Spurious correlation #4,929 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Kerosene used in Italy and the second variable is The average number of likes on MinuteEarth YouTube videos. The chart goes from 2013 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 Kerosene used in Italy and the second variable is The average number of likes on MinuteEarth YouTube videos. The chart goes from 2013 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As Italy used less kerosene, it led to a dimmer overall mood. This dimmer mood meant people weren't as easily 'ignited' by MinuteEarth's content, so they didn't feel the 'spark' to hit the like button. The 'likeability' of the videos was quite literally affected by the 'reduced fuel' for enthusiasm in Italy. It's a case of Kerosene-haustion leading to a real 'combustible' situation for MinuteEarth's online engagement. The 'bright spark' of entertainment just couldn't 'ignite' the same level of interest without the 'kerosene' for a positive response.

^{Show image generation prompt} Model: dalle-3
Prompt: In a dimly lit Italian village, a group of people gathered within the cozy warmth of a piazza, surrounded by centuries-old buildings. The evening sun cast faint but warm hues through narrow cobblestone pathways. As they sat around a flickering screen displaying MinuteEarth videos, the subdued atmosphere was palpable. The lack of kerosene use in Italy was evident in the subdued surroundings, and the villagers' expressions reflected the tangible impact. While the videos played, the usual spark of enthusiasm was missing from their faces, and the like button on the screen remained untouched. This atmosphere painted a realistic and poignant scene, highlighting the tangible impact of reduced kerosene use on the engagement of the villagers with the content. 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: Kerosene used in Italy and The average number of likes on MinuteEarth YouTube videos. 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: a meme Glitch art Cyberpunk Realistic Magic realism
ChatGPT: Realistic

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 Kerosene used in Italy and The average number of likes on MinuteEarth YouTube videos, in the following situation: As Italy used less kerosene, it led to a dimmer overall mood. This dimmer mood meant people weren't as easily 'ignited' by MinuteEarth's content, so they didn't feel the 'spark' to hit the like button. The 'likeability' of the videos was quite literally affected by the 'reduced fuel' for enthusiasm in Italy. It's a case of Kerosene-haustion leading to a real 'combustible' situation for MinuteEarth's online engagement. The 'bright spark' of entertainment just couldn't 'ignite' the same level of interest without the 'kerosene' for a positive response.. Keep the description simple.
ChatGPT: In a dimly lit Italian village, a group of people gathered around a screen displaying MinuteEarth videos. However, the usual spark of enthusiasm was missing from their faces as they watched the content. The like button on the screen remained untouched, reflecting the decreased engagement. The lack of kerosene use in Italy was visible in the subdued atmosphere, impacting the likeability of the videos in a tangible way.

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: [ Realistic ] and depicting this scene: In a dimly lit Italian village, a group of people gathered around a screen displaying MinuteEarth videos. However, the usual spark of enthusiasm was missing from their faces as they watched the content. The like button on the screen remained untouched, reflecting the decreased engagement. The lack of kerosene use in Italy was visible in the subdued atmosphere, impacting the likeability of the videos in a tangible way. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: In a dimly lit Italian village, a group of people gathered within the cozy warmth of a piazza, surrounded by centuries-old buildings. The evening sun cast faint but warm hues through narrow cobblestone pathways. As they sat around a flickering screen displaying MinuteEarth videos, the subdued atmosphere was palpable. The lack of kerosene use in Italy was evident in the subdued surroundings, and the villagers' expressions reflected the tangible impact. While the videos played, the usual spark of enthusiasm was missing from their faces, and the like button on the screen remained untouched. This atmosphere painted a realistic and poignant scene, highlighting the tangible impact of reduced kerosene use on the engagement of the villagers with the content.

*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 Kerosene used in Italy caused The average number of likes on MinuteEarth YouTube videos to decrease. Include one good pun.

AI academic paper

(Because p < 0.01)

Kerosene Comedy: Illuminating the Connection Between Kerosene Use in Italy and the Liking Patterns of MinuteEarth YouTube Videos

The International Journal of Quirky Energy and Offbeat Observations

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 Kerosene used in Italy and The average number of likes on MinuteEarth YouTube videos. Make lots of jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
"Kerosene Comedy: Illuminating the Connection Between Kerosene Use in Italy and the Liking Patterns of MinuteEarth YouTube Videos"

[[ABSTRACT]]
This study sheds light on the curious link between household kerosene use in Italy and the average number of likes received by MinuteEarth on their YouTube videos. Using data from the Energy Information Administration and the YouTube analytics, our research team conducted a thorough analysis from 2013 to 2022. We discovered a surprisingly high correlation coefficient of 0.8196290 and a statistically significant p-value of less than 0.01, indicating a strong relationship between these seemingly unrelated variables. The implications of these findings are both enlightening and amusing, highlighting the interconnectedness of seemingly unrelated phenomena and illuminating the importance of taking a closer look at the quirky connections in the world around us.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The interplay between kerosene use in Italy and the average number of likes on MinuteEarth YouTube videos is an unlikely and, dare I say, illuminating topic for investigation. One might question the connection between the burning of kerosene in Italian households and the clicking of the "like" button on MinuteEarth's videos, as one pertains to domestic lighting and the other to digital appreciation. However, as we delve into this peculiar correlation, it becomes evident that there is more than meets the eye.
The relationship between energy consumption and online engagement may seem as incongruous as trying to light a candle with a computer mouse, but we aim to demonstrate otherwise. By shedding light on this unexpected link, we aim to uncover the underlying factors and shed some humorous and informative commentary on these seemingly disparate realms.
In our pursuit of understanding this unlikely association, we have employed data from the Energy Information Administration and the labyrinthine depths of YouTube analytics from the years 2013 to 2022. Our meticulous examination has yielded a correlation coefficient of 0.8196290, and a p-value of less than 0.01, indicating a robust and statistically significant relationship between these two variables. It is our hope that by elucidating this unexpected connection, we can bring a spark of curiosity and amusement to the world of research and foster a greater appreciation for the whimsical quirks of statistical analysis.
So, without further ado, let us embark on a journey to illuminate the enigmatic relationship between household kerosene use in Italy and the virtual affirmations bestowed upon MinuteEarth's enlightening videos.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

Early investigations into the juxtaposition of household kerosene use in Italy and the average number of likes on MinuteEarth YouTube videos were conducted by Smith (2015) and Doe (2018). They initially approached this curious connection with the utmost seriousness, aiming to unravel the perplexing interplay between these two seemingly unrelated domains. However, it soon became apparent that the task at hand was not as straightforward as attempting to balance a wobbly IKEA table.

Smith (2015) noted some initial perplexity in his findings, remarking upon the bewitching nature of the statistical correlation between kerosene usage and digital affirmations. Nevertheless, despite his earnest efforts, Smith's work inadvertently laid the groundwork for a series of increasingly eccentric inquiries into the realm of research.

Building upon these early investigations, Jones (2020) delved deeper into the enigmatic relationship, drawing on insights from non-fiction works such as "Oil and Power: The Economics of an Energy Problem" by Cardy and "The Medium is the Massage: An Inventory of Effects" by McLuhan and Fiore. At this juncture, the intersection of household energy consumption and online interaction had become as enigmatic as a game of Clue played in the dark - with Professor Plum in the YouTube studio with the kerosene lamp, perhaps?

As the inquiry progressed, the literature began to take unpredictable turns, with works of fiction making unexpected cameos in the scholarly discourse. The likes of "The Da Vinci Code" by Dan Brown and "The Shadow of the Wind" by Carlos Ruiz Zafón made their way into the discussion, as if to illustrate that the plot thickened like a hearty stew simmering on a kerosene stove.

It appears that the whimsical nature of this investigation was not lost on some, as evidenced by the appearance of board game titles such as "Mystery of the Abbey" and "Sherlock Holmes Consulting Detective" in the researchers' off-duty discussions. The parallel between unraveling the mysteries of kerosene and cracking the code of digital approval was not lost on those involved, leading to more than a few lighthearted comparisons between the two, as if the research process were akin to traversing a convoluted game board to unravel the secrets hidden within the data.

With each twist and turn, the literature continued to provide unexpected amusement, much like an unexpected punchline in a comedy routine. As the sources grew increasingly diverse and the inquiry took on a life of its own, it became evident that the journey to unravel the unexpected connection between kerosene use in Italy and the liking patterns of MinuteEarth YouTube videos had become a quest as intricate and comical as a tapestry woven by the hands of fate itself.

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 Kerosene used in Italy and The average number of likes on MinuteEarth YouTube videos. Make lots of jokes, goofy observations, and puns.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
"Kerosene Comedy: Illuminating the Connection Between Kerosene Use in Italy and the Liking Patterns of MinuteEarth YouTube Videos"

[[ABSTRACT]]
This study sheds light on the curious link between household kerosene use in Italy and the average number of likes received by MinuteEarth on their YouTube videos. Using data from the Energy Information Administration and the YouTube analytics, our research team conducted a thorough analysis from 2013 to 2022. We discovered a surprisingly high correlation coefficient of 0.8196290 and a statistically significant p-value of less than 0.01, indicating a strong relationship between these seemingly unrelated variables. The implications of these findings are both enlightening and amusing, highlighting the interconnectedness of seemingly unrelated phenomena and illuminating the importance of taking a closer look at the quirky connections in the world around us.

[[LITERATURE REVIEW]]
Early investigations into the juxtaposition of household kerosene use in Italy and the average number of likes on MinuteEarth YouTube videos were conducted by Smith (2015) and Doe (2018). They initially approached this curious connection with the utmost seriousness, aiming to unravel the perplexing interplay between these two seemingly unrelated domains. However, it soon became apparent that the task at hand was not as straightforward as attempting to balance a wobbly IKEA table.
Smith (2015) noted some initial perplexity in his findings, remarking upon the bewitching nature of the statistical correlation between kerosene usage and digital affirmations. Nevertheless, despite his earnest efforts, Smith's work inadvertently laid the groundwork for a series of increasingly eccentric inquiries into the realm of research.
Building upon these early investigations, Jones (2020) delved deeper into the enigmatic relationship, drawing on insights from non-fiction works such as "Oil and Power: The Economics of an Energy Problem" by Cardy and "The Medium is the Massage: An Inventory of Effects" by McLuhan and Fiore. At this juncture, the intersection of household energy consumption and online interaction had become as enigmatic as a game of Clue played in the dark - with Professor Plum in the YouTube studio with the kerosene lamp, perhaps?
As the inquiry progressed, the literature began to take unpredictable turns, with works of fiction making unexpected cameos in the scholarly discourse. The likes of "The Da Vinci Code" by Dan Brown and "The Shadow of the Wind" by Carlos Ruiz Zafón made their way into the discussion, as if to illustrate that the plot thickened like a hearty stew simmering on a kerosene stove.
It appears that the whimsical nature of this investigation was not lost on some, as evidenced by the appearance of board game titles such as "Mystery of the Abbey" and "Sherlock Holmes Consulting Detective" in the researchers' off-duty discussions. The parallel between unraveling the mysteries of kerosene and cracking the code of digital approval was not lost on those involved, leading to more than a few lighthearted comparisons between the two, as if the research process were akin to traversing a convoluted game board to unravel the secrets hidden within the data.
With each twist and turn, the literature continued to provide unexpected amusement, much like an unexpected punchline in a comedy routine. As the sources grew increasingly diverse and the inquiry took on a life of its own, it became evident that the journey to unravel the unexpected connection between kerosene use in Italy and the liking patterns of MinuteEarth YouTube videos had become a quest as intricate and comical as a tapestry woven by the hands of fate itself.

[[RESULTS]]
The results of our investigation into the relationship between kerosene use in Italy and the average number of likes on MinuteEarth YouTube videos unveil a striking and unexpected connection. The correlation coefficient of 0.8196290 indicates a strong positive relationship between these seemingly unrelated variables. This finding suggests that as kerosene consumption in Italian households fluctuated over the years 2013 to 2022, so too did the average number of likes garnered by MinuteEarth's captivating videos.
The r-squared value of 0.6717917 further elucidates the robustness of this relationship, explaining approximately 67.18% of the variability in YouTube likes through the fluctuations in kerosene use. It appears that the flickering flames of kerosene lamps may have cast a metaphorical light on the digital realm, influencing the virtual appreciation bestowed upon MinuteEarth's educational content.
Furthermore, the p-value of less than 0.01 provides compelling evidence to reject the null hypothesis of no relationship between these variables. The statistical significance of this p-value strengthens the argument that the observed connection is not merely a statistical fluke, but rather a genuine and impactful association.
In summary, our analysis has brought to light an unexpected correlation between two seemingly disparate domains. The implications of these findings are far-reaching, shedding light on the interconnectedness of energy consumption and online engagement and underscoring the whimsical quirks that permeate the world of statistical analysis. Fig. 1 provides a visual representation of the strong correlation between kerosene use in Italy and the average number of likes on MinuteEarth YouTube videos.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The correlation uncovered in this study between kerosene use in Italy and the average number of likes on MinuteEarth YouTube videos presents a perplexing yet tantalizing puzzle for researchers and laymen alike. Our findings not only supported the prior research but also shed further light on the whimsical interplay between seemingly incongruous domains. The initial investigations by Smith (2015) and Doe (2018), although earnest in their pursuit, inadvertently set the stage for a series of increasingly curious inquiries into this enigmatic connection. Our results have not only validated their earlier findings but have also amplified the unusual nature of this association, much like a kerosene lamp illuminating a darkened room.

As Jones (2020) delved deeper into the mysterious relationship, drawing upon insights from unconventional sources, our study has solidified and expanded upon his earlier discoveries. The unexpected parallels between household energy consumption and online interaction have turned the research process into a veritable game of "Clue" played in the dark, with each new finding unraveling clues much like solving a riddle whispered in the dim glow of a kerosene lamp.

The literature review's unexpected turn towards works of fiction and board games now appears not as quirk but as prescient indicators of the surprising twists and turns this research would take. Just as how "The Da Vinci Code" and "The Shadow of the Wind" made their way into the scholarly discourse, our study has unearthed a connection as mysterious and compelling as the plots of these literary works – a connection that has now been revealed with the clarity of a well-lit kerosene lantern.

Indeed, the unexpected amusement provided by the diverse sources and unusual comparisons now seems fitting, as our results undoubtedly push the boundaries of traditional research into uncharted and entertaining territory. The quest to unravel the connection between kerosene use in Italy and MinuteEarth YouTube video likes has been a journey as intricate and comical as a tapestry woven by the hands of fate itself, with our findings serving as the unexpected punchline in a long and winding comedy routine. Our study has, quite literally, brought to light an unexpected correlation, glimmering like a kerosene lamp, between two seemingly unrelated domains. Fig. 1 serves as a visual testament to the surprising illumination our analysis has cast on this curious connection.

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 Kerosene used in Italy and The average number of likes on MinuteEarth YouTube videos. Make lots of jokes, goofy observations, and puns.

Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"Kerosene Comedy: Illuminating the Connection Between Kerosene Use in Italy and the Liking Patterns of MinuteEarth YouTube Videos"

[[ABSTRACT]]
This study sheds light on the curious link between household kerosene use in Italy and the average number of likes received by MinuteEarth on their YouTube videos. Using data from the Energy Information Administration and the YouTube analytics, our research team conducted a thorough analysis from 2013 to 2022. We discovered a surprisingly high correlation coefficient of 0.8196290 and a statistically significant p-value of less than 0.01, indicating a strong relationship between these seemingly unrelated variables. The implications of these findings are both enlightening and amusing, highlighting the interconnectedness of seemingly unrelated phenomena and illuminating the importance of taking a closer look at the quirky connections in the world around us.

[[INTRDUCTION]]
The interplay between kerosene use in Italy and the average number of likes on MinuteEarth YouTube videos is an unlikely and, dare I say, illuminating topic for investigation. One might question the connection between the burning of kerosene in Italian households and the clicking of the "like" button on MinuteEarth's videos, as one pertains to domestic lighting and the other to digital appreciation. However, as we delve into this peculiar correlation, it becomes evident that there is more than meets the eye.
The relationship between energy consumption and online engagement may seem as incongruous as trying to light a candle with a computer mouse, but we aim to demonstrate otherwise. By shedding light on this unexpected link, we aim to uncover the underlying factors and shed some humorous and informative commentary on these seemingly disparate realms.
In our pursuit of understanding this unlikely association, we have employed data from the Energy Information Administration and the labyrinthine depths of YouTube analytics from the years 2013 to 2022. Our meticulous examination has yielded a correlation coefficient of 0.8196290, and a p-value of less than 0.01, indicating a robust and statistically significant relationship between these two variables. It is our hope that by elucidating this unexpected connection, we can bring a spark of curiosity and amusement to the world of research and foster a greater appreciation for the whimsical quirks of statistical analysis.
So, without further ado, let us embark on a journey to illuminate the enigmatic relationship between household kerosene use in Italy and the virtual affirmations bestowed upon MinuteEarth's enlightening videos.

[[RESULTS]]
The results of our investigation into the relationship between kerosene use in Italy and the average number of likes on MinuteEarth YouTube videos unveil a striking and unexpected connection. The correlation coefficient of 0.8196290 indicates a strong positive relationship between these seemingly unrelated variables. This finding suggests that as kerosene consumption in Italian households fluctuated over the years 2013 to 2022, so too did the average number of likes garnered by MinuteEarth's captivating videos.
The r-squared value of 0.6717917 further elucidates the robustness of this relationship, explaining approximately 67.18% of the variability in YouTube likes through the fluctuations in kerosene use. It appears that the flickering flames of kerosene lamps may have cast a metaphorical light on the digital realm, influencing the virtual appreciation bestowed upon MinuteEarth's educational content.
Furthermore, the p-value of less than 0.01 provides compelling evidence to reject the null hypothesis of no relationship between these variables. The statistical significance of this p-value strengthens the argument that the observed connection is not merely a statistical fluke, but rather a genuine and impactful association.
In summary, our analysis has brought to light an unexpected correlation between two seemingly disparate domains. The implications of these findings are far-reaching, shedding light on the interconnectedness of energy consumption and online engagement and underscoring the whimsical quirks that permeate the world of statistical analysis. Fig. 1 provides a visual representation of the strong correlation between kerosene use in Italy and the average number of likes on MinuteEarth YouTube videos.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has illuminated the unexpected yet undeniable link between the consumption of kerosene in Italian households and the virtual affirmations bestowed upon MinuteEarth's educational videos. The findings have sparked much amusement and pondering in our research team, as we contemplated the analogy of kerosene as a metaphorical "like" button for the physical realm, casting its influence on the digital appreciation realm. Who knew that the flickering flames of kerosene lamps could metaphorically shed light on the digital realm? It seems that the connection between kerosene use and YouTube likes is as bright as a well-lit room!

These intriguing findings challenge the conventional boundaries of statistical analysis, throwing proverbial kerosene on the fire of curiosity and sparking a renewed appreciation for the whimsical and unexpected connections in the world around us. It seems that our statistical examination has uncovered a relationship that is not merely a statistical fluke, but rather a genuine and impactful association, shining a light on the complex interplay between energy consumption and online engagement.

The implications of these findings are both enlightening and entertaining. However, we firmly assert that no further research on this topic is warranted, as our results have undoubtedly shed enough light on the curious connection between kerosene use in Italy and the liking patterns of MinuteEarth YouTube videos. It's time to dim the proverbial lights on this topic and bask in the glow of our illuminating findings.

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 Kerosene Comedy: Illuminating the Connection Between Kerosene Use in Italy and the Liking Patterns of MinuteEarth YouTube Videos. Respond only with the name of a fictional academic journal.

ChatGPT:

The International Journal of Quirky Energy and Offbeat Observations

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 Kerosene Comedy: Illuminating the Connection Between Kerosene Use in Italy and the Liking Patterns of MinuteEarth YouTube Videos. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Unusual Correlations in Research (SUCR)

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 Kerosene Comedy: Illuminating the Connection Between Kerosene Use in Italy and the Liking Patterns of MinuteEarth YouTube Videos with an abstract of This study sheds light on the curious link between household kerosene use in Italy and the average number of likes received by MinuteEarth on their YouTube videos. Using data from the Energy Information Administration and the YouTube analytics, our research team conducted a thorough analysis from 2013 to 2022. We discovered a surprisingly high correlation coefficient of 0.8196290 and a statistically significant p-value of less than 0.01, indicating a strong relationship between these seemingly unrelated variables. The implications of these findings are both enlightening and amusing, highlighting the interconnectedness of seemingly unrelated phenomena and illuminating the importance of taking a closer look at the quirky connections in the world around us.

ChatGPT:

kerosene use Italy, household kerosene use, kerosene consumption correlation, MinuteEarth YouTube videos, YouTube analytics, Energy Information Administration, Italy energy consumption, quirky connections, seemingly unrelated phenomena

*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.

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Data details

Kerosene used in Italy
Detailed data title: Volume of kerosene used consumed in Italy in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Kerosene used in Italy

The average number of likes on MinuteEarth YouTube videos
Detailed data title: The average number of likes on MinuteEarth YouTube videos.
Source: YouTube
See what else correlates with The average number of likes on MinuteEarth YouTube videos

Correlation r = 0.8196290 (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 = 0.6717917 (Coefficient of determination)
This means 67.2% of the change in the one variable (i.e., The average number of likes on MinuteEarth YouTube videos) is predictable based on the change in the other (i.e., Kerosene used in Italy) over the 10 years from 2013 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0037.^Show 0.0037017767829540106000000000
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 0.82 in 0.37% of random cases. Said differently, if you correlated 270 random variables^Note Which I absolutely did.
with the same 9 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 9 because we have two variables measured over a period of 10 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.

[ 0.39, 0.96 ] 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.

	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Kerosene used in Italy (Million Barrels/Day)	5.61918	12.5918	5.64657	7.28415	8.0411	6.3863	5.81918	6.02459	8.15616	2.54247
The average number of likes on MinuteEarth YouTube videos (Average likes)	27354.7	48456.1	32329	29328.9	32800.7	20047.4	22309.3	20142.4	40750.8	21972.8

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([5.61918,12.5918,5.64657,7.28415,8.0411,6.3863,5.81918,6.02459,8.15616,2.54247,])
array_2 = np.array([27354.7,48456.1,32329,29328.9,32800.7,20047.4,22309.3,20142.4,40750.8,21972.8,])
array_1_name = "Kerosene used in Italy"
array_2_name = "The average number of likes on MinuteEarth YouTube videos"

# 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 Kerosene used in Italy
Line chart for only The average number of likes on MinuteEarth YouTube videos
AI-generated correlation image
The spurious research paper: Kerosene Comedy: Illuminating the Connection Between Kerosene Use in Italy and the Liking Patterns of MinuteEarth YouTube Videos

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Correlation ID: 4929 · Black Variable ID: 24567 · Red Variable ID: 25605


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