Spurious correlation #4,326 · 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 Republican Presidential candidate in Louisiana and the second variable is Biomass power generated in Taiwan. The chart goes from 1989 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 Republican Presidential candidate in Louisiana and the second variable is Biomass power generated in Taiwan. The chart goes from 1989 to 2020, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As more people in Louisiana voted for the Republican Presidential candidate, the collective support somehow generated an unexpected surge in political power, which was then harnessed and converted into energy. This energy was inexplicably transmitted all the way to Taiwan, where it was used to supercharge the biomass power production, creating a truly 'powerful' connection between American politics and renewable energy!

^{Show image generation prompt} Model: dalle-3
Prompt: Create an image that depicts a captivating transformation of vibrant energy flowing from a group of people in Louisiana, who are shown casting their votes for a Republican Presidential candidate. The energy should be illustrated as a dynamic surge radiating from the group. The scene then transitions to an abstract representation of Taiwan, where the incoming energy from the US is converted into a powerful force that fuels the biomass power production, depicted by a visually striking landscape with towering green energy turbines. The image should capture the seamless connection between the political support in Louisiana and the sustainable energy production in Taiwan, symbolizing the fusion of determination and renewable power. 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 Republican Presidential candidate in Louisiana and Biomass power generated in Taiwan. 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 TIME magazine cover image Data cyber art Pop art An image that looks like it is a scene from 'Star Wars' Cybernetic art
ChatGPT: A TIME magazine cover image

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 Republican Presidential candidate in Louisiana and Biomass power generated in Taiwan, in the following situation: As more people in Louisiana voted for the Republican Presidential candidate, the collective support somehow generated an unexpected surge in political power, which was then harnessed and converted into energy. This energy was inexplicably transmitted all the way to Taiwan, where it was used to supercharge the biomass power production, creating a truly 'powerful' connection between American politics and renewable energy!. Keep the description simple.
ChatGPT: In Louisiana, as more people voted for the Republican Presidential candidate, their collective support transformed into a vibrant surge of energy. This energy was then mysteriously channeled across the vast ocean to Taiwan. In Taiwan, the influx of energy boosted the biomass power production to new heights. It created an astonishing connection between American politics and renewable energy, symbolizing a fusion of political determination and sustainable power.

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: [ A TIME magazine cover image ] and depicting this scene: In Louisiana, as more people voted for the Republican Presidential candidate, their collective support transformed into a vibrant surge of energy. This energy was then mysteriously channeled across the vast ocean to Taiwan. In Taiwan, the influx of energy boosted the biomass power production to new heights. It created an astonishing connection between American politics and renewable energy, symbolizing a fusion of political determination and sustainable power. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create an image that depicts a captivating transformation of vibrant energy flowing from a group of people in Louisiana, who are shown casting their votes for a Republican Presidential candidate. The energy should be illustrated as a dynamic surge radiating from the group. The scene then transitions to an abstract representation of Taiwan, where the incoming energy from the US is converted into a powerful force that fuels the biomass power production, depicted by a visually striking landscape with towering green energy turbines. The image should capture the seamless connection between the political support in Louisiana and the sustainable energy production in Taiwan, symbolizing the fusion of determination and renewable power.

*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 Republican Presidential candidate in Louisiana caused Biomass power generated in Taiwan to increase. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

From Red States to Efficient Plates: A Biomass of Political Power in Louisiana and Taiwan

Journal of Cross-Cultural Energy Politics

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 Votes for the Republican Presidential candidate in Louisiana and Biomass power generated in Taiwan. 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. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading the backs of shampoo bottles.

Here is the title and abstract of the paper:
[[TITLE]]
"From Red States to Efficient Plates: A Biomass of Political Power in Louisiana and Taiwan"

[[ABSTRACT]]
In this research paper, we embark on an unconventional journey through the nexus of American politics and Taiwanese energy production to examine the surprising correlation between votes for the Republican presidential candidate in Louisiana and the generation of biomass power in Taiwan. Our research team delved into datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration to unravel this enigmatic connection that has baffled many scholars and pundits alike.

With a correlation coefficient of 0.9869037 and a statistically significant p-value of less than 0.01 across the years 1989 to 2020, the findings humorously point towards a peculiar parallel between the political leanings in one corner of the world and the renewable energy practices in another. Just as political ideologies can ignite fiery debates, it appears they may also have a curious influence on the generation of sustainable power sources. With a bit of wit and statistical rigor, our academic pursuit reveals that sometimes the most unexpected correlations can unveil the humor and quirks of the data world, providing valuable insight into the interconnectedness of seemingly unrelated phenomena.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
As we embark on this research journey, we aim to shed light on a correlation that is as unexpected as finding a vegan food truck at a Texas BBQ festival. Our investigation led us to the intriguing connection between the political landscape in Louisiana and the generation of biomass power in Taiwan. It's like stumbling upon a hidden treasure map in the footnotes of a political science textbook – a surprising, yet exhilarating discovery.
The field of political science often has the appeal of a dry martini, while the realm of energy production can feel as thrilling as watching paint dry. Yet, in this unconventional study, we ventured to mix these seemingly unrelated domains like a bartender experimenting with new cocktail recipes. The results? A blend of statistical significance and unexpected correlations that tickled our academic sensibilities and left us chuckling at the whimsy of data analysis.
Picture this: Republican voters in the bayous of Louisiana unknowingly wielding influence on the bioenergy practices in the lush landscapes of Taiwan. It's as if a butterfly flapping its wings in Baton Rouge could spark a renewable energy revolution in Taipei. Who would have thought that political leanings in the land of Mardi Gras could have such a surprising impact on the generation of sustainable power halfway across the globe?
In the spirit of scholarly inquiry, we approached this investigation with rigor, but not without a healthy dose of humor. After all, what's the point of delving into data if you can't crack a joke or two along the way? So, join us as we unravel the antics of the data world, where the unexpected thrives and the conventional norms of correlation take a back seat to the whimsy of statistical analysis.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "Smith et al. (2015)," the authors find a positive correlation between votes for the Republican presidential candidate in Louisiana and the generation of biomass power in Taiwan. This unexpected link challenges conventional wisdom and tickles the fancy of political pundits and energy enthusiasts alike. As we delve deeper into this whimsical connection, it's akin to stumbling upon a hidden banana in a fruit salad - surprising, yet oddly satisfying.

Further insights from "Doe and Jones (2017)" reveal a statistically significant relationship between political affiliations in Louisiana and the production of biomass energy in Taiwan. It's like discovering a unicorn in the wild - rare, magical, and altogether delightful. The authors' findings serve as a refreshing reminder that the world of data analysis is not without its share of unexpected surprises and humorous curiosities.

As we navigate through this labyrinth of unlikely associations, we consult real-world literature to juxtapose our academic findings. In "The Politics of Pellets: A Comprehensive Study of Biomass Power," the authors delve into the intricate web of political influences on the biomass energy sector, providing a serious backdrop to our lighthearted exploration. However, in "Biomass and Bayous: The Intrigue of Political Energy," the authors humorously dance through the marshy realms of Louisiana politics and its whimsical effects on faraway lands, presenting a playful counterpoint to the scholarly discourse.

Turning the page to fictional accounts, works such as "The Republican Rainbow: A Political Odyssey" and "Taiwan's Tales of Biomass" add a touch of whimsy to our academic pursuits, reminding us that truth can be stranger than fiction, and correlation may indeed be found in the most unexpected places.

And now, as we dive headfirst into the depths of unconventional research practices, we humorously confess that our literature review extended to the backs of shampoo bottles, where we gleaned insights into the surprising correlations between herbal essences and political preferences. Who knew that lathering up with lavender-scented shampoo could lead to an electoral shift? The conundrums of correlation never cease to amuse, and our scholarly journey takes us to unpredictable, and sometimes downright ridiculous, destinations.

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 Votes for the Republican Presidential candidate in Louisiana and Biomass power generated in Taiwan. Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from MIT Election Data and Science Lab, Harvard Dataverse and Energy Information Administration .

For the time period 1989 to 2020, you found a correlation 0.9869037, r-squared of 0.9739789, 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]]
"From Red States to Efficient Plates: A Biomass of Political Power in Louisiana and Taiwan"

[[ABSTRACT]]
In this research paper, we embark on an unconventional journey through the nexus of American politics and Taiwanese energy production to examine the surprising correlation between votes for the Republican presidential candidate in Louisiana and the generation of biomass power in Taiwan. Our research team delved into datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration to unravel this enigmatic connection that has baffled many scholars and pundits alike.

With a correlation coefficient of 0.9869037 and a statistically significant p-value of less than 0.01 across the years 1989 to 2020, the findings humorously point towards a peculiar parallel between the political leanings in one corner of the world and the renewable energy practices in another. Just as political ideologies can ignite fiery debates, it appears they may also have a curious influence on the generation of sustainable power sources. With a bit of wit and statistical rigor, our academic pursuit reveals that sometimes the most unexpected correlations can unveil the humor and quirks of the data world, providing valuable insight into the interconnectedness of seemingly unrelated phenomena.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
As we embark on this research journey, we aim to shed light on a correlation that is as unexpected as finding a vegan food truck at a Texas BBQ festival. Our investigation led us to the intriguing connection between the political landscape in Louisiana and the generation of biomass power in Taiwan. It's like stumbling upon a hidden treasure map in the footnotes of a political science textbook – a surprising, yet exhilarating discovery.
The field of political science often has the appeal of a dry martini, while the realm of energy production can feel as thrilling as watching paint dry. Yet, in this unconventional study, we ventured to mix these seemingly unrelated domains like a bartender experimenting with new cocktail recipes. The results? A blend of statistical significance and unexpected correlations that tickled our academic sensibilities and left us chuckling at the whimsy of data analysis.
Picture this: Republican voters in the bayous of Louisiana unknowingly wielding influence on the bioenergy practices in the lush landscapes of Taiwan. It's as if a butterfly flapping its wings in Baton Rouge could spark a renewable energy revolution in Taipei. Who would have thought that political leanings in the land of Mardi Gras could have such a surprising impact on the generation of sustainable power halfway across the globe?
In the spirit of scholarly inquiry, we approached this investigation with rigor, but not without a healthy dose of humor. After all, what's the point of delving into data if you can't crack a joke or two along the way? So, join us as we unravel the antics of the data world, where the unexpected thrives and the conventional norms of correlation take a back seat to the whimsy of statistical analysis.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The results of our analysis unveil a correlation of 0.9869037 between the votes for the Republican presidential candidate in Louisiana and the generation of biomass power in Taiwan. This correlation is about as strong as a Louisiana gumbo seasoned with statistical significance and served with a side of Taiwanese tofu. With an r-squared value of 0.9739789, it's safe to say that the connection is as robust as a Cajun spice blend, leaving little room for doubt about the association between these seemingly disparate variables.

The scatterplot (Fig. 1) included in this paper elegantly visualizes the strong relationship between these two variables. It's like witnessing a dance-off between American red states and Taiwanese green energy, with the data points grooving to the beat of statistical significance. Who knew that political preferences and sustainable energy could make such unlikely dance partners? But here they are, twirling across the graph in a harmonious spectacle of data dynamics.

Furthermore, the p-value of less than 0.01 adds a delightful cherry on top of this statistical sundae, confirming that the correlation is not just a fluke but a bona fide discovery that has caught the attention of our research team. It's as if the data itself is winking at us, nudging us to acknowledge that sometimes, truth is indeed stranger than fiction – or in this case, stranger than political punditry and energy policy combined.

In conclusion, our findings provide compelling evidence of a connection between political dynamics in Louisiana and the renewable energy landscape in Taiwan. Just as the "Gumbo Capital of the World" exerts its influence on American politics, it seems to have a quirky, albeit statistically significant, impact on energy practices across the Pacific. This research not only illuminates an intriguing correlation but also invites scholars and enthusiasts alike to marvel at the captivating unpredictability of data, where the unlikeliest of pairs can set the stage for meaningful 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 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 Republican Presidential candidate in Louisiana and Biomass power generated in Taiwan. 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]]
"From Red States to Efficient Plates: A Biomass of Political Power in Louisiana and Taiwan"

[[ABSTRACT]]
In this research paper, we embark on an unconventional journey through the nexus of American politics and Taiwanese energy production to examine the surprising correlation between votes for the Republican presidential candidate in Louisiana and the generation of biomass power in Taiwan. Our research team delved into datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration to unravel this enigmatic connection that has baffled many scholars and pundits alike.

With a correlation coefficient of 0.9869037 and a statistically significant p-value of less than 0.01 across the years 1989 to 2020, the findings humorously point towards a peculiar parallel between the political leanings in one corner of the world and the renewable energy practices in another. Just as political ideologies can ignite fiery debates, it appears they may also have a curious influence on the generation of sustainable power sources. With a bit of wit and statistical rigor, our academic pursuit reveals that sometimes the most unexpected correlations can unveil the humor and quirks of the data world, providing valuable insight into the interconnectedness of seemingly unrelated phenomena.

[[LITERATURE REVIEW]]
In "Smith et al. (2015)," the authors find a positive correlation between votes for the Republican presidential candidate in Louisiana and the generation of biomass power in Taiwan. This unexpected link challenges conventional wisdom and tickles the fancy of political pundits and energy enthusiasts alike. As we delve deeper into this whimsical connection, it's akin to stumbling upon a hidden banana in a fruit salad - surprising, yet oddly satisfying.
Further insights from "Doe and Jones (2017)" reveal a statistically significant relationship between political affiliations in Louisiana and the production of biomass energy in Taiwan. It's like discovering a unicorn in the wild - rare, magical, and altogether delightful. The authors' findings serve as a refreshing reminder that the world of data analysis is not without its share of unexpected surprises and humorous curiosities.
As we navigate through this labyrinth of unlikely associations, we consult real-world literature to juxtapose our academic findings. In "The Politics of Pellets: A Comprehensive Study of Biomass Power," the authors delve into the intricate web of political influences on the biomass energy sector, providing a serious backdrop to our lighthearted exploration. However, in "Biomass and Bayous: The Intrigue of Political Energy," the authors humorously dance through the marshy realms of Louisiana politics and its whimsical effects on faraway lands, presenting a playful counterpoint to the scholarly discourse.
Turning the page to fictional accounts, works such as "The Republican Rainbow: A Political Odyssey" and "Taiwan's Tales of Biomass" add a touch of whimsy to our academic pursuits, reminding us that truth can be stranger than fiction, and correlation may indeed be found in the most unexpected places.
And now, as we dive headfirst into the depths of unconventional research practices, we humorously confess that our literature review extended to the backs of shampoo bottles, where we gleaned insights into the surprising correlations between herbal essences and political preferences. Who knew that lathering up with lavender-scented shampoo could lead to an electoral shift? The conundrums of correlation never cease to amuse, and our scholarly journey takes us to unpredictable, and sometimes downright ridiculous, destinations.

[[RESULTS]]
The results of our analysis unveil a correlation of 0.9869037 between the votes for the Republican presidential candidate in Louisiana and the generation of biomass power in Taiwan. This correlation is about as strong as a Louisiana gumbo seasoned with statistical significance and served with a side of Taiwanese tofu. With an r-squared value of 0.9739789, it's safe to say that the connection is as robust as a Cajun spice blend, leaving little room for doubt about the association between these seemingly disparate variables.
The scatterplot (Fig. 1) included in this paper elegantly visualizes the strong relationship between these two variables. It's like witnessing a dance-off between American red states and Taiwanese green energy, with the data points grooving to the beat of statistical significance. Who knew that political preferences and sustainable energy could make such unlikely dance partners? But here they are, twirling across the graph in a harmonious spectacle of data dynamics.
Furthermore, the p-value of less than 0.01 adds a delightful cherry on top of this statistical sundae, confirming that the correlation is not just a fluke but a bona fide discovery that has caught the attention of our research team. It's as if the data itself is winking at us, nudging us to acknowledge that sometimes, truth is indeed stranger than fiction – or in this case, stranger than political punditry and energy policy combined.
In conclusion, our findings provide compelling evidence of a connection between political dynamics in Louisiana and the renewable energy landscape in Taiwan. Just as the "Gumbo Capital of the World" exerts its influence on American politics, it seems to have a quirky, albeit statistically significant, impact on energy practices across the Pacific. This research not only illuminates an intriguing correlation but also invites scholars and enthusiasts alike to marvel at the captivating unpredictability of data, where the unlikeliest of pairs can set the stage for meaningful revelations.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Delving into the enigmatic connection between Louisiana red states and Taiwanese green energy, our study hilariously unveils a correlation that's as strong as a beignet’s aroma wafting through a French Quarter jazz club. As we harken back to the whimsical insights from "Smith et al. (2015)" and "Doe and Jones (2017)," it's clear that our findings dance a merry jig in tune with prior research, much like a Mardi Gras parade through the streets of academic scholarship.

The statistical significance we uncovered is about as unexpected as catching a crawfish in the Yangtze River - a delightful surprise that underscores the interconnectedness of seemingly unrelated phenomena. Our results not only support, but shimmy hand-in-hand with the previous studies, much like a funky Louisiana zydeco band finding its groove alongside a Taiwanese tai-chi master.

The scatterplot in Figure 1 may just be the most unexpected dance-off since Napoleon Dynamite busted out his moves - with American red states and Taiwanese green energy twirling across the graph in a display of statistical significance that can only be described as epic. It’s as if the data points are performing a delightful ballet, gracefully illustrating the unlikely yet robust correlation between political leanings in the Bayou State and the sustainable energy practices in the Far East.

The p-value of less than 0.01 is like finding a four-leaf clover in a field of data - a rare, charming validation that our results are not just a statistical fling but a genuine, long-term relationship between these variables. It's as if the data itself is inviting us to join in on the revelry, nudging us to acknowledge the enchanting dance between political preferences and green energy generation.

In this academic pursuit, we've captured the whimsy and humor of uncovering unexpected correlations, proving that truth can indeed be wackier than fiction. Our findings don't just enlighten, they entertain, beckoning scholars and enthusiasts to revel in the merry twists and turns of the data world, where even the most improbable duos can steal the show and leave us grinning in delight. And if this study is anything, it's a lighthearted celebration of the captivating unpredictability of data, where the unlikeliest of couples can tango their way into compelling 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 Votes for the Republican Presidential candidate in Louisiana and Biomass power generated in Taiwan. 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]]
"From Red States to Efficient Plates: A Biomass of Political Power in Louisiana and Taiwan"

[[ABSTRACT]]
In this research paper, we embark on an unconventional journey through the nexus of American politics and Taiwanese energy production to examine the surprising correlation between votes for the Republican presidential candidate in Louisiana and the generation of biomass power in Taiwan. Our research team delved into datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration to unravel this enigmatic connection that has baffled many scholars and pundits alike.

With a correlation coefficient of 0.9869037 and a statistically significant p-value of less than 0.01 across the years 1989 to 2020, the findings humorously point towards a peculiar parallel between the political leanings in one corner of the world and the renewable energy practices in another. Just as political ideologies can ignite fiery debates, it appears they may also have a curious influence on the generation of sustainable power sources. With a bit of wit and statistical rigor, our academic pursuit reveals that sometimes the most unexpected correlations can unveil the humor and quirks of the data world, providing valuable insight into the interconnectedness of seemingly unrelated phenomena.

[[INTRDUCTION]]
As we embark on this research journey, we aim to shed light on a correlation that is as unexpected as finding a vegan food truck at a Texas BBQ festival. Our investigation led us to the intriguing connection between the political landscape in Louisiana and the generation of biomass power in Taiwan. It's like stumbling upon a hidden treasure map in the footnotes of a political science textbook – a surprising, yet exhilarating discovery.
The field of political science often has the appeal of a dry martini, while the realm of energy production can feel as thrilling as watching paint dry. Yet, in this unconventional study, we ventured to mix these seemingly unrelated domains like a bartender experimenting with new cocktail recipes. The results? A blend of statistical significance and unexpected correlations that tickled our academic sensibilities and left us chuckling at the whimsy of data analysis.
Picture this: Republican voters in the bayous of Louisiana unknowingly wielding influence on the bioenergy practices in the lush landscapes of Taiwan. It's as if a butterfly flapping its wings in Baton Rouge could spark a renewable energy revolution in Taipei. Who would have thought that political leanings in the land of Mardi Gras could have such a surprising impact on the generation of sustainable power halfway across the globe?
In the spirit of scholarly inquiry, we approached this investigation with rigor, but not without a healthy dose of humor. After all, what's the point of delving into data if you can't crack a joke or two along the way? So, join us as we unravel the antics of the data world, where the unexpected thrives and the conventional norms of correlation take a back seat to the whimsy of statistical analysis.

[[RESULTS]]
The results of our analysis unveil a correlation of 0.9869037 between the votes for the Republican presidential candidate in Louisiana and the generation of biomass power in Taiwan. This correlation is about as strong as a Louisiana gumbo seasoned with statistical significance and served with a side of Taiwanese tofu. With an r-squared value of 0.9739789, it's safe to say that the connection is as robust as a Cajun spice blend, leaving little room for doubt about the association between these seemingly disparate variables.
The scatterplot (Fig. 1) included in this paper elegantly visualizes the strong relationship between these two variables. It's like witnessing a dance-off between American red states and Taiwanese green energy, with the data points grooving to the beat of statistical significance. Who knew that political preferences and sustainable energy could make such unlikely dance partners? But here they are, twirling across the graph in a harmonious spectacle of data dynamics.
Furthermore, the p-value of less than 0.01 adds a delightful cherry on top of this statistical sundae, confirming that the correlation is not just a fluke but a bona fide discovery that has caught the attention of our research team. It's as if the data itself is winking at us, nudging us to acknowledge that sometimes, truth is indeed stranger than fiction – or in this case, stranger than political punditry and energy policy combined.
In conclusion, our findings provide compelling evidence of a connection between political dynamics in Louisiana and the renewable energy landscape in Taiwan. Just as the "Gumbo Capital of the World" exerts its influence on American politics, it seems to have a quirky, albeit statistically significant, impact on energy practices across the Pacific. This research not only illuminates an intriguing correlation but also invites scholars and enthusiasts alike to marvel at the captivating unpredictability of data, where the unlikeliest of pairs can set the stage for meaningful revelations.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In the grand symphony of data analysis, our research has uncovered a harmonious duet between the political pulse of Louisiana and the renewable energy rhythm of Taiwan. Who would have thought that the red of Republican votes could tango so gracefully with the green of biomass power? It's like witnessing a dance-off between a crawfish boil and a tea ceremony - unexpected, yet strangely captivating.

As we wrap up this scholarly escapade, it's clear that the statistical waltz between these variables is no mere happenstance. It's like that perfect recipe where all the ingredients come together - a dash of partisanship, a sprinkle of sustainable energy policies, and a pinch of global interconnectedness. We've shown that data analysis can be as delightful as a surprise Mardi Gras parade, where the unexpected becomes the main attraction.

In light of these captivating findings, it's safe to say that the connection between Louisiana's political landscape and Taiwan's energy production is no longer an enigma but a whimsical revelation. So, as we bid adieu to this lighthearted foray into the data world, we assert with confidence that further research in this area is about as necessary as a sack of crawfish at a vegetarian convention. Our findings stand as a testament to the delightful unpredictability of data analysis, where the quirks and surprises are just waiting to be uncovered.

In the wise words of Mark Twain, "Truth is stranger than fiction, but it is because Fiction is obliged to stick to possibilities; Truth isn't." With that, we can confidently declare that this peculiar correlation is no longer just a possibility, but a delightful truth that adds a touch of humor and wonder to the scholarly pursuit. Thus, no further research is needed in this area, but laughter and amusement are always welcome.

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 From Red States to Efficient Plates: A Biomass of Political Power in Louisiana and Taiwan. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Cross-Cultural Energy Politics

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 From Red States to Efficient Plates: A Biomass of Political Power in Louisiana and Taiwan. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Comparative Politics and Culinary Sustainability

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 From Red States to Efficient Plates: A Biomass of Political Power in Louisiana and Taiwan with an abstract of In this research paper, we embark on an unconventional journey through the nexus of American politics and Taiwanese energy production to examine the surprising correlation between votes for the Republican presidential candidate in Louisiana and the generation of biomass power in Taiwan. Our research team delved into datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration to unravel this enigmatic connection that has baffled many scholars and pundits alike.
With a correlation coefficient of 0.9869037 and a statistically significant p-value of less than 0.01 across the years 1989 to 2020, the findings humorously point towards a peculiar parallel between the political leanings in one corner of the world and the renewable energy practices in another. Just as political ideologies can ignite fiery debates, it appears they may also have a curious influence on the generation of sustainable power sources. With a bit of wit and statistical rigor, our academic pursuit reveals that sometimes the most unexpected correlations can unveil the humor and quirks of the data world, providing valuable insight into the interconnectedness of seemingly unrelated phenomena.

ChatGPT:

Red States, Louisiana, Taiwan, biomass power, Republican presidential candidate, correlation, energy production, MIT Election Data and Science Lab, Harvard Dataverse, Energy Information Administration, renewable energy, political influence, statistical rigor, interconnectedness of 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 Republican Presidential candidate in Louisiana
Detailed data title: Total votes cast for the Republican Presidential candidate in Louisiana
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for the Republican Presidential candidate in Louisiana

Biomass power generated in Taiwan
Detailed data title: Total biomass power generated in Taiwan in billion kWh
Source: Energy Information Administration
See what else correlates with Biomass power generated in Taiwan

Correlation r = 0.9869037 (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 = 0.9739789 (Coefficient of determination)
This means 97.4% of the change in the one variable (i.e., Biomass power generated in Taiwan) is predictable based on the change in the other (i.e., Votes for the Republican Presidential candidate in Louisiana) over the 8 years from 1989 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 5.6E-6.^Show 0.0000055604684235995450000000
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 0.99 in 0.00056% of random cases. Said differently, if you correlated 179,841 random variables^Note You don't actually need 179 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 7 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 7 because we have two variables measured over a period of 8 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.

[ 0.93, 1 ] 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.

	1992	1996	2000	2004	2008	2012	2016	2020
Votes for the Republican Presidential candidate in Louisiana (Total votes)	733386	712586	927871	1102170	1148280	1152260	1178640	1255780
Biomass power generated in Taiwan (Billion kWh)	0.275204	0.690862	1.79272	3.3493	3.36123	3.4192	3.60197	3.75309

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.
Very low n: There are not many data points included in this analysis. Even if the p-value is high, we should be suspicious of using so few datapoints in a correlation.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

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([733386,712586,927871,1102170,1148280,1152260,1178640,1255780,])
array_2 = np.array([0.275204,0.690862,1.79272,3.3493,3.36123,3.4192,3.60197,3.75309,])
array_1_name = "Votes for the Republican Presidential candidate in Louisiana"
array_2_name = "Biomass power generated in Taiwan"

# 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 Votes for the Republican Presidential candidate in Louisiana
Line chart for only Biomass power generated in Taiwan
AI-generated correlation image
The spurious research paper: From Red States to Efficient Plates: A Biomass of Political Power in Louisiana and Taiwan

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Correlation ID: 4326 · Black Variable ID: 25985 · Red Variable ID: 24024


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