Spurious correlation #1,940 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air pollution in Helena, Montana and the second variable is Electricity generation in Benin. The chart goes from 1988 to 2021, 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 Air pollution in Helena, Montana and the second variable is Electricity generation in Benin. The chart goes from 1988 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The smog from Helena drifted all the way to Benin, where it inadvertently charged up the local power plants. It just goes to show, when life gives you smog, make electricity!

^{Show image generation prompt} Model: dalle-3
Prompt: Create an image that embodies the contrast between environmental challenges and sustainable solutions. In a scene of Helena, Montana shrouded in hazy smoke from wildfires, showcase a brutalist architectural structure with a sense of weathered resilience. In contrast, depict a simple, rural setting in Benin with a small, vibrant solar panel generating sustainable energy amidst the community. Capture the stark difference in environmental impact and the hopeful promise of renewable energy. 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: Unhealthy air quality in Helena, Montana and Electricity generation in Benin. 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: Cubism Photorealism Pop art Steampunk Brutalism
ChatGPT: Brutalism

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 Unhealthy air quality in Helena, Montana and Electricity generation in Benin together. Keep the description simple.
ChatGPT: In Helena, Montana, the air is hazy and filled with smoke from nearby wildfires, indicating unhealthy air quality. At the same time, in Benin, a small solar panel on a roof is generating electricity, providing sustainable energy for a local community.

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: [ Brutalism ] and depicting this scene: In Helena, Montana, the air is hazy and filled with smoke from nearby wildfires, indicating unhealthy air quality. At the same time, in Benin, a small solar panel on a roof is generating electricity, providing sustainable energy for a local community. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create an image that embodies the contrast between environmental challenges and sustainable solutions. In a scene of Helena, Montana shrouded in hazy smoke from wildfires, showcase a brutalist architectural structure with a sense of weathered resilience. In contrast, depict a simple, rural setting in Benin with a small, vibrant solar panel generating sustainable energy amidst the community. Capture the stark difference in environmental impact and the hopeful promise of renewable energy.

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

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

AI academic paper

(Because p < 0.01)

The Air-Pollution-Electricity Evolution Solution Revolution: A Co-Relation Navigation Investigation

The Journal of Eco-Technology and Environmental Innovation

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Air pollution in Helena, Montana and Electricity generation in Benin. 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. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
The Air-Pollution-Electricity Evolution Solution Revolution: A Co-Relation Navigation Investigation

[[ABSTRACT]]
In this study, we delved deep into the connection between air pollution levels in Helena, Montana, and electricity generation in Benin. With tongues firmly in our cheeks and magnifying glasses in hand, we have diligently combed through the Environmental Protection Agency and Energy Information Administration data from 1988 to 2021 to assess this curious correlation. Lo and behold, we uncovered a correlation coefficient of 0.7015194 and p < 0.01, revealing a rather striking relationship between these seemingly unrelated realms. Our findings not only shed light on the interconnectedness of global environmental phenomena but also highlight the potential for electrifying discoveries in the field of cross-continental pollution-proliferation relations.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intricate interplay between environmental factors and energy generation has long captivated the curious minds of researchers across the globe. This study embarks on a peculiar journey, venturing into the convoluted realm of air pollution levels in Helena, Montana, and the seemingly distant domain of electricity generation in Benin. While the layperson may perceive these entities as disparate as chalk and cheese, the discerning eye of scientific inquiry often unravels surprising connections, much like discovering that "pineapple" is neither a pine nor an apple.
As we enter the labyrinth of data analysis and correlation examination, it is essential to recognize the multifaceted nature of our quest. Like intrepid explorers navigating uncharted territories, we tread amongst vast arrays of numerical values, statistical measures, and regression analyses. Yet, fear not, dear reader, for we shall endeavor to illuminate this expedition with moments of levity and perhaps the occasional statistical pun, as we sally forth toward the heart of this empirical puzzle.
Air pollution, a topic that hangs heavy in the conscience of Earth's inhabitants, has garnered substantial attention in the scientific community. Conversely, the mechanisms and repercussions of electricity generation in various regions have woven a complex tapestry of challenges and innovations. Together, these two domains stand as the game pieces in the board game of our investigation, where the dice are rolled in the form of statistical tests and the goal is to uncover patterns and relationships, much like searching for a needle in a haystack, if that needle were a statistically significant finding and the haystack were a vast dataset.
In the following pages, we shall weave a narrative through the labyrinth of numerical values and methodological intricacies, aiming to shed light on the enigmatic connection between these seemingly incongruous facets of the environment and energy production. Let us embark on this intellectual voyage with open minds, a spirit of curiosity, and a readiness to embrace both the serious scientific discourse and the occasional lighthearted jest, if you will permit us a small measure of statistical merriment.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

Unraveling the enigmatic connections between air pollution in Helena, Montana, and electricity generation in Benin has led many a researcher down a perplexing path, much like navigating through a forest of statistical significance. The journey begins with Smith et al. (2010) who undertook a comprehensive analysis of air pollutant dispersion patterns in mountainous regions, providing foundational insights into the complexities of atmospheric dynamics. Meanwhile, Doe and Jones (2015) investigated the socioeconomic determinants of electricity consumption in West African nations, casting a spotlight on the intricate dance between energy demand and regional development.

Delving further into the realm of environmental impact, "Environmental Economics: A Very Short Introduction" by Smith (2003) and "Electricity Economics: Production Functions with Electricity" by Doe (2007) shed light on the economic underpinnings of pollution and power generation. However, as we wade through the sea of scholarly works, it is crucial to infuse a touch of literary whimsy into our discourse. Hence, we turn to "The Electric Kool-Aid Acid Test" by Tom Wolfe, not because it offers any substantive insight into the correlation between air pollution and electricity generation, but simply because the title sounds electrifyingly intriguing.

Venturing further into the realm of fiction, the mysterious and captivating allure of "The Shock Doctrine" by Naomi Klein beckons us to contemplate the electrifying shockwaves of industrialization and its ecological repercussions. Furthermore, the classic board game, "Power Grid," loosely parallels our investigation, challenging players to navigate the intricate landscape of power generation and resource management – a metaphorical journey not unlike our scholarly pursuit, albeit with considerably fewer statistical variables and noticeably more colorful game pieces.

As we traverse the academic landscape, veering from scholarly rigor to literary flights of fancy, let us remain ever mindful of the wondrous interplay of seriousness and mirth, as we endeavor to untangle the web of interconnections that underpin the intercontinental interplay of air pollution and electricity generation. For in the quest for knowledge, a dash of whimsy may just be the spark that ignites the fires of discovery.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between Air pollution in Helena, Montana and Electricity generation in Benin. 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 Environmental Protection Agency and Energy Information Administration . You used data from 1988 to 2021

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 Air-Pollution-Electricity Evolution Solution Revolution: A Co-Relation Navigation Investigation

[[ABSTRACT]]
In this study, we delved deep into the connection between air pollution levels in Helena, Montana, and electricity generation in Benin. With tongues firmly in our cheeks and magnifying glasses in hand, we have diligently combed through the Environmental Protection Agency and Energy Information Administration data from 1988 to 2021 to assess this curious correlation. Lo and behold, we uncovered a correlation coefficient of 0.7015194 and p < 0.01, revealing a rather striking relationship between these seemingly unrelated realms. Our findings not only shed light on the interconnectedness of global environmental phenomena but also highlight the potential for electrifying discoveries in the field of cross-continental pollution-proliferation relations.

[[INTRODUCTION]]
The intricate interplay between environmental factors and energy generation has long captivated the curious minds of researchers across the globe. This study embarks on a peculiar journey, venturing into the convoluted realm of air pollution levels in Helena, Montana, and the seemingly distant domain of electricity generation in Benin. While the layperson may perceive these entities as disparate as chalk and cheese, the discerning eye of scientific inquiry often unravels surprising connections, much like discovering that "pineapple" is neither a pine nor an apple.
As we enter the labyrinth of data analysis and correlation examination, it is essential to recognize the multifaceted nature of our quest. Like intrepid explorers navigating uncharted territories, we tread amongst vast arrays of numerical values, statistical measures, and regression analyses. Yet, fear not, dear reader, for we shall endeavor to illuminate this expedition with moments of levity and perhaps the occasional statistical pun, as we sally forth toward the heart of this empirical puzzle.
Air pollution, a topic that hangs heavy in the conscience of Earth's inhabitants, has garnered substantial attention in the scientific community. Conversely, the mechanisms and repercussions of electricity generation in various regions have woven a complex tapestry of challenges and innovations. Together, these two domains stand as the game pieces in the board game of our investigation, where the dice are rolled in the form of statistical tests and the goal is to uncover patterns and relationships, much like searching for a needle in a haystack, if that needle were a statistically significant finding and the haystack were a vast dataset.
In the following pages, we shall weave a narrative through the labyrinth of numerical values and methodological intricacies, aiming to shed light on the enigmatic connection between these seemingly incongruous facets of the environment and energy production. Let us embark on this intellectual voyage with open minds, a spirit of curiosity, and a readiness to embrace both the serious scientific discourse and the occasional lighthearted jest, if you will permit us a small measure of statistical merriment.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To unearth the link between air pollution in Helena, Montana, and electricity generation in Benin, we donned our metaphorical Sherlock Holmes hats and embarked on a data-gathering escapade that would make even the most intrepid investigators envious. Our cunning plan involved navigating the labyrinthine corridors of the Environmental Protection Agency (EPA) and the Energy Information Administration (EIA) databases, where tales of numerical intrigue lay waiting to be uncovered.

Firstly, we engaged in a rigorous search and retrieval operation, scouring through gigabytes of data spanning from 1988 to 2021, resembling a quest for the Holy Grail but with significantly more Excel spreadsheets. We meticulously gathered information on air quality indices, particulate matter concentrations, electricity generation statistics, and various socio-economic indicators, because if you're going to explore a correlation, you might as well throw in the kitchen sink too.

Once we had assembled an armada of datasets, we diligently performed data wrangling and cleaning, ensuring that no erroneous outliers or misbehaving missing values would dare sabotage our noble pursuit of knowledge. As any seasoned statistician will tell you, a clean dataset is the first step towards statistical enlightenment.

Next, we pried open Pandora's box of statistical analyses, unleashing an assortment of correlation coefficients, regression models, and hypothesis tests to scrutinize the extent of the relationship between air pollution levels in Helena and electricity generation in Benin. We didn’t just shake the data, we put it through a vigorous aerobics workout of statistical procedures.

To quantify this captivating co-relation navigation, we employed the Pearson correlation coefficient, aiding us in measuring the strength and direction of the relationship between our chosen variables. Like skillful matchmakers, we scrutinized the p-values with bated breath, seeking the elusive <0.01 threshold that would signify a meaningful connection between our star-crossed variables.

Let it be known that where the data ventured, statistical software like SPSS and R faithfully followed, acting as our trusty companions in this scientific odyssey. With their assistance, we performed multiple linear regressions to disentangle the complex web of interactions among our quantitative predictors and pinpoint the influence of air pollution on electricity generation with the precision of a laser-guided statistical missile.

As a final touch, we dabbled in some time series analysis, appreciating how the trends and fluctuations of air pollution and electricity generation over time resembled a captivating dance between two partners on the scientific stage, albeit with significantly less grace and more missing data points.

In summary, our methodology involved equal parts data excavation, statistical acrobatics, and a touch of digital alchemy to transform raw numbers into nuggets of research gold. With our methodological wizardry honed to a fine point, we were prepared to venture boldly into the uncharted realms of correlation and causation, armed with our trusty statistical wands and a penchant for levity amidst the empirical rigors.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the 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 Air pollution in Helena, Montana and Electricity generation in Benin. 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 Environmental Protection Agency and Energy Information Administration .

For the time period 1988 to 2021, you found a correlation 0.7015194, r-squared of 0.4921294, 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 Air-Pollution-Electricity Evolution Solution Revolution: A Co-Relation Navigation Investigation

[[ABSTRACT]]
In this study, we delved deep into the connection between air pollution levels in Helena, Montana, and electricity generation in Benin. With tongues firmly in our cheeks and magnifying glasses in hand, we have diligently combed through the Environmental Protection Agency and Energy Information Administration data from 1988 to 2021 to assess this curious correlation. Lo and behold, we uncovered a correlation coefficient of 0.7015194 and p < 0.01, revealing a rather striking relationship between these seemingly unrelated realms. Our findings not only shed light on the interconnectedness of global environmental phenomena but also highlight the potential for electrifying discoveries in the field of cross-continental pollution-proliferation relations.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intricate interplay between environmental factors and energy generation has long captivated the curious minds of researchers across the globe. This study embarks on a peculiar journey, venturing into the convoluted realm of air pollution levels in Helena, Montana, and the seemingly distant domain of electricity generation in Benin. While the layperson may perceive these entities as disparate as chalk and cheese, the discerning eye of scientific inquiry often unravels surprising connections, much like discovering that "pineapple" is neither a pine nor an apple.
As we enter the labyrinth of data analysis and correlation examination, it is essential to recognize the multifaceted nature of our quest. Like intrepid explorers navigating uncharted territories, we tread amongst vast arrays of numerical values, statistical measures, and regression analyses. Yet, fear not, dear reader, for we shall endeavor to illuminate this expedition with moments of levity and perhaps the occasional statistical pun, as we sally forth toward the heart of this empirical puzzle.
Air pollution, a topic that hangs heavy in the conscience of Earth's inhabitants, has garnered substantial attention in the scientific community. Conversely, the mechanisms and repercussions of electricity generation in various regions have woven a complex tapestry of challenges and innovations. Together, these two domains stand as the game pieces in the board game of our investigation, where the dice are rolled in the form of statistical tests and the goal is to uncover patterns and relationships, much like searching for a needle in a haystack, if that needle were a statistically significant finding and the haystack were a vast dataset.
In the following pages, we shall weave a narrative through the labyrinth of numerical values and methodological intricacies, aiming to shed light on the enigmatic connection between these seemingly incongruous facets of the environment and energy production. Let us embark on this intellectual voyage with open minds, a spirit of curiosity, and a readiness to embrace both the serious scientific discourse and the occasional lighthearted jest, if you will permit us a small measure of statistical merriment.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The statistical analysis revealed a rather remarkable correlation coefficient of 0.7015194 between air pollution levels in Helena, Montana, and electricity generation in Benin. This positive correlation indicates that as air pollution levels in Helena increased, electricity generation in Benin also exhibited a corresponding increase. In layman's terms, it's as if the fumes from Helena managed to travel across oceans to spur electricity generation in Benin – a true testament to the global interconnectedness of environmental and energy systems.

The strength of this correlation was further underscored by an r-squared value of 0.4921294, indicating that approximately 49.21% of the variability in electricity generation in Benin was accounted for by the variability in air pollution levels in Helena. It's as if these two variables were engaged in an intricate dance, with nearly half of the steps mirroring each other's movements.

Furthermore, the p-value, which came in at less than 0.01, bestowed an air of statistical significance upon this correlation, affirming that the likelihood of observing such a substantial relationship due to random chance alone is comparable to finding a four-leaf clover in a field of three-leaf clovers – quite rare indeed.

Fig. 1 vividly depicts the relationship between air pollution in Helena and electricity generation in Benin, showcasing the impressive alignment of data points that reinforces the findings of this investigation. It's almost as if the data points themselves were eager to demonstrate the strength of their connection, standing shoulder to shoulder like old friends sharing an inside joke.

In conclusion, our study not only unraveled a compelling correlation between two seemingly disparate variables but also demonstrated the power of statistical analysis in uncovering hidden patterns. The implications of this correlation are as electrifying as they are thought-provoking, underscoring the need for further exploration into the intricate dance of environmental and energy systems. This research paves the way for future studies to delve into the underlying mechanisms driving this connection and to consider the broader impact of such intercontinental environmental resonances.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between Air pollution in Helena, Montana and Electricity generation in Benin. 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 Air-Pollution-Electricity Evolution Solution Revolution: A Co-Relation Navigation Investigation

[[ABSTRACT]]
In this study, we delved deep into the connection between air pollution levels in Helena, Montana, and electricity generation in Benin. With tongues firmly in our cheeks and magnifying glasses in hand, we have diligently combed through the Environmental Protection Agency and Energy Information Administration data from 1988 to 2021 to assess this curious correlation. Lo and behold, we uncovered a correlation coefficient of 0.7015194 and p < 0.01, revealing a rather striking relationship between these seemingly unrelated realms. Our findings not only shed light on the interconnectedness of global environmental phenomena but also highlight the potential for electrifying discoveries in the field of cross-continental pollution-proliferation relations.

[[LITERATURE REVIEW]]
Unraveling the enigmatic connections between air pollution in Helena, Montana, and electricity generation in Benin has led many a researcher down a perplexing path, much like navigating through a forest of statistical significance. The journey begins with Smith et al. (2010) who undertook a comprehensive analysis of air pollutant dispersion patterns in mountainous regions, providing foundational insights into the complexities of atmospheric dynamics. Meanwhile, Doe and Jones (2015) investigated the socioeconomic determinants of electricity consumption in West African nations, casting a spotlight on the intricate dance between energy demand and regional development.
Delving further into the realm of environmental impact, "Environmental Economics: A Very Short Introduction" by Smith (2003) and "Electricity Economics: Production Functions with Electricity" by Doe (2007) shed light on the economic underpinnings of pollution and power generation. However, as we wade through the sea of scholarly works, it is crucial to infuse a touch of literary whimsy into our discourse. Hence, we turn to "The Electric Kool-Aid Acid Test" by Tom Wolfe, not because it offers any substantive insight into the correlation between air pollution and electricity generation, but simply because the title sounds electrifyingly intriguing.
Venturing further into the realm of fiction, the mysterious and captivating allure of "The Shock Doctrine" by Naomi Klein beckons us to contemplate the electrifying shockwaves of industrialization and its ecological repercussions. Furthermore, the classic board game, "Power Grid," loosely parallels our investigation, challenging players to navigate the intricate landscape of power generation and resource management – a metaphorical journey not unlike our scholarly pursuit, albeit with considerably fewer statistical variables and noticeably more colorful game pieces.
As we traverse the academic landscape, veering from scholarly rigor to literary flights of fancy, let us remain ever mindful of the wondrous interplay of seriousness and mirth, as we endeavor to untangle the web of interconnections that underpin the intercontinental interplay of air pollution and electricity generation. For in the quest for knowledge, a dash of whimsy may just be the spark that ignites the fires of discovery.

[[RESULTS]]
The statistical analysis revealed a rather remarkable correlation coefficient of 0.7015194 between air pollution levels in Helena, Montana, and electricity generation in Benin. This positive correlation indicates that as air pollution levels in Helena increased, electricity generation in Benin also exhibited a corresponding increase. In layman's terms, it's as if the fumes from Helena managed to travel across oceans to spur electricity generation in Benin – a true testament to the global interconnectedness of environmental and energy systems.
The strength of this correlation was further underscored by an r-squared value of 0.4921294, indicating that approximately 49.21% of the variability in electricity generation in Benin was accounted for by the variability in air pollution levels in Helena. It's as if these two variables were engaged in an intricate dance, with nearly half of the steps mirroring each other's movements.
Furthermore, the p-value, which came in at less than 0.01, bestowed an air of statistical significance upon this correlation, affirming that the likelihood of observing such a substantial relationship due to random chance alone is comparable to finding a four-leaf clover in a field of three-leaf clovers – quite rare indeed.
Fig. 1 vividly depicts the relationship between air pollution in Helena and electricity generation in Benin, showcasing the impressive alignment of data points that reinforces the findings of this investigation. It's almost as if the data points themselves were eager to demonstrate the strength of their connection, standing shoulder to shoulder like old friends sharing an inside joke.
In conclusion, our study not only unraveled a compelling correlation between two seemingly disparate variables but also demonstrated the power of statistical analysis in uncovering hidden patterns. The implications of this correlation are as electrifying as they are thought-provoking, underscoring the need for further exploration into the intricate dance of environmental and energy systems. This research paves the way for future studies to delve into the underlying mechanisms driving this connection and to consider the broader impact of such intercontinental environmental resonances.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our findings have illuminated a remarkable correlation between air pollution levels in Helena, Montana, and electricity generation in Benin, akin to a scientific "bromance" between these seemingly unrelated entities. The statistical significance of this relationship echoes the profound words of Shakespeare: "Though this be madness, yet there is method in't." Indeed, the robust correlation coefficient and minuscule p-value underscore the need to take this seemingly whimsical connection with the utmost seriousness.

Harking back to the unconventional foray into literary whimsy in our literature review, it is evident that the meandering path of scholarly pursuit, much like the Yellow Brick Road, can be peppered with moments of levity and unexpected insights—akin to stumbling upon a "eureka!" moment while in search of statistical significance. The allure of literary references serves as a reminder that science and mirth need not exist in mutual exclusion; in fact, they often underpin the very fabric of scientific exploration.

Our results bolster and build upon prior research, serving as a veritable nod to the eclectic mix of scholarly endeavors explored in the literature review. Smith et al.'s pioneering work on air pollutant dispersion patterns in mountainous regions finds resonance in our study, albeit with a continental twist. Furthermore, Doe and Jones' exploration of the socioeconomic determinants of electricity consumption in West African nations gains further credence with our findings, weaving a tapestry of interdisciplinary collaboration that would make even the most seasoned statistician grin with scholarly satisfaction.

The correlation coefficient and r-squared value serve as undeniable evidence of the intricate dance between air pollution in Helena and electricity generation in Benin, much like a celestial waltz choreographed by the gravitational pull of statistical significance. This symbiotic relationship, reminiscent of a scientific tango, underscores the interconnectedness of environmental and energy systems—ultimately challenging us to ponder the ripple effects of seemingly distant phenomena on a global scale.

In closing, our findings serve as a beacon of illumination into the enigmatic interplay of air pollution and electricity generation, prompting further exploration into the nuanced mechanisms underpinning this correlation. As the scholarly community embarks on future investigations, it is imperative to maintain a sense of scholarly whimsy, for in the grand tapestry of scientific discovery, mirth and method are not mutually exclusive but rather the twin beacons that light our path toward electrifying revelations.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Air pollution in Helena, Montana and Electricity generation in Benin. 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 Air-Pollution-Electricity Evolution Solution Revolution: A Co-Relation Navigation Investigation

[[ABSTRACT]]
In this study, we delved deep into the connection between air pollution levels in Helena, Montana, and electricity generation in Benin. With tongues firmly in our cheeks and magnifying glasses in hand, we have diligently combed through the Environmental Protection Agency and Energy Information Administration data from 1988 to 2021 to assess this curious correlation. Lo and behold, we uncovered a correlation coefficient of 0.7015194 and p < 0.01, revealing a rather striking relationship between these seemingly unrelated realms. Our findings not only shed light on the interconnectedness of global environmental phenomena but also highlight the potential for electrifying discoveries in the field of cross-continental pollution-proliferation relations.

[[INTRDUCTION]]
The intricate interplay between environmental factors and energy generation has long captivated the curious minds of researchers across the globe. This study embarks on a peculiar journey, venturing into the convoluted realm of air pollution levels in Helena, Montana, and the seemingly distant domain of electricity generation in Benin. While the layperson may perceive these entities as disparate as chalk and cheese, the discerning eye of scientific inquiry often unravels surprising connections, much like discovering that "pineapple" is neither a pine nor an apple.
As we enter the labyrinth of data analysis and correlation examination, it is essential to recognize the multifaceted nature of our quest. Like intrepid explorers navigating uncharted territories, we tread amongst vast arrays of numerical values, statistical measures, and regression analyses. Yet, fear not, dear reader, for we shall endeavor to illuminate this expedition with moments of levity and perhaps the occasional statistical pun, as we sally forth toward the heart of this empirical puzzle.
Air pollution, a topic that hangs heavy in the conscience of Earth's inhabitants, has garnered substantial attention in the scientific community. Conversely, the mechanisms and repercussions of electricity generation in various regions have woven a complex tapestry of challenges and innovations. Together, these two domains stand as the game pieces in the board game of our investigation, where the dice are rolled in the form of statistical tests and the goal is to uncover patterns and relationships, much like searching for a needle in a haystack, if that needle were a statistically significant finding and the haystack were a vast dataset.
In the following pages, we shall weave a narrative through the labyrinth of numerical values and methodological intricacies, aiming to shed light on the enigmatic connection between these seemingly incongruous facets of the environment and energy production. Let us embark on this intellectual voyage with open minds, a spirit of curiosity, and a readiness to embrace both the serious scientific discourse and the occasional lighthearted jest, if you will permit us a small measure of statistical merriment.

[[RESULTS]]
The statistical analysis revealed a rather remarkable correlation coefficient of 0.7015194 between air pollution levels in Helena, Montana, and electricity generation in Benin. This positive correlation indicates that as air pollution levels in Helena increased, electricity generation in Benin also exhibited a corresponding increase. In layman's terms, it's as if the fumes from Helena managed to travel across oceans to spur electricity generation in Benin – a true testament to the global interconnectedness of environmental and energy systems.
The strength of this correlation was further underscored by an r-squared value of 0.4921294, indicating that approximately 49.21% of the variability in electricity generation in Benin was accounted for by the variability in air pollution levels in Helena. It's as if these two variables were engaged in an intricate dance, with nearly half of the steps mirroring each other's movements.
Furthermore, the p-value, which came in at less than 0.01, bestowed an air of statistical significance upon this correlation, affirming that the likelihood of observing such a substantial relationship due to random chance alone is comparable to finding a four-leaf clover in a field of three-leaf clovers – quite rare indeed.
Fig. 1 vividly depicts the relationship between air pollution in Helena and electricity generation in Benin, showcasing the impressive alignment of data points that reinforces the findings of this investigation. It's almost as if the data points themselves were eager to demonstrate the strength of their connection, standing shoulder to shoulder like old friends sharing an inside joke.
In conclusion, our study not only unraveled a compelling correlation between two seemingly disparate variables but also demonstrated the power of statistical analysis in uncovering hidden patterns. The implications of this correlation are as electrifying as they are thought-provoking, underscoring the need for further exploration into the intricate dance of environmental and energy systems. This research paves the way for future studies to delve into the underlying mechanisms driving this connection and to consider the broader impact of such intercontinental environmental resonances.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In the immortal words of Newton, "What goes up must come down." Our foray into the entwined realms of air pollution in Helena, Montana, and electricity generation in Benin has indeed painted a picture of intercontinental symphony, where the particles in the air seemingly perform a ballet that resonates across oceans. This innovative investigation has, quite literally, sparked connections that transcend geographical boundaries, akin to a scientific rendition of a long-distance love story.

The correlation coefficient of 0.7015194, akin to a match made in statistical heaven, has illuminated the synchrony between these disparate variables with a glow brighter than a bio-luminescent jellyfish. The r-squared value of 0.4921294 signifies a harmonious dance, reminiscent of a celestial waltz, wherein nearly half of the steps mirror each other – a statistical pas de deux, if you will.

Moreover, the p-value, resplendent in its rarity, stands as a testament to the unlikelihood of such a striking relationship occurring by mere chance, much like stumbling upon a unicorn in a petting zoo. Fig. 1, a veritable Mona Lisa of data visualization, captures the essence of this connection with a resonance akin to a perfect pitch in a symphony orchestra.

As we bid adieu to this exploration, we do so with a nod to the potential for further discovery. Nevertheless, we confidently assert that no further research is required in this area. The connection has been made, and it's as solid as a rock in a geology museum. It's time to close this chapter and move on to more pressing scientific puzzles, like figuring out why toast always falls butter-side down.

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 Air-Pollution-Electricity Evolution Solution Revolution: A Co-Relation Navigation Investigation. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Eco-Technology and Environmental Innovation

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 Air-Pollution-Electricity Evolution Solution Revolution: A Co-Relation Navigation Investigation. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Sustainable Energy Solutions (SSES)

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 Air-Pollution-Electricity Evolution Solution Revolution: A Co-Relation Navigation Investigation with an abstract of In this study, we delved deep into the connection between air pollution levels in Helena, Montana, and electricity generation in Benin. With tongues firmly in our cheeks and magnifying glasses in hand, we have diligently combed through the Environmental Protection Agency and Energy Information Administration data from 1988 to 2021 to assess this curious correlation. Lo and behold, we uncovered a correlation coefficient of 0.7015194 and p < 0.01, revealing a rather striking relationship between these seemingly unrelated realms. Our findings not only shed light on the interconnectedness of global environmental phenomena but also highlight the potential for electrifying discoveries in the field of cross-continental pollution-proliferation relations.

ChatGPT:

air pollution levels, electricity generation, correlation, Helena Montana, Benin, Environmental Protection Agency data, Energy Information Administration data, 1988-2021, correlation coefficient, global environmental phenomena, cross-continental pollution-proliferation relations

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Data details

Air pollution in Helena, Montana
Detailed data title: Percentage of days with air quality at 'unhealthy for sensitive groups' or worse in Helena, MT
Source: Environmental Protection Agency
See what else correlates with Air pollution in Helena, Montana

Electricity generation in Benin
Detailed data title: Total electricity generation in Benin in billion kWh
Source: Energy Information Administration
See what else correlates with Electricity generation in Benin

Correlation r = 0.7015194 (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 = 0.4921294 (Coefficient of determination)
This means 49.2% of the change in the one variable (i.e., Electricity generation in Benin) is predictable based on the change in the other (i.e., Air pollution in Helena, Montana) over the 34 years from 1988 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.8E-6.^Show 0.0000037986401823455515000000
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 0.7 in 0.00038% of random cases. Said differently, if you correlated 263,252 random variables^Note You don't actually need 263 thousand 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 33 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 33 because we have two variables measured over a period of 34 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.

[ 0.48, 0.84 ] 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.

	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
Air pollution in Helena, Montana (Bad air quality days)	0.00840336	0	0.00558659	0	0	0	0	0	0	0	0.00295858	0	0.0164835	0.00833333	0.00862069	0.0115607	0.00643087	0.00837989	0.00280112	0.0311615	0.00824176	0.00837989	0.0388889	0.00547945	0.0191257	0	0.0136986	0.0356164	0.0191257	0.0794521	0.0273973	0.00547945	0.0136612	0.0465753
Electricity generation in Benin (Billion kWh)	0.005	0.005	0.019	0.022	0.023	0.024	0.048	0.029	0.0487	0.0517	0.0597	0.0677	0.07908	0.06216	0.05934	0.07532	0.0762	0.10064	0.142	0.20498	0.21344	0.12038	0.10816	0.09594	0.04612	0.08848	0.22666	0.31056	0.26004	0.3115	0.19028	0.2017	0.22608	0.241097

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([0.00840336,0,0.00558659,0,0,0,0,0,0,0,0.00295858,0,0.0164835,0.00833333,0.00862069,0.0115607,0.00643087,0.00837989,0.00280112,0.0311615,0.00824176,0.00837989,0.0388889,0.00547945,0.0191257,0,0.0136986,0.0356164,0.0191257,0.0794521,0.0273973,0.00547945,0.0136612,0.0465753,])
array_2 = np.array([0.005,0.005,0.019,0.022,0.023,0.024,0.048,0.029,0.0487,0.0517,0.0597,0.0677,0.07908,0.06216,0.05934,0.07532,0.0762,0.10064,0.142,0.20498,0.21344,0.12038,0.10816,0.09594,0.04612,0.08848,0.22666,0.31056,0.26004,0.3115,0.19028,0.2017,0.22608,0.241097,])
array_1_name = "Air pollution in Helena, Montana"
array_2_name = "Electricity generation in Benin"

# 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
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Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only Air pollution in Helena, Montana
Line chart for only Electricity generation in Benin
AI-generated correlation image
The spurious research paper: The Air-Pollution-Electricity Evolution Solution Revolution: A Co-Relation Navigation Investigation

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Correlation ID: 1940 · Black Variable ID: 22011 · Red Variable ID: 23516


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