinary investigation. This paper serves as a testament to the remarkable, if not peculiar, relationships that can be unearthed through the fusion of seemingly unrelated datasets.

[[LITERATURE REVIEW]]
The existing body of literature on the intersection of nomenclature and environmental influences has thus far been characterized by a paucity of empirical investigations, lending an air of enigma to this peculiar research domain. However, recent forays into interdisciplinary inquiry have illuminated hitherto uncharted connections, conveying a sense of intrigue that beckons further exploration.
In "Smith et al.," the authors find a tepid link between naming trends and localized atmospheric conditions, akin to the perfunctory stirrings of a zephyr. Building upon this foundational work, the investigation by "Doe and colleagues" introduces a nuanced consideration of region-specific naming proclivities and their ostensible effects on air quality, evoking a semblance of intrigue.
Venturing beyond the conventional purview of empirical research, "Jones et al." embark upon an unconventional odyssey into the realm of nomenclature and its purported reverberations on environmental realms, hinting at the clandestine interplay of nomenclature and atmospheric phenomena that remains shrouded in mystery.
Expanding the scope to include broader sociocultural implications, the compendium "The Names We Give" posits nuanced reflections on the metaphorical ethos of nomenclature and its potential ripples through the fabric of environmental conditions. Conversely, the treatise "Essence of Odalys" proffers an in-depth exploration of the historical and cultural connotations embedded within the namesake of interest, paving the way for a multifaceted interrogation of the nomenclatural enigma.
Delving further into the annals of literary exegesis, the speculative fiction novel "Winds of Naming" invokes a whimsical narrative that intertwines the intrinsic vibrancy of names with the ineffable currents of the atmosphere, blurring the boundaries between the quotidian and the surreal. In a similar vein, the enigmatic tome "The Airborne Odyssey of Odalys" weaves a tapestry of fantastical realms wherein the eponymous name assumes an ethereal significance, echoing the arcane harmonies of the ambient atmosphere.
In the digital realm, social media discourse has provided an unanticipated wellspring of anecdotal revelations and ephemeral ponderings concerning the purported interplay between the prevalence of the name Odalys and the atmospheric milieu in Prescott, Arizona. Proffering an informal yet intriguing lens into the popular consciousness, such online forums offer a tantalizing glimpse into the collective musings that have permeated the cultural milieu.
Whilst the scholastic landscape may have afforded only sparse insights into this curious nexus thus far, the burgeoning interest and emerging strands of inquiry collectively impel a concerted effort towards unraveling the enigmatic intertwinement of nomenclature and atmospheric phenomena.

[[RESULTS]]
The analysis unveiled a compelling correlation between the popularity of the first name Odalys and air pollution levels in Prescott, Arizona. Over the 42-year period from 1981 to 2022, a striking correlation coefficient of 0.8624824 was observed, signifying a robust statistical relationship between the naming trends and ambient air quality. This finding indicates that as the frequency of the name Odalys increased, so did the levels of air pollution, unveiling an unexpected interplay between nomenclature and environmental conditions.
Furthermore, the coefficient of determination (r-squared) was calculated to be 0.7438758, suggesting that approximately 74.39% of the variability in air pollution in Prescott, Arizona can be explained by the prevalence of the name Odalys. This substantial r-squared value provides further evidence of the strong association between the two variables and underscores the potential impact of nomenclature on local atmospheric dynamics.
The significance of this relationship is underscored by the p-value of less than 0.01, indicating that the observed association is unlikely to have occurred by random chance. This lends additional weight to the assertion that the popularity of the name Odalys is intricately linked to the quality of the air in Prescott, Arizona.
Overall, the findings of this study offer a captivating foray into the uncharted territory of quirky correlations, where the seemingly innocuous world of nomenclature intersects with the ethereal currents of ambient air quality. The implications of these results may prompt a reevaluation of the intertwined influences of human behavior and environmental conditions, opening up a peculiar yet fascinating avenue for further interdisciplinary inquiry.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The empirical findings of this investigation serve to reinforce and substantiate the earlier suppositions proffered within the extant literature. The palpable correlation between the ascendancy of the appellation Odalys and the proliferation of air pollutants in Prescott, Arizona not only augments prior speculations but also elucidates the convoluted interweaving of nomenclature and environmental dynamics.

Building upon the tepid linkages discerned by "Smith et al.," our study has unshackled this association from the shackles of obscurity, resolutely substantiating the purported reverberations of nomenclature on atmospheric constituents. The robust statistical relationship documented herein resonates with the nascent stirrings of a zephyr alluded to within the precursory literature, signifying a transformative stride in unraveling this enigmatic odyssey.

The nuanced consideration of region-specific naming proclivities introduced by "Doe and colleagues" assumes a poignant relevance in light of our findings, infusing a semblance of intrigue into the hitherto elusive interplay of nomenclature and atmospheric phenomena. Furthermore, the meta-reflections and cultural connotations embedded within the namesake of interest, as advanced by "Essence of Odalys," have been imbued with a newfound resonance in light of the empirical revelations proffered by our analysis.

The substantial r-squared value documented herein, evocative of the startling precipice of a 74.39% explanatory potential, reverberates with the multifaceted interrogation of the nomenclatural enigma, as envisaged within the compendium "The Names We Give." Indeed, the clandestine interplay of nomenclature and atmospheric phenomena that remains shrouded in mystery, alluded to by "Jones et al.," has been laid bare by the compelling evidentiary corroboration furnished by our investigation.

The digital discourse surrounding the purported interplay between the prevalence of the name Odalys and the atmospheric milieu in Prescott, Arizona has, in the wake of our empirical revelations, assumed an unexpected poignancy, akin to a surreptitious ripple amidst a vast ocean of collective musings. Thus, the burgeoning interest and emerging strands of inquiry collectively impel a concerted effort towards unraveling the enigmatic intertwinement of nomenclature and atmospheric phenomena, as we have strived to accomplish within the compass of this study.

In conclusion, the discernible correlation delineated herein not only converges with the speculative conjectures prevalent within the nascent landscape of interdisciplinary inquiry but also serves as a clarion call for further forays into the odyssey of quirky correlations, where the ostensibly innocuous world of nomenclature intersects with the ineffable currents of ambient air quality.

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 Odalys and Air pollution in Prescott, Arizona. 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 Curious Case of Odalys: A Study of Air in Prescott Skies

[[ABSTRACT]]
This research paper seeks to examine the association between the popularity of the first name Odalys and air pollution in Prescott, Arizona. Leveraging data from the US Social Security Administration and the Environmental Protection Agency, our research team undertook a comprehensive analysis spanning the years 1981 to 2022. The study revealed a striking correlation coefficient of 0.8624824, indicating a robust statistical relationship between the naming trends and ambient air quality. Additionally, with p < 0.01, the significance of this connection cannot be disregarded. Our findings may prompt a reevaluation of the impact of nomenclature on environmental conditions, offering an unexpected avenue for future interdisciplinary investigation. This paper serves as a testament to the remarkable, if not peculiar, relationships that can be unearthed through the fusion of seemingly unrelated datasets.

[[INTRDUCTION]]
The intersection of nomenclature and environmental factors has long been a subject of fascination and speculation, often relegated to the realms of folklore or the musings of eccentric observers. However, the etymology of names and their potential influence on the surrounding environment has, until recently, largely evaded the scrutiny of systematic inquiry. The present study seeks to shed light on this enigmatic juncture by exploring the connection between the prevalence of the first name Odalys and the state of air quality in Prescott, Arizona.
The ethereal nature of names, often bestowed upon individuals without consideration of their potential repercussions on atmospheric conditions, has relegated such inquiries to the periphery of scholarly discourse. However, as we embark on this expedition into the peculiar realm of nomenclature, it is imperative to maintain a spirit of open-minded inquiry and embrace the unexpected turns that may arise.
The nominal choice of Odalys, a name imbued with its own historical and cultural resonance, intertwines with the intangible currents of ambient air quality in ways that demand attention. As we navigate this unexplored terrain, it is crucial to approach our findings with a modicum of skepticism and a healthy dose of curiosity, lest we be swept away by the whimsical whims of fate and nomenclature.

[[RESULTS]]
The analysis unveiled a compelling correlation between the popularity of the first name Odalys and air pollution levels in Prescott, Arizona. Over the 42-year period from 1981 to 2022, a striking correlation coefficient of 0.8624824 was observed, signifying a robust statistical relationship between the naming trends and ambient air quality. This finding indicates that as the frequency of the name Odalys increased, so did the levels of air pollution, unveiling an unexpected interplay between nomenclature and environmental conditions.
Furthermore, the coefficient of determination (r-squared) was calculated to be 0.7438758, suggesting that approximately 74.39% of the variability in air pollution in Prescott, Arizona can be explained by the prevalence of the name Odalys. This substantial r-squared value provides further evidence of the strong association between the two variables and underscores the potential impact of nomenclature on local atmospheric dynamics.
The significance of this relationship is underscored by the p-value of less than 0.01, indicating that the observed association is unlikely to have occurred by random chance. This lends additional weight to the assertion that the popularity of the name Odalys is intricately linked to the quality of the air in Prescott, Arizona.
Overall, the findings of this study offer a captivating foray into the uncharted territory of quirky correlations, where the seemingly innocuous world of nomenclature intersects with the ethereal currents of ambient air quality. The implications of these results may prompt a reevaluation of the intertwined influences of human behavior and environmental conditions, opening up a peculiar yet fascinating avenue for further interdisciplinary inquiry.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, the results of this study provide compelling evidence of a significant correlation between the popularity of the name Odalys and air pollution levels in Prescott, Arizona. The magnitude of the correlation coefficient and the low p-value emphasize the robust statistical relationship between these seemingly unrelated variables, highlighting the whimsical whims of fate and nomenclature, as well as the prodigious potential for unexpected turns in interdisciplinary investigation.

This unanticipated linkage between nomenclature and ambient air quality underscores the need for a broader perspective on the potential influences of human behavior on environmental conditions. The etymological threads of the name Odalys, woven into the atmospheric tapestry, serve as a whimsical reminder of the inexorable interconnectedness of seemingly disparate phenomena.

However, it is important to approach these findings with a modicum of skepticism, as the dynamics of nomenclature and atmospheric interplay may harbor complexities beyond the scope of this study. Future research in this area may benefit from exploring additional variables and employing more elaborate analytical methods to deepen our understanding of this peculiar relationship.

Nevertheless, the tentacles of our findings reach into uncharted scholarly waters and may serve as a beacon for future investigations into the enigmatic nexus of nomenclature and environmental conditions. Above all, the curious case of Odalys in the Prescott skies beckons researchers to embrace the whimsical interplay of unexpected correlations and the inscrutable dances of data.

It is our resolute assertion that no further research into this particular correlation is necessary, as it has certainly left a lasting impression on both the field of academia and the idiosyncrasies of scholarly inquiry.

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 Curious Case of Odalys: A Study of Air in Prescott Skies. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Atmospheric Anomalies

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 Curious Case of Odalys: A Study of Air in Prescott Skies. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Atmospheric Anomalies Research and Inquiry (SAARI)

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 Curious Case of Odalys: A Study of Air in Prescott Skies with an abstract of This research paper seeks to examine the association between the popularity of the first name Odalys and air pollution in Prescott, Arizona. Leveraging data from the US Social Security Administration and the Environmental Protection Agency, our research team undertook a comprehensive analysis spanning the years 1981 to 2022. The study revealed a striking correlation coefficient of 0.8624824, indicating a robust statistical relationship between the naming trends and ambient air quality. Additionally, with p < 0.01, the significance of this connection cannot be disregarded. Our findings may prompt a reevaluation of the impact of nomenclature on environmental conditions, offering an unexpected avenue for future interdisciplinary investigation. This paper serves as a testament to the remarkable, if not peculiar, relationships that can be unearthed through the fusion of seemingly unrelated datasets.

ChatGPT:

Odalys, air pollution, Prescott, Arizona, popularity of first name, US Social Security Administration, Environmental Protection Agency, correlation coefficient, statistical relationship, ambient air quality, naming trends, interdisciplinary investigation, fusion of datasets

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

Air pollution in Prescott, Arizona
Detailed data title: Percentage of days with moderate or worse air quality in Prescott, AZ
Source: Environmental Protection Agency
See what else correlates with Air pollution in Prescott, Arizona

Correlation r = 0.8624824 (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 = 0.7438758 (Coefficient of determination)
This means 74.4% of the change in the one variable (i.e., Air pollution in Prescott, Arizona) is predictable based on the change in the other (i.e., Popularity of the first name Odalys) over the 42 years from 1981 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.1E-13.^Show 0.0000000000002127961053279961
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.86 in 2.1E-11% of random cases. Said differently, if you correlated 4,699,334,127,655 random variables^Note You don't actually need 4 trillion 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 41 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 41 because we have two variables measured over a period of 42 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.

[ 0.76, 0.92 ] 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.

	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 Odalys (Babies born)	9	0	22	13	18	9	14	11	8	10	11	11	18	31	42	322	196	234	273	222	234	235	138	107	108	117	68	81	94	79	68	45	46	46	38	51	29	37	30	21	31	23
Air pollution in Prescott, Arizona (Bad air quality days)	0.0699301	0.113074	0.0426829	0.0760234	0	0	0	0	0	0	0	0	0	0	0	0.620321	0.595556	0.689498	0.641844	0.443503	0.474684	0.524715	0.414449	0.457364	0.335443	0.00806452	0.0166667	0.236025	0.147727	0.1673	0.300412	0.272085	0.215278	0.339286	0.216667	0.140401	0.320588	0.2	0.162465	0.199396	0.0876133	0.0657534

Why this works

Data dredging: I have 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 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([9,0,22,13,18,9,14,11,8,10,11,11,18,31,42,322,196,234,273,222,234,235,138,107,108,117,68,81,94,79,68,45,46,46,38,51,29,37,30,21,31,23,])
array_2 = np.array([0.0699301,0.113074,0.0426829,0.0760234,0,0,0,0,0,0,0,0,0,0,0,0.620321,0.595556,0.689498,0.641844,0.443503,0.474684,0.524715,0.414449,0.457364,0.335443,0.00806452,0.0166667,0.236025,0.147727,0.1673,0.300412,0.272085,0.215278,0.339286,0.216667,0.140401,0.320588,0.2,0.162465,0.199396,0.0876133,0.0657534,])
array_1_name = "Popularity of the first name Odalys"
array_2_name = "Air pollution in Prescott, Arizona"

# 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 Odalys
Line chart for only Air pollution in Prescott, Arizona
AI-generated correlation image
The spurious research paper: The Curious Case of Odalys: A Study of Air in Prescott Skies

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Correlation ID: 5318 · Black Variable ID: 3210 · Red Variable ID: 21580


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