Spurious correlation #5,159 · 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 Republican Senators in Maryland and the second variable is Google searches for 'burn centers'. The chart goes from 2004 to 2018, 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 Republican Senators in Maryland and the second variable is Google searches for 'burn centers'. The chart goes from 2004 to 2018, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As Republican votes for Senators in Maryland decreased, there was a corresponding decrease in political burn. With fewer fiery debates and heated discussions, the need for burn centers diminished, leading to a cooler political climate in more ways than one. It seems like even the healthcare system couldn't escape the influence of the political landscape - talk about feeling the political heat!

^{Show image generation prompt} Model: dalle-3
Prompt: You can try generating an image with the following prompt: [ Photorealism ] Create an image of a bustling city with a large billboard prominently displaying a graph depicting the decline in Republican votes for Senators in Maryland. Adjacent to the billboard, there is a computer screen displaying a decreasing trend in Google searches for "burn centers". In the foreground, depict a serene political debate where individuals engage in calm and respectful discussions, reflecting a cooler political climate. In the background, include a hospital with fewer patients in the burn unit, symbolizing the indirect impact of the political landscape on healthcare. 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: Republican votes for Senators in Maryland and Google searches for 'burn centers'. 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: Vaporwave Photorealism Realistic Cyber folk Scalar art
ChatGPT: Photorealism

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 Republican votes for Senators in Maryland and Google searches for 'burn centers', in the following situation: As Republican votes for Senators in Maryland decreased, there was a corresponding decrease in political burn. With fewer fiery debates and heated discussions, the need for burn centers diminished, leading to a cooler political climate in more ways than one. It seems like even the healthcare system couldn't escape the influence of the political landscape - talk about feeling the political heat!. Keep the description simple.
ChatGPT: In a bustling city, a graph on a billboard shows a decline in Republican votes for Senators in Maryland. Alongside it, a computer screen displays a decreasing trend in Google searches for "burn centers". The scene is juxtaposed with a serene political debate, as people engage in calm and respectful discussions, reflecting a cooler political climate. In the background, a hospital with fewer patients in the burn unit symbolizes the indirect impact of the political landscape on healthcare.

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: [ Photorealism ] and depicting this scene: In a bustling city, a graph on a billboard shows a decline in Republican votes for Senators in Maryland. Alongside it, a computer screen displays a decreasing trend in Google searches for "burn centers". The scene is juxtaposed with a serene political debate, as people engage in calm and respectful discussions, reflecting a cooler political climate. In the background, a hospital with fewer patients in the burn unit symbolizes the indirect impact of the political landscape on healthcare. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: You can try generating an image with the following prompt: [ Photorealism ] Create an image of a bustling city with a large billboard prominently displaying a graph depicting the decline in Republican votes for Senators in Maryland. Adjacent to the billboard, there is a computer screen displaying a decreasing trend in Google searches for "burn centers". In the foreground, depict a serene political debate where individuals engage in calm and respectful discussions, reflecting a cooler political climate. In the background, include a hospital with fewer patients in the burn unit, symbolizing the indirect impact of the political landscape on healthcare.

*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 a decrease in Republican votes for Senators in Maryland caused burn centers (Google searches for 'burn centers') to decrease. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.05)

Feeling the Heat: Exploring the Relationship Between Republican Votes for Senators in Maryland and Google Searches for Burn Centers

The Journal of Political Google Searches and Public Health Voting Patterns

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 Republican votes for Senators in Maryland and Google searches for 'burn centers'. Make lots of jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name a couple popular internet memes that are related to one of the topics.

Here is the title and abstract of the paper:
[[TITLE]]
Feeling the Heat: Exploring the Relationship Between Republican Votes for Senators in Maryland and Google Searches for Burn Centers

[[ABSTRACT]]
In this study, we set out to investigate the surprising correlation between Republican votes for Senators in Maryland and the frequency of Google searches for "burn centers." With data spanning from 2004 to 2018 sourced from the MIT Election Data and Science Lab, Harvard Dataverse, and Google Trends, we embarked on a quirky quest to shed light on this peculiar association.

Uncovering a correlation coefficient of 0.9031699 and a statistically significant p-value, our findings raise eyebrows and generate more questions than answers. Is there a smoldering political divide that drives people to search for burn centers? Could it be a metaphorical reflection of voters feeling "burned" by political results? Or is it simply a fluke of the data, an odd flaring of coincidence?

This research not only adds a touch of sizzle to the typically serious realm of political analysis but also underscores the need for exploring unanticipated correlations that may ignite new avenues of inquiry. So, as we navigate the labyrinth of causation and correlation, let's fan the flames of curiosity and delve into the unexpected heat radiating from this offbeat fusion of politics and search behavior.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
In the scorching arena of political analysis, where debates often turn to fiery exchanges, one might not expect to find a link to the seemingly unrelated world of burn centers. Yet, as we dive into the labyrinth of data and statistics, we stumbled upon a peculiar connection that left us feeling quite singed – the relationship between Republican votes for Senators in Maryland and the frequency of Google searches for burn centers. This unexpected correlation sparked our curiosity, leading us to embark on a journey through the wild and whimsical realm of unanticipated statistical relationships.
As we fanned the flames of inquiry, we found ourselves face to face with a correlation coefficient of 0.9031699, igniting both fascination and bewilderment. With a statistically significant p-value, our findings not only turned up the heat on conventional political analysis but also left us scrambling to make sense of this odd pairing of variables.
Now, one might wonder, what could possibly fuel such a connection? Could it be a metaphorical reflection of voters feeling the political heat and seeking refuge in the form of burn centers? Or perhaps, a sly manifestation of political "burnout" casting a smoldering shadow on search behavior? As we navigate this enigmatic landscape of causation and correlation, it's clear that we've stumbled upon a finding that is anything but a flash in the pan.
This quirky quest for understanding not only adds a touch of sizzle to the otherwise serious world of political research but also reminds us that in the realm of data analysis, the unexpected can often set the stage for scientific discovery. So, as we delve into the fiery fusion of politics and search behavior, let's embrace the heat and kindle the flames of curiosity as we explore the uncommon and unconventional in the landscape of statistical relationships.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

To begin our exploration of the perplexing nexus between Republican votes for Senators in Maryland and the frequency of Google searches for burn centers, we turn our attention to the scholarly pursuit of uncovering unexpected correlations. Smith (2015) aptly captures the essence of such endeavors in their seminal work, "Unearthing Unforeseen Associations in Political Data," where they illuminate the thrill and challenge of unraveling enigmatic statistical relationships.

Continuing in the realm of serious inquiry, Doe (2018) delves into the intricacies of voter behavior and the underlying motivations that drive political affiliations in "Voting Patterns and Their Psychological Underpinnings," providing valuable insights into the factors that may influence the choices of Maryland residents at the ballot box.

However, as we venture further into this unusual intersection of politics and seemingly unrelated phenomena, it becomes evident that the landscape is not devoid of whimsical and unconventional connections. "Burn Centers: A Comprehensive Guide" by Jones (2016) offers a detailed exploration of the critical role played by burn centers in healthcare, providing a foundation for understanding the significance of the term in the public consciousness.

Shifting our focus slightly, we pivot to the world of fiction, where we encounter works that, albeit not directly related to our topic, carry intriguing titles that add a touch of levity to our scholarly pursuits. "Burning Bridges: A Political Saga" by A. Novel (2017) and "The Fahrenheit Follies" by S. Story (2014) may not offer empirical insights, but their titles provide a whimsical nod to the fiery theme we find ourselves engulfed in.

In a surprising twist of internet culture intersecting with our subject matter, the viral meme "Feeling the Bern" emerges as a playful reflection of political fervor, injecting an unexpected note of humor into our investigation. As we seek to untangle the web of correlations, it's evident that the unconventional can often prove to be an unexpected source of inspiration in the pursuit of knowledge.

With this eclectic array of sources, both serious and lighthearted, we set the stage for an examination that not only sheds light on this curious correlation but also underscores the compelling and sometimes comical nature of unanticipated statistical relationships.

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 Republican votes for Senators in Maryland and Google searches for 'burn centers'. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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 Google Trends . You used data from 2004 to 2018

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]]
Feeling the Heat: Exploring the Relationship Between Republican Votes for Senators in Maryland and Google Searches for Burn Centers

[[ABSTRACT]]
In this study, we set out to investigate the surprising correlation between Republican votes for Senators in Maryland and the frequency of Google searches for "burn centers." With data spanning from 2004 to 2018 sourced from the MIT Election Data and Science Lab, Harvard Dataverse, and Google Trends, we embarked on a quirky quest to shed light on this peculiar association.

Uncovering a correlation coefficient of 0.9031699 and a statistically significant p-value, our findings raise eyebrows and generate more questions than answers. Is there a smoldering political divide that drives people to search for burn centers? Could it be a metaphorical reflection of voters feeling "burned" by political results? Or is it simply a fluke of the data, an odd flaring of coincidence?

This research not only adds a touch of sizzle to the typically serious realm of political analysis but also underscores the need for exploring unanticipated correlations that may ignite new avenues of inquiry. So, as we navigate the labyrinth of causation and correlation, let's fan the flames of curiosity and delve into the unexpected heat radiating from this offbeat fusion of politics and search behavior.

[[INTRODUCTION]]
In the scorching arena of political analysis, where debates often turn to fiery exchanges, one might not expect to find a link to the seemingly unrelated world of burn centers. Yet, as we dive into the labyrinth of data and statistics, we stumbled upon a peculiar connection that left us feeling quite singed – the relationship between Republican votes for Senators in Maryland and the frequency of Google searches for burn centers. This unexpected correlation sparked our curiosity, leading us to embark on a journey through the wild and whimsical realm of unanticipated statistical relationships.
As we fanned the flames of inquiry, we found ourselves face to face with a correlation coefficient of 0.9031699, igniting both fascination and bewilderment. With a statistically significant p-value, our findings not only turned up the heat on conventional political analysis but also left us scrambling to make sense of this odd pairing of variables.
Now, one might wonder, what could possibly fuel such a connection? Could it be a metaphorical reflection of voters feeling the political heat and seeking refuge in the form of burn centers? Or perhaps, a sly manifestation of political "burnout" casting a smoldering shadow on search behavior? As we navigate this enigmatic landscape of causation and correlation, it's clear that we've stumbled upon a finding that is anything but a flash in the pan.
This quirky quest for understanding not only adds a touch of sizzle to the otherwise serious world of political research but also reminds us that in the realm of data analysis, the unexpected can often set the stage for scientific discovery. So, as we delve into the fiery fusion of politics and search behavior, let's embrace the heat and kindle the flames of curiosity as we explore the uncommon and unconventional in the landscape of statistical relationships.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To uncover the mysterious connection between Republican votes for Senators in Maryland and the frequency of Google searches for burn centers, our research team embarked on a journey through the convoluted jungle of data analysis. With a mix of whimsy, bewilderment, and a dash of scientific rigor, we employed a multifaceted approach to tackle the enigmatic relationship between political preferences and the urge to Google the nearest burn center.

First and foremost, we gathered data from the MIT Election Data and Science Lab and the Harvard Dataverse, casting a wide net across the electoral landscape from 2004 to 2018. With meticulous precision, we harvested the votes cast for Republican Senators in Maryland, capturing the ebb and flow of political allegiance over the years.

Next, we turned to the digital realm, harnessing the power of Google Trends to capture the frequency of searches for "burn centers" within the same timeframe. As we skimmed through the scintillating peaks and valleys of search trends, our curiosity sparked bright as we pondered the peculiar correlation that lay just beneath the surface.

Armed with this eclectic mix of political and Google search data, we braved the statistical wilderness, unleashing the full arsenal of correlation analysis and hypothesis testing. With bated breath and a touch of trepidation, we sought to unravel the tangled web of causation and correlation, all while keeping our eyes peeled for any unexpected statistical blazes.

Employing the formidable tool of correlation coefficient computation, we endeavored to measure the strength and direction of the relationship between Republican votes for Senators in Maryland and Google searches for burn centers. As the numbers danced across our screens, we couldn't help but marvel at the unexpected warmth radiating from the resulting coefficient of 0.9031699.

And yet, the adventure did not stop there. With a statistical significance test in hand, we braved the untamed territory of p-values, aiming to distinguish between true relationships and mere statistical phantoms. As the p-value stood staunch and significant, we found ourselves staring at a finding that defied conventional political analysis and sparked more questions than it answered.

In this journey through the labyrinth of data and statistics, our methodology stood as a testament to both the rigors of scientific inquiry and the whimsical nature of unexpected connections. From political votes to Google searches, we navigated a landscape that was anything but predictable, reveling in the unconventional and fanning the flames of curiosity with each step.

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 Republican votes for Senators in Maryland and Google searches for 'burn centers'. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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]]
Feeling the Heat: Exploring the Relationship Between Republican Votes for Senators in Maryland and Google Searches for Burn Centers

[[ABSTRACT]]
In this study, we set out to investigate the surprising correlation between Republican votes for Senators in Maryland and the frequency of Google searches for "burn centers." With data spanning from 2004 to 2018 sourced from the MIT Election Data and Science Lab, Harvard Dataverse, and Google Trends, we embarked on a quirky quest to shed light on this peculiar association.

Uncovering a correlation coefficient of 0.9031699 and a statistically significant p-value, our findings raise eyebrows and generate more questions than answers. Is there a smoldering political divide that drives people to search for burn centers? Could it be a metaphorical reflection of voters feeling "burned" by political results? Or is it simply a fluke of the data, an odd flaring of coincidence?

This research not only adds a touch of sizzle to the typically serious realm of political analysis but also underscores the need for exploring unanticipated correlations that may ignite new avenues of inquiry. So, as we navigate the labyrinth of causation and correlation, let's fan the flames of curiosity and delve into the unexpected heat radiating from this offbeat fusion of politics and search behavior.

[[LITERATURE REVIEW]]
To begin our exploration of the perplexing nexus between Republican votes for Senators in Maryland and the frequency of Google searches for burn centers, we turn our attention to the scholarly pursuit of uncovering unexpected correlations. Smith (2015) aptly captures the essence of such endeavors in their seminal work, "Unearthing Unforeseen Associations in Political Data," where they illuminate the thrill and challenge of unraveling enigmatic statistical relationships.
Continuing in the realm of serious inquiry, Doe (2018) delves into the intricacies of voter behavior and the underlying motivations that drive political affiliations in "Voting Patterns and Their Psychological Underpinnings," providing valuable insights into the factors that may influence the choices of Maryland residents at the ballot box.
However, as we venture further into this unusual intersection of politics and seemingly unrelated phenomena, it becomes evident that the landscape is not devoid of whimsical and unconventional connections. "Burn Centers: A Comprehensive Guide" by Jones (2016) offers a detailed exploration of the critical role played by burn centers in healthcare, providing a foundation for understanding the significance of the term in the public consciousness.
Shifting our focus slightly, we pivot to the world of fiction, where we encounter works that, albeit not directly related to our topic, carry intriguing titles that add a touch of levity to our scholarly pursuits. "Burning Bridges: A Political Saga" by A. Novel (2017) and "The Fahrenheit Follies" by S. Story (2014) may not offer empirical insights, but their titles provide a whimsical nod to the fiery theme we find ourselves engulfed in.
In a surprising twist of internet culture intersecting with our subject matter, the viral meme "Feeling the Bern" emerges as a playful reflection of political fervor, injecting an unexpected note of humor into our investigation. As we seek to untangle the web of correlations, it's evident that the unconventional can often prove to be an unexpected source of inspiration in the pursuit of knowledge.
With this eclectic array of sources, both serious and lighthearted, we set the stage for an examination that not only sheds light on this curious correlation but also underscores the compelling and sometimes comical nature of unanticipated statistical relationships.

[[RESULTS]]
The scorching saga of our investigation into the surprising correlation between Republican votes for Senators in Maryland and Google searches for burn centers has yielded some smoking-hot results. With a correlation coefficient of 0.9031699 and an r-squared of 0.8157159, it's clear that there's a fiery connection between these seemingly unrelated variables.
The statistically significant p-value of less than 0.05 adds fuel to the flames of curiosity, leaving us to ponder the reasons behind this unexpected association. Could it be that political fervor ignites a desire to seek refuge in burn centers? Or does it speak to a deeper metaphorical burn experienced by voters dissatisfied with political outcomes?
Fig. 1 showcases the scatterplot, providing a visually stunning display of the strong correlation between Republican votes for Senators in Maryland and Google searches for burn centers. It's a visual spectacle that's sure to set statistical hearts ablaze with excitement.
In conclusion, our findings not only add an unexpected twist to the realm of political analysis but also serve as a reminder that in the world of data and statistics, the unanticipated can lead to surprising discoveries. Our discovery of this curious correlation emphasizes the need to explore unconventional avenues of inquiry and to approach data analysis with a willingness to embrace the unexpected.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

As we ignite the discussion on the scorching correlation between Republican votes for Senators in Maryland and Google searches for burn centers, we find ourselves enveloped in a veritable inferno of astonishment. Our findings not only fanned the flames of curiosity but also stoked the coals of statistical inquiry, uncovering an unexpected rapport between seemingly unrelated variables.

Our results alight with the earlier works of Smith (2015) and Doe (2018), who laid the groundwork for unearthing unforeseen associations in political data and delving into the psychological underpinnings of voting patterns. While we initially approached our investigation with tongue firmly in cheek, the statistical conflagration we've uncovered supports the notion that unanticipated correlations may indeed hold substantive significance in the realm of political analysis. Sometimes, the most unexpected blazes lead to the hottest revelations.

In direct parallel to the serious literature, our study also hearkens back to the whimsical and unconventional connections highlighted by Jones (2016) and the playful titles of works by A. Novel (2017) and S. Story (2014). Who would have thought that such lighthearted anecdotes would offer a spark of insight into the unexpected nexus we've unraveled? It goes to show that even the most seemingly outlandish connections can kindle the flames of genuine scientific inquiry.

It's clear that our findings have provided the kindling to spark further discussion and inquiry into the multifaceted nature of voter behavior and its interplay with seemingly unrelated phenomena. The statistically significant p-value serves as a beacon, guiding us through the smokescreen of potential confounding variables and beckoning us to delve deeper into the root causes of this fiery correlation.

While our study delved into the political landscape with a humorous twist, it's important to acknowledge that our findings carry implications that reach far beyond the realm of amusement. The need to explore unconventional avenues of inquiry, coupled with an openness to unexpected statistical relationships, has never been more pressing in an era where data-driven insights carry substantial weight in shaping informed decision-making.

As we extinguish the flames of this discussion, let us not forget that even in the realm of science and research, there's always room for a touch of whimsy and a spritz of unexpected discovery. It's in these colorful, unexpected moments that we often find the brightest embers of knowledge, flickering and dancing in the unlikeliest of places.

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 Republican votes for Senators in Maryland and Google searches for 'burn centers'. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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]]
Feeling the Heat: Exploring the Relationship Between Republican Votes for Senators in Maryland and Google Searches for Burn Centers

[[ABSTRACT]]
In this study, we set out to investigate the surprising correlation between Republican votes for Senators in Maryland and the frequency of Google searches for "burn centers." With data spanning from 2004 to 2018 sourced from the MIT Election Data and Science Lab, Harvard Dataverse, and Google Trends, we embarked on a quirky quest to shed light on this peculiar association.

Uncovering a correlation coefficient of 0.9031699 and a statistically significant p-value, our findings raise eyebrows and generate more questions than answers. Is there a smoldering political divide that drives people to search for burn centers? Could it be a metaphorical reflection of voters feeling "burned" by political results? Or is it simply a fluke of the data, an odd flaring of coincidence?

This research not only adds a touch of sizzle to the typically serious realm of political analysis but also underscores the need for exploring unanticipated correlations that may ignite new avenues of inquiry. So, as we navigate the labyrinth of causation and correlation, let's fan the flames of curiosity and delve into the unexpected heat radiating from this offbeat fusion of politics and search behavior.

[[INTRDUCTION]]
In the scorching arena of political analysis, where debates often turn to fiery exchanges, one might not expect to find a link to the seemingly unrelated world of burn centers. Yet, as we dive into the labyrinth of data and statistics, we stumbled upon a peculiar connection that left us feeling quite singed – the relationship between Republican votes for Senators in Maryland and the frequency of Google searches for burn centers. This unexpected correlation sparked our curiosity, leading us to embark on a journey through the wild and whimsical realm of unanticipated statistical relationships.
As we fanned the flames of inquiry, we found ourselves face to face with a correlation coefficient of 0.9031699, igniting both fascination and bewilderment. With a statistically significant p-value, our findings not only turned up the heat on conventional political analysis but also left us scrambling to make sense of this odd pairing of variables.
Now, one might wonder, what could possibly fuel such a connection? Could it be a metaphorical reflection of voters feeling the political heat and seeking refuge in the form of burn centers? Or perhaps, a sly manifestation of political "burnout" casting a smoldering shadow on search behavior? As we navigate this enigmatic landscape of causation and correlation, it's clear that we've stumbled upon a finding that is anything but a flash in the pan.
This quirky quest for understanding not only adds a touch of sizzle to the otherwise serious world of political research but also reminds us that in the realm of data analysis, the unexpected can often set the stage for scientific discovery. So, as we delve into the fiery fusion of politics and search behavior, let's embrace the heat and kindle the flames of curiosity as we explore the uncommon and unconventional in the landscape of statistical relationships.

[[RESULTS]]
The scorching saga of our investigation into the surprising correlation between Republican votes for Senators in Maryland and Google searches for burn centers has yielded some smoking-hot results. With a correlation coefficient of 0.9031699 and an r-squared of 0.8157159, it's clear that there's a fiery connection between these seemingly unrelated variables.
The statistically significant p-value of less than 0.05 adds fuel to the flames of curiosity, leaving us to ponder the reasons behind this unexpected association. Could it be that political fervor ignites a desire to seek refuge in burn centers? Or does it speak to a deeper metaphorical burn experienced by voters dissatisfied with political outcomes?
Fig. 1 showcases the scatterplot, providing a visually stunning display of the strong correlation between Republican votes for Senators in Maryland and Google searches for burn centers. It's a visual spectacle that's sure to set statistical hearts ablaze with excitement.
In conclusion, our findings not only add an unexpected twist to the realm of political analysis but also serve as a reminder that in the world of data and statistics, the unanticipated can lead to surprising discoveries. Our discovery of this curious correlation emphasizes the need to explore unconventional avenues of inquiry and to approach data analysis with a willingness to embrace the unexpected.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In the scorching conclusion of our investigation, we find ourselves grappling with a burning question: what in the world could be igniting the curious correlation between Republican votes for Senators in Maryland and Google searches for burn centers? It's a conundrum hotter than a fresh batch of data straight from the statistical oven.

As we reflect on our sizzling findings, it becomes clear that this unlikely association has set our research journey ablaze with excitement. Whether it's a manifestation of voters feeling the political heat or a fiery metaphor for dissatisfaction with political outcomes, one thing is certain – this correlation is no mere statistical fluke; it's the real deal, hot and sizzling like a well-conducted experiment.

But before we get all fired up about this unexpected fusion of political fervor and search behavior, let's cool down and acknowledge the limitations of our study. While our findings raise eyebrows and temperature readings, further research is needed to fully understand the sparks that fly between these variables.

However, our eccentric expedition through the landscape of statistical relationships has unquestionably added a splash of hot sauce to the bland dish of traditional political analysis. It's a reminder that in the world of data and statistics, the unexpected can turn up the heat and lead to new avenues of inquiry, even if those avenues take us to unexpected places like burn centers.

So, as the flames of curiosity flicker and dance around this peculiar correlation, let's call it a day and extinguish any lingering research embers. Because sometimes, in the wild and whimsical world of statistics, there are correlations that are best left unexplained – and this just might be one of them. No more research is needed in this area; we've already danced dangerously close to the fire.

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 Feeling the Heat: Exploring the Relationship Between Republican Votes for Senators in Maryland and Google Searches for Burn Centers. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Political Google Searches and Public Health Voting Patterns

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 Feeling the Heat: Exploring the Relationship Between Republican Votes for Senators in Maryland and Google Searches for Burn Centers. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Political Internet Search Studies

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 Feeling the Heat: Exploring the Relationship Between Republican Votes for Senators in Maryland and Google Searches for Burn Centers with an abstract of In this study, we set out to investigate the surprising correlation between Republican votes for Senators in Maryland and the frequency of Google searches for "burn centers." With data spanning from 2004 to 2018 sourced from the MIT Election Data and Science Lab, Harvard Dataverse, and Google Trends, we embarked on a quirky quest to shed light on this peculiar association.
Uncovering a correlation coefficient of 0.9031699 and a statistically significant p-value, our findings raise eyebrows and generate more questions than answers. Is there a smoldering political divide that drives people to search for burn centers? Could it be a metaphorical reflection of voters feeling "burned" by political results? Or is it simply a fluke of the data, an odd flaring of coincidence?
This research not only adds a touch of sizzle to the typically serious realm of political analysis but also underscores the need for exploring unanticipated correlations that may ignite new avenues of inquiry. So, as we navigate the labyrinth of causation and correlation, let's fan the flames of curiosity and delve into the unexpected heat radiating from this offbeat fusion of politics and search behavior.

ChatGPT:

Republican votes, Senators in Maryland, Google searches, burn centers, correlation, correlation coefficient, statistical significance, political divide, metaphorical reflection, voters, political results, data analysis, unexpected correlations, political analysis

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

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Data details

Votes for Republican Senators in Maryland
Detailed data title: Total number of votes cast for Federal Republican Senate candidates in Maryland
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for Republican Senators in Maryland

Google searches for 'burn centers'
Detailed data title: Relative volume of Google searches for 'burn centers' (Worldwide, without quotes)
Source: Google Trends
Additional Info: ^{See full details} Relative search volume (not absolute numbers)

See what else correlates with Google searches for 'burn centers'

Correlation r = 0.9031699 (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 = 0.8157159 (Coefficient of determination)
This means 81.6% of the change in the one variable (i.e., Google searches for 'burn centers') is predictable based on the change in the other (i.e., Votes for Republican Senators in Maryland) over the 6 years from 2004 through 2018.

p < 0.05, which statistically significant(Null hypothesis significance test)
The p-value is 0.014.^Show 0.0136101496473324270000000000
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 0.9 in 1.4% of random cases. Said differently, if you correlated 73 random variables^Note Which I absolutely did.
with the same 5 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 5 because we have two variables measured over a period of 6 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.34, 0.99 ] 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.

	2004	2006	2010	2012	2016	2018
Votes for Republican Senators in Maryland (Total votes)	783055	787182	655666	693291	972557	697017
Google searches for 'burn centers' (Rel. search volume)	22.5	22.4167	14.5833	16.4167	25.75	19.75

Why this works

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

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([783055,787182,655666,693291,972557,697017,])
array_2 = np.array([22.5,22.4167,14.5833,16.4167,25.75,19.75,])
array_1_name = "Votes for Republican Senators in Maryland"
array_2_name = "Google searches for 'burn centers'"

# 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 Republican Senators in Maryland
Line chart for only Google searches for 'burn centers'
AI-generated correlation image
The spurious research paper: Feeling the Heat: Exploring the Relationship Between Republican Votes for Senators in Maryland and Google Searches for Burn Centers

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Correlation ID: 5159 · Black Variable ID: 26253 · Red Variable ID: 1343


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