Spurious correlation #1,619 · 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 Jason and the second variable is Air pollution in Denver, Colorado. 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 Jason and the second variable is Air pollution in Denver, Colorado. The chart goes from 1980 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

Fewer people were requesting personalized license plates with the name JASON, leading to a decline in production at the local license plate manufacturing plant. This reduction in manufacturing activity resulted in lower emissions of pollutants into the atmosphere, consequently improving the air quality in Denver.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image that looks like a scene from 'Star Wars' and depicts a bustling Denver park, with a large group of people gathering around a man named Jason, cheering and clapping as he gives a speech. The city skyline is shrouded in a haze of smog, and a sign nearby warns of unhealthy air quality. The futuristic yet polluted cityscape creates an otherworldly aura reminiscent of a 'Star Wars' setting. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

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

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: Popularity of the first name Jason and Unhealthy air quality in Denver, Colorado. 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: Voxel art Celtic art An image that looks like it is a scene from 'Star Wars' Celtic art Vaporwave
ChatGPT: An image that looks like it is a scene from 'Star Wars'

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 Jason and Unhealthy air quality in Denver, Colorado together. Keep the description simple.
ChatGPT: In a bustling Denver park, a large group of people gathers around a man named Jason, cheering and clapping as he gives a speech. However, the city skyline is shrouded in a haze of smog, and a sign nearby warns of unhealthy air quality.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ An image that looks like it is a scene from 'Star Wars' ] and depicting this scene: In a bustling Denver park, a large group of people gathers around a man named Jason, cheering and clapping as he gives a speech. However, the city skyline is shrouded in a haze of smog, and a sign nearby warns of unhealthy air quality. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that looks like a scene from 'Star Wars' and depicts a bustling Denver park, with a large group of people gathering around a man named Jason, cheering and clapping as he gives a speech. The city skyline is shrouded in a haze of smog, and a sign nearby warns of unhealthy air quality. The futuristic yet polluted cityscape creates an otherworldly aura reminiscent of a 'Star Wars' setting.

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

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

AI academic paper

(Because p < 0.01)

The Ties between Jason and Haze in Denver's Days: A Correlation Analysis

The Journal of Urban Connections

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 Jason and Air pollution in Denver, Colorado. 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 name some movies that you watched that are tangentially related to the topic.

Here is the title and abstract of the paper:
[[TITLE]]
The Ties between "Jason" and Haze in Denver's Days: A Correlation Analysis

[[ABSTRACT]]
This research paper investigates the intriguing interplay between the popularity of the first name "Jason" and air pollution levels in Denver, Colorado. Leveraging data from the US Social Security Administration and the Environmental Protection Agency, our study uncovered a noteworthy correlation. Over the period from 1980 to 2022, a correlation coefficient of 0.8700527 was observed, with a p-value of less than 0.01. The implications of these findings, while surprising, raise questions about the hidden influences of nomenclature and atmospheric conditions. This study promises to shed light on the unconventional connections that may lurk beneath the surface of seemingly unrelated phenomena, inviting both lighthearted curiosity and rigorous academic inquiry.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
The connection between human nomenclature and environmental factors has long been a source of both amusement and skepticism in the scientific community. Rarely does one entertain the idea that the popularity of a given first name could be tied to the atmospheric conditions of a specific geographical location. However, in the illustrious tradition of scientific inquiry, we set out to investigate this curious relationship between the frequency of the name "Jason" and the air quality in the environs of Denver, Colorado.
While some may consider this endeavor to be a flight of fancy, we approached the task with all due solemnity, armed with statistical tools and a keen eye for uncovering the unexpected. It is often the case in scientific pursuits that the most unexpected findings yield the most tantalizing implications, and so we delved into the realm of data sets, correlation analyses, and p-values, poised to extract the truth from the numbers.
The US Social Security Administration provided us with a veritable smorgasbord of Jason-related data, while the Environmental Protection Agency, ever the stalwart guardian of atmospheric quality, offered up decades' worth of air pollution measurements in the Denver area. Embracing the spirit of interdisciplinary collaboration, we merged these seemingly disparate datasets and embarked upon the endeavor of quantifying the relationship, if any, between the popularity of the name "Jason" and the haze that graced the denizens of Denver.
As we began our statistical odyssey, we held in our minds the guiding principle that in the crucible of research, any variable, no matter how seemingly ludicrous or inconsequential, ought to be subjected to the rigors of analysis. It is only by allowing the data to speak unfettered that we may uncover the secrets of the world, whether they be grand and profound or peculiar and whimsical.
And so, with measured steps and a twinkle of skepticism, we present our findings, confident that they will provoke both mirth and contemplation, and perhaps prompt future scholars to delve into the uncharted territory of unanticipated correlations.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The surprising intersection of human nomenclature and atmospheric conditions has garnered attention in various academic circles. The heretofore unforeseen relationship between the popularity of the first name "Jason" and air pollution levels in Denver, Colorado has piqued the curiosity of researchers and enthusiasts alike. Smith (2010) conducted a comprehensive analysis of naming trends and their potential impact on environmental variables, positing that societal nomenclature may hold unexplored connections to climatic phenomena. Doe (2015) examined the influence of individual name preferences on local environmental factors, laying the groundwork for further investigations into the curious interplay between personal identification and atmospheric qualities. Jones (2018) delved into the realm of geographical peculiarities and naming patterns, hinting at the intriguing depth of potential correlations waiting to be unraveled.

Turning to non-fiction publications, "The Air Pollution Problem" by Green (2017) and "Names and Identities" by White (2019) provide insightful perspectives on the complexities of air quality challenges and the psychological significance of personal names, respectively. In addition, the historical novel "Smogtown: The Lung-Burning History of Pollution in Los Angeles" by Jacobs and Kelly (2008) offers a captivating portrayal of environmental struggles, albeit in a different locale. Similarly, Dostoyevsky's "Crime and Punishment" may appear unrelated at first glance, but the themes of guilt and redemption resonate with the hidden layers of cognitive dissonance underlying the "Jason" and haze correlation.

Furthermore, cinematic experiences cannot be overlooked in their potential to inspire multidisciplinary contemplation. "Airplane!" and "Up in the Air" stand as cinematic testaments to the intermingling of human presence and atmospheric elements, provoking contemplation on the complexities of human-nature interactions. The unanticipated union of lighthearted humor and scholarly inquiry in these films serves as a testament to the far-reaching implications of unconventional correlations.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between Popularity of the first name Jason and Air pollution in Denver, Colorado. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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 Ties between "Jason" and Haze in Denver's Days: A Correlation Analysis

[[ABSTRACT]]
This research paper investigates the intriguing interplay between the popularity of the first name "Jason" and air pollution levels in Denver, Colorado. Leveraging data from the US Social Security Administration and the Environmental Protection Agency, our study uncovered a noteworthy correlation. Over the period from 1980 to 2022, a correlation coefficient of 0.8700527 was observed, with a p-value of less than 0.01. The implications of these findings, while surprising, raise questions about the hidden influences of nomenclature and atmospheric conditions. This study promises to shed light on the unconventional connections that may lurk beneath the surface of seemingly unrelated phenomena, inviting both lighthearted curiosity and rigorous academic inquiry.

[[INTRODUCTION]]
INTRODUCTION
The connection between human nomenclature and environmental factors has long been a source of both amusement and skepticism in the scientific community. Rarely does one entertain the idea that the popularity of a given first name could be tied to the atmospheric conditions of a specific geographical location. However, in the illustrious tradition of scientific inquiry, we set out to investigate this curious relationship between the frequency of the name "Jason" and the air quality in the environs of Denver, Colorado.
While some may consider this endeavor to be a flight of fancy, we approached the task with all due solemnity, armed with statistical tools and a keen eye for uncovering the unexpected. It is often the case in scientific pursuits that the most unexpected findings yield the most tantalizing implications, and so we delved into the realm of data sets, correlation analyses, and p-values, poised to extract the truth from the numbers.
The US Social Security Administration provided us with a veritable smorgasbord of Jason-related data, while the Environmental Protection Agency, ever the stalwart guardian of atmospheric quality, offered up decades' worth of air pollution measurements in the Denver area. Embracing the spirit of interdisciplinary collaboration, we merged these seemingly disparate datasets and embarked upon the endeavor of quantifying the relationship, if any, between the popularity of the name "Jason" and the haze that graced the denizens of Denver.
As we began our statistical odyssey, we held in our minds the guiding principle that in the crucible of research, any variable, no matter how seemingly ludicrous or inconsequential, ought to be subjected to the rigors of analysis. It is only by allowing the data to speak unfettered that we may uncover the secrets of the world, whether they be grand and profound or peculiar and whimsical.
And so, with measured steps and a twinkle of skepticism, we present our findings, confident that they will provoke both mirth and contemplation, and perhaps prompt future scholars to delve into the uncharted territory of unanticipated correlations.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The primary sources of data for this study were the US Social Security Administration and the Environmental Protection Agency. From the former, we gathered the frequency of the first name "Jason" in the census records spanning the years 1980 to 2022. These records, while initially intended for administrative purposes, conveniently provided a rich tapestry of Jason-related occurrences. Meanwhile, the Environmental Protection Agency furnished us with comprehensive air pollution measurements in the Denver, Colorado area over the same timeframe. These measurements, encompassing a profusion of particulate matter and gaseous contaminants, formed the fulcrum upon which our atmospheric analyses pivoted.

Data Processing:
After procuring these disparate datasets, we commenced the ritualistic dance of data processing. The Jason-related data was meticulously scrubbed and polished to remove any aberrations or outliers, ensuring that the essence of "Jason-ness" was captured with fidelity. Meanwhile, the air pollution measurements received similarly exacting treatment, as we sieved through the haze of pollutants to distill the purest expression of atmospheric opacity.

Correlation Analysis:
With our datasets primed and preened, we engaged in the venerable tradition of correlation analysis. Employing the trusty Pearson correlation coefficient, we measured the strength and direction of the relationship between the frequency of the name "Jason" and the level of air pollution in Denver. A p-value of less than 0.01 was deemed the threshold for significance, in deference to the skepticism that often surrounds unconventional correlations.

Controlling for Confounding Variables:
In acknowledgment of the potential confounding factors that may surreptitiously influence our findings, we undertook a rigorous process of controlling for extraneous variables. Demographic shifts, meteorological anomalies, and the caprices of societal naming trends were all subjected to systematic scrutiny to ensure that our observed correlation was robust and not prone to spurious associations.

Reliability and Limitations:
It is imperative to acknowledge that while our methodology was steadfast and our analyses meticulous, the nature of observational research introduces the specter of unobserved variables and lurking causality. The validity of our findings must therefore be interpreted within the context of our methodological framework and the known limitations of observational studies. Nonetheless, we are confident that our study serves as a compelling springboard for further inquiries into the idiosyncratic interplay of nomenclature and environmental 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 results section of an academic research paper. You speak in the past tense (you found...).
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 results section of an academic research paper presenting the findings of the connection between Popularity of the first name Jason and Air pollution in Denver, Colorado. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from US Social Security Administration and Environmental Protection Agency .

For the time period 1980 to 2022, you found a correlation 0.8700527, r-squared of 0.7569917, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
The Ties between "Jason" and Haze in Denver's Days: A Correlation Analysis

[[ABSTRACT]]
This research paper investigates the intriguing interplay between the popularity of the first name "Jason" and air pollution levels in Denver, Colorado. Leveraging data from the US Social Security Administration and the Environmental Protection Agency, our study uncovered a noteworthy correlation. Over the period from 1980 to 2022, a correlation coefficient of 0.8700527 was observed, with a p-value of less than 0.01. The implications of these findings, while surprising, raise questions about the hidden influences of nomenclature and atmospheric conditions. This study promises to shed light on the unconventional connections that may lurk beneath the surface of seemingly unrelated phenomena, inviting both lighthearted curiosity and rigorous academic inquiry.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
INTRODUCTION
The connection between human nomenclature and environmental factors has long been a source of both amusement and skepticism in the scientific community. Rarely does one entertain the idea that the popularity of a given first name could be tied to the atmospheric conditions of a specific geographical location. However, in the illustrious tradition of scientific inquiry, we set out to investigate this curious relationship between the frequency of the name "Jason" and the air quality in the environs of Denver, Colorado.
While some may consider this endeavor to be a flight of fancy, we approached the task with all due solemnity, armed with statistical tools and a keen eye for uncovering the unexpected. It is often the case in scientific pursuits that the most unexpected findings yield the most tantalizing implications, and so we delved into the realm of data sets, correlation analyses, and p-values, poised to extract the truth from the numbers.
The US Social Security Administration provided us with a veritable smorgasbord of Jason-related data, while the Environmental Protection Agency, ever the stalwart guardian of atmospheric quality, offered up decades' worth of air pollution measurements in the Denver area. Embracing the spirit of interdisciplinary collaboration, we merged these seemingly disparate datasets and embarked upon the endeavor of quantifying the relationship, if any, between the popularity of the name "Jason" and the haze that graced the denizens of Denver.
As we began our statistical odyssey, we held in our minds the guiding principle that in the crucible of research, any variable, no matter how seemingly ludicrous or inconsequential, ought to be subjected to the rigors of analysis. It is only by allowing the data to speak unfettered that we may uncover the secrets of the world, whether they be grand and profound or peculiar and whimsical.
And so, with measured steps and a twinkle of skepticism, we present our findings, confident that they will provoke both mirth and contemplation, and perhaps prompt future scholars to delve into the uncharted territory of unanticipated correlations.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

A strong positive correlation was found between the popularity of the first name "Jason" and air pollution levels in Denver, Colorado. Over the 1980 to 2022 period, the correlation coefficient stood at an impressive 0.8700527, indicating a robust relationship between the frequency of the name "Jason" and the atmospheric conditions of the Denver area. Furthermore, the coefficient of determination (r-squared) of 0.7569917 suggests that approximately 76% of the variability in air pollution levels can be explained by the variability in the popularity of the name "Jason."

The p-value of less than 0.01 further bolsters the significance of this correlation, lending support to the notion that the observed relationship is unlikely to be the result of random chance. In other words, the probability of obtaining such a strong correlation purely by fluke is exceedingly low, prompting us to cautiously accept the possibility of a genuine association between the two variables.

Fig. 1 depicts a scatterplot of the data, visually capturing the compelling correlation between the popularity of the name "Jason" and air pollution levels in Denver, Colorado. The scatterplot serves as a striking visual representation of the positive relationship uncovered in our analysis.

These findings, while unexpected and seemingly fanciful, point to the potential influence of nomenclature on environmental phenomena. The implications of this correlation extend beyond the realms of mere statistical curiosity and evoke contemplation on the intricate interplay between human behavior and atmospheric conditions. It is a poignant reminder of the serendipitous discoveries that lie in wait within the labyrinthine structures of data, encouraging scholars to approach research endeavors with a dash of whimsy and an open mind.

The statistical evidence presented here not only unveils an unanticipated connection between the eponymous moniker "Jason" and the haze in Denver's skies but also lays bare the delightful surprises that can emerge from rigorous empirical inquiry.

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 Jason and Air pollution in Denver, Colorado. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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 Ties between "Jason" and Haze in Denver's Days: A Correlation Analysis

[[ABSTRACT]]
This research paper investigates the intriguing interplay between the popularity of the first name "Jason" and air pollution levels in Denver, Colorado. Leveraging data from the US Social Security Administration and the Environmental Protection Agency, our study uncovered a noteworthy correlation. Over the period from 1980 to 2022, a correlation coefficient of 0.8700527 was observed, with a p-value of less than 0.01. The implications of these findings, while surprising, raise questions about the hidden influences of nomenclature and atmospheric conditions. This study promises to shed light on the unconventional connections that may lurk beneath the surface of seemingly unrelated phenomena, inviting both lighthearted curiosity and rigorous academic inquiry.

[[LITERATURE REVIEW]]
The surprising intersection of human nomenclature and atmospheric conditions has garnered attention in various academic circles. The heretofore unforeseen relationship between the popularity of the first name "Jason" and air pollution levels in Denver, Colorado has piqued the curiosity of researchers and enthusiasts alike. Smith (2010) conducted a comprehensive analysis of naming trends and their potential impact on environmental variables, positing that societal nomenclature may hold unexplored connections to climatic phenomena. Doe (2015) examined the influence of individual name preferences on local environmental factors, laying the groundwork for further investigations into the curious interplay between personal identification and atmospheric qualities. Jones (2018) delved into the realm of geographical peculiarities and naming patterns, hinting at the intriguing depth of potential correlations waiting to be unraveled.
Turning to non-fiction publications, "The Air Pollution Problem" by Green (2017) and "Names and Identities" by White (2019) provide insightful perspectives on the complexities of air quality challenges and the psychological significance of personal names, respectively. In addition, the historical novel "Smogtown: The Lung-Burning History of Pollution in Los Angeles" by Jacobs and Kelly (2008) offers a captivating portrayal of environmental struggles, albeit in a different locale. Similarly, Dostoyevsky's "Crime and Punishment" may appear unrelated at first glance, but the themes of guilt and redemption resonate with the hidden layers of cognitive dissonance underlying the "Jason" and haze correlation.
Furthermore, cinematic experiences cannot be overlooked in their potential to inspire multidisciplinary contemplation. "Airplane!" and "Up in the Air" stand as cinematic testaments to the intermingling of human presence and atmospheric elements, provoking contemplation on the complexities of human-nature interactions. The unanticipated union of lighthearted humor and scholarly inquiry in these films serves as a testament to the far-reaching implications of unconventional correlations.

[[RESULTS]]
A strong positive correlation was found between the popularity of the first name "Jason" and air pollution levels in Denver, Colorado. Over the 1980 to 2022 period, the correlation coefficient stood at an impressive 0.8700527, indicating a robust relationship between the frequency of the name "Jason" and the atmospheric conditions of the Denver area. Furthermore, the coefficient of determination (r-squared) of 0.7569917 suggests that approximately 76% of the variability in air pollution levels can be explained by the variability in the popularity of the name "Jason."
The p-value of less than 0.01 further bolsters the significance of this correlation, lending support to the notion that the observed relationship is unlikely to be the result of random chance. In other words, the probability of obtaining such a strong correlation purely by fluke is exceedingly low, prompting us to cautiously accept the possibility of a genuine association between the two variables.
Fig. 1 depicts a scatterplot of the data, visually capturing the compelling correlation between the popularity of the name "Jason" and air pollution levels in Denver, Colorado. The scatterplot serves as a striking visual representation of the positive relationship uncovered in our analysis.
These findings, while unexpected and seemingly fanciful, point to the potential influence of nomenclature on environmental phenomena. The implications of this correlation extend beyond the realms of mere statistical curiosity and evoke contemplation on the intricate interplay between human behavior and atmospheric conditions. It is a poignant reminder of the serendipitous discoveries that lie in wait within the labyrinthine structures of data, encouraging scholars to approach research endeavors with a dash of whimsy and an open mind.
The statistical evidence presented here not only unveils an unanticipated connection between the eponymous moniker "Jason" and the haze in Denver's skies but also lays bare the delightful surprises that can emerge from rigorous empirical inquiry.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of this study offer compelling support for the idea that the popularity of the first name "Jason" and air pollution levels in Denver, Colorado are closely intertwined. The correlation coefficient of 0.8700527, with a p-value of less than 0.01, corroborates the prior research that hinted at the enigmatic relationship between nomenclature and atmospheric conditions.

In line with Smith's (2010) assertion that naming trends may have unexplored connections to climatic phenomena, our results underscore the significance of societal nomenclature in potentially influencing environmental variables. Furthermore, Doe's (2015) exploration of the impact of individual name preferences on local environmental factors finds resonance in the robust correlation detected in our analysis. The unexpected union of lighthearted humor and scholarly inquiry in films such as "Airplane!" and "Up in the Air" takes on newfound relevance in light of our findings, highlighting the multidisciplinary contemplation inspired by unconventional correlations.

The coefficient of determination (r-squared) of 0.7569917 suggests that a substantial proportion of the variability in air pollution levels can be attributed to the variability in the popularity of the name "Jason." This statistical insight elevates the seemingly fanciful connection between nomenclature and atmospheric phenomena to a level of substantive significance, inviting further inquiry into the intricate interplay between human behavior and environmental conditions.

While the link between the eponymous moniker "Jason" and the haze in Denver's skies may appear far-fetched at first glance, the statistical evidence presented here unveils a delightful surprise within the labyrinthine structures of data. These results emphasize the potential for unexpected discoveries to emerge from rigorous empirical inquiry, reminding scholars to approach research endeavors with a dash of whimsy and an open mind.

In conclusion, the correlation between the popularity of the first name "Jason" and air pollution levels in Denver, Colorado not only challenges conventional notions of unrelated phenomena but also underscores the captivating allure of uncovering hidden connections in the realm of empirical research. With these thought-provoking findings, we are compelled to embrace the complexity and unpredictability of the scientific endeavor, recognizing that the most unexpected correlations may yet hold the key to unlocking new frontiers of knowledge.

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 Jason and Air pollution in Denver, Colorado. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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 Ties between "Jason" and Haze in Denver's Days: A Correlation Analysis

[[ABSTRACT]]
This research paper investigates the intriguing interplay between the popularity of the first name "Jason" and air pollution levels in Denver, Colorado. Leveraging data from the US Social Security Administration and the Environmental Protection Agency, our study uncovered a noteworthy correlation. Over the period from 1980 to 2022, a correlation coefficient of 0.8700527 was observed, with a p-value of less than 0.01. The implications of these findings, while surprising, raise questions about the hidden influences of nomenclature and atmospheric conditions. This study promises to shed light on the unconventional connections that may lurk beneath the surface of seemingly unrelated phenomena, inviting both lighthearted curiosity and rigorous academic inquiry.

[[INTRDUCTION]]
INTRODUCTION
The connection between human nomenclature and environmental factors has long been a source of both amusement and skepticism in the scientific community. Rarely does one entertain the idea that the popularity of a given first name could be tied to the atmospheric conditions of a specific geographical location. However, in the illustrious tradition of scientific inquiry, we set out to investigate this curious relationship between the frequency of the name "Jason" and the air quality in the environs of Denver, Colorado.
While some may consider this endeavor to be a flight of fancy, we approached the task with all due solemnity, armed with statistical tools and a keen eye for uncovering the unexpected. It is often the case in scientific pursuits that the most unexpected findings yield the most tantalizing implications, and so we delved into the realm of data sets, correlation analyses, and p-values, poised to extract the truth from the numbers.
The US Social Security Administration provided us with a veritable smorgasbord of Jason-related data, while the Environmental Protection Agency, ever the stalwart guardian of atmospheric quality, offered up decades' worth of air pollution measurements in the Denver area. Embracing the spirit of interdisciplinary collaboration, we merged these seemingly disparate datasets and embarked upon the endeavor of quantifying the relationship, if any, between the popularity of the name "Jason" and the haze that graced the denizens of Denver.
As we began our statistical odyssey, we held in our minds the guiding principle that in the crucible of research, any variable, no matter how seemingly ludicrous or inconsequential, ought to be subjected to the rigors of analysis. It is only by allowing the data to speak unfettered that we may uncover the secrets of the world, whether they be grand and profound or peculiar and whimsical.
And so, with measured steps and a twinkle of skepticism, we present our findings, confident that they will provoke both mirth and contemplation, and perhaps prompt future scholars to delve into the uncharted territory of unanticipated correlations.

[[RESULTS]]
A strong positive correlation was found between the popularity of the first name "Jason" and air pollution levels in Denver, Colorado. Over the 1980 to 2022 period, the correlation coefficient stood at an impressive 0.8700527, indicating a robust relationship between the frequency of the name "Jason" and the atmospheric conditions of the Denver area. Furthermore, the coefficient of determination (r-squared) of 0.7569917 suggests that approximately 76% of the variability in air pollution levels can be explained by the variability in the popularity of the name "Jason."
The p-value of less than 0.01 further bolsters the significance of this correlation, lending support to the notion that the observed relationship is unlikely to be the result of random chance. In other words, the probability of obtaining such a strong correlation purely by fluke is exceedingly low, prompting us to cautiously accept the possibility of a genuine association between the two variables.
Fig. 1 depicts a scatterplot of the data, visually capturing the compelling correlation between the popularity of the name "Jason" and air pollution levels in Denver, Colorado. The scatterplot serves as a striking visual representation of the positive relationship uncovered in our analysis.
These findings, while unexpected and seemingly fanciful, point to the potential influence of nomenclature on environmental phenomena. The implications of this correlation extend beyond the realms of mere statistical curiosity and evoke contemplation on the intricate interplay between human behavior and atmospheric conditions. It is a poignant reminder of the serendipitous discoveries that lie in wait within the labyrinthine structures of data, encouraging scholars to approach research endeavors with a dash of whimsy and an open mind.
The statistical evidence presented here not only unveils an unanticipated connection between the eponymous moniker "Jason" and the haze in Denver's skies but also lays bare the delightful surprises that can emerge from rigorous empirical inquiry.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our investigation into the relationship between the popularity of the first name "Jason" and air pollution levels in Denver, Colorado has yielded remarkable and certainly unexpected results. The robust correlation coefficient of 0.8700527, accompanied by a minuscule p-value, points to a striking association that defies conventional wisdom and deftly challenges the boundaries of what may be considered as interconnected in our intricate world.

The implications of these findings do not simply rest on the statistical significance of the correlation but extend to the amusing contemplation of the unseen forces at play in shaping our environment. The idea that a mere name could bear such influence over atmospheric conditions prompts lighthearted curiosity and tickles the fancy of those who appreciate the whimsical aspects of scientific inquiry. Indeed, the notion of a "Jason effect" on air pollution levels evokes both amusement and contemplation, urging us to look beyond the obvious and venture into the realms of unconventional correlations.

It is a testament to the serendipitous nature of data analysis, a reminder that within the sea of numbers and variables, unexpected treasures of insight may be uncovered. The scatterplot, a visual testament to the compelling relationship elucidated by our study, stands as a testament to the unanticipated connections that may arise from rigorous empirical inquiry.

In light of these findings, it is evident that the interplay between nomenclature and environmental factors holds potential for further exploration. However, it is our contention that the elegant correlation observed in this study stands as a singular and delightful revelation, requiring no further probing or repetition. The "Jason effect" on air pollution in Denver, Colorado has been brought to light, leaving us with a droll yet illuminating tale of the quirks and caprices of statistical associations.

In summary, this investigation has illuminated a surprising nexus between the first name "Jason" and the atmospheric conditions of Denver, imparting both amusement and insight. We would cautiously assert, with a hint of mirth and intrigue, that perhaps no more research is needed in this unfathomable domain of enquiry. The "Jason effect," a unique quirk of statistical fate, stands as a testament to the whimsy that resides within the seemingly serious realm of correlation analysis.

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 Ties between Jason and Haze in Denver's Days: A Correlation Analysis. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Urban Connections

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 Ties between Jason and Haze in Denver's Days: A Correlation Analysis. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Atmospheric Anecdotes

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 Ties between Jason and Haze in Denver's Days: A Correlation Analysis with an abstract of This research paper investigates the intriguing interplay between the popularity of the first name "Jason" and air pollution levels in Denver, Colorado. Leveraging data from the US Social Security Administration and the Environmental Protection Agency, our study uncovered a noteworthy correlation. Over the period from 1980 to 2022, a correlation coefficient of 0.8700527 was observed, with a p-value of less than 0.01. The implications of these findings, while surprising, raise questions about the hidden influences of nomenclature and atmospheric conditions. This study promises to shed light on the unconventional connections that may lurk beneath the surface of seemingly unrelated phenomena, inviting both lighthearted curiosity and rigorous academic inquiry.

ChatGPT:

"Jason popularity Denver," "air pollution levels correlation," "US Social Security Administration data," "Environmental Protection Agency data," "nomenclature influences atmospheric conditions," "correlation coefficient significance," "hidden influences of names," "unconventional connections in research," "implications of name popularity," "atmospheric conditions impact on names"

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

Air pollution in Denver, Colorado
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Denver-Aurora-Lakewood, CO
Source: Environmental Protection Agency
See what else correlates with Air pollution in Denver, Colorado

Correlation r = 0.8700527 (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 = 0.7569917 (Coefficient of determination)
This means 75.7% of the change in the one variable (i.e., Air pollution in Denver, Colorado) is predictable based on the change in the other (i.e., Popularity of the first name Jason) over the 43 years from 1980 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.6E-14.^Show 0.0000000000000359251654009675
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 0.87 in 3.6E-12% of random cases. Said differently, if you correlated 27,835,640,806,070 random variables^Note You don't actually need 27 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 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.

[ 0.77, 0.93 ] 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 Jason (Babies born)	48476	42166	40913	35363	30158	26225	22570	19459	16005	12655	10737	9419	9267	8657	8584	9031	8723	8762	9418	9381	9801	10186	9976	9846	9096	8619	8460	8131	7673	6932	6288	5858	5761	5517	5570	5029	4887	4251	3873	3579	3098	2855	2599
Air pollution in Denver, Colorado (Bad air quality days)	39.6694	35.4396	37.8082	41.3699	32.2404	28.2192	33.6986	27.6712	25.4098	23.2877	6.84932	9.86301	11.2022	12.8767	11.2329	9.58904	14.2077	8.21918	11.5068	7.39726	13.9344	11.2329	12.3288	16.9863	7.10383	11.2329	16.1644	25.2055	14.4809	4.10959	10.411	15.6164	19.1257	12.0548	9.31507	7.12329	7.9235	10.9589	15.6164	6.0274	9.28962	18.3562	9.58904

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. ^{Show the steps to do this.} Step 1: Download and install Python on your computer.

Step 2: Open a plaintext editor like Notepad and paste the code below into it.

Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

Step 7: Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."

# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([48476,42166,40913,35363,30158,26225,22570,19459,16005,12655,10737,9419,9267,8657,8584,9031,8723,8762,9418,9381,9801,10186,9976,9846,9096,8619,8460,8131,7673,6932,6288,5858,5761,5517,5570,5029,4887,4251,3873,3579,3098,2855,2599,])
array_2 = np.array([39.6694,35.4396,37.8082,41.3699,32.2404,28.2192,33.6986,27.6712,25.4098,23.2877,6.84932,9.86301,11.2022,12.8767,11.2329,9.58904,14.2077,8.21918,11.5068,7.39726,13.9344,11.2329,12.3288,16.9863,7.10383,11.2329,16.1644,25.2055,14.4809,4.10959,10.411,15.6164,19.1257,12.0548,9.31507,7.12329,7.9235,10.9589,15.6164,6.0274,9.28962,18.3562,9.58904,])
array_1_name = "Popularity of the first name Jason"
array_2_name = "Air pollution in Denver, Colorado"

# 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 Jason
Line chart for only Air pollution in Denver, Colorado
AI-generated correlation image
The spurious research paper: The Ties between Jason and Haze in Denver's Days: A Correlation Analysis

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Correlation ID: 1619 · Black Variable ID: 2045 · Red Variable ID: 20541


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