Spurious correlation #4,405 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Votes for the Democratic Presidential candidate in Maryland and the second variable is Electricity generation in Costa Rica. The chart goes from 1980 to 2020, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Votes for the Democratic Presidential candidate in Maryland and the second variable is Electricity generation in Costa Rica. The chart goes from 1980 to 2020, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the support for the Democratic candidate surged in Maryland, it sparked a political current that couldn't be contained. This shocking development sent a wave of excitement through the state, leading to a boost in energy levels. Meanwhile, in a power play of their own, Costa Rica felt the positive momentum and decided to amp up their electricity generation to show their electrifying support, proving that when it comes to politics, it's all about power distribution!

^{Show image generation prompt} Model: dalle-3
Prompt: Prompt: Create an image of a lively scene in which cute cartoon characters representing Maryland and Costa Rica are depicted. In Maryland, show voters with excited expressions lining up to cast their ballots for the Democratic presidential candidate, with vibrant waves of energy and excitement emanating from them. In Costa Rica, illustrate a sunny landscape dotted with cheerful wind turbines and solar panels working hard to generate electricity, with their cute, determined expressions reflecting the surge of positive momentum. The energy distribution network should convey the electrifying support spreading throughout the country. 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 Democrat Presidential candidate in Maryland and Electricity generation in Costa Rica. 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: Parallax art Macabre Vaporwave Cute cartoon Art nouveau
ChatGPT: Cute cartoon

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 Democrat Presidential candidate in Maryland and Electricity generation in Costa Rica, in the following situation: As the support for the Democratic candidate surged in Maryland, it sparked a political current that couldn't be contained. This shocking development sent a wave of excitement through the state, leading to a boost in energy levels. Meanwhile, in a power play of their own, Costa Rica felt the positive momentum and decided to amp up their electricity generation to show their electrifying support, proving that when it comes to politics, it's all about power distribution!. Keep the description simple.
ChatGPT: In Maryland, a surge of support for the Democratic presidential candidate sent waves of excitement through the state, with voters lining up to cast their ballots. As the political current sparked by the surge couldn't be contained, the energy levels rose, and the buzz of anticipation filled the air. Meanwhile, in Costa Rica, the positive momentum resonated, prompting an increase in electricity generation. The sunny landscape was dotted with wind turbines and solar panels working hard to amplify their electrifying support, as the power distribution surged through the country.

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: [ Cute cartoon ] and depicting this scene: In Maryland, a surge of support for the Democratic presidential candidate sent waves of excitement through the state, with voters lining up to cast their ballots. As the political current sparked by the surge couldn't be contained, the energy levels rose, and the buzz of anticipation filled the air. Meanwhile, in Costa Rica, the positive momentum resonated, prompting an increase in electricity generation. The sunny landscape was dotted with wind turbines and solar panels working hard to amplify their electrifying support, as the power distribution surged through the country. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Create an image of a lively scene in which cute cartoon characters representing Maryland and Costa Rica are depicted. In Maryland, show voters with excited expressions lining up to cast their ballots for the Democratic presidential candidate, with vibrant waves of energy and excitement emanating from them. In Costa Rica, illustrate a sunny landscape dotted with cheerful wind turbines and solar panels working hard to generate electricity, with their cute, determined expressions reflecting the surge of positive momentum. The energy distribution network should convey the electrifying support spreading throughout the country.

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

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

AI academic paper

(Because p < 0.01)

Power Play: Shedding Light on the Relationship Between Democratic Votes in Maryland and Electricity Generation in Costa Rica

The Journal of Eclectic Electoral Studies

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 Democrat Presidential candidate in Maryland and Electricity generation in Costa Rica. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
"Power Play: Shedding Light on the Relationship Between Democratic Votes in Maryland and Electricity Generation in Costa Rica"

[[ABSTRACT]]
The correlation between political preferences and electricity generation has been a topic of intrigue, with potential implications for understanding the interplay between democratic activities and power generation. In this study, we investigate the connection between votes for the Democratic Presidential candidate in the state of Maryland and electricity generation in the picturesque nation of Costa Rica. Through meticulous data analysis utilizing information from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we have uncovered a notable correlation coefficient of 0.9877987 and p < 0.01 for the period spanning from 1980 to 2020. The findings of this study offer intriguing insights into the potential influence of political leanings on the dynamics of electricity generation in a geographically divergent region. Our research serves to illuminate the intersection of electoral dynamics and energy production, shedding light on a mirthful dance between political inclinations and power generation patterns.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intricate tapestry of global politics and energy production has long been a subject of profound fascination, with numerous researchers endeavoring to unravel the entwined threads of these seemingly disparate realms. As we delve into the depths of this complex nexus, one can't help but ponder the curious concatenation of events and forces that shape the world around us. In the midst of this captivating exploration, we turn our attention to the relationship between votes for the Democratic Presidential candidate in the state of Maryland and the enigmatic realm of electricity generation in the charming haven of Costa Rica.
While at first glance, one might be inclined to chuckle at the seemingly whimsical juxtaposition of these two disparate entities – the political aspirations of citizens in the Old Line State and the electrifying ventures of a tropical paradise – our scholarly pursuit is underpinned by a deep-rooted commitment to ferret out hidden connections and uncover the unexpected. With tongues only slightly in cheek, we embark on this cerebral expedition into the realm where the polling booth intersects with the power grid, with the fervent hope of unearthing insights that are as illuminating as they are electrifying.
The crux of this investigation lies in our desire to discern whether there exists a discernible relationship between the voting patterns in Maryland and the fluctuating rhythms of electricity generation in Costa Rica. Though some may jest at the notion of a correlation between the electoral fervor of Marylanders and the buzz of Costa Rican energy infrastructure, our foray into the labyrinth of data and statistical analysis has yielded findings that are anything but laughable. Through the unwavering application of meticulous methodologies and an unyielding commitment to scholarly rigor, we have unearthed a remarkable correlation coefficient of 0.9877987, with a p-value of less than 0.01, spanning from the annals of 1980 to the present day.
In unveiling these remarkable findings, we aim not only to tantalize the intellect of the academic community but also to tease out the implications of this correlation for the broader understanding of the intricate interplay between political inclinations and the imperceptible hum of energy production. As we traverse the terrain of political landscapes and energy grids, the narrative that emerges is one that sheds a comical yet thought-provoking light on the interconnections that underpin our global tapestry.
In the subsequent sections of this paper, we shall delve into the data sources, methodological approaches, and, of course, the revelatory findings that have emerged from our earnest exploration of this quirky yet captivating relationship between democratic votes in Maryland and electricity generation in Costa Rica. As we unravel this engaging tale of electoral dynamics and energy generation, we invite our fellow scholars to walk alongside us on this mirthful odyssey, where the serious pursuit of knowledge is punctuated by the occasional twinkle of levity.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The relationship between political preferences and energy production has been a subject of considerable interest, with researchers seeking to untangle the interwoven strands of these seemingly incongruent domains. While the initial focus of our inquiry may evoke a lighthearted chuckle at the notion of a connection between votes for the Democratic Presidential candidate in Maryland and electricity generation in Costa Rica, our pursuit is grounded in an earnest endeavor to tease out unexpected connections and uncover the hidden forces at play.

Smith et al. (2015) investigated the potential links between electoral behaviors and energy generation patterns, setting the stage for our exploration into this intriguing correlation. Building upon this foundation, Doe and Jones (2018) delved into the nuanced dynamics of political inclinations and their possible impact on energy infrastructure, laying the groundwork for our own analytical foray.

As we shift from these foundational studies to the broader context, it is worth noting the palpable resonance in the literary realm. In "The Geography of Bliss" by Eric Weiner (2008), the author traverses the world in search of happiness, offering a whimsical parallel to our own quest to untangle the merry dance between democratic votes and energy production. Furthermore, Doris Kearns Goodwin's "Team of Rivals" (2005) provides a compelling insight into the intricate dynamics of political alignments, albeit in a different context, yet serving as a delightfully tangential reference point for our exploration.

Deviation from the conventional literature leads us to ponder the impact of fantasy and fiction on our understanding of these seemingly unconnected domains. With a nod to the realm of speculative fiction, the works of Philip K. Dick and Ursula K. Le Guin beckon us to contemplate alternate realities where the connection between political leanings and energy generation may take on entirely unforeseen dimensions.

Taking a step further into the realm of unlikely influences, a binge-watch of "The Magic School Bus" and "Scooby-Doo" offers an unconventional yet illuminating perspective on the whimsical interplay between electoral dynamics and the enigmatic pulse of energy generation. While these may appear to be jests on the surface, the underlying principle of unanticipated connections remains a guiding light in our scholarly pursuit.

In the following sections, we delve into the intricate methodology and revelatory findings that have emerged from our earnest expedition into the intriguing relationship between democratic votes in Maryland and electricity generation in Costa Rica. As we navigate this path with scholarly seriousness peppered with the occasional witticism, we invite our fellow researchers to join us on this merry odyssey, where the pursuit of knowledge unfurls amidst the playfulness of unexpected interconnections.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between Votes for the Democrat Presidential candidate in Maryland and Electricity generation in Costa Rica. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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 1980 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]]
"Power Play: Shedding Light on the Relationship Between Democratic Votes in Maryland and Electricity Generation in Costa Rica"

[[ABSTRACT]]
The correlation between political preferences and electricity generation has been a topic of intrigue, with potential implications for understanding the interplay between democratic activities and power generation. In this study, we investigate the connection between votes for the Democratic Presidential candidate in the state of Maryland and electricity generation in the picturesque nation of Costa Rica. Through meticulous data analysis utilizing information from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we have uncovered a notable correlation coefficient of 0.9877987 and p < 0.01 for the period spanning from 1980 to 2020. The findings of this study offer intriguing insights into the potential influence of political leanings on the dynamics of electricity generation in a geographically divergent region. Our research serves to illuminate the intersection of electoral dynamics and energy production, shedding light on a mirthful dance between political inclinations and power generation patterns.

[[INTRODUCTION]]
The intricate tapestry of global politics and energy production has long been a subject of profound fascination, with numerous researchers endeavoring to unravel the entwined threads of these seemingly disparate realms. As we delve into the depths of this complex nexus, one can't help but ponder the curious concatenation of events and forces that shape the world around us. In the midst of this captivating exploration, we turn our attention to the relationship between votes for the Democratic Presidential candidate in the state of Maryland and the enigmatic realm of electricity generation in the charming haven of Costa Rica.
While at first glance, one might be inclined to chuckle at the seemingly whimsical juxtaposition of these two disparate entities – the political aspirations of citizens in the Old Line State and the electrifying ventures of a tropical paradise – our scholarly pursuit is underpinned by a deep-rooted commitment to ferret out hidden connections and uncover the unexpected. With tongues only slightly in cheek, we embark on this cerebral expedition into the realm where the polling booth intersects with the power grid, with the fervent hope of unearthing insights that are as illuminating as they are electrifying.
The crux of this investigation lies in our desire to discern whether there exists a discernible relationship between the voting patterns in Maryland and the fluctuating rhythms of electricity generation in Costa Rica. Though some may jest at the notion of a correlation between the electoral fervor of Marylanders and the buzz of Costa Rican energy infrastructure, our foray into the labyrinth of data and statistical analysis has yielded findings that are anything but laughable. Through the unwavering application of meticulous methodologies and an unyielding commitment to scholarly rigor, we have unearthed a remarkable correlation coefficient of 0.9877987, with a p-value of less than 0.01, spanning from the annals of 1980 to the present day.
In unveiling these remarkable findings, we aim not only to tantalize the intellect of the academic community but also to tease out the implications of this correlation for the broader understanding of the intricate interplay between political inclinations and the imperceptible hum of energy production. As we traverse the terrain of political landscapes and energy grids, the narrative that emerges is one that sheds a comical yet thought-provoking light on the interconnections that underpin our global tapestry.
In the subsequent sections of this paper, we shall delve into the data sources, methodological approaches, and, of course, the revelatory findings that have emerged from our earnest exploration of this quirky yet captivating relationship between democratic votes in Maryland and electricity generation in Costa Rica. As we unravel this engaging tale of electoral dynamics and energy generation, we invite our fellow scholars to walk alongside us on this mirthful odyssey, where the serious pursuit of knowledge is punctuated by the occasional twinkle of levity.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To investigate the curious correlation between votes for the Democratic Presidential candidate in Maryland and electricity generation in Costa Rica, we employed a blend of serious statistical analysis and a pinch of whimsy. Our data, spanning from 1980 to 2020, was aggregated from various sources, including the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration. We sifted through a veritable mountain of digits, diligently teasing out the elusive connections between political preferences and the generation of electrical currents.

The initial step in our methodological ballet involved the collection of electoral data from the state of Maryland, embracing the spirited fluctuations in votes for the Democratic Presidential candidate over the past four decades. Simultaneously, we harnessed data on the electricity generation in the tropical haven of Costa Rica, capturing the ebb and flow of kilowatt-hours in this enchanting locale. These datasets were then coaxed, cajoled, and coerced to reveal their hidden secrets, employing time-series analysis techniques with a dash of sorcery – oops, we meant to say rigorous statistical methodologies.

To quantify the relationship between Maryland's democratic fervor and Costa Rica's electrical heartbeat, we meticulously calculated correlation coefficients and p-values, with an arsenal of statistical tools at our disposal. Our computation of these measures was infused with the utmost precision, as we carefully navigated the labyrinth of numbers and p-values, all while trying to avoid the siren call of spurious correlations and apophenia. We also utilized sophisticated multivariate regression models, commingling potent predictors from the realms of politics and energy to unravel the interwoven strands of correlation.

Further fortifying our methodological arsenal, we factored in various control variables, accounting for nuances such as socio-economic factors, environmental policies, and the occasional disruption caused by wayward toucans or mischievous sloths. These adjustments helped to ensure that our findings were robust and impervious to the caprices of confounding variables, offering a clear-eyed perspective on the relationship between political proclivities and the generation of electricity in this paradise of pura vida.

In the pursuit of academic rigor and intellectual honesty, we paid heed to the potential limitations of our study. While we danced with the data, we remained mindful of the necessity to tread cautiously in inferring causation from correlation, lest we succumb to the siren song of spurious explanations. With an unwavering commitment to scientific integrity, we donned our methodological lab coats and marched forward, determined to unearth the authentic patterns inherent in the enigmatic interplay between Maryland's electoral ballet and Costa Rica's voltaic pas de deux.

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 Democrat Presidential candidate in Maryland and Electricity generation in Costa Rica. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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 1980 to 2020, you found a correlation 0.9877987, r-squared of 0.9757463, 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]]
"Power Play: Shedding Light on the Relationship Between Democratic Votes in Maryland and Electricity Generation in Costa Rica"

[[ABSTRACT]]
The correlation between political preferences and electricity generation has been a topic of intrigue, with potential implications for understanding the interplay between democratic activities and power generation. In this study, we investigate the connection between votes for the Democratic Presidential candidate in the state of Maryland and electricity generation in the picturesque nation of Costa Rica. Through meticulous data analysis utilizing information from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we have uncovered a notable correlation coefficient of 0.9877987 and p < 0.01 for the period spanning from 1980 to 2020. The findings of this study offer intriguing insights into the potential influence of political leanings on the dynamics of electricity generation in a geographically divergent region. Our research serves to illuminate the intersection of electoral dynamics and energy production, shedding light on a mirthful dance between political inclinations and power generation patterns.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intricate tapestry of global politics and energy production has long been a subject of profound fascination, with numerous researchers endeavoring to unravel the entwined threads of these seemingly disparate realms. As we delve into the depths of this complex nexus, one can't help but ponder the curious concatenation of events and forces that shape the world around us. In the midst of this captivating exploration, we turn our attention to the relationship between votes for the Democratic Presidential candidate in the state of Maryland and the enigmatic realm of electricity generation in the charming haven of Costa Rica.
While at first glance, one might be inclined to chuckle at the seemingly whimsical juxtaposition of these two disparate entities – the political aspirations of citizens in the Old Line State and the electrifying ventures of a tropical paradise – our scholarly pursuit is underpinned by a deep-rooted commitment to ferret out hidden connections and uncover the unexpected. With tongues only slightly in cheek, we embark on this cerebral expedition into the realm where the polling booth intersects with the power grid, with the fervent hope of unearthing insights that are as illuminating as they are electrifying.
The crux of this investigation lies in our desire to discern whether there exists a discernible relationship between the voting patterns in Maryland and the fluctuating rhythms of electricity generation in Costa Rica. Though some may jest at the notion of a correlation between the electoral fervor of Marylanders and the buzz of Costa Rican energy infrastructure, our foray into the labyrinth of data and statistical analysis has yielded findings that are anything but laughable. Through the unwavering application of meticulous methodologies and an unyielding commitment to scholarly rigor, we have unearthed a remarkable correlation coefficient of 0.9877987, with a p-value of less than 0.01, spanning from the annals of 1980 to the present day.
In unveiling these remarkable findings, we aim not only to tantalize the intellect of the academic community but also to tease out the implications of this correlation for the broader understanding of the intricate interplay between political inclinations and the imperceptible hum of energy production. As we traverse the terrain of political landscapes and energy grids, the narrative that emerges is one that sheds a comical yet thought-provoking light on the interconnections that underpin our global tapestry.
In the subsequent sections of this paper, we shall delve into the data sources, methodological approaches, and, of course, the revelatory findings that have emerged from our earnest exploration of this quirky yet captivating relationship between democratic votes in Maryland and electricity generation in Costa Rica. As we unravel this engaging tale of electoral dynamics and energy generation, we invite our fellow scholars to walk alongside us on this mirthful odyssey, where the serious pursuit of knowledge is punctuated by the occasional twinkle of levity.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

Our investigation into the nexus between votes for the Democratic Presidential candidate in Maryland and electricity generation in Costa Rica yielded a correlation coefficient of 0.9877987, suggesting a remarkably strong relationship between these ostensibly incongruent variables. The r-squared value of 0.9757463 further underscores the robustness of this correlation, indicating that approximately 97.57% of the variance in electricity generation in Costa Rica can be explained by the votes for the Democratic candidate in Maryland. With a p-value of less than 0.01, we can confidently reject the null hypothesis and assert that this correlation is highly significant.

The figure (Fig. 1) included in this paper visually represents the compelling correlation between Democratic votes in Maryland and electricity generation in Costa Rica. As the adage goes, a picture is worth a thousand words, and in this case, it vividly illustrates the striking relationship that we have uncovered through our rigorous statistical analysis.

While some may raise an eyebrow at the seemingly farcical juxtaposition of political preferences in Maryland and electricity generation in Costa Rica, our findings underscore the remarkable interconnectedness of seemingly disparate phenomena. As we reflect on the implications of this discovery, one can't help but appreciate the whimsical dance between electoral dynamics and energy generation patterns. The interplay of these forces illuminates the intricate web of global dynamics, offering a humorous yet thought-provoking insight into the unanticipated links that permeate our world.

In the forthcoming sections, we will expound upon the implications of these findings and their significance in broadening our understanding of both political phenomena and energy production. Prepare for a riveting journey through the gastronomical delights of statistical analysis and the power plays of democratic fervor, as we unravel the profound yet lighthearted tale of this peculiar relationship.

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 Democrat Presidential candidate in Maryland and Electricity generation in Costa Rica. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
"Power Play: Shedding Light on the Relationship Between Democratic Votes in Maryland and Electricity Generation in Costa Rica"

[[ABSTRACT]]
The correlation between political preferences and electricity generation has been a topic of intrigue, with potential implications for understanding the interplay between democratic activities and power generation. In this study, we investigate the connection between votes for the Democratic Presidential candidate in the state of Maryland and electricity generation in the picturesque nation of Costa Rica. Through meticulous data analysis utilizing information from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we have uncovered a notable correlation coefficient of 0.9877987 and p < 0.01 for the period spanning from 1980 to 2020. The findings of this study offer intriguing insights into the potential influence of political leanings on the dynamics of electricity generation in a geographically divergent region. Our research serves to illuminate the intersection of electoral dynamics and energy production, shedding light on a mirthful dance between political inclinations and power generation patterns.

[[LITERATURE REVIEW]]
The relationship between political preferences and energy production has been a subject of considerable interest, with researchers seeking to untangle the interwoven strands of these seemingly incongruent domains. While the initial focus of our inquiry may evoke a lighthearted chuckle at the notion of a connection between votes for the Democratic Presidential candidate in Maryland and electricity generation in Costa Rica, our pursuit is grounded in an earnest endeavor to tease out unexpected connections and uncover the hidden forces at play.
Smith et al. (2015) investigated the potential links between electoral behaviors and energy generation patterns, setting the stage for our exploration into this intriguing correlation. Building upon this foundation, Doe and Jones (2018) delved into the nuanced dynamics of political inclinations and their possible impact on energy infrastructure, laying the groundwork for our own analytical foray.
As we shift from these foundational studies to the broader context, it is worth noting the palpable resonance in the literary realm. In "The Geography of Bliss" by Eric Weiner (2008), the author traverses the world in search of happiness, offering a whimsical parallel to our own quest to untangle the merry dance between democratic votes and energy production. Furthermore, Doris Kearns Goodwin's "Team of Rivals" (2005) provides a compelling insight into the intricate dynamics of political alignments, albeit in a different context, yet serving as a delightfully tangential reference point for our exploration.
Deviation from the conventional literature leads us to ponder the impact of fantasy and fiction on our understanding of these seemingly unconnected domains. With a nod to the realm of speculative fiction, the works of Philip K. Dick and Ursula K. Le Guin beckon us to contemplate alternate realities where the connection between political leanings and energy generation may take on entirely unforeseen dimensions.
Taking a step further into the realm of unlikely influences, a binge-watch of "The Magic School Bus" and "Scooby-Doo" offers an unconventional yet illuminating perspective on the whimsical interplay between electoral dynamics and the enigmatic pulse of energy generation. While these may appear to be jests on the surface, the underlying principle of unanticipated connections remains a guiding light in our scholarly pursuit.
In the following sections, we delve into the intricate methodology and revelatory findings that have emerged from our earnest expedition into the intriguing relationship between democratic votes in Maryland and electricity generation in Costa Rica. As we navigate this path with scholarly seriousness peppered with the occasional witticism, we invite our fellow researchers to join us on this merry odyssey, where the pursuit of knowledge unfurls amidst the playfulness of unexpected interconnections.

[[RESULTS]]
Our investigation into the nexus between votes for the Democratic Presidential candidate in Maryland and electricity generation in Costa Rica yielded a correlation coefficient of 0.9877987, suggesting a remarkably strong relationship between these ostensibly incongruent variables. The r-squared value of 0.9757463 further underscores the robustness of this correlation, indicating that approximately 97.57% of the variance in electricity generation in Costa Rica can be explained by the votes for the Democratic candidate in Maryland. With a p-value of less than 0.01, we can confidently reject the null hypothesis and assert that this correlation is highly significant.
The figure (Fig. 1) included in this paper visually represents the compelling correlation between Democratic votes in Maryland and electricity generation in Costa Rica. As the adage goes, a picture is worth a thousand words, and in this case, it vividly illustrates the striking relationship that we have uncovered through our rigorous statistical analysis.
While some may raise an eyebrow at the seemingly farcical juxtaposition of political preferences in Maryland and electricity generation in Costa Rica, our findings underscore the remarkable interconnectedness of seemingly disparate phenomena. As we reflect on the implications of this discovery, one can't help but appreciate the whimsical dance between electoral dynamics and energy generation patterns. The interplay of these forces illuminates the intricate web of global dynamics, offering a humorous yet thought-provoking insight into the unanticipated links that permeate our world.
In the forthcoming sections, we will expound upon the implications of these findings and their significance in broadening our understanding of both political phenomena and energy production. Prepare for a riveting journey through the gastronomical delights of statistical analysis and the power plays of democratic fervor, as we unravel the profound yet lighthearted tale of this peculiar relationship.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of our study have unveiled a delightful yet surprisingly robust connection between votes for the Democratic Presidential candidate in Maryland and electricity generation in Costa Rica. While the initial impression of this correlation may evoke a raised eyebrow or a slight chuckle, the statistical weight behind our results commands earnest attention.

Our research has lent support to the prior work of Smith et al. (2015) and Doe and Jones (2018), who laid the groundwork for our exploration into the whimsical interplay between democratic fervor and power production. The correlation coefficient of 0.9877987 that we have unearthed mirrors and amplifies the resonance of their earlier inquiries, signaling the substantial influence of political inclinations on energy generation patterns. It's almost as if the ballots cast in the serene streets of Maryland whisper through the ether, resonating with the hum of turbines and the crackle of electrical currents across the verdant landscapes of Costa Rica.

The unmistakable correlation highlighted in our study not only contributes to the scholarly discourse but also prompts a fond reflection on the comical yet profound intertwining of democratic votes and energy generation. It's as though the electoral pulse of one region extends its ethereal influence to mold the very currents of an entirely disparate land. The results of our analysis stand as a testament to the mirthful dance between seemingly distant domains, inviting us to contemplate the lively interconnections that animate the tapestry of global dynamics.

Indeed, our findings serve to illuminate the unexpected nexus between electoral dynamics and energy production, underscoring the pertinence of seemingly improbable connections. As we bask in the glow of statistical significance and revel in the picturesque coupling of ballots and kilowatts, the scholarly pursuit takes on a whimsical charm, reminding us that the pursuit of knowledge often unfurls amidst the playfulness of unforeseen interconnections.

In the forthcoming sections, we will delve into the nuanced implications of this correlation, weaving a tapestry that celebrates the delightfully unexpected interplay between political leanings and power generation. Brace yourselves for an academic expedition that promises to entertain and enlighten in equal measure, as we journey through the intellectual landscape marked by the fascinating camaraderie of democratic votes and electrical currents.

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 Democrat Presidential candidate in Maryland and Electricity generation in Costa Rica. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
"Power Play: Shedding Light on the Relationship Between Democratic Votes in Maryland and Electricity Generation in Costa Rica"

[[ABSTRACT]]
The correlation between political preferences and electricity generation has been a topic of intrigue, with potential implications for understanding the interplay between democratic activities and power generation. In this study, we investigate the connection between votes for the Democratic Presidential candidate in the state of Maryland and electricity generation in the picturesque nation of Costa Rica. Through meticulous data analysis utilizing information from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we have uncovered a notable correlation coefficient of 0.9877987 and p < 0.01 for the period spanning from 1980 to 2020. The findings of this study offer intriguing insights into the potential influence of political leanings on the dynamics of electricity generation in a geographically divergent region. Our research serves to illuminate the intersection of electoral dynamics and energy production, shedding light on a mirthful dance between political inclinations and power generation patterns.

[[INTRDUCTION]]
The intricate tapestry of global politics and energy production has long been a subject of profound fascination, with numerous researchers endeavoring to unravel the entwined threads of these seemingly disparate realms. As we delve into the depths of this complex nexus, one can't help but ponder the curious concatenation of events and forces that shape the world around us. In the midst of this captivating exploration, we turn our attention to the relationship between votes for the Democratic Presidential candidate in the state of Maryland and the enigmatic realm of electricity generation in the charming haven of Costa Rica.
While at first glance, one might be inclined to chuckle at the seemingly whimsical juxtaposition of these two disparate entities – the political aspirations of citizens in the Old Line State and the electrifying ventures of a tropical paradise – our scholarly pursuit is underpinned by a deep-rooted commitment to ferret out hidden connections and uncover the unexpected. With tongues only slightly in cheek, we embark on this cerebral expedition into the realm where the polling booth intersects with the power grid, with the fervent hope of unearthing insights that are as illuminating as they are electrifying.
The crux of this investigation lies in our desire to discern whether there exists a discernible relationship between the voting patterns in Maryland and the fluctuating rhythms of electricity generation in Costa Rica. Though some may jest at the notion of a correlation between the electoral fervor of Marylanders and the buzz of Costa Rican energy infrastructure, our foray into the labyrinth of data and statistical analysis has yielded findings that are anything but laughable. Through the unwavering application of meticulous methodologies and an unyielding commitment to scholarly rigor, we have unearthed a remarkable correlation coefficient of 0.9877987, with a p-value of less than 0.01, spanning from the annals of 1980 to the present day.
In unveiling these remarkable findings, we aim not only to tantalize the intellect of the academic community but also to tease out the implications of this correlation for the broader understanding of the intricate interplay between political inclinations and the imperceptible hum of energy production. As we traverse the terrain of political landscapes and energy grids, the narrative that emerges is one that sheds a comical yet thought-provoking light on the interconnections that underpin our global tapestry.
In the subsequent sections of this paper, we shall delve into the data sources, methodological approaches, and, of course, the revelatory findings that have emerged from our earnest exploration of this quirky yet captivating relationship between democratic votes in Maryland and electricity generation in Costa Rica. As we unravel this engaging tale of electoral dynamics and energy generation, we invite our fellow scholars to walk alongside us on this mirthful odyssey, where the serious pursuit of knowledge is punctuated by the occasional twinkle of levity.

[[RESULTS]]
Our investigation into the nexus between votes for the Democratic Presidential candidate in Maryland and electricity generation in Costa Rica yielded a correlation coefficient of 0.9877987, suggesting a remarkably strong relationship between these ostensibly incongruent variables. The r-squared value of 0.9757463 further underscores the robustness of this correlation, indicating that approximately 97.57% of the variance in electricity generation in Costa Rica can be explained by the votes for the Democratic candidate in Maryland. With a p-value of less than 0.01, we can confidently reject the null hypothesis and assert that this correlation is highly significant.
The figure (Fig. 1) included in this paper visually represents the compelling correlation between Democratic votes in Maryland and electricity generation in Costa Rica. As the adage goes, a picture is worth a thousand words, and in this case, it vividly illustrates the striking relationship that we have uncovered through our rigorous statistical analysis.
While some may raise an eyebrow at the seemingly farcical juxtaposition of political preferences in Maryland and electricity generation in Costa Rica, our findings underscore the remarkable interconnectedness of seemingly disparate phenomena. As we reflect on the implications of this discovery, one can't help but appreciate the whimsical dance between electoral dynamics and energy generation patterns. The interplay of these forces illuminates the intricate web of global dynamics, offering a humorous yet thought-provoking insight into the unanticipated links that permeate our world.
In the forthcoming sections, we will expound upon the implications of these findings and their significance in broadening our understanding of both political phenomena and energy production. Prepare for a riveting journey through the gastronomical delights of statistical analysis and the power plays of democratic fervor, as we unravel the profound yet lighthearted tale of this peculiar relationship.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

With our study shedding light on the correlation between votes for the Democratic Presidential candidate in Maryland and electricity generation in Costa Rica, we have uncovered a comically strong link that challenges conventional expectations. Our findings present a startling intertwining of political leanings and power generation patterns, illustrating the whimsical dance between seemingly incongruent realms.

As we conclude our investigation, one cannot help but marvel at the enchanting symphony of statistics and electoral eccentricities. The intersecting narratives of Maryland's political inclinations and Costa Rica's energy dynamics offer a delightful quirkiness that defies traditional scholarly solemnity.

While, at first glance, this correlation may invite a chuckle or two, our rigorous statistical analysis leaves no room for doubt regarding the significance of this connection. The robust correlation coefficient and r-squared value emphasize the gravity of this unlikely relationship, reminding us that the world of data analysis is as full of surprises as an unexpected plot twist in a mystery novel.

This peculiar relationship between democratic votes and electricity generation stands as a testament to the beguiling complexity of the world around us. It epitomizes the delightful unpredictability that lies at the heart of our scholarly pursuits, infusing levity into the oftentimes sober realm of academic inquiry.

In the spirit of whimsical curiosity and a profound appreciation for the unexpected, we emphatically declare that further research in this area is unnecessary. Our findings encapsulate the humorous richness of this correlation, leaving little doubt that sometimes, academic inquiry can also offer an entertaining twist of fate.

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 Power Play: Shedding Light on the Relationship Between Democratic Votes in Maryland and Electricity Generation in Costa Rica. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Eclectic Electoral Studies

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 Power Play: Shedding Light on the Relationship Between Democratic Votes in Maryland and Electricity Generation in Costa Rica. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Transnational Energy and Political Analysis

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Power Play: Shedding Light on the Relationship Between Democratic Votes in Maryland and Electricity Generation in Costa Rica with an abstract of The correlation between political preferences and electricity generation has been a topic of intrigue, with potential implications for understanding the interplay between democratic activities and power generation. In this study, we investigate the connection between votes for the Democratic Presidential candidate in the state of Maryland and electricity generation in the picturesque nation of Costa Rica. Through meticulous data analysis utilizing information from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, we have uncovered a notable correlation coefficient of 0.9877987 and p < 0.01 for the period spanning from 1980 to 2020. The findings of this study offer intriguing insights into the potential influence of political leanings on the dynamics of electricity generation in a geographically divergent region. Our research serves to illuminate the intersection of electoral dynamics and energy production, shedding light on a mirthful dance between political inclinations and power generation patterns.

ChatGPT:

Maryland, Democratic votes, electricity generation, Costa Rica, correlation, political preferences, power generation, MIT Election Data and Science Lab, Harvard Dataverse, Energy Information Administration, electoral dynamics, energy production patterns.

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

Random correlation

Discover a new correlation

View all correlations

View all research papers

Report an error

Data details

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

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

Correlation r = 0.9877987 (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 = 0.9757463 (Coefficient of determination)
This means 97.6% of the change in the one variable (i.e., Electricity generation in Costa Rica) is predictable based on the change in the other (i.e., Votes for the Democratic Presidential candidate in Maryland) over the 11 years from 1980 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.41E-8.^Show 0.0000000140811034504688400000
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 1.41E-6% of random cases. Said differently, if you correlated 71,017,162 random variables^Note You don't actually need 71 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.

In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 10 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 10 because we have two variables measured over a period of 11 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.95, 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.

	1980	1984	1988	1992	1996	2000	2004	2008	2012	2016	2020
Votes for the Democratic Presidential candidate in Maryland (Total votes)	726161	787935	826304	988571	966207	1144010	1334500	1629470	1677840	1678010	1985020
Electricity generation in Costa Rica (Billion kWh)	2.142	3.035	3.109	4.57	4.749	6.84846	7.96304	9.36038	10.0552	10.8394	11.7409

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([726161,787935,826304,988571,966207,1144010,1334500,1629470,1677840,1678010,1985020,])
array_2 = np.array([2.142,3.035,3.109,4.57,4.749,6.84846,7.96304,9.36038,10.0552,10.8394,11.7409,])
array_1_name = "Votes for the Democratic Presidential candidate in Maryland"
array_2_name = "Electricity generation in Costa Rica"

# 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 Democratic Presidential candidate in Maryland
Line chart for only Electricity generation in Costa Rica
AI-generated correlation image
The spurious research paper: Power Play: Shedding Light on the Relationship Between Democratic Votes in Maryland and Electricity Generation in Costa Rica

View another random correlation

How fun was this correlation?

Big thanks for reviewing!

Correlation ID: 4405 · Black Variable ID: 25988 · Red Variable ID: 23605


Problem variable:
Issue:
Additional details: Optional
Confirm you are a human: