Spurious correlation #5,362 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Votes for the Democratic Presidential candidate in Florida and the second variable is Fossil fuel use in Guam. The chart goes from 1980 to 2020, 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 Votes for the Democratic Presidential candidate in Florida and the second variable is Fossil fuel use in Guam. The chart goes from 1980 to 2020, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the support for the Democratic candidate surged in Florida, it created a ripple effect all the way to Guam. This led to a sudden spike in demand for campaign merchandise, including those catchy bumper stickers. With the increase in production and shipping of these items, it's no wonder there was a rise in fossil fuel use in Guam - talk about a politi-coal domino effect!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image that looks like it is a scene from a Pixar movie, showing a vibrant and bustling Floridian voting site. The scene should display a busy sea of voting booths, with enthusiastic individuals casting their ballots for the Democratic Presidential candidate. There should be a noticeable frenzy of activity, as people eagerly adorn themselves with bumper stickers and campaign merchandise bearing the candidate's name. Meanwhile, the image should also convey a strong sense of movement and excitement, illustrating the surge in production and shipping of these items. In the background, the distinct landscape of Guam should be subtly integrated, dotted with an increase in fossil fuel use as shipments of campaign merchandise arrive, creating a politi-coal domino effect. 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: Votes for the Democratic Presidential candidate in Florida and Fossil fuel use in Guam. 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: Cutsy art illustration Minimalism An image that looks like it is a scene from a Pixar movie Pop art Hyper-realistic
ChatGPT: An image that looks like it is a scene from a Pixar movie

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 Votes for the Democratic Presidential candidate in Florida and Fossil fuel use in Guam, in the following situation: As the support for the Democratic candidate surged in Florida, it created a ripple effect all the way to Guam. This led to a sudden spike in demand for campaign merchandise, including those catchy bumper stickers. With the increase in production and shipping of these items, it's no wonder there was a rise in fossil fuel use in Guam - talk about a politi-coal domino effect!. Keep the description simple.
ChatGPT: In Florida, a sea of voting booths is filled with people casting their ballots for the Democratic Presidential candidate. Bumper stickers and campaign merchandise with the candidate's name are in high demand, leading to a surge in production and shipping. Over in Guam, the island's landscape is dotted with a spike in fossil fuel use as shipments of the campaign merchandise arrive, creating a politi-coal domino effect.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ An image that looks like it is a scene from a Pixar movie ] and depicting this scene: In Florida, a sea of voting booths is filled with people casting their ballots for the Democratic Presidential candidate. Bumper stickers and campaign merchandise with the candidate's name are in high demand, leading to a surge in production and shipping. Over in Guam, the island's landscape is dotted with a spike in fossil fuel use as shipments of the campaign merchandise arrive, creating a politi-coal domino effect. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that looks like it is a scene from a Pixar movie, showing a vibrant and bustling Floridian voting site. The scene should display a busy sea of voting booths, with enthusiastic individuals casting their ballots for the Democratic Presidential candidate. There should be a noticeable frenzy of activity, as people eagerly adorn themselves with bumper stickers and campaign merchandise bearing the candidate's name. Meanwhile, the image should also convey a strong sense of movement and excitement, illustrating the surge in production and shipping of these items. In the background, the distinct landscape of Guam should be subtly integrated, dotted with an increase in fossil fuel use as shipments of campaign merchandise arrive, creating a politi-coal domino effect.

*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 Votes for the Democrat Presidential candidate in Florida caused Fossil fuel use in Guam to increase. Include one good pun.

AI academic paper

(Because p < 0.01)

Going Against the Current: Exploring the Surprising Relationship Between Votes for the Democrat Presidential Candidate in Florida and Fossil Fuel Use in Guam

The Journal of Political Geodynamics

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 Votes for the Democrat Presidential candidate in Florida and Fossil fuel use in Guam. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then you might move on to cartoons and children's shows that you watched for research.

Here is the title and abstract of the paper:
[[TITLE]]
"Going Against the Current: Exploring the Surprising Relationship Between Votes for the Democrat Presidential Candidate in Florida and Fossil Fuel Use in Guam"

[[ABSTRACT]]
This paper explores the perplexing relationship between votes for the Democrat presidential candidate in Florida and fossil fuel use in Guam. Despite being geographically distant and politically distinct, these two factors demonstrate an unexpected correlation. Utilizing data from MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, our research team uncovered a striking correlation coefficient of 0.9395069 and p < 0.01 for the years 1980 to 2020. The results highlight the need for further investigation into the potential influences at play, as well as the implications for understanding the interconnectedness of seemingly disparate phenomena.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The relationship between voting patterns in Florida and fossil fuel use in Guam is as unexpected as finding a pineapple on a pizza. One might initially dismiss the idea of any connection between these two geographically distant and politically distinct entities. However, much like the fusion of flavors in an unusual culinary pairing, our study revealed a surprising correlation that challenges conventional understanding.
While conventional wisdom suggests that voting preferences in a swing state like Florida and energy consumption in a small Pacific territory like Guam would have little in common, our research uncovered a rather robust association between the two. This unexpected correlation has raised eyebrows in the academic community and prompted us to delve deeper into the underlying dynamics driving this unlikely relationship.
This research aims to shed light on the hitherto unexplored links between political behavior in the contiguous United States and energy usage in an overseas territory. By employing rigorous statistical analyses and drawing on extensive datasets, we endeavor to map out the contours of this intriguing confluence and offer insights that may have far-reaching implications. Our findings beckon to be examined and, to borrow a phrase from the culinary world, leave us hungry for more understanding.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In their seminal work, "Election Dynamics and Energy Consumption: An Unlikely Affair," Smith et al. (2017) laid the groundwork for our exploration by hinting at the potential interplay between voting behavior and energy usage. Their initial observations, although subtle, set the stage for our own investigation into this peculiar relationship. Building upon this foundation, Doe and co-authors (2019) further probed into the nuanced connections between political preferences and environmental factors in "The Enigmatic Dance of Democracy and Fuel."

As we venture into the unexpected intersection of political voting patterns and fossil fuel utilization, we are compelled to consider the broader literature on interdisciplinary studies that challenge traditional assumptions. Jones' "Crossing Boundaries: Surprising Correlations in Divergent Domains" provides an insightful framework for interpreting the unanticipated connection between seemingly disparate variables. The work of Smith, Doe, and Jones constitutes essential reading for any scholar seeking to grasp the uncanny entanglement of voting choices and energy consumption.

Expanding our perspective beyond scholarly publications, it is intriguing to note the narrative parallels in non-fiction works such as "Energy Manifesto: Decoding the Political Powerplay" by Klein (2019) and "Election Alchemy: Unearthing Hidden Energies" by Patel (2020). These compelling analyses offer a literary segue into the fantastical realm of fiction, where the likes of "The Polls of Pandora" by Rowling (2007) and "The Carbon Chronicles" by Tolkien (1954) imaginatively explore the confluence of political agency and environmental impact.

Adding a touch of whimsy to our review, it is worth mentioning that the authors, in the pursuit of a comprehensive understanding, have also sought insights from popular culture, including animated television series such as "Captain Planet and the Planeteers" and "The Magic School Bus." While these sources may not offer direct empirical evidence, they serve as a reminder of the breadth of influences that can shape our perception of the world.

In summary, the literature presents a tapestry of perspectives, from scholarly discourse to imaginative musings, that collectively steer our quest to unravel the enigmatic bond between votes for the Democrat presidential candidate in Florida and fossil fuel use in Guam. As we proceed to analyze our own empirical findings, we remain mindful of the diverse intellectual tapestry that colors our understanding, proving that truth, much like a complex meal, can often be stranger than fiction.

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 Votes for the Democrat Presidential candidate in Florida and Fossil fuel use in Guam. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Limit your response to 500 tokens.

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

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

[[TITLE]]
"Going Against the Current: Exploring the Surprising Relationship Between Votes for the Democrat Presidential Candidate in Florida and Fossil Fuel Use in Guam"

[[ABSTRACT]]
This paper explores the perplexing relationship between votes for the Democrat presidential candidate in Florida and fossil fuel use in Guam. Despite being geographically distant and politically distinct, these two factors demonstrate an unexpected correlation. Utilizing data from MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, our research team uncovered a striking correlation coefficient of 0.9395069 and p < 0.01 for the years 1980 to 2020. The results highlight the need for further investigation into the potential influences at play, as well as the implications for understanding the interconnectedness of seemingly disparate phenomena.

[[LITERATURE REVIEW]]
In their seminal work, "Election Dynamics and Energy Consumption: An Unlikely Affair," Smith et al. (2017) laid the groundwork for our exploration by hinting at the potential interplay between voting behavior and energy usage. Their initial observations, although subtle, set the stage for our own investigation into this peculiar relationship. Building upon this foundation, Doe and co-authors (2019) further probed into the nuanced connections between political preferences and environmental factors in "The Enigmatic Dance of Democracy and Fuel."
As we venture into the unexpected intersection of political voting patterns and fossil fuel utilization, we are compelled to consider the broader literature on interdisciplinary studies that challenge traditional assumptions. Jones' "Crossing Boundaries: Surprising Correlations in Divergent Domains" provides an insightful framework for interpreting the unanticipated connection between seemingly disparate variables. The work of Smith, Doe, and Jones constitutes essential reading for any scholar seeking to grasp the uncanny entanglement of voting choices and energy consumption.
Expanding our perspective beyond scholarly publications, it is intriguing to note the narrative parallels in non-fiction works such as "Energy Manifesto: Decoding the Political Powerplay" by Klein (2019) and "Election Alchemy: Unearthing Hidden Energies" by Patel (2020). These compelling analyses offer a literary segue into the fantastical realm of fiction, where the likes of "The Polls of Pandora" by Rowling (2007) and "The Carbon Chronicles" by Tolkien (1954) imaginatively explore the confluence of political agency and environmental impact.
Adding a touch of whimsy to our review, it is worth mentioning that the authors, in the pursuit of a comprehensive understanding, have also sought insights from popular culture, including animated television series such as "Captain Planet and the Planeteers" and "The Magic School Bus." While these sources may not offer direct empirical evidence, they serve as a reminder of the breadth of influences that can shape our perception of the world.
In summary, the literature presents a tapestry of perspectives, from scholarly discourse to imaginative musings, that collectively steer our quest to unravel the enigmatic bond between votes for the Democrat presidential candidate in Florida and fossil fuel use in Guam. As we proceed to analyze our own empirical findings, we remain mindful of the diverse intellectual tapestry that colors our understanding, proving that truth, much like a complex meal, can often be stranger than fiction.

[[RESULTS]]
The analysis revealed a striking correlation coefficient of 0.9395069, with an r-squared value of 0.8826731, and a p-value less than 0.01, indicating a statistically significant relationship between votes for the Democrat presidential candidate in Florida and fossil fuel use in Guam. The strength of this correlation is as surprising as stumbling upon a pineapple on a pizza – unexpected, yet undeniably present.
Furthermore, the scatterplot depicted in Fig. 1 visually encapsulates this remarkable correlation, akin to the visual perplexity one experiences upon encountering an unexpected pairing. The robustness of this relationship challenges previous assumptions and underlines the need for deeper understanding of the underlying mechanisms at play.
The unexpected connection between these seemingly unrelated variables defies conventional wisdom, much like the unexpected harmony in a seemingly mismatched blend of flavors. Our findings beckon the need for further exploration into the potential social, economic, and political factors that may contribute to this unexpected relationship.
In conclusion, the surprising correlation between votes for the Democrat presidential candidate in Florida and fossil fuel use in Guam prompts us to re-evaluate our understanding of seemingly unconnected phenomena. This research opens the door to exploring new avenues of inquiry, much like the unexpected discovery of an unlikely combination that challenges preconceived notions.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of our empirical analysis have unveiled a perplexing and unexpected correlation between votes for the Democrat presidential candidate in Florida and fossil fuel use in Guam. Our results echo the subtle hints and suggestive whispers of prior scholarly works, such as the seminal contribution of Smith et al. (2017) and the inquisitive probing of Doe and co-authors (2019). The robust correlation coefficient we uncovered lends credence to the unanticipated connection between political voting patterns and fossil fuel utilization, thereby amplifying the resonance of these earlier scholarly conversations.

Delving deeper, our analysis not only reinforces the interconnectedness of seemingly disparate variables but also highlights the need to unravel the mysterious drivers underpinning this alliance. We embraced the unexpected nature of our findings, much like stumbling upon a surprise ingredient in a recipe – a discovery that challenges conventional taste profiles and prompts a re-evaluation of culinary expectations. In a similar vein, the statistical significance of the relationship between Democratic votes in Florida and fossil fuel use in Guam challenges established understandings, much like a quirky fusion cuisine that defies traditional gastronomic norms.

Nevertheless, the unexpected interplay between these distant and distinct variables sparks curiosity regarding the potential social and political forces at play. Our results beckon the need for a concerted effort to peel back the layers of this enigmatic correlation, much like unraveling a captivating mystery novel. Just as the revelation of the culprit in a whodunit elevates the reader's comprehension, unraveling the underlying mechanisms governing this unexpected relationship promises to enrich our understanding of the intricate dance between political agency and environmental impact.

In conclusion, our findings serve as a reminder that scholarly inquiry often mirrors life's capricious nature, presenting unexpected twists that challenge preconceived notions. Our research opens the door to a new realm of exploration, akin to embarking on a culinary adventure that defies traditional recipe boundaries, compelling us to question conventional thought patterns and embrace the unforeseen blending of seemingly incongruous elements.

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 Votes for the Democrat Presidential candidate in Florida and Fossil fuel use in Guam. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"Going Against the Current: Exploring the Surprising Relationship Between Votes for the Democrat Presidential Candidate in Florida and Fossil Fuel Use in Guam"

[[ABSTRACT]]
This paper explores the perplexing relationship between votes for the Democrat presidential candidate in Florida and fossil fuel use in Guam. Despite being geographically distant and politically distinct, these two factors demonstrate an unexpected correlation. Utilizing data from MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, our research team uncovered a striking correlation coefficient of 0.9395069 and p < 0.01 for the years 1980 to 2020. The results highlight the need for further investigation into the potential influences at play, as well as the implications for understanding the interconnectedness of seemingly disparate phenomena.

[[INTRDUCTION]]
The relationship between voting patterns in Florida and fossil fuel use in Guam is as unexpected as finding a pineapple on a pizza. One might initially dismiss the idea of any connection between these two geographically distant and politically distinct entities. However, much like the fusion of flavors in an unusual culinary pairing, our study revealed a surprising correlation that challenges conventional understanding.
While conventional wisdom suggests that voting preferences in a swing state like Florida and energy consumption in a small Pacific territory like Guam would have little in common, our research uncovered a rather robust association between the two. This unexpected correlation has raised eyebrows in the academic community and prompted us to delve deeper into the underlying dynamics driving this unlikely relationship.
This research aims to shed light on the hitherto unexplored links between political behavior in the contiguous United States and energy usage in an overseas territory. By employing rigorous statistical analyses and drawing on extensive datasets, we endeavor to map out the contours of this intriguing confluence and offer insights that may have far-reaching implications. Our findings beckon to be examined and, to borrow a phrase from the culinary world, leave us hungry for more understanding.

[[RESULTS]]
The analysis revealed a striking correlation coefficient of 0.9395069, with an r-squared value of 0.8826731, and a p-value less than 0.01, indicating a statistically significant relationship between votes for the Democrat presidential candidate in Florida and fossil fuel use in Guam. The strength of this correlation is as surprising as stumbling upon a pineapple on a pizza – unexpected, yet undeniably present.
Furthermore, the scatterplot depicted in Fig. 1 visually encapsulates this remarkable correlation, akin to the visual perplexity one experiences upon encountering an unexpected pairing. The robustness of this relationship challenges previous assumptions and underlines the need for deeper understanding of the underlying mechanisms at play.
The unexpected connection between these seemingly unrelated variables defies conventional wisdom, much like the unexpected harmony in a seemingly mismatched blend of flavors. Our findings beckon the need for further exploration into the potential social, economic, and political factors that may contribute to this unexpected relationship.
In conclusion, the surprising correlation between votes for the Democrat presidential candidate in Florida and fossil fuel use in Guam prompts us to re-evaluate our understanding of seemingly unconnected phenomena. This research opens the door to exploring new avenues of inquiry, much like the unexpected discovery of an unlikely combination that challenges preconceived notions.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

The unexpected correlation between votes for the Democrat presidential candidate in Florida and fossil fuel use in Guam has left researchers buoyed by a sense of bemusement. The statistically significant relationship between these disparate variables is as surprising as finding a coconut in Alaska – an unexpected occurrence that prompts a reevaluation of conventional perspectives. While the exact mechanisms driving this unlikely association remain as elusive as a tropical breeze on a snowy day, our study has laid bare the need for further investigation into the underlying forces at play, much like searching for buried treasure in uncharted waters.

The implications of this research extend beyond the field of political and energy economics, akin to the reverberations felt when a stone skips unexpectedly far across a still pond. This unlikely relationship challenges traditional paradigms, infusing the academic discourse with a sense of intellectual whimsy that is as surprising as discovering a hidden mural in an unexpected location. However, it may be that the discovery lies not in uncovering the mysterious connection itself, but in questioning why we were surprised in the first place.

The unexpected linkage between votes in Florida and energy use in Guam evokes a sense of curiosity that is as persistent as a tropical storm in the Pacific. It is clear that further research in this area is not only warranted but should be pursued with an eagerness that rivals that of a quest for an elusive recipe. However, this humorously unexpected association may ultimately serve as a reminder that, in the grand tapestry of research, the threads of inquiry are as serendipitous as stumbling upon a rare artifact in a most unusual location. Therefore, we assert that no further research is needed in this area.

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 Going Against the Current: Exploring the Surprising Relationship Between Votes for the Democrat Presidential Candidate in Florida and Fossil Fuel Use in Guam. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Political Geodynamics

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 Going Against the Current: Exploring the Surprising Relationship Between Votes for the Democrat Presidential Candidate in Florida and Fossil Fuel Use in Guam. Respond only with the name of a fictional organization.

ChatGPT:

The Astounding Institute for Interdisciplinary Research and Analysis

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 Going Against the Current: Exploring the Surprising Relationship Between Votes for the Democrat Presidential Candidate in Florida and Fossil Fuel Use in Guam with an abstract of This paper explores the perplexing relationship between votes for the Democrat presidential candidate in Florida and fossil fuel use in Guam. Despite being geographically distant and politically distinct, these two factors demonstrate an unexpected correlation. Utilizing data from MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, our research team uncovered a striking correlation coefficient of 0.9395069 and p < 0.01 for the years 1980 to 2020. The results highlight the need for further investigation into the potential influences at play, as well as the implications for understanding the interconnectedness of seemingly disparate phenomena.

ChatGPT:

Democrat presidential candidate, Florida votes, fossil fuel use, Guam correlation, MIT Election Data and Science Lab, Harvard Dataverse, Energy Information Administration, correlation coefficient, political geography, interconnected 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

Votes for the Democratic Presidential candidate in Florida
Detailed data title: Percentage of all votes cast for the Democrat Presidential candidate in Florida
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for the Democratic Presidential candidate in Florida

Fossil fuel use in Guam
Detailed data title: Total fossil fuel use in Guam in billion kWh
Source: Energy Information Administration
See what else correlates with Fossil fuel use in Guam

Correlation r = 0.9395069 (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 = 0.8826731 (Coefficient of determination)
This means 88.3% of the change in the one variable (i.e., Fossil fuel use in Guam) is predictable based on the change in the other (i.e., Votes for the Democratic Presidential candidate in Florida) over the 11 years from 1980 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.8E-5.^Show 0.0000176621719778293850000000
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 0.94 in 0.0018% of random cases. Said differently, if you correlated 56,618 random variables^Note You don't actually need 56 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 10 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 10 because we have two variables measured over a period of 11 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.

[ 0.78, 0.98 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: ^Note If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	1980	1984	1988	1992	1996	2000	2004	2008	2012	2016	2020
Votes for the Democratic Presidential candidate in Florida (Percentage of votes)	38.5002	34.6602	38.5072	38.9901	48.0256	48.8378	47.0911	51.0333	50.0079	47.8233	47.8615
Fossil fuel use in Guam (Billion kWh)	1.077	0.94	1.15929	1.4318	1.73498	1.84976	1.775	1.7643	1.684	1.656	1.6309

Why this works

Data dredging: I have 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([38.5002,34.6602,38.5072,38.9901,48.0256,48.8378,47.0911,51.0333,50.0079,47.8233,47.8615,])
array_2 = np.array([1.077,0.94,1.15929,1.4318,1.73498,1.84976,1.775,1.7643,1.684,1.656,1.6309,])
array_1_name = "Votes for the Democratic Presidential candidate in Florida"
array_2_name = "Fossil fuel use in Guam"

# 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)

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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.

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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 Votes for the Democratic Presidential candidate in Florida
Line chart for only Fossil fuel use in Guam
AI-generated correlation image
The spurious research paper: Going Against the Current: Exploring the Surprising Relationship Between Votes for the Democrat Presidential Candidate in Florida and Fossil Fuel Use in Guam

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Correlation ID: 5362 · Black Variable ID: 26076 · Red Variable ID: 23704


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