Spurious correlation #4,795 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Nolan and the second variable is Votes for Republican Senators in South Carolina. The chart goes from 1978 to 2020, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Nolan and the second variable is Votes for Republican Senators in South Carolina. The chart goes from 1978 to 2020, and the two variables track closely in value over that time.

Download png, svg

AI explanation

Every time someone said “Nolan” a nearby Republican in South Carolina felt a sudden inexplicable urge to cast their vote. It was like a secret trigger word, but for political participation.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image where in a lively town square in South Carolina, a towering billboard proudly showcases the name "Nolan" in vibrant, bold letters. The surrounding area is adorned with patriotic red, white, and blue decorations, adding to the fervent atmosphere. As pedestrians stroll along, a noticeable phenomenon occurs; every time someone utters the name "Nolan," a group of Republican Senators nearby experience a sudden surge of enthusiasm, gathering eagerly to engage in fervent discussions about political affairs. The name "Nolan" has evolved into a powerful and enigmatic force, sparking intense political involvement within the Republican community of South Carolina. 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: Popularity of the first name Nolan and Republican votes for Senators in South Carolina. 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 funny image macro An image that looks like it is a scene from 'Star Wars' Cute cartoon Realistic a funny image macro
ChatGPT: a funny image macro

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 Popularity of the first name Nolan and Republican votes for Senators in South Carolina, in the following situation: Every time someone said “Nolan” a nearby Republican in South Carolina felt a sudden inexplicable urge to cast their vote. It was like a secret trigger word, but for political participation.. Keep the description simple.
ChatGPT: In a bustling town square in South Carolina, a large billboard displays the name "Nolan" in bold letters, surrounded by red, white, and blue decorations. As people walk by and say "Nolan," a group of Republican Senators nearby feel an sudden urge to cast their votes, gathering excitedly to discuss political matters. The name "Nolan" has become a powerful and mysterious catalyst for political participation among the Republican community in South Carolina.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ a funny image macro ] and depicting this scene: In a bustling town square in South Carolina, a large billboard displays the name "Nolan" in bold letters, surrounded by red, white, and blue decorations. As people walk by and say "Nolan," a group of Republican Senators nearby feel an sudden urge to cast their votes, gathering excitedly to discuss political matters. The name "Nolan" has become a powerful and mysterious catalyst for political participation among the Republican community in South Carolina. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image where in a lively town square in South Carolina, a towering billboard proudly showcases the name "Nolan" in vibrant, bold letters. The surrounding area is adorned with patriotic red, white, and blue decorations, adding to the fervent atmosphere. As pedestrians stroll along, a noticeable phenomenon occurs; every time someone utters the name "Nolan," a group of Republican Senators nearby experience a sudden surge of enthusiasm, gathering eagerly to engage in fervent discussions about political affairs. The name "Nolan" has evolved into a powerful and enigmatic force, sparking intense political involvement within the Republican community of South Carolina.

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

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in Popularity of the first name Nolan caused Republican votes for Senators in South Carolina to increase. Make it feel plausible.

AI academic paper

(Because p < 0.01)

Rollin' with Nolan: The Impact of the Name Nolan on Republican Senators' Votes in South Carolina

Journal of Political Nameology

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 Popularity of the first name Nolan and Republican votes for Senators in South Carolina. Make lots of jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading CVS receipts.

Here is the title and abstract of the paper:
[[TITLE]]
Rollin' with Nolan: The Impact of the Name Nolan on Republican Senators' Votes in South Carolina

[[ABSTRACT]]
Nomenclature plays a pivotal role in shaping societal trends, even in the realm of politics. This study delves into the curious relationship between the popularity of the first name Nolan and the voting patterns for Republican senators in the charming state of South Carolina. Utilizing data from the US Social Security Administration and the MIT Election Data and Science Lab, supplemented by the Harvard Dataverse, our research team uncovered an intriguing correlation coefficient of 0.9408137. We pored over data from 1978 to 2020, unearthing a connection so compelling that it's got us feeling more excited than a kid in a candy store.

Our findings reveal a statistically significant relationship, with p < 0.01, sparking ripples of curiosity and a splash of pun-fueled enthusiasm within the academic community. As we unravel the intricate web of factors contributing to this curious phenomenon, we invite readers to join us on this scholarly escapade, where we aim to illuminate the serious business of political nomenclature with a side of lighthearted, whimsical musings. So, buckle up and get ready to dive headfirst into the wacky world of name popularity and political preferences in the Palmetto State!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
In the realm of politics, the influence of various factors on voting patterns has long been a subject of scholarly scrutiny. However, amidst the exhaustive analyses of demographics, policies, and campaign strategies, there lies an often overlooked yet undeniably impactful element - the humble first name. Our study sets out to explore the intriguing intersection of nomenclature and political allegiances, focusing specifically on the correlation between the prevalence of the moniker "Nolan" and Republican senators' votes in the charming state of South Carolina.
As we embark on this whimsical odyssey through the world of statistical analysis and political nomenclature, it becomes abundantly clear that the influence of a name can extend far beyond mere semantics. Harnessing data from the US Social Security Administration, the MIT Election Data and Science Lab, and the Harvard Dataverse, our research team has ventured into uncharted territory, aiming to unravel the enigma surrounding the name "Nolan" and its dance with Republican votes in the Palmetto State.
The allure of this study lies not only in its statistical significance but also in the recognition of the often-overlooked role of nomenclature in shaping societal trends. With a correlation coefficient of 0.9408137 and a p-value < 0.01, our findings have sparked a palatable sense of curiosity within the academic community, akin to the allure of a freshly baked apple pie on a crisp autumn day.
In the pursuit of scholarly inquiry, we must not shy away from melding serenity with whimsy, for it is the unexpected tangents and playful musings that add a dash of flavor to the otherwise austere landscape of academic discourse. Therefore, brace yourselves for a scholarly escapade like no other, as we unravel the curious symphony of political preferences and nomenclature, with a liberal sprinkling of puns and witticisms to keep the journey light-hearted and engaging.
So, with our tongues firmly in our cheeks and our statistical tools in hand, let us peel back the layers of this electoral onion and unearth the captivating correlation between the name "Nolan" and Republican senators' votes in South Carolina. Buckle up, dear reader, for we are about to embark on a rollicking adventure through the quirky realm of political nomenclature, where names carry the weight of votes, and statistical analyses are seasoned with a generous pinch of levity.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

Previous studies have examined the impact of various sociodemographic factors on political preferences, from income levels to education, and even the influence of regional cultural norms. However, as we wade into the labyrinth of electoral eccentricities, a rather unorthodox piece of the puzzle emerges - the first name. Smith et al. (2017) investigated the influence of first names on political inclinations, shedding light on the subtle yet surprisingly influential role of nomenclature.

Doe and Jones (2015) delved into the societal implications of names, uncovering correlations between given names and career choices, but this burgeoning field of inquiry seems to have overlooked the delightful intersection of name popularity and political affiliation, particularly within the charming abode of South Carolina.

Venturing into the realm of non-fiction literature, "Freakonomics" by Steven D. Levitt and Stephen J. Dubner introduces us to the captivating world of unconventional societal phenomena, encouraging scholarly minds to unravel the mysteries that lie beneath seemingly inconspicuous patterns. Meanwhile, "Thinking, Fast and Slow" by Daniel Kahneman offers a compelling exploration of human decision-making, urging us to consider the whimsical factors that underpin our choices, just as the name Nolan may whimsically sway political preferences.

Delving further into the world of fiction, the enigmatic intrigue of "The Da Vinci Code" by Dan Brown stimulates our imaginations, reminding us that beneath the facade of the ordinary lies an intricate tapestry of underlying connections - much like the charming allure of a seemingly ordinary name like Nolan exerting its influence on political landscapes.

As we've traversed the serious and pseudo-serious, it's worth acknowledging that this paper's narrative is about to take a dramatic, albeit humorous, turn. In the quest for scholarly enlightenment, our research team delved into an unorthodox array of sources, including the musings of Mr. Bob at the local diner, the elaborately woven tales of Spaghetti and Meatball recipes from a family cookbook, not to mention the compelling hypotheses derived from ancient hieroglyphics found at the bottom of a cereal box.

In a particularly daring move, we even sought insights from the cryptic wisdom etched upon CVS receipts, where amidst the mundane details of purchases, lay the untold secrets of political nomenclature. While some may scoff at our unconventional approach, we embraced it with the unyielding enthusiasm of a puppy chasing its tail, determined to capture the essence of the Nolan phenomenon in South Carolinian politics from all conceivable angles.

Stay tuned, dear reader, for the scholarly journey ahead promises not only rigorous statistical analyses and profound insights but also a healthy dose of whimsy and mirth to keep the academic spirit steadfast amid the maelstrom of political nomenclature and quirky correlations.

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 Popularity of the first name Nolan and Republican votes for Senators in South Carolina. Make lots of 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]]
Rollin' with Nolan: The Impact of the Name Nolan on Republican Senators' Votes in South Carolina

[[ABSTRACT]]
Nomenclature plays a pivotal role in shaping societal trends, even in the realm of politics. This study delves into the curious relationship between the popularity of the first name Nolan and the voting patterns for Republican senators in the charming state of South Carolina. Utilizing data from the US Social Security Administration and the MIT Election Data and Science Lab, supplemented by the Harvard Dataverse, our research team uncovered an intriguing correlation coefficient of 0.9408137. We pored over data from 1978 to 2020, unearthing a connection so compelling that it's got us feeling more excited than a kid in a candy store.

Our findings reveal a statistically significant relationship, with p < 0.01, sparking ripples of curiosity and a splash of pun-fueled enthusiasm within the academic community. As we unravel the intricate web of factors contributing to this curious phenomenon, we invite readers to join us on this scholarly escapade, where we aim to illuminate the serious business of political nomenclature with a side of lighthearted, whimsical musings. So, buckle up and get ready to dive headfirst into the wacky world of name popularity and political preferences in the Palmetto State!

[[LITERATURE REVIEW]]
Previous studies have examined the impact of various sociodemographic factors on political preferences, from income levels to education, and even the influence of regional cultural norms. However, as we wade into the labyrinth of electoral eccentricities, a rather unorthodox piece of the puzzle emerges - the first name. Smith et al. (2017) investigated the influence of first names on political inclinations, shedding light on the subtle yet surprisingly influential role of nomenclature.
Doe and Jones (2015) delved into the societal implications of names, uncovering correlations between given names and career choices, but this burgeoning field of inquiry seems to have overlooked the delightful intersection of name popularity and political affiliation, particularly within the charming abode of South Carolina.
Venturing into the realm of non-fiction literature, "Freakonomics" by Steven D. Levitt and Stephen J. Dubner introduces us to the captivating world of unconventional societal phenomena, encouraging scholarly minds to unravel the mysteries that lie beneath seemingly inconspicuous patterns. Meanwhile, "Thinking, Fast and Slow" by Daniel Kahneman offers a compelling exploration of human decision-making, urging us to consider the whimsical factors that underpin our choices, just as the name Nolan may whimsically sway political preferences.
Delving further into the world of fiction, the enigmatic intrigue of "The Da Vinci Code" by Dan Brown stimulates our imaginations, reminding us that beneath the facade of the ordinary lies an intricate tapestry of underlying connections - much like the charming allure of a seemingly ordinary name like Nolan exerting its influence on political landscapes.
As we've traversed the serious and pseudo-serious, it's worth acknowledging that this paper's narrative is about to take a dramatic, albeit humorous, turn. In the quest for scholarly enlightenment, our research team delved into an unorthodox array of sources, including the musings of Mr. Bob at the local diner, the elaborately woven tales of Spaghetti and Meatball recipes from a family cookbook, not to mention the compelling hypotheses derived from ancient hieroglyphics found at the bottom of a cereal box.
In a particularly daring move, we even sought insights from the cryptic wisdom etched upon CVS receipts, where amidst the mundane details of purchases, lay the untold secrets of political nomenclature. While some may scoff at our unconventional approach, we embraced it with the unyielding enthusiasm of a puppy chasing its tail, determined to capture the essence of the Nolan phenomenon in South Carolinian politics from all conceivable angles.
Stay tuned, dear reader, for the scholarly journey ahead promises not only rigorous statistical analyses and profound insights but also a healthy dose of whimsy and mirth to keep the academic spirit steadfast amid the maelstrom of political nomenclature and quirky correlations.

[[RESULTS]]
The analysis of data covering the years 1978 to 2020 revealed a striking correlation between the popularity of the first name "Nolan" and the votes for Republican senators in South Carolina. The correlation coefficient of 0.9408137 suggests a remarkably strong relationship, as if "Nolan" and Republican votes were engaged in a waltz of statistical significance, twirling around the dance floor of electoral data with a grace that would make even the most seasoned ballroom dancer envious.
The r-squared value of 0.8851304 further solidifies the robustness of this correlation, indicating that approximately 88.51% of the variability in Republican votes for senators in South Carolina can be explained by the variations in the popularity of the name "Nolan." It's almost as if the name "Nolan" has been whispering its political preferences into the ears of South Carolinian voters, swaying their decisions with an almost hypnotic charm.
The p-value of less than 0.01 adds an exclamation point to our discovery, signifying a level of statistical significance that would make even the most rigid skeptic raise an eyebrow in curiosity. The evidence overwhelmingly supports the notion that there is more to a name than meets the eye, and in the case of "Nolan" and South Carolina, that name seems to have a magnetic pull toward a particular political sentiment.
Our findings are encapsulated in Figure 1, a scatterplot that visually captures the awe-inspiring correlation between the prevalence of the name "Nolan" and the Republican votes for senators in South Carolina. This plot graphically illustrates the compelling connection that our research has unearthed, akin to an artistic masterpiece that blends data points with a touch of whimsy and intrigue.
As we reflect on the implications of our findings, it becomes abundantly clear that the impact of a name can permeate even the most intricate facets of societal phenomena. The allure of this correlation is not just in its statistical robustness but in the quirky charm of how a seemingly innocuous factor such as a name can delicately weave its way into the political fabric of a state. It's like stumbling upon a hidden gem in a pile of electoral hay, with the name "Nolan" emerging as a unique and captivating thread in the colorful tapestry of South Carolina's political landscape.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our research has revealed a remarkable correlation between the popularity of the first name "Nolan" and the votes for Republican senators in South Carolina, a correlation so strong it's like finding a needle in a haystack, except in this case, the needle befriends the haystack and they start line-dancing together.

Building on the goofily serious, albeit intriguing, theoretical underpinnings explored in our literature review, our findings lend empirical support to the notion that names can wield a surprisingly influential sway over political affiliations. It's as if the quiet, unassuming "Nolan" has stepped into the limelight of sociopolitical analysis, shimmying its way into the hearts and minds of South Carolinian voters with an almost theatrical flair.

The robust correlation coefficient of 0.9408137 showcases the strong magnetic pull that "Nolan" exerts on Republican votes, akin to a gravitational force so potent it pulls voters into its orbit with the charm of a 1950s crooner. This finding lends credence to the prior scholarly endeavors investigating the subtle yet impactful role of nomenclature in shaping societal phenomena, demonstrating that even a seemingly innocuous factor like a name can yield statistically significant insights, much like discovering a serious book hidden behind a comically illustrated cover.

In a nod to the whimsically lighthearted musings of Levitt and Dubner in "Freakonomics," our study underscores the sheer delight in unraveling the enigmatic connections between seemingly unrelated variables, resembling the joy of solving an unexpected riddle within the labyrinth of electoral data. The surprising correlation between the name "Nolan" and Republican votes in South Carolina echoes the playful spirit of untangling a particularly convoluted tongue twister, where each twist and turn leads to the delightful revelation behind a seemingly ordinary name.

Furthermore, our results reflect the sentiment espoused by Kahneman in "Thinking, Fast and Slow," as they exemplify the intricate dance between swift, intuitive decision-making and deliberate, analytical contemplation. The compelling relationship unveiled in our study mirrors the whimsical interplay of swift intuition and deliberate reasoning, much like witnessing a confluence of unexpected events that unfold with the surprising elegance of a well-executed magic trick.

As we shed light on the vibrant tapestry of electoral data in South Carolina, the captivating allure of the name "Nolan" emerges as a captivating thread that weaves its way into the rich fabric of political preferences. This finding affirms that there is more to a name than mere letters and syllables, delving into the captivating interplay of individual nomenclature and broader societal dynamics. Just as the mischievous "Nolan" seems to wield its influence over Republican votes in South Carolina, our study leaves us with an invitation to ponder the whimsical, mysterious facets of nomenclature and their unexpected impact on the intricate dance of politics.

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 Popularity of the first name Nolan and Republican votes for Senators in South Carolina. Make lots of 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]]
Rollin' with Nolan: The Impact of the Name Nolan on Republican Senators' Votes in South Carolina

[[ABSTRACT]]
Nomenclature plays a pivotal role in shaping societal trends, even in the realm of politics. This study delves into the curious relationship between the popularity of the first name Nolan and the voting patterns for Republican senators in the charming state of South Carolina. Utilizing data from the US Social Security Administration and the MIT Election Data and Science Lab, supplemented by the Harvard Dataverse, our research team uncovered an intriguing correlation coefficient of 0.9408137. We pored over data from 1978 to 2020, unearthing a connection so compelling that it's got us feeling more excited than a kid in a candy store.

Our findings reveal a statistically significant relationship, with p < 0.01, sparking ripples of curiosity and a splash of pun-fueled enthusiasm within the academic community. As we unravel the intricate web of factors contributing to this curious phenomenon, we invite readers to join us on this scholarly escapade, where we aim to illuminate the serious business of political nomenclature with a side of lighthearted, whimsical musings. So, buckle up and get ready to dive headfirst into the wacky world of name popularity and political preferences in the Palmetto State!

[[INTRDUCTION]]
In the realm of politics, the influence of various factors on voting patterns has long been a subject of scholarly scrutiny. However, amidst the exhaustive analyses of demographics, policies, and campaign strategies, there lies an often overlooked yet undeniably impactful element - the humble first name. Our study sets out to explore the intriguing intersection of nomenclature and political allegiances, focusing specifically on the correlation between the prevalence of the moniker "Nolan" and Republican senators' votes in the charming state of South Carolina.
As we embark on this whimsical odyssey through the world of statistical analysis and political nomenclature, it becomes abundantly clear that the influence of a name can extend far beyond mere semantics. Harnessing data from the US Social Security Administration, the MIT Election Data and Science Lab, and the Harvard Dataverse, our research team has ventured into uncharted territory, aiming to unravel the enigma surrounding the name "Nolan" and its dance with Republican votes in the Palmetto State.
The allure of this study lies not only in its statistical significance but also in the recognition of the often-overlooked role of nomenclature in shaping societal trends. With a correlation coefficient of 0.9408137 and a p-value < 0.01, our findings have sparked a palatable sense of curiosity within the academic community, akin to the allure of a freshly baked apple pie on a crisp autumn day.
In the pursuit of scholarly inquiry, we must not shy away from melding serenity with whimsy, for it is the unexpected tangents and playful musings that add a dash of flavor to the otherwise austere landscape of academic discourse. Therefore, brace yourselves for a scholarly escapade like no other, as we unravel the curious symphony of political preferences and nomenclature, with a liberal sprinkling of puns and witticisms to keep the journey light-hearted and engaging.
So, with our tongues firmly in our cheeks and our statistical tools in hand, let us peel back the layers of this electoral onion and unearth the captivating correlation between the name "Nolan" and Republican senators' votes in South Carolina. Buckle up, dear reader, for we are about to embark on a rollicking adventure through the quirky realm of political nomenclature, where names carry the weight of votes, and statistical analyses are seasoned with a generous pinch of levity.

[[RESULTS]]
The analysis of data covering the years 1978 to 2020 revealed a striking correlation between the popularity of the first name "Nolan" and the votes for Republican senators in South Carolina. The correlation coefficient of 0.9408137 suggests a remarkably strong relationship, as if "Nolan" and Republican votes were engaged in a waltz of statistical significance, twirling around the dance floor of electoral data with a grace that would make even the most seasoned ballroom dancer envious.
The r-squared value of 0.8851304 further solidifies the robustness of this correlation, indicating that approximately 88.51% of the variability in Republican votes for senators in South Carolina can be explained by the variations in the popularity of the name "Nolan." It's almost as if the name "Nolan" has been whispering its political preferences into the ears of South Carolinian voters, swaying their decisions with an almost hypnotic charm.
The p-value of less than 0.01 adds an exclamation point to our discovery, signifying a level of statistical significance that would make even the most rigid skeptic raise an eyebrow in curiosity. The evidence overwhelmingly supports the notion that there is more to a name than meets the eye, and in the case of "Nolan" and South Carolina, that name seems to have a magnetic pull toward a particular political sentiment.
Our findings are encapsulated in Figure 1, a scatterplot that visually captures the awe-inspiring correlation between the prevalence of the name "Nolan" and the Republican votes for senators in South Carolina. This plot graphically illustrates the compelling connection that our research has unearthed, akin to an artistic masterpiece that blends data points with a touch of whimsy and intrigue.
As we reflect on the implications of our findings, it becomes abundantly clear that the impact of a name can permeate even the most intricate facets of societal phenomena. The allure of this correlation is not just in its statistical robustness but in the quirky charm of how a seemingly innocuous factor such as a name can delicately weave its way into the political fabric of a state. It's like stumbling upon a hidden gem in a pile of electoral hay, with the name "Nolan" emerging as a unique and captivating thread in the colorful tapestry of South Carolina's political landscape.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In closing, our study has shed light on the captivating correlation between the prevalence of the first name "Nolan" and Republican senators' votes in South Carolina. With a correlation coefficient resembling the strength of a Herculean handshake, and an r-squared value as robust as a determined squirrel cracking a nut, our findings beckon us to ponder the sway of nomenclature in the realm of politics.

Much like a well-crafted pun, the statistical significance of our results cannot be ignored. The p-value of less than 0.01 stands as a beacon of empirical rigor, much like a lighthouse guiding wavering ships of skepticism through the tumultuous seas of academic inquiry. The evidence has spoken, and it whispers the peculiar tale of a name shaping not only identities but also political proclivities.

As we gaze upon our scatterplot, it's as if the data points are engaged in a rhythmic dance, the name "Nolan" leading the Republican votes in South Carolina with the finesse of a seasoned ballroom maestro. It's a waltz of statistical significance, a tango of tantalizing correlations that beckon us to ponder the mysterious ways in which a name can influence the political landscape.

In the spirit of scholarly inquiry, we must recognize that sometimes, amidst the austere academic discourse, a dash of whimsical lightheartedness can add a sprinkle of glee to the pursuit of knowledge. Indeed, this study has demonstrated that even in the realm of politics, statistical analyses and pun-fueled witticisms can coalesce into a thought-provoking tapestry of scholarly exploration.

In conclusion, the correlation between the name "Nolan" and Republican votes in South Carolina stands as a testament to the multifaceted influence of nomenclature in shaping societal phenomena. Therefore, with a confident nod and a touch of levity, we assert that further research in this area is as unnecessary as an inflatable dartboard – there's simply no need to keep throwing darts at this peculiar yet enlightening correlation.

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 Rollin' with Nolan: The Impact of the Name Nolan on Republican Senators' Votes in South Carolina. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Political Nameology

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 Rollin' with Nolan: The Impact of the Name Nolan on Republican Senators' Votes in South Carolina. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Eccentric Sociology and Political Analysis

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Rollin' with Nolan: The Impact of the Name Nolan on Republican Senators' Votes in South Carolina with an abstract of Nomenclature plays a pivotal role in shaping societal trends, even in the realm of politics. This study delves into the curious relationship between the popularity of the first name Nolan and the voting patterns for Republican senators in the charming state of South Carolina. Utilizing data from the US Social Security Administration and the MIT Election Data and Science Lab, supplemented by the Harvard Dataverse, our research team uncovered an intriguing correlation coefficient of 0.9408137. We pored over data from 1978 to 2020, unearthing a connection so compelling that it's got us feeling more excited than a kid in a candy store.
Our findings reveal a statistically significant relationship, with p < 0.01, sparking ripples of curiosity and a splash of pun-fueled enthusiasm within the academic community. As we unravel the intricate web of factors contributing to this curious phenomenon, we invite readers to join us on this scholarly escapade, where we aim to illuminate the serious business of political nomenclature with a side of lighthearted, whimsical musings. So, buckle up and get ready to dive headfirst into the wacky world of name popularity and political preferences in the Palmetto State!

ChatGPT:

name popularity, Nolan, Republican senators, South Carolina, voting patterns, US Social Security Administration, MIT Election Data and Science Lab, Harvard Dataverse, correlation coefficient, political nomenclature, political preferences, Palmetto State

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

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Data details

Popularity of the first name Nolan
Detailed data title: Babies of all sexes born in the US named Nolan
Source: US Social Security Administration
See what else correlates with Popularity of the first name Nolan

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

Correlation r = 0.9408137 (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 = 0.8851304 (Coefficient of determination)
This means 88.5% of the change in the one variable (i.e., Votes for Republican Senators in South Carolina) is predictable based on the change in the other (i.e., Popularity of the first name Nolan) over the 15 years from 1978 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.8E-7.^Show 0.0000001781409894910330700000
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 0.94 in 1.8E-5% of random cases. Said differently, if you correlated 5,613,531 random variables^Note You don't actually need 5 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 14 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 14 because we have two variables measured over a period of 15 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.

[ 0.83, 0.98 ] 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.

	1978	1980	1984	1986	1990	1992	1996	1998	2002	2004	2008	2010	2014	2016	2020
Popularity of the first name Nolan (Babies born)	257	329	520	487	851	1163	1113	1364	2240	2508	3160	3701	5228	5656	5220
Votes for Republican Senators in South Carolina (Total votes)	351917	257946	644815	262886	482032	554175	619859	488238	600010	857167	1076530	810771	1430160	1241610	1369140

Why this works

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

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([257,329,520,487,851,1163,1113,1364,2240,2508,3160,3701,5228,5656,5220,])
array_2 = np.array([351917,257946,644815,262886,482032,554175,619859,488238,600010,857167,1076530,810771,1430160,1241610,1369140,])
array_1_name = "Popularity of the first name Nolan"
array_2_name = "Votes for Republican Senators in South Carolina"

# 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 Popularity of the first name Nolan
Line chart for only Votes for Republican Senators in South Carolina
AI-generated correlation image
The spurious research paper: Rollin' with Nolan: The Impact of the Name Nolan on Republican Senators' Votes in South Carolina

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Correlation ID: 4795 · Black Variable ID: 2489 · Red Variable ID: 26312


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