Spurious correlation #5,475 · 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 Libertarian Presidential candidate in Maryland and the second variable is Biomass power generated in Uganda. The chart goes from 2000 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 Libertarian Presidential candidate in Maryland and the second variable is Biomass power generated in Uganda. The chart goes from 2000 to 2020, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As more people in Maryland supported the Libertarian candidate, it created a surge in 'vot-ality.' This increase in 'vot-um' somehow resonated across the oceans and sparked a corresponding 'power-itical' movement in Uganda. The connection between political preferences in Maryland and renewable energy output in Uganda may seem 'p-odd' at first, but it just goes to show that when it comes to global influence, every ballot counts!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a bustling city street in Maryland, with voters proudly sporting "Libertarian for President" stickers. The scene should convey the excitement and energy of their collective support for the Libertarian candidate, with a pulsating 'vot-ality' charging the atmosphere. In the background, an imaginative transition across the ocean to a biomass power plant in Uganda humming with increased activity, depicting the generators producing a greater amount of clean and renewable energy. Emphasize an invisible connection or thread linking the scenes to show how the surge of 'vot-um' in Maryland is resonating across the ocean, sparking a 'power-itical' movement that mirrors the growing support for the Libertarian candidate. The image should convey the unlikely yet impactful link between the two locations due to political preferences, showcasing the influence of ballots and political movements on global impact. 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 Libertarian Presidential candidate in Maryland and Biomass power generated in Uganda. 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: Celtic art An image that looks like it is a scene from 'Star Wars' A scene from Skyrim Oragami Scalar art
ChatGPT: A scene from 'Star Wars'

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Votes for the Libertarian Presidential candidate in Maryland and Biomass power generated in Uganda, in the following situation: As more people in Maryland supported the Libertarian candidate, it created a surge in 'vot-ality.' This increase in 'vot-um' somehow resonated across the oceans and sparked a corresponding 'power-itical' movement in Uganda. The connection between political preferences in Maryland and renewable energy output in Uganda may seem 'p-odd' at first, but it just goes to show that when it comes to global influence, every ballot counts!. Keep the description simple.
ChatGPT: In Maryland, a growing number of voters proudly sport "Libertarian for President" stickers as they participate in the electoral process. The excitement and energy of their collective support seem to ripple through the air, creating a tangible 'vot-ality' that charges the atmosphere. Meanwhile, in Uganda, a biomass power plant hums with increased activity, its generators producing a greater amount of clean and renewable energy. As if connected by an invisible thread, the surge of 'vot-um' in Maryland resonates across the ocean, sparking a 'power-itical' movement that mirrors the growing support for the Libertarian candidate. The unlikely link between the two may seem 'p-odd' at first, but it becomes clear that the influence of political preferences in Maryland has somehow sparked a corresponding increase in renewable energy output in Uganda. The scene conveys the message that every ballot and every political movement, no matter how far apart, has the power to shape global impact.

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 scene from 'Star Wars' ] and depicting this scene: In Maryland, a growing number of voters proudly sport "Libertarian for President" stickers as they participate in the electoral process. The excitement and energy of their collective support seem to ripple through the air, creating a tangible 'vot-ality' that charges the atmosphere. Meanwhile, in Uganda, a biomass power plant hums with increased activity, its generators producing a greater amount of clean and renewable energy. As if connected by an invisible thread, the surge of 'vot-um' in Maryland resonates across the ocean, sparking a 'power-itical' movement that mirrors the growing support for the Libertarian candidate. The unlikely link between the two may seem 'p-odd' at first, but it becomes clear that the influence of political preferences in Maryland has somehow sparked a corresponding increase in renewable energy output in Uganda. The scene conveys the message that every ballot and every political movement, no matter how far apart, has the power to shape global impact. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a bustling city street in Maryland, with voters proudly sporting "Libertarian for President" stickers. The scene should convey the excitement and energy of their collective support for the Libertarian candidate, with a pulsating 'vot-ality' charging the atmosphere. In the background, an imaginative transition across the ocean to a biomass power plant in Uganda humming with increased activity, depicting the generators producing a greater amount of clean and renewable energy. Emphasize an invisible connection or thread linking the scenes to show how the surge of 'vot-um' in Maryland is resonating across the ocean, sparking a 'power-itical' movement that mirrors the growing support for the Libertarian candidate. The image should convey the unlikely yet impactful link between the two locations due to political preferences, showcasing the influence of ballots and political movements on global impact.

*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 Libertarian Presidential candidate in Maryland caused Biomass power generated in Uganda to increase. Include one good pun.

AI academic paper

(Because p < 0.01)

From Libertarian Leaning to Lively Lignin: Unveiling the Unanticipated Link Between Votes for the Libertarian Presidential Candidate in Maryland and Biomass Power Generated in Uganda

The Journal of Eclectic Ecological Economics

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 Libertarian Presidential candidate in Maryland and Biomass power generated in Uganda. 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 cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
"From Libertarian Leaning to Lively Lignin: Unveiling the Unanticipated Link Between Votes for the Libertarian Presidential Candidate in Maryland and Biomass Power Generated in Uganda"

[[ABSTRACT]]
This research paper presents the unexpected relationship between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda. Using data from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration for the years 2000 to 2020, our research team discovered a remarkably high correlation coefficient of 0.9780902 with a p-value of less than 0.01.

The findings shed light on the intricate, underlying mechanisms that connect political preferences and energy generation half a world apart. Despite the geographic and cultural divide, the results suggest a strong association that goes beyond mere coincidence. The implications of this study not only challenge traditional assumptions but also invite further investigation into the intertwined nature of seemingly unrelated phenomena.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of politics and energy generation has been a subject of considerable academic interest in recent years. However, the connection between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda represents a particularly unexpected and, some might say, eccentric area of investigation. While pundits and policymakers may initially scoff at the notion of any correlation, our study aims to demonstrate the surprising harmony between these ostensibly disparate phenomena.
The data collection process, like a scavenger hunt through the digital archives, involved sourcing information from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration. The meticulous assembly of these datasets laid the foundation for our analysis, which revealed a correlation coefficient that would make statisticians raise an intrigued eyebrow – 0.9780902, coupled with a diminutive p-value of less than 0.01.
In scrutinizing the statistical relationship between votes cast in the Free State and the generation of biomass energy in the "Pearl of Africa," our findings challenge conventional wisdom and encourage readers to cast a more discerning eye on the intricate web of global interconnections. While some may view this research as a mere statistical oddity, the implications stretch far beyond the confines of conventional wisdom, beckoning scholars and policymakers alike to delve deeper into the bedrock of unexpected relationships.
This paper aims to unravel the enigmatic coalescence of voter behavior in Maryland and the utilization of biomass power in Uganda. By shedding light on this serendipitous connection, we invite readers to reassess their assumptions regarding associations between political preferences and energy generation, demonstrating that sometimes, analytical inquiry can unearth the most improbable of bedfellows.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "A Statistical Analysis of Political Behavior" by Smith, the authors find numerous correlations between political preferences and various socioeconomic factors, but fail to mention any connection to biomass power generated in Uganda. In "Energy Generation and Sustainable Development" by Doe, the authors examine the global landscape of renewable energy sources, yet make no mention of the Libertarian presidential candidate in Maryland. Similarly, Jones, in "The Complex Dynamics of Political Ecology," overlooks any potential link between political voting patterns in the United States and biomass power generation in Uganda.

Turning to non-fiction books, "Biomass Energy for a Sustainable Future" and "The Subtle Art of Political Punditry" delve into separate aspects of the two subjects at hand, but do not address any potential relationship between them. On the other hand, fictional works such as "Libertarian Dreams in the Land of Biomass" and "The Biomass Conspiracy: A Political Thriller" offer imaginative, albeit unrelated, narratives that capture the reader's attention.

In the realm of social media, a tweet from @BiomassEnthusiast claims, "The political leanings of Maryland voters certainly impact global energy dynamics more than we realize," hinting at a possible connection. Another post from @Libertarian4Eva reads, "You'd be surprised how much your vote in Maryland affects energy decisions around the world," further fueling speculation regarding the unexpected link between the two seemingly incongruous phenomena.

The dearth of empirical evidence in the existing literature regarding the relationship between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda underscores the novelty and significance of the present study. While previous inquiries have overlooked this unprecedented connection, this paper seeks to fill this scholarly gap and unravel the intriguing juncture between political behavior and energy generation.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between Votes for the Libertarian Presidential candidate in Maryland and Biomass power generated in Uganda. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 . You used data from 2000 to 2020

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"From Libertarian Leaning to Lively Lignin: Unveiling the Unanticipated Link Between Votes for the Libertarian Presidential Candidate in Maryland and Biomass Power Generated in Uganda"

[[ABSTRACT]]
This research paper presents the unexpected relationship between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda. Using data from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration for the years 2000 to 2020, our research team discovered a remarkably high correlation coefficient of 0.9780902 with a p-value of less than 0.01.

The findings shed light on the intricate, underlying mechanisms that connect political preferences and energy generation half a world apart. Despite the geographic and cultural divide, the results suggest a strong association that goes beyond mere coincidence. The implications of this study not only challenge traditional assumptions but also invite further investigation into the intertwined nature of seemingly unrelated phenomena.

[[INTRODUCTION]]
The intersection of politics and energy generation has been a subject of considerable academic interest in recent years. However, the connection between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda represents a particularly unexpected and, some might say, eccentric area of investigation. While pundits and policymakers may initially scoff at the notion of any correlation, our study aims to demonstrate the surprising harmony between these ostensibly disparate phenomena.
The data collection process, like a scavenger hunt through the digital archives, involved sourcing information from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration. The meticulous assembly of these datasets laid the foundation for our analysis, which revealed a correlation coefficient that would make statisticians raise an intrigued eyebrow – 0.9780902, coupled with a diminutive p-value of less than 0.01.
In scrutinizing the statistical relationship between votes cast in the Free State and the generation of biomass energy in the "Pearl of Africa," our findings challenge conventional wisdom and encourage readers to cast a more discerning eye on the intricate web of global interconnections. While some may view this research as a mere statistical oddity, the implications stretch far beyond the confines of conventional wisdom, beckoning scholars and policymakers alike to delve deeper into the bedrock of unexpected relationships.
This paper aims to unravel the enigmatic coalescence of voter behavior in Maryland and the utilization of biomass power in Uganda. By shedding light on this serendipitous connection, we invite readers to reassess their assumptions regarding associations between political preferences and energy generation, demonstrating that sometimes, analytical inquiry can unearth the most improbable of bedfellows.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To unravel the perplexing relationship between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda, a multi-faceted and eclectic blend of research methods was employed. Beginning with a digital sleuthing expedition, data was meticulously harvested from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration. This process was akin to navigating a labyrinth of information, with each dataset serving as a potential clue in our quest for correlation.

The data, spanning the years 2000 to 2020, was akin to a historical quilt of political leanings and energy output, providing a diverse tapestry of information to weave into our analysis. Once the data was gathered, it underwent a thorough scrubbing and grooming process to ensure its suitability for statistical interrogation.

Following this, the data underwent a metaphorical tango with statistical software, where a complex dance of correlation analysis, regression modeling, and hypothesis testing took place. This statistical soirée was not for the faint of heart, as we navigated the intricacies of multivariate analysis and regression diagnostics to extract meaningful insights from the dataset.

The correlation coefficient, akin to the maestro of a symphony orchestra, wielded its numerical baton, revealing a stunning harmony between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda, represented by a correlation coefficient of 0.9780902. The p-value, serving as the discerning critic in the audience, applauded this statistical harmony by displaying a diminutive value of less than 0.01, signifying the significance of the relationship uncovered.

While the dance of data and statistics formed the crux of our methodology, it is imperative to acknowledge the limitations of our approach. The ecological fallacy lurked as a potential specter, given the inherent challenges of drawing conclusions about individual behavior based on aggregate data. Additionally, the potential for omitted variable bias and confounding factors posed as formidable adversaries in our quest for causation.

Nevertheless, armed with an arsenal of statistical tools and a zealous pursuit of insight, our research team embarked on this methodological odyssey to uncover the unexpected correlation between political proclivities in Maryland and the generation of biomass power in Uganda.

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

Please make up the results section of an academic research paper presenting the findings of the connection between Votes for the Libertarian Presidential candidate in Maryland and Biomass power generated in Uganda. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 2000 to 2020, you found a correlation 0.9780902, r-squared of 0.9566604, 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 Libertarian Leaning to Lively Lignin: Unveiling the Unanticipated Link Between Votes for the Libertarian Presidential Candidate in Maryland and Biomass Power Generated in Uganda"

[[ABSTRACT]]
This research paper presents the unexpected relationship between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda. Using data from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration for the years 2000 to 2020, our research team discovered a remarkably high correlation coefficient of 0.9780902 with a p-value of less than 0.01.

The findings shed light on the intricate, underlying mechanisms that connect political preferences and energy generation half a world apart. Despite the geographic and cultural divide, the results suggest a strong association that goes beyond mere coincidence. The implications of this study not only challenge traditional assumptions but also invite further investigation into the intertwined nature of seemingly unrelated phenomena.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intersection of politics and energy generation has been a subject of considerable academic interest in recent years. However, the connection between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda represents a particularly unexpected and, some might say, eccentric area of investigation. While pundits and policymakers may initially scoff at the notion of any correlation, our study aims to demonstrate the surprising harmony between these ostensibly disparate phenomena.
The data collection process, like a scavenger hunt through the digital archives, involved sourcing information from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration. The meticulous assembly of these datasets laid the foundation for our analysis, which revealed a correlation coefficient that would make statisticians raise an intrigued eyebrow – 0.9780902, coupled with a diminutive p-value of less than 0.01.
In scrutinizing the statistical relationship between votes cast in the Free State and the generation of biomass energy in the "Pearl of Africa," our findings challenge conventional wisdom and encourage readers to cast a more discerning eye on the intricate web of global interconnections. While some may view this research as a mere statistical oddity, the implications stretch far beyond the confines of conventional wisdom, beckoning scholars and policymakers alike to delve deeper into the bedrock of unexpected relationships.
This paper aims to unravel the enigmatic coalescence of voter behavior in Maryland and the utilization of biomass power in Uganda. By shedding light on this serendipitous connection, we invite readers to reassess their assumptions regarding associations between political preferences and energy generation, demonstrating that sometimes, analytical inquiry can unearth the most improbable of bedfellows.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The results of our analysis revealed a strikingly high correlation coefficient of 0.9780902 and a r-squared value of 0.9566604, indicating a robust and significant relationship between the votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda from 2000 to 2020. The probability value of less than 0.01 further substantiates the strength of this connection, leaving little room for statistical skepticism.

Figure 1 displays a scatterplot illustrating the close alignment between the two variables, resembling two star-crossed lovers finding their place in the tangled web of global data. The tight clustering of data points mirrors the unanticipated harmony between political inclinations in Maryland and the production of biomass energy in Uganda, proving that sometimes, the most seemingly incongruous pairings can form unexpectedly intimate relationships.

In the realm of statistical analysis, such a robust correlation between disparate variables is akin to stumbling upon a hidden treasure in the labyrinth of data exploration. Much like the serendipitous discovery of a valuable gem, these findings challenge conventional perceptions and beckon us to peel back the layers of seemingly unrelated phenomena.

The implications of these results not only defy the preconceived notions of logical association but also open the door to a new realm of inquiry, where the unexpected dance of political choices and energy generation unveils a rich tapestry of interconnectedness. This study invites a reevaluation of traditional assumptions, reminding us that sometimes, the most intriguing relationships lie beneath the surface of statistical normalcy.

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 Libertarian Presidential candidate in Maryland and Biomass power generated in Uganda. 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]]
"From Libertarian Leaning to Lively Lignin: Unveiling the Unanticipated Link Between Votes for the Libertarian Presidential Candidate in Maryland and Biomass Power Generated in Uganda"

[[ABSTRACT]]
This research paper presents the unexpected relationship between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda. Using data from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration for the years 2000 to 2020, our research team discovered a remarkably high correlation coefficient of 0.9780902 with a p-value of less than 0.01.

The findings shed light on the intricate, underlying mechanisms that connect political preferences and energy generation half a world apart. Despite the geographic and cultural divide, the results suggest a strong association that goes beyond mere coincidence. The implications of this study not only challenge traditional assumptions but also invite further investigation into the intertwined nature of seemingly unrelated phenomena.

[[LITERATURE REVIEW]]
In "A Statistical Analysis of Political Behavior" by Smith, the authors find numerous correlations between political preferences and various socioeconomic factors, but fail to mention any connection to biomass power generated in Uganda. In "Energy Generation and Sustainable Development" by Doe, the authors examine the global landscape of renewable energy sources, yet make no mention of the Libertarian presidential candidate in Maryland. Similarly, Jones, in "The Complex Dynamics of Political Ecology," overlooks any potential link between political voting patterns in the United States and biomass power generation in Uganda.
Turning to non-fiction books, "Biomass Energy for a Sustainable Future" and "The Subtle Art of Political Punditry" delve into separate aspects of the two subjects at hand, but do not address any potential relationship between them. On the other hand, fictional works such as "Libertarian Dreams in the Land of Biomass" and "The Biomass Conspiracy: A Political Thriller" offer imaginative, albeit unrelated, narratives that capture the reader's attention.
In the realm of social media, a tweet from @BiomassEnthusiast claims, "The political leanings of Maryland voters certainly impact global energy dynamics more than we realize," hinting at a possible connection. Another post from @Libertarian4Eva reads, "You'd be surprised how much your vote in Maryland affects energy decisions around the world," further fueling speculation regarding the unexpected link between the two seemingly incongruous phenomena.
The dearth of empirical evidence in the existing literature regarding the relationship between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda underscores the novelty and significance of the present study. While previous inquiries have overlooked this unprecedented connection, this paper seeks to fill this scholarly gap and unravel the intriguing juncture between political behavior and energy generation.

[[RESULTS]]
The results of our analysis revealed a strikingly high correlation coefficient of 0.9780902 and a r-squared value of 0.9566604, indicating a robust and significant relationship between the votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda from 2000 to 2020. The probability value of less than 0.01 further substantiates the strength of this connection, leaving little room for statistical skepticism.
Figure 1 displays a scatterplot illustrating the close alignment between the two variables, resembling two star-crossed lovers finding their place in the tangled web of global data. The tight clustering of data points mirrors the unanticipated harmony between political inclinations in Maryland and the production of biomass energy in Uganda, proving that sometimes, the most seemingly incongruous pairings can form unexpectedly intimate relationships.
In the realm of statistical analysis, such a robust correlation between disparate variables is akin to stumbling upon a hidden treasure in the labyrinth of data exploration. Much like the serendipitous discovery of a valuable gem, these findings challenge conventional perceptions and beckon us to peel back the layers of seemingly unrelated phenomena.
The implications of these results not only defy the preconceived notions of logical association but also open the door to a new realm of inquiry, where the unexpected dance of political choices and energy generation unveils a rich tapestry of interconnectedness. This study invites a reevaluation of traditional assumptions, reminding us that sometimes, the most intriguing relationships lie beneath the surface of statistical normalcy.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of this study present a rather peculiar yet undeniably robust relationship between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda. The remarkably high correlation coefficient of 0.9780902 serves as a striking testament to the interconnectedness of seemingly disparate phenomena. It appears that the political leanings of Maryland voters have a substantial impact on global energy dynamics, much to the surprise of those who have yet to delve into the intricacies of this peculiar association.

Our research has not only shed light on the unexpected harmony between political preferences in Maryland and biomass power generation in Uganda but has also challenged the conventional wisdom of statistical associations. The close alignment depicted in the scatterplot, reminiscent of two star-crossed lovers finding their place in the tangled web of global data, is a testament to the unanticipated harmony between political inclinations and the production of biomass energy. It's almost as if these two variables were destined to be together, despite their seemingly incongruous nature.

The implications of these findings extend far beyond their statistical significance. They invite us to reevaluate traditional assumptions and recognize that sometimes, the most intriguing relationships lie beneath the surface of statistical normalcy. Much like stumbling upon a hidden treasure in the labyrinth of data exploration, the discovery of this strong correlation challenges our perceptions of logical association and beckons us to delve deeper into the complex interplay of political choices and energy generation.

It is imperative for future research to build upon these unexpected findings and explore the intricate mechanisms that underpin this connection. While the traditional literature might have overlooked this unprecedented correlation, our study has opened the door to a new realm of inquiry, where the unexpected dance of political choices and energy generation unveils a rich tapestry of interconnectedness. Perhaps, after all, there is a lively lignin linking libertarian leanings in Maryland to the biomass power in Uganda.

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 Libertarian Presidential candidate in Maryland and Biomass power generated in Uganda. 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]]
"From Libertarian Leaning to Lively Lignin: Unveiling the Unanticipated Link Between Votes for the Libertarian Presidential Candidate in Maryland and Biomass Power Generated in Uganda"

[[ABSTRACT]]
This research paper presents the unexpected relationship between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda. Using data from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration for the years 2000 to 2020, our research team discovered a remarkably high correlation coefficient of 0.9780902 with a p-value of less than 0.01.

The findings shed light on the intricate, underlying mechanisms that connect political preferences and energy generation half a world apart. Despite the geographic and cultural divide, the results suggest a strong association that goes beyond mere coincidence. The implications of this study not only challenge traditional assumptions but also invite further investigation into the intertwined nature of seemingly unrelated phenomena.

[[INTRDUCTION]]
The intersection of politics and energy generation has been a subject of considerable academic interest in recent years. However, the connection between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda represents a particularly unexpected and, some might say, eccentric area of investigation. While pundits and policymakers may initially scoff at the notion of any correlation, our study aims to demonstrate the surprising harmony between these ostensibly disparate phenomena.
The data collection process, like a scavenger hunt through the digital archives, involved sourcing information from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration. The meticulous assembly of these datasets laid the foundation for our analysis, which revealed a correlation coefficient that would make statisticians raise an intrigued eyebrow – 0.9780902, coupled with a diminutive p-value of less than 0.01.
In scrutinizing the statistical relationship between votes cast in the Free State and the generation of biomass energy in the "Pearl of Africa," our findings challenge conventional wisdom and encourage readers to cast a more discerning eye on the intricate web of global interconnections. While some may view this research as a mere statistical oddity, the implications stretch far beyond the confines of conventional wisdom, beckoning scholars and policymakers alike to delve deeper into the bedrock of unexpected relationships.
This paper aims to unravel the enigmatic coalescence of voter behavior in Maryland and the utilization of biomass power in Uganda. By shedding light on this serendipitous connection, we invite readers to reassess their assumptions regarding associations between political preferences and energy generation, demonstrating that sometimes, analytical inquiry can unearth the most improbable of bedfellows.

[[RESULTS]]
The results of our analysis revealed a strikingly high correlation coefficient of 0.9780902 and a r-squared value of 0.9566604, indicating a robust and significant relationship between the votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda from 2000 to 2020. The probability value of less than 0.01 further substantiates the strength of this connection, leaving little room for statistical skepticism.
Figure 1 displays a scatterplot illustrating the close alignment between the two variables, resembling two star-crossed lovers finding their place in the tangled web of global data. The tight clustering of data points mirrors the unanticipated harmony between political inclinations in Maryland and the production of biomass energy in Uganda, proving that sometimes, the most seemingly incongruous pairings can form unexpectedly intimate relationships.
In the realm of statistical analysis, such a robust correlation between disparate variables is akin to stumbling upon a hidden treasure in the labyrinth of data exploration. Much like the serendipitous discovery of a valuable gem, these findings challenge conventional perceptions and beckon us to peel back the layers of seemingly unrelated phenomena.
The implications of these results not only defy the preconceived notions of logical association but also open the door to a new realm of inquiry, where the unexpected dance of political choices and energy generation unveils a rich tapestry of interconnectedness. This study invites a reevaluation of traditional assumptions, reminding us that sometimes, the most intriguing relationships lie beneath the surface of statistical normalcy.

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Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has illuminated a remarkably robust and statistically significant connection between votes for the Libertarian presidential candidate in Maryland and the generation of biomass power in Uganda. The high correlation coefficient of 0.9780902 practically screams, "We're a match made in data heaven!" The p-value of less than 0.01 reinforces the notion that this is not just a case of statistical infatuation, but a genuine and enduring relationship.

Our findings challenge the conventional wisdom that political preferences and energy generation are as distinct as apples and oranges. Instead, they suggest that these seemingly unrelated phenomena share a captivating and enigmatic bond, much like a budding romance in the realm of statistical analysis. Figure 1's scatterplot resembles a cosmic dance of variables, a statistical waltz if you will, demonstrating that even the most unexpected pairings can find harmonious resonance in the data universe.

This study encourages a paradigm shift in the way we perceive the interplay between political choices and energy production. Rather than dismissing the connection between votes in Maryland and biomass power in Uganda as a mere statistical fluke, we should embrace the depth of their relationship and celebrate the serendipitous nature of their association.

In essence, our research has uncovered a clandestine dalliance between political inclinations and energy utilization, inviting us to acknowledge that sometimes, the most profound connections arise from the unlikeliest of origins. Consequently, we assert that no further research in this particular area is necessary as our findings have left no statistical stone unturned.

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 Libertarian Leaning to Lively Lignin: Unveiling the Unanticipated Link Between Votes for the Libertarian Presidential Candidate in Maryland and Biomass Power Generated in Uganda. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Eclectic Ecological Economics

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 Libertarian Leaning to Lively Lignin: Unveiling the Unanticipated Link Between Votes for the Libertarian Presidential Candidate in Maryland and Biomass Power Generated in Uganda. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Ecological Anomalies and Global Understanding (SEAGU)

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 Libertarian Leaning to Lively Lignin: Unveiling the Unanticipated Link Between Votes for the Libertarian Presidential Candidate in Maryland and Biomass Power Generated in Uganda with an abstract of This research paper presents the unexpected relationship between votes for the Libertarian presidential candidate in Maryland and biomass power generated in Uganda. Using data from the MIT Election Data and Science Lab, Harvard Dataverse, and Energy Information Administration for the years 2000 to 2020, our research team discovered a remarkably high correlation coefficient of 0.9780902 with a p-value of less than 0.01.
The findings shed light on the intricate, underlying mechanisms that connect political preferences and energy generation half a world apart. Despite the geographic and cultural divide, the results suggest a strong association that goes beyond mere coincidence. The implications of this study not only challenge traditional assumptions but also invite further investigation into the intertwined nature of seemingly unrelated phenomena.

ChatGPT:

Libertarian presidential candidate, Maryland, biomass power, Uganda, correlation coefficient, MIT Election Data and Science Lab, Harvard Dataverse, Energy Information Administration, political preferences, energy generation, geographic divide, cultural divide, traditional assumptions, intertwined nature, unrelated phenomena

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

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Data details

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

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

Correlation r = 0.9780902 (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 = 0.9566604 (Coefficient of determination)
This means 95.7% of the change in the one variable (i.e., Biomass power generated in Uganda) is predictable based on the change in the other (i.e., Votes for the Libertarian Presidential candidate in Maryland) over the 6 years from 2000 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00071.^Show 0.0007148014131218885000000000
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 0.98 in 0.071% of random cases. Said differently, if you correlated 1,399 random variables^Note Which I absolutely did.
with the same 5 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 5 because we have two variables measured over a period of 6 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.

[ 0.81, 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.

	2000	2004	2008	2012	2016	2020
Votes for the Libertarian Presidential candidate in Maryland (Percentage of votes)	0.262195	0.255595	0.373994	1.11531	2.862	1.10266
Biomass power generated in Uganda (Billion kWh)	0.03	0.047	0.11	0.16	0.363	0.188

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.
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.

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([0.262195,0.255595,0.373994,1.11531,2.862,1.10266,])
array_2 = np.array([0.03,0.047,0.11,0.16,0.363,0.188,])
array_1_name = "Votes for the Libertarian Presidential candidate in Maryland"
array_2_name = "Biomass power generated in Uganda"

# 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 Libertarian Presidential candidate in Maryland
Line chart for only Biomass power generated in Uganda
AI-generated correlation image
The spurious research paper: From Libertarian Leaning to Lively Lignin: Unveiling the Unanticipated Link Between Votes for the Libertarian Presidential Candidate in Maryland and Biomass Power Generated in Uganda

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Correlation ID: 5475 · Black Variable ID: 26110 · Red Variable ID: 24059


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