Download png, svg
AI explanation
As the support for the Democrat candidate surged in Wisconsin, it created a 'powerful' political wave. This 'energy' wave somehow influenced Slovenian petrol consumption, sparking a 'fuelish' connection. It's as if the candidate's votes 'ignited' a chain reaction, leading to an unexpected 'diesel' of approval for petroleum products in Slovenia. The 'political climate change' in Wisconsin had an unforeseen ripple effect across the globe, leaving everyone 'oil' shook in disbelief at this 'unconventional' correlation.
Model: dalle-3
Prompt: Generate an image with a bold, futuristic aesthetic, inspired by data cyber art. The scene features a dynamic graph display, showcasing a sharp surge in Votes for the Democrat Presidential candidate in Wisconsin, depicted as a vibrant wave-like pattern. This powerful political movement is visually striking and indicative of a significant shift. From the same graph, integrate a juxtaposed set of petrol pumps in Slovenia, illustrating an unexpected increase in Petroleum consumption. These pumps should emanate a sense of energy and connection, symbolizing the fuelish correlation ignited by the political wave. The imagery should emphasize the unexpected relationship between these two variables, with the political wave in Wisconsin creating a ripple effect that sends shockwaves across the globe. Capture the essence of this correlation in a visually compelling and simplified manner, devoid of any words or speech bubbles. 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 Wisconsin and Petroluem consumption in Slovenia. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: a Polaroid photo High-tech A scene from Skyrim Concept art Data cyber art
ChatGPT: Data cyber art
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 Wisconsin and Petroluem consumption in Slovenia, in the following situation: As the support for the Democrat candidate surged in Wisconsin, it created a 'powerful' political wave. This 'energy' wave somehow influenced Slovenian petrol consumption, sparking a 'fuelish' connection. It's as if the candidate's votes 'ignited' a chain reaction, leading to an unexpected 'diesel' of approval for petroleum products in Slovenia. The 'political climate change' in Wisconsin had an unforeseen ripple effect across the globe, leaving everyone 'oil' shook in disbelief at this 'unconventional' correlation. . Keep the description simple.
ChatGPT: In a visual scene, imagine a graph showing a dramatic surge in Votes for the Democrat Presidential candidate in Wisconsin, represented by a wave-like pattern reflecting the powerful political movement. From the same graph, imagine a set of petrol pumps in Slovenia, showing an unexpected increase in Petroleum consumption, symbolizing the fuelish connection ignited by the political wave. The scene captures the unexpected correlation between the two variables, with the political wave in Wisconsin creating a ripple effect that sends shockwaves across the globe.
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: [ Data cyber art ] and depicting this scene: In a visual scene, imagine a graph showing a dramatic surge in Votes for the Democrat Presidential candidate in Wisconsin, represented by a wave-like pattern reflecting the powerful political movement. From the same graph, imagine a set of petrol pumps in Slovenia, showing an unexpected increase in Petroleum consumption, symbolizing the fuelish connection ignited by the political wave. The scene captures the unexpected correlation between the two variables, with the political wave in Wisconsin creating a ripple effect that sends shockwaves across the globe. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image with a bold, futuristic aesthetic, inspired by data cyber art. The scene features a dynamic graph display, showcasing a sharp surge in Votes for the Democrat Presidential candidate in Wisconsin, depicted as a vibrant wave-like pattern. This powerful political movement is visually striking and indicative of a significant shift. From the same graph, integrate a juxtaposed set of petrol pumps in Slovenia, illustrating an unexpected increase in Petroleum consumption. These pumps should emanate a sense of energy and connection, symbolizing the fuelish correlation ignited by the political wave. The imagery should emphasize the unexpected relationship between these two variables, with the political wave in Wisconsin creating a ripple effect that sends shockwaves across the globe. Capture the essence of this correlation in a visually compelling and simplified manner, devoid of any words or speech bubbles.
*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.
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 Wisconsin caused Petroluem consumption in Slovenia to increase. Include one good pun.
AI academic paper
 |
Flights of Votes: A Correlational Study of Democrat Presidential Support in Wisconsin and Petroleum Consumption in Slovenia The Journal of International Energy and Political Dynamics Jan 2024 Reminder: This paper is AI-generated. Not real! |
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.You write titles and abstracts of goofy academic research papers.
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, and add unexpected twists in your writing.
Please draft the title and abstract of an academic research paper presenting the findings of the connection between Votes for the Democrat Presidential candidate in Wisconsin and Petroluem consumption in Slovenia. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.
Rhyme words in the title.
Your research team used data from MIT Election Data and Science Lab, Harvard Dataverse and Energy Information Administration to assess this nagging question. You found a correlation coefficient of 0.8791495 and p < 0.01 for 1992 to 2020.
Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)
[[TITLE]]
Lorem ipsum dolor sit
[[ABSTRACT]]
Lorem ipsum dolor sit
ChatGPT:
[[TITLE]]
Flights of Votes: A Correlational Study of Democrat Presidential Support in Wisconsin and Petroleum Consumption in Slovenia
[[ABSTRACT]]
This paper presents the findings of a comprehensive correlational study examining the relationship between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, our research team embarked on this unlikely journey to unravel the mystery of how the political landscape in Wisconsin may echo across the petroleum consumption patterns in Slovenia.
Our analysis, spanning the years 1992 to 2020, revealed a striking correlation coefficient of 0.8791495 and a significance level of p < 0.01, prompting our team to approach the data with both caution and curiosity. While the causative mechanisms underlying this linkage remain elusive, our findings offer a plethora of puzzling possibilities and whimsical conjectures. We invite readers to join us in this tantalizing expedition through the intersection of political inclinations and petroleum predilections, where each statistical coefficient may hold more than meets the eye.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.You draft the introduction 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 introduction section of an academic research paper presenting the findings of the connection between Votes for the Democrat Presidential candidate in Wisconsin and Petroluem consumption in Slovenia. 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.
Here is the title and abstract of the paper:
[[TITLE]]
Flights of Votes: A Correlational Study of Democrat Presidential Support in Wisconsin and Petroleum Consumption in Slovenia
[[ABSTRACT]]
This paper presents the findings of a comprehensive correlational study examining the relationship between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, our research team embarked on this unlikely journey to unravel the mystery of how the political landscape in Wisconsin may echo across the petroleum consumption patterns in Slovenia.
Our analysis, spanning the years 1992 to 2020, revealed a striking correlation coefficient of 0.8791495 and a significance level of p < 0.01, prompting our team to approach the data with both caution and curiosity. While the causative mechanisms underlying this linkage remain elusive, our findings offer a plethora of puzzling possibilities and whimsical conjectures. We invite readers to join us in this tantalizing expedition through the intersection of political inclinations and petroleum predilections, where each statistical coefficient may hold more than meets the eye.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
[[INTRODUCTION]]
The interplay between the political landscape and societal behaviors continues to fascinate researchers and pundits alike. In this vein, our study delves into the curious nexus between support for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia. While seemingly disparate realms, the allure of uncovering potential associations between these two variables proved irresistible to our inquisitive research team.
One may question the rationale behind this unusual pairing of variables, but as the saying goes, "When in doubt, let the data do the talking." With quantitative rigor and unwavering determination, we embarked on an analytical odyssey that transcended geographical borders and political divides. Armed with election data from the MIT Election Data and Science Lab and petroleum consumption statistics from the Energy Information Administration, we sought to unravel the enigmatic threads connecting a Midwestern state's political preferences to a Central European nation's fuel consumption habits.
The synergy between statistical analysis and intellectual curiosity propelled us to unearth a correlation coefficient of 0.8791495 between the aforementioned variables. Now, before you raise an incredulous eyebrow, rest assured that we, too, initially did a double-take. This eyebrow-raising correlation, which achieved a significance level of p < 0.01, beckoned us into an arena of inquiry where speculation and skepticism coalesce.
As we venture further into the heartland of our statistical findings, we encourage readers to adopt an open-minded stance and embrace the serendipitous discoveries that may lie in wait. The tango between voter sentiment in Wisconsin and Slovenia's petroleum preferences unfurls a captivating narrative, one enriched with unexpected twists and confounding connections. As we wade through the numerical underbrush, our minds remain buoyant with tantalizing possibilities, for each coefficient may indeed harbor more than meets the eye.
In the following sections, we shall unfurl the tapestry of our methodology, data sources, empirical analyses, and, synonymously, the mirthful musings that have emerged from our scholarly escapade. Embracing the spirit of empirical inquiry and embracing the lighthearted allure of statistical revelations, we invite you to join us on this scholarly sojourn through the whimsical world of correlational curiosities.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up a literature review section of an academic research paper presenting the findings of the connection between Votes for the Democrat Presidential candidate in Wisconsin and Petroluem consumption in Slovenia. 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]]
Flights of Votes: A Correlational Study of Democrat Presidential Support in Wisconsin and Petroleum Consumption in Slovenia
[[ABSTRACT]]
This paper presents the findings of a comprehensive correlational study examining the relationship between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, our research team embarked on this unlikely journey to unravel the mystery of how the political landscape in Wisconsin may echo across the petroleum consumption patterns in Slovenia.
Our analysis, spanning the years 1992 to 2020, revealed a striking correlation coefficient of 0.8791495 and a significance level of p < 0.01, prompting our team to approach the data with both caution and curiosity. While the causative mechanisms underlying this linkage remain elusive, our findings offer a plethora of puzzling possibilities and whimsical conjectures. We invite readers to join us in this tantalizing expedition through the intersection of political inclinations and petroleum predilections, where each statistical coefficient may hold more than meets the eye.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The interplay between the political landscape and societal behaviors continues to fascinate researchers and pundits alike. In this vein, our study delves into the curious nexus between support for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia. While seemingly disparate realms, the allure of uncovering potential associations between these two variables proved irresistible to our inquisitive research team.
One may question the rationale behind this unusual pairing of variables, but as the saying goes, "When in doubt, let the data do the talking." With quantitative rigor and unwavering determination, we embarked on an analytical odyssey that transcended geographical borders and political divides. Armed with election data from the MIT Election Data and Science Lab and petroleum consumption statistics from the Energy Information Administration, we sought to unravel the enigmatic threads connecting a Midwestern state's political preferences to a Central European nation's fuel consumption habits.
The synergy between statistical analysis and intellectual curiosity propelled us to unearth a correlation coefficient of 0.8791495 between the aforementioned variables. Now, before you raise an incredulous eyebrow, rest assured that we, too, initially did a double-take. This eyebrow-raising correlation, which achieved a significance level of p < 0.01, beckoned us into an arena of inquiry where speculation and skepticism coalesce.
As we venture further into the heartland of our statistical findings, we encourage readers to adopt an open-minded stance and embrace the serendipitous discoveries that may lie in wait. The tango between voter sentiment in Wisconsin and Slovenia's petroleum preferences unfurls a captivating narrative, one enriched with unexpected twists and confounding connections. As we wade through the numerical underbrush, our minds remain buoyant with tantalizing possibilities, for each coefficient may indeed harbor more than meets the eye.
In the following sections, we shall unfurl the tapestry of our methodology, data sources, empirical analyses, and, synonymously, the mirthful musings that have emerged from our scholarly escapade. Embracing the spirit of empirical inquiry and embracing the lighthearted allure of statistical revelations, we invite you to join us on this scholarly sojourn through the whimsical world of correlational curiosities.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
The pursuit of knowledge, particularly within the realm of statistical inquiry, has led researchers across various domains to stumble upon the most unexpected and improbable correlations. In the quest to shed light on the seemingly incongruous relationship between the votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia, we delve into a plethora of scholarly works, each bearing its own nuggets of wisdom and, in some cases, unexpected wit.
In "Statistical Correlations: Uncovering Unlikely Connections," Smith et al. artfully elucidate the intricate dance of statistical significance, cautioning readers against falling prey to spurious correlations and serendipitous findings. While their work remains a beacon of methodological rigor, it inadvertently piqued our curiosity about the tantalizing prospect of uncovering correlations that defy conventional expectations. This prompt led us down an academic rabbit hole on the quest to explore the hitherto uncharted territory of political and petroleum peculiarities.
Doe and Jones, in their seminal work "Quantitative Quirks: Statistical Anomalies Beyond Belief," laid bare an array of seemingly inexplicable statistical associations, igniting a flame of intellectual inquisitiveness within our research team. Though their focus may not have been on the juxtaposition of political preferences and energy consumption, their work inspired us to embrace the whimsical side of statistical analysis, leading to unforeseen revelations and chuckle-inducing conjectures.
Transitioning from the venerable world of academic literature, we found ourselves drawn to non-fiction works that, though unrelated to our immediate research pursuit, held an undercurrent of relevance. In "The Art of Political Persuasion," the author deftly navigates the complex landscape of electoral dynamics, shedding light on the kaleidoscope of factors that shape voter sentiment. While the book may not directly address the correlation between electoral tendencies in Wisconsin and petroleum consumption in Slovenia, its exploration of political nuances and persuasive tactics offers a joyous contrast to the sobering world of statistical inquiry.
Moving further afield, we turned our attention to fiction works that, by some fortuitous twist of fate, seemed to resonate with the curious interplay of politics and petroleum. "Fueling the Fires of Democracy" by an acclaimed novelist weaves a tale of political intrigue set against the backdrop of a fictional Midwestern town, offering a whimsical parallel to our exploration of voter predilections in Wisconsin. While the parallels between fiction and empirical research may be tenuous at best, the subtle synchronicities between this narrative and our statistical odyssey served as a welcome respite from the analytical rigor that permeates our scholarly endeavors.
As our intellectual escapades transcended traditional academic literature, we found ourselves traversing the realm of social media, where snippets of insight and curious observations awaited. A tweet by a political pundit, wherein they mused about the potential impact of international elections on global energy trends, offered a moment of contemplation and, dare we say, amusement. While social media may not be the traditional bastion of scholarly discourse, the offhand remarks and pithy observations peppered throughout various platforms added an unexpected layer of levity to our scholarly pursuits.
In essence, our foray into the nexus between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia led us down an intellectually rich and occasionally whimsical path. As we navigate the terrain of scholarly inquiry, we are reminded that even in the most unexpected of correlations, humor and light-hearted musings may be found, paving the way for a scholarly sojourn that transcends the bounds of traditional statistical analysis.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Democrat Presidential candidate in Wisconsin and Petroluem consumption in Slovenia. 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 1992 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]]
Flights of Votes: A Correlational Study of Democrat Presidential Support in Wisconsin and Petroleum Consumption in Slovenia
[[ABSTRACT]]
This paper presents the findings of a comprehensive correlational study examining the relationship between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, our research team embarked on this unlikely journey to unravel the mystery of how the political landscape in Wisconsin may echo across the petroleum consumption patterns in Slovenia.
Our analysis, spanning the years 1992 to 2020, revealed a striking correlation coefficient of 0.8791495 and a significance level of p < 0.01, prompting our team to approach the data with both caution and curiosity. While the causative mechanisms underlying this linkage remain elusive, our findings offer a plethora of puzzling possibilities and whimsical conjectures. We invite readers to join us in this tantalizing expedition through the intersection of political inclinations and petroleum predilections, where each statistical coefficient may hold more than meets the eye.
[[INTRODUCTION]]
The interplay between the political landscape and societal behaviors continues to fascinate researchers and pundits alike. In this vein, our study delves into the curious nexus between support for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia. While seemingly disparate realms, the allure of uncovering potential associations between these two variables proved irresistible to our inquisitive research team.
One may question the rationale behind this unusual pairing of variables, but as the saying goes, "When in doubt, let the data do the talking." With quantitative rigor and unwavering determination, we embarked on an analytical odyssey that transcended geographical borders and political divides. Armed with election data from the MIT Election Data and Science Lab and petroleum consumption statistics from the Energy Information Administration, we sought to unravel the enigmatic threads connecting a Midwestern state's political preferences to a Central European nation's fuel consumption habits.
The synergy between statistical analysis and intellectual curiosity propelled us to unearth a correlation coefficient of 0.8791495 between the aforementioned variables. Now, before you raise an incredulous eyebrow, rest assured that we, too, initially did a double-take. This eyebrow-raising correlation, which achieved a significance level of p < 0.01, beckoned us into an arena of inquiry where speculation and skepticism coalesce.
As we venture further into the heartland of our statistical findings, we encourage readers to adopt an open-minded stance and embrace the serendipitous discoveries that may lie in wait. The tango between voter sentiment in Wisconsin and Slovenia's petroleum preferences unfurls a captivating narrative, one enriched with unexpected twists and confounding connections. As we wade through the numerical underbrush, our minds remain buoyant with tantalizing possibilities, for each coefficient may indeed harbor more than meets the eye.
In the following sections, we shall unfurl the tapestry of our methodology, data sources, empirical analyses, and, synonymously, the mirthful musings that have emerged from our scholarly escapade. Embracing the spirit of empirical inquiry and embracing the lighthearted allure of statistical revelations, we invite you to join us on this scholarly sojourn through the whimsical world of correlational curiosities.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
To embark on our esoteric exploration of the interplay between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia, we devised a methodological framework that blended quantitative prowess with inquisitive acumen. Our endeavor commenced with an arduous endeavor to source, curate, and reconcile data from disparate domains, akin to voyaging through a labyrinth of statistical serendipity.
Drawing from the MIT Election Data and Science Lab, we meticulously gathered historical records of votes for the Democrat presidential candidate in Wisconsin from 1992 to 2020, traversing the electoral landscape with the gaiety of a data-driven bard. Concurrently, our foray into the realm of petroleum predilections led us to the Energy Information Administration, where we unearthed a trove of statistical gems pertaining to Slovenia's petroleum consumption during the aforementioned period. This harmonizing of election data and energy statistics incited a scholarly pas de deux that transcended traditional disciplinary boundaries with finesse and fervor.
In aligning the idiosyncrasies of these datasets, we confronted the bewildering task of synchronizing temporal, regional, and contextual nuances, akin to weaving a statistical tapestry that unified the disparate threads of political proclivities and petrochemical ardor. In acquiescing to the tenets of quantitative scholarship, we meticulously scrubbed the data, honing our sleuthing skills to discern any anomalies, outliers, or mischievous data points that dared to taint the purity of our statistical tableau.
Once the data were cleansed of any blemishes, we indulged in the waltz of statistical analysis, summoning the venerable spear of correlation to discern the dance of association between the electoral proclivities of Wisconsin's denizens and the fuel-sipping habits of Slovenia's populace. Augmenting our analytical gambit with the pythonic elegance of regression, we sought to trace the potential causal threads that may underlie the observed correlations, akin to practicing the statistical sorcery that elicits both wonder and intrigue.
Moreover, we ventured into the orb of robustness checks and sensitivity analyses, akin to peering through the kaleidoscope of statistical validation to discern if our findings would remain resolute under the inexorable gaze of scrutiny. Through this methodological safari, we sought to fortify the foundations of our statistical musings, ensuring that the whimsical correlations we uncovered were not mere phantasms of statistical caprice.
As we unravel the narrative of our empirical analyses in the subsequent sections, we implore our esteemed readers to embrace the statistical spectacle that has unfolded, replete with unexpected twists and scholarly sallies into the realm of correlational intrigue. This methodological odyssey, though steeped in empirical rigor, is embellished with the dalliances of academic wit, beckoning readers to revel in the mirth that oftentimes ensues from scholarly escapades through the realms of data and discovery.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Democrat Presidential candidate in Wisconsin and Petroluem consumption in Slovenia. 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 1992 to 2020, you found a correlation 0.8791495, r-squared of 0.7729038, 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]]
Flights of Votes: A Correlational Study of Democrat Presidential Support in Wisconsin and Petroleum Consumption in Slovenia
[[ABSTRACT]]
This paper presents the findings of a comprehensive correlational study examining the relationship between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, our research team embarked on this unlikely journey to unravel the mystery of how the political landscape in Wisconsin may echo across the petroleum consumption patterns in Slovenia.
Our analysis, spanning the years 1992 to 2020, revealed a striking correlation coefficient of 0.8791495 and a significance level of p < 0.01, prompting our team to approach the data with both caution and curiosity. While the causative mechanisms underlying this linkage remain elusive, our findings offer a plethora of puzzling possibilities and whimsical conjectures. We invite readers to join us in this tantalizing expedition through the intersection of political inclinations and petroleum predilections, where each statistical coefficient may hold more than meets the eye.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
The interplay between the political landscape and societal behaviors continues to fascinate researchers and pundits alike. In this vein, our study delves into the curious nexus between support for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia. While seemingly disparate realms, the allure of uncovering potential associations between these two variables proved irresistible to our inquisitive research team.
One may question the rationale behind this unusual pairing of variables, but as the saying goes, "When in doubt, let the data do the talking." With quantitative rigor and unwavering determination, we embarked on an analytical odyssey that transcended geographical borders and political divides. Armed with election data from the MIT Election Data and Science Lab and petroleum consumption statistics from the Energy Information Administration, we sought to unravel the enigmatic threads connecting a Midwestern state's political preferences to a Central European nation's fuel consumption habits.
The synergy between statistical analysis and intellectual curiosity propelled us to unearth a correlation coefficient of 0.8791495 between the aforementioned variables. Now, before you raise an incredulous eyebrow, rest assured that we, too, initially did a double-take. This eyebrow-raising correlation, which achieved a significance level of p < 0.01, beckoned us into an arena of inquiry where speculation and skepticism coalesce.
As we venture further into the heartland of our statistical findings, we encourage readers to adopt an open-minded stance and embrace the serendipitous discoveries that may lie in wait. The tango between voter sentiment in Wisconsin and Slovenia's petroleum preferences unfurls a captivating narrative, one enriched with unexpected twists and confounding connections. As we wade through the numerical underbrush, our minds remain buoyant with tantalizing possibilities, for each coefficient may indeed harbor more than meets the eye.
In the following sections, we shall unfurl the tapestry of our methodology, data sources, empirical analyses, and, synonymously, the mirthful musings that have emerged from our scholarly escapade. Embracing the spirit of empirical inquiry and embracing the lighthearted allure of statistical revelations, we invite you to join us on this scholarly sojourn through the whimsical world of correlational curiosities.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
The correlation analysis between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia yielded an intriguing coefficient of 0.8791495, with an r-squared of 0.7729038. This robust correlation, surpassing the conventional threshold for statistical significance at p < 0.01, beckons contemplation and further scrutiny.
To visually encapsulate this seemingly outlandish yet captivating relationship, we present Fig. 1, a scatterplot encapsulating the entwined trajectories of political support in Wisconsin and petroleum consumption in Slovenia. This graphic representation vividly underscores the striking association between the two variables, akin to an unexpected serendipity in the realm of statistical inquiry.
While the underlying mechanisms engendering this correlation remain veiled in ambiguity, our findings provoke an intellectual dance between skepticism and intrigue. The apparition of this pronounced correlation serves as a testament to the unfathomable quirks of statistical reality. As much as the scholarly mind yearns for tidy, straightforward explanations, the contours of causation in this case elicit a wry smile and a raised eyebrow.
In light of these findings, we invite readers to partake in this academic symbiosis of gravitas and gaiety, as we navigate the tantalizing labyrinth of correlational peculiarities. The resonance between the political pulse of Wisconsin and Slovenia's proclivity for petroleum offers a melange of possibilities, each one stirring the fabric of conventional wisdom and kindling the embers of intellectual amusement.
In summation, our findings evoke a harmonious discord, an enigmatic riddle that teases the intellect and propels us to embrace the whimsical vicissitudes of statistical inquiry. It is within this tapestry of correlations and conjectures that the true essence of scholarly exploration emerges, unraveling the nexus between seemingly distant phenomena with an irrepressible spirit of curiosity and mirthful contemplation.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the discussion section of an academic research paper presenting the findings of the connection between Votes for the Democrat Presidential candidate in Wisconsin and Petroluem consumption in Slovenia. 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]]
Flights of Votes: A Correlational Study of Democrat Presidential Support in Wisconsin and Petroleum Consumption in Slovenia
[[ABSTRACT]]
This paper presents the findings of a comprehensive correlational study examining the relationship between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, our research team embarked on this unlikely journey to unravel the mystery of how the political landscape in Wisconsin may echo across the petroleum consumption patterns in Slovenia.
Our analysis, spanning the years 1992 to 2020, revealed a striking correlation coefficient of 0.8791495 and a significance level of p < 0.01, prompting our team to approach the data with both caution and curiosity. While the causative mechanisms underlying this linkage remain elusive, our findings offer a plethora of puzzling possibilities and whimsical conjectures. We invite readers to join us in this tantalizing expedition through the intersection of political inclinations and petroleum predilections, where each statistical coefficient may hold more than meets the eye.
[[LITERATURE REVIEW]]
The pursuit of knowledge, particularly within the realm of statistical inquiry, has led researchers across various domains to stumble upon the most unexpected and improbable correlations. In the quest to shed light on the seemingly incongruous relationship between the votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia, we delve into a plethora of scholarly works, each bearing its own nuggets of wisdom and, in some cases, unexpected wit.
In "Statistical Correlations: Uncovering Unlikely Connections," Smith et al. artfully elucidate the intricate dance of statistical significance, cautioning readers against falling prey to spurious correlations and serendipitous findings. While their work remains a beacon of methodological rigor, it inadvertently piqued our curiosity about the tantalizing prospect of uncovering correlations that defy conventional expectations. This prompt led us down an academic rabbit hole on the quest to explore the hitherto uncharted territory of political and petroleum peculiarities.
Doe and Jones, in their seminal work "Quantitative Quirks: Statistical Anomalies Beyond Belief," laid bare an array of seemingly inexplicable statistical associations, igniting a flame of intellectual inquisitiveness within our research team. Though their focus may not have been on the juxtaposition of political preferences and energy consumption, their work inspired us to embrace the whimsical side of statistical analysis, leading to unforeseen revelations and chuckle-inducing conjectures.
Transitioning from the venerable world of academic literature, we found ourselves drawn to non-fiction works that, though unrelated to our immediate research pursuit, held an undercurrent of relevance. In "The Art of Political Persuasion," the author deftly navigates the complex landscape of electoral dynamics, shedding light on the kaleidoscope of factors that shape voter sentiment. While the book may not directly address the correlation between electoral tendencies in Wisconsin and petroleum consumption in Slovenia, its exploration of political nuances and persuasive tactics offers a joyous contrast to the sobering world of statistical inquiry.
Moving further afield, we turned our attention to fiction works that, by some fortuitous twist of fate, seemed to resonate with the curious interplay of politics and petroleum. "Fueling the Fires of Democracy" by an acclaimed novelist weaves a tale of political intrigue set against the backdrop of a fictional Midwestern town, offering a whimsical parallel to our exploration of voter predilections in Wisconsin. While the parallels between fiction and empirical research may be tenuous at best, the subtle synchronicities between this narrative and our statistical odyssey served as a welcome respite from the analytical rigor that permeates our scholarly endeavors.
As our intellectual escapades transcended traditional academic literature, we found ourselves traversing the realm of social media, where snippets of insight and curious observations awaited. A tweet by a political pundit, wherein they mused about the potential impact of international elections on global energy trends, offered a moment of contemplation and, dare we say, amusement. While social media may not be the traditional bastion of scholarly discourse, the offhand remarks and pithy observations peppered throughout various platforms added an unexpected layer of levity to our scholarly pursuits.
In essence, our foray into the nexus between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia led us down an intellectually rich and occasionally whimsical path. As we navigate the terrain of scholarly inquiry, we are reminded that even in the most unexpected of correlations, humor and light-hearted musings may be found, paving the way for a scholarly sojourn that transcends the bounds of traditional statistical analysis.
[[RESULTS]]
The correlation analysis between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia yielded an intriguing coefficient of 0.8791495, with an r-squared of 0.7729038. This robust correlation, surpassing the conventional threshold for statistical significance at p < 0.01, beckons contemplation and further scrutiny.
To visually encapsulate this seemingly outlandish yet captivating relationship, we present Fig. 1, a scatterplot encapsulating the entwined trajectories of political support in Wisconsin and petroleum consumption in Slovenia. This graphic representation vividly underscores the striking association between the two variables, akin to an unexpected serendipity in the realm of statistical inquiry.
While the underlying mechanisms engendering this correlation remain veiled in ambiguity, our findings provoke an intellectual dance between skepticism and intrigue. The apparition of this pronounced correlation serves as a testament to the unfathomable quirks of statistical reality. As much as the scholarly mind yearns for tidy, straightforward explanations, the contours of causation in this case elicit a wry smile and a raised eyebrow.
In light of these findings, we invite readers to partake in this academic symbiosis of gravitas and gaiety, as we navigate the tantalizing labyrinth of correlational peculiarities. The resonance between the political pulse of Wisconsin and Slovenia's proclivity for petroleum offers a melange of possibilities, each one stirring the fabric of conventional wisdom and kindling the embers of intellectual amusement.
In summation, our findings evoke a harmonious discord, an enigmatic riddle that teases the intellect and propels us to embrace the whimsical vicissitudes of statistical inquiry. It is within this tapestry of correlations and conjectures that the true essence of scholarly exploration emerges, unraveling the nexus between seemingly distant phenomena with an irrepressible spirit of curiosity and mirthful contemplation.
-------
Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
The uncanny connection between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia, as unveiled by our correlational analysis, invites a merry dance of scholarly discourse. Our findings tantalizingly concur with prior research that hinted at the whimsical nature of statistical associations. The fabled statistical quirk celebrated by Smith et al. has found a spirited counterpart in the improbable rapport between political predilections and energy indulgences. In a wry twist, the quantitative anomalies championed by Doe and Jones seem to have sown the seeds of our own delightfully unexpected revelation.
The robust correlation coefficient, glimmering with significance at p < 0.01, stands as a testament to the delightfully capricious nature of statistical reality. This curious liaison between Wisconsin's electoral tapestry and Slovenia's petroleum penchant defies the conventional doldrums of causative certitude, stirring the scholarly spirit with a subtle nod to the serendipitous landscapes that statistical analysis often unveils.
As we ponder the befuddling embrace of these seemingly disparate variables, one cannot help but be reminded of the amusing resonances echoing from the interdisciplinary fringes of research. The subtle parallel to "Fueling the Fires of Democracy," a fictional tale of political intrigue, tugs at the threads of statistical whimsy, threading a playful harmony between the realm of scholarly inquiry and the captivating allure of literary fancy.
Nestled within the robustness of our findings lies an enigmatic riddle, an intellectual soiree that beckons both the somber-faced empiricist and the mirthful sage of statistical whimsy. Could it be that the echoes of democracy in the heartlands of America ripple across the oceans to breathe life into the engines of industry in Slovenia? Our study, with its robust correlation coefficient and tantalizing r-squared, leaves us with more questions than answers, igniting the flames of scholarly curiosity and mirthful contemplation.
In this tantalizing tango between politics and petroleum, we are emboldened to embrace the lighthearted reverie that underscores the most unlikely of statistical associations. As we peer through the statistical looking glass, it becomes evident that the corridors of academia harbor more than just the staid echoes of empirical inquiry; they also reverberate with the vivacious hum of intellectual amusement and unexpected conjecture.
Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up a short conclusion of an academic research paper presenting the findings of the connection between Votes for the Democrat Presidential candidate in Wisconsin and Petroluem consumption in Slovenia. 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]]
Flights of Votes: A Correlational Study of Democrat Presidential Support in Wisconsin and Petroleum Consumption in Slovenia
[[ABSTRACT]]
This paper presents the findings of a comprehensive correlational study examining the relationship between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, our research team embarked on this unlikely journey to unravel the mystery of how the political landscape in Wisconsin may echo across the petroleum consumption patterns in Slovenia.
Our analysis, spanning the years 1992 to 2020, revealed a striking correlation coefficient of 0.8791495 and a significance level of p < 0.01, prompting our team to approach the data with both caution and curiosity. While the causative mechanisms underlying this linkage remain elusive, our findings offer a plethora of puzzling possibilities and whimsical conjectures. We invite readers to join us in this tantalizing expedition through the intersection of political inclinations and petroleum predilections, where each statistical coefficient may hold more than meets the eye.
[[INTRDUCTION]]
The interplay between the political landscape and societal behaviors continues to fascinate researchers and pundits alike. In this vein, our study delves into the curious nexus between support for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia. While seemingly disparate realms, the allure of uncovering potential associations between these two variables proved irresistible to our inquisitive research team.
One may question the rationale behind this unusual pairing of variables, but as the saying goes, "When in doubt, let the data do the talking." With quantitative rigor and unwavering determination, we embarked on an analytical odyssey that transcended geographical borders and political divides. Armed with election data from the MIT Election Data and Science Lab and petroleum consumption statistics from the Energy Information Administration, we sought to unravel the enigmatic threads connecting a Midwestern state's political preferences to a Central European nation's fuel consumption habits.
The synergy between statistical analysis and intellectual curiosity propelled us to unearth a correlation coefficient of 0.8791495 between the aforementioned variables. Now, before you raise an incredulous eyebrow, rest assured that we, too, initially did a double-take. This eyebrow-raising correlation, which achieved a significance level of p < 0.01, beckoned us into an arena of inquiry where speculation and skepticism coalesce.
As we venture further into the heartland of our statistical findings, we encourage readers to adopt an open-minded stance and embrace the serendipitous discoveries that may lie in wait. The tango between voter sentiment in Wisconsin and Slovenia's petroleum preferences unfurls a captivating narrative, one enriched with unexpected twists and confounding connections. As we wade through the numerical underbrush, our minds remain buoyant with tantalizing possibilities, for each coefficient may indeed harbor more than meets the eye.
In the following sections, we shall unfurl the tapestry of our methodology, data sources, empirical analyses, and, synonymously, the mirthful musings that have emerged from our scholarly escapade. Embracing the spirit of empirical inquiry and embracing the lighthearted allure of statistical revelations, we invite you to join us on this scholarly sojourn through the whimsical world of correlational curiosities.
[[RESULTS]]
The correlation analysis between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia yielded an intriguing coefficient of 0.8791495, with an r-squared of 0.7729038. This robust correlation, surpassing the conventional threshold for statistical significance at p < 0.01, beckons contemplation and further scrutiny.
To visually encapsulate this seemingly outlandish yet captivating relationship, we present Fig. 1, a scatterplot encapsulating the entwined trajectories of political support in Wisconsin and petroleum consumption in Slovenia. This graphic representation vividly underscores the striking association between the two variables, akin to an unexpected serendipity in the realm of statistical inquiry.
While the underlying mechanisms engendering this correlation remain veiled in ambiguity, our findings provoke an intellectual dance between skepticism and intrigue. The apparition of this pronounced correlation serves as a testament to the unfathomable quirks of statistical reality. As much as the scholarly mind yearns for tidy, straightforward explanations, the contours of causation in this case elicit a wry smile and a raised eyebrow.
In light of these findings, we invite readers to partake in this academic symbiosis of gravitas and gaiety, as we navigate the tantalizing labyrinth of correlational peculiarities. The resonance between the political pulse of Wisconsin and Slovenia's proclivity for petroleum offers a melange of possibilities, each one stirring the fabric of conventional wisdom and kindling the embers of intellectual amusement.
In summation, our findings evoke a harmonious discord, an enigmatic riddle that teases the intellect and propels us to embrace the whimsical vicissitudes of statistical inquiry. It is within this tapestry of correlations and conjectures that the true essence of scholarly exploration emerges, unraveling the nexus between seemingly distant phenomena with an irrepressible spirit of curiosity and mirthful contemplation.
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In conclusion, the correlation between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia has unfurled a tapestry of statistical astonishment and whimsical reverie. The robust coefficient of 0.8791495, coupled with its resounding significance at p < 0.01, paints a portrait of interconnectedness that transcends mere happenstance. While the Sisyphean quest for causation may linger on the scholarly horizon, our findings beckon with a sly wink and a nod to the capricious capers of statistical inquiry.
As we bid adieu to this serendipitous saga, we cannot help but marvel at the confounding correlations that bridge geographical expanse and political proclivity. The dance between Wisconsin's democratic sentiments and Slovenia's petroleum pursuits unearths a symphony of statistical curiosity, resonating with a cacophony of conjectures and mischievous musings.
In light of these findings, it seems that no further expedition into this enigmatic alliance between electoral allegiance and oil consumption is warranted. The scholarly quest for correlation may halt here, leaving us with tantalizing mysteries and a wistful smile, as we embrace the capricious and comedic contours of statistical reality.
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 Flights of Votes: A Correlational Study of Democrat Presidential Support in Wisconsin and Petroleum Consumption in Slovenia. Respond only with the name of a fictional academic journal.
ChatGPT:
The Journal of International Energy and Political Dynamics
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 Flights of Votes: A Correlational Study of Democrat Presidential Support in Wisconsin and Petroleum Consumption in Slovenia. Respond only with the name of a fictional organization.
ChatGPT:
The Institute for Comparative Political Consumption
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 Flights of Votes: A Correlational Study of Democrat Presidential Support in Wisconsin and Petroleum Consumption in Slovenia with an abstract of This paper presents the findings of a comprehensive correlational study examining the relationship between votes for the Democrat presidential candidate in Wisconsin and petroleum consumption in Slovenia. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Energy Information Administration, our research team embarked on this unlikely journey to unravel the mystery of how the political landscape in Wisconsin may echo across the petroleum consumption patterns in Slovenia.
Our analysis, spanning the years 1992 to 2020, revealed a striking correlation coefficient of 0.8791495 and a significance level of p < 0.01, prompting our team to approach the data with both caution and curiosity. While the causative mechanisms underlying this linkage remain elusive, our findings offer a plethora of puzzling possibilities and whimsical conjectures. We invite readers to join us in this tantalizing expedition through the intersection of political inclinations and petroleum predilections, where each statistical coefficient may hold more than meets the eye.
ChatGPT:
Democrat presidential support, Wisconsin, petroleum consumption, Slovenia, correlational study, MIT Election Data and Science Lab, Harvard Dataverse, Energy Information Administration, political landscape, correlation coefficient, significance level, causative mechanisms, statistical coefficient, political inclinations, petroleum predilections
*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.
Discover a new correlation
View all correlations
View all research papers
Report an error
Data details
Votes for the Democratic Presidential candidate in WisconsinDetailed data title: Percentage of all votes cast for the Democrat Presidential candidate in Wisconsin
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for the Democratic Presidential candidate in Wisconsin
Petroluem consumption in Slovenia
Detailed data title: Volume of petroluem consumption consumed in Slovenia in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Petroluem consumption in Slovenia
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 = 0.7729038 (Coefficient of determination)
This means 77.3% of the change in the one variable (i.e., Petroluem consumption in Slovenia) is predictable based on the change in the other (i.e., Votes for the Democratic Presidential candidate in Wisconsin) over the 8 years from 1992 through 2020.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00402. 0.0040222357362305810000000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. 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 0.88 in 0.402% of random cases. Said differently, if you correlated 249 random variables Which I absolutely did.
with the same 7 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 7 because we have two variables measured over a period of 8 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.
[ 0.46, 0.98 ] 95% correlation confidence interval (using the Fisher z-transformation)
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: If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.
Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.
| 1992 | 1996 | 2000 | 2004 | 2008 | 2012 | 2016 | 2020 | |
| Votes for the Democratic Presidential candidate in Wisconsin (Percentage of votes) | 41.1316 | 48.811 | 47.8328 | 49.6997 | 56.2178 | 52.7762 | 46.4538 | 49.4495 |
| Petroluem consumption in Slovenia (Million Barrels/Day) | 33.2268 | 55.8142 | 50.1612 | 52.4317 | 64.0656 | 54.0683 | 52.3197 | 45.8797 |
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
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
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. 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([41.1316,48.811,47.8328,49.6997,56.2178,52.7762,46.4538,49.4495,])
array_2 = np.array([33.2268,55.8142,50.1612,52.4317,64.0656,54.0683,52.3197,45.8797,])
array_1_name = "Votes for the Democratic Presidential candidate in Wisconsin"
array_2_name = "Petroluem consumption in Slovenia"
# 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. 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
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 Wisconsin
- Line chart for only Petroluem consumption in Slovenia
- AI-generated correlation image
- The spurious research paper: Flights of Votes: A Correlational Study of Democrat Presidential Support in Wisconsin and Petroleum Consumption in Slovenia
Kudos for rating!
Correlation ID: 4615 · Black Variable ID: 26190 · Red Variable ID: 24907
