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Spurious correlation #4,612 · 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 Kansas and the second variable is Domino's Pizza Group's Earnings per Share.  The chart goes from 2003 to 2020, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

As the number of Republican votes for Senators in Kansas increased, more people were drawn to the idea of firing up their own ovens and making homemade pizzas from scratch, leading to a decline in orders from Domino's Pizza. This resulted in a decrease in Domino's Pizza Group's Earnings per Share, as their traditional customer base shifted from delivery to political dough-making activites.

Model: dalle-3
Prompt: Image Generation Prompt: Generate an image of a suburban Kansas kitchen, bustling with activity as families engage in making homemade pizzas. The kitchen counters are filled with various ingredients, and there is a warm, inviting glow from the oven as pizzas bake. In contrast, show Domino's Pizza delivery scooters driving by with fewer stops, illustrating the impact of homemade pizza-making on the company's earnings. The meme style should incorporate humor and subtly convey the shift in customer choices from delivery to political dough-making activities. 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 Kansas and Domino's Pizza Group's Earnings per Share. 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: Cutsy art illustration Vaporwave 3D Pixar Style a meme An image that looks like it is a scene from 'Star Wars'
ChatGPT: a meme

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 Kansas and Domino's Pizza Group's Earnings per Share, in the following situation: As the number of Republican votes for Senators in Kansas increased, more people were drawn to the idea of firing up their own ovens and making homemade pizzas from scratch, leading to a decline in orders from Domino's Pizza. This resulted in a decrease in Domino's Pizza Group's Earnings per Share, as their traditional customer base shifted from delivery to political dough-making activites.. Keep the description simple.
ChatGPT: In Kansas, as the Republican votes for Senators increased, more households gathered around kitchen counters, making their own homemade pizzas from scratch. The aroma of fresh dough and toppings filled the air, while Domino's Pizza delivery scooters were seen making fewer stops around the neighborhood. A graph showing Domino's Pizza Group's Earnings per Share dipped downwards, reflecting the shift in customer choices from delivery to political dough-making activities.

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 meme ] and depicting this scene: In Kansas, as the Republican votes for Senators increased, more households gathered around kitchen counters, making their own homemade pizzas from scratch. The aroma of fresh dough and toppings filled the air, while Domino's Pizza delivery scooters were seen making fewer stops around the neighborhood. A graph showing Domino's Pizza Group's Earnings per Share dipped downwards, reflecting the shift in customer choices from delivery to political dough-making activities. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Image Generation Prompt: Generate an image of a suburban Kansas kitchen, bustling with activity as families engage in making homemade pizzas. The kitchen counters are filled with various ingredients, and there is a warm, inviting glow from the oven as pizzas bake. In contrast, show Domino's Pizza delivery scooters driving by with fewer stops, illustrating the impact of homemade pizza-making on the company's earnings. The meme style should incorporate humor and subtly convey the shift in customer choices from delivery to political dough-making activities.

*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.

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 increases in Republican votes for Senators in Kansas negatively influenced Domino's Pizza Group's Earnings per Share. Make it feel plausible.

AI academic paper

(Because p < 0.01)
The Pizza and Politics Paradox: Exploring the Connection between Republican Votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share

The Journal of Gastronomical Governance

Jan 2024

Reminder: This paper is AI-generated. Not real!
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 silly researcher writing an academic paper.You write titles and abstracts of goofy academic research papers.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, and add unexpected twists in your writing.

Please draft the title and abstract of an academic research paper presenting the findings of the connection between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Make the title an alliteration.

Your research team used data from MIT Election Data and Science Lab, Harvard Dataverse and Wikipedia to assess this nagging question. You found a correlation coefficient of -0.9501201 and p < 0.01 for 2003 to 2020.

Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)

[[TITLE]]
Lorem ipsum dolor sit

[[ABSTRACT]]
Lorem ipsum dolor sit



ChatGPT:
[[TITLE]]
The Pizza and Politics Paradox: Exploring the Connection between Republican Votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share

[[ABSTRACT]]
In this paper, we delve into the unlikely link between political preferences and pizza profits by examining the correlation between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. Our research team embarked on a quest to unravel this mysterious association, blending the savory world of pizza with the nuanced realm of political decision-making. As we peppered our analysis with statistical rigor, we discovered a correlation coefficient of -0.9501201 and p < 0.01, provoking an incredulous "you had me at pizza!" response.



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

Please make up the introduction section of an academic research paper presenting the findings of the connection between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Here is the title and abstract of the paper:
[[TITLE]]
The Pizza and Politics Paradox: Exploring the Connection between Republican Votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share

[[ABSTRACT]]
In this paper, we delve into the unlikely link between political preferences and pizza profits by examining the correlation between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. Our research team embarked on a quest to unravel this mysterious association, blending the savory world of pizza with the nuanced realm of political decision-making. As we peppered our analysis with statistical rigor, we discovered a correlation coefficient of -0.9501201 and p < 0.01, provoking an incredulous "you had me at pizza!" response.


-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

Picture this: it's election night, and the political fervor in Kansas is as hot as a fresh slice of pepperoni pizza. Meanwhile, investors are on the edge of their seats, anxiously eyeing Domino's Pizza Group's Earnings per Share like it's the last slice in the box. As improbable as it sounds, our research sets out to unravel the curious connection between these two seemingly unrelated phenomena. It's a paradoxical puzzle that could rival the twistiest of pretzels.

Now, you might be thinking, "What's the deal with the correlation between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share? Who ordered that combo?" Well, we're here to serve up some answers fresher than a dough tossed in the air. You might say our hypothesis is as bold as extra-spicy jalapenos.

Research like this can sometimes be as elusive as catching a slippery greased pig, but after analyzing mountains of data - and, let's be honest, indulging in our fair share of pizza - we stumbled upon a correlation coefficient of -0.9501201, an unexpected finding that could cause even the most stoic statistician to do a double take. Our p-value of less than 0.01 resulted in a "statistically significant" stamp that even a pizza delivery driver would be proud to hand over.

Just when you thought you've seen it all - pizza, politics, and profit walk into a bar, and the bartender says, "What's the correlation coefficient here?" It's like a joke with a punchline that leaves you scratching your head, but ultimately makes you chuckle. And speaking of chuckles, why did the researcher go to art class? To brush up on his statistical modeling!

The intersection of public opinion and corporate earnings may seem like an unlikely pair, akin to anchovies and pineapple on a pizza, but our findings beg the question: is there something deeper at play here, or is it just a coincidence as tasty as a perfectly-seasoned slice of pizza? It's a riddle wrapped in a mystery inside a pizza box, so join us as we venture into the delightfully cheesy world of politics-meets-pizza economics!


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Kansas and Domino's Pizza Group's Earnings per Share. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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 some movies that you watched that are tangentially related to the topic.

Here is the title and abstract of the paper:
[[TITLE]]
The Pizza and Politics Paradox: Exploring the Connection between Republican Votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share

[[ABSTRACT]]
In this paper, we delve into the unlikely link between political preferences and pizza profits by examining the correlation between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. Our research team embarked on a quest to unravel this mysterious association, blending the savory world of pizza with the nuanced realm of political decision-making. As we peppered our analysis with statistical rigor, we discovered a correlation coefficient of -0.9501201 and p < 0.01, provoking an incredulous "you had me at pizza!" response.


-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Picture this: it's election night, and the political fervor in Kansas is as hot as a fresh slice of pepperoni pizza. Meanwhile, investors are on the edge of their seats, anxiously eyeing Domino's Pizza Group's Earnings per Share like it's the last slice in the box. As improbable as it sounds, our research sets out to unravel the curious connection between these two seemingly unrelated phenomena. It's a paradoxical puzzle that could rival the twistiest of pretzels.
Now, you might be thinking, "What's the deal with the correlation between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share? Who ordered that combo?" Well, we're here to serve up some answers fresher than a dough tossed in the air. You might say our hypothesis is as bold as extra-spicy jalapenos.
Research like this can sometimes be as elusive as catching a slippery greased pig, but after analyzing mountains of data - and, let's be honest, indulging in our fair share of pizza - we stumbled upon a correlation coefficient of -0.9501201, an unexpected finding that could cause even the most stoic statistician to do a double take. Our p-value of less than 0.01 resulted in a "statistically significant" stamp that even a pizza delivery driver would be proud to hand over.
Just when you thought you've seen it all - pizza, politics, and profit walk into a bar, and the bartender says, "What's the correlation coefficient here?" It's like a joke with a punchline that leaves you scratching your head, but ultimately makes you chuckle. And speaking of chuckles, why did the researcher go to art class? To brush up on his statistical modeling!
The intersection of public opinion and corporate earnings may seem like an unlikely pair, akin to anchovies and pineapple on a pizza, but our findings beg the question: is there something deeper at play here, or is it just a coincidence as tasty as a perfectly-seasoned slice of pizza? It's a riddle wrapped in a mystery inside a pizza box, so join us as we venture into the delightfully cheesy world of politics-meets-pizza economics!

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The study of political preferences and their impact on corporate success has been a topic of interest for many researchers. Smith et al. (2016) discovered correlations between political party alignment and consumer behavior, shedding light on the potential influence of political ideology on market dynamics. Similarly, Doe and Jones (2018) examined the intersection of public sentiment and corporate performance, revealing intriguing connections between state politics and business outcomes.

Now, let's take a slice of the literature that delves into the delectable world of pizzas. In "Extra Cheese: A Deep-Dish Dive into Pizza Economics" by Pizza Lover (2017), the author delves into the complex dynamics of the pizza industry, exploring the impact of consumer preferences and economic factors on pizza sales. In "The Art of Toppings: A Gourmet Guide to Pizza Innovation" by Pizza Aficionado (2019), the author uncovers the unique relationship between pizza flavor profiles and consumer satisfaction, offering a tantalizing perspective on the role of taste in market success.

Turning to fictional works that may offer tangential insights, "Cheesy Mysteries: A Novel Approach to Pizza Paradoxes" by Fictional Writer (2020) entraps readers in a web of intrigue as they unravel the enigmatic connections between pizzas and peculiar phenomena. In "The Politics of Pepperoni: A Pizza-Infused Political Thriller" by Imaginary Author (2015), the author spins a political tale intertwined with the tantalizing aroma of freshly baked pizzas, blurring the lines between electoral strategies and savory sensations.

In the realm of cinema, the movie "Pizza & Prosperity: A Culinary Conspiracy" explores the clandestine world of pizza magnates and political machinations, painting a visually captivating narrative that hints at the hidden influence of pizzas on public decisions. Meanwhile, in "Election Pie: A Political Feast" viewers are treated to a comedic exploration of political campaigning that unfolds against the backdrop of a bustling pizza parlor, serving up a generous helping of laughter alongside food for thought.

In "Dough and Donkeys: The Political Culinary Conundrum" by Silly Researcher (2021), the author stirs up a blend of humor and intrigue, utilizing unconventional correlations to shed light on the whimsical connections between political preferences and pizza profits. The study's findings may provoke a chuckle or two, but they also serve as a reminder that sometimes, the most unexpected pairings can yield fascinating insights.

And now for a quick joke! Why did the pizza maker go to therapy? He needed to get a pizza his mind!


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Kansas and Domino's Pizza Group's Earnings per Share. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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 Wikipedia . You used data from 2003 to 2020

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

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
The Pizza and Politics Paradox: Exploring the Connection between Republican Votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share

[[ABSTRACT]]
In this paper, we delve into the unlikely link between political preferences and pizza profits by examining the correlation between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. Our research team embarked on a quest to unravel this mysterious association, blending the savory world of pizza with the nuanced realm of political decision-making. As we peppered our analysis with statistical rigor, we discovered a correlation coefficient of -0.9501201 and p < 0.01, provoking an incredulous "you had me at pizza!" response.


[[INTRODUCTION]]
Picture this: it's election night, and the political fervor in Kansas is as hot as a fresh slice of pepperoni pizza. Meanwhile, investors are on the edge of their seats, anxiously eyeing Domino's Pizza Group's Earnings per Share like it's the last slice in the box. As improbable as it sounds, our research sets out to unravel the curious connection between these two seemingly unrelated phenomena. It's a paradoxical puzzle that could rival the twistiest of pretzels.
Now, you might be thinking, "What's the deal with the correlation between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share? Who ordered that combo?" Well, we're here to serve up some answers fresher than a dough tossed in the air. You might say our hypothesis is as bold as extra-spicy jalapenos.
Research like this can sometimes be as elusive as catching a slippery greased pig, but after analyzing mountains of data - and, let's be honest, indulging in our fair share of pizza - we stumbled upon a correlation coefficient of -0.9501201, an unexpected finding that could cause even the most stoic statistician to do a double take. Our p-value of less than 0.01 resulted in a "statistically significant" stamp that even a pizza delivery driver would be proud to hand over.
Just when you thought you've seen it all - pizza, politics, and profit walk into a bar, and the bartender says, "What's the correlation coefficient here?" It's like a joke with a punchline that leaves you scratching your head, but ultimately makes you chuckle. And speaking of chuckles, why did the researcher go to art class? To brush up on his statistical modeling!
The intersection of public opinion and corporate earnings may seem like an unlikely pair, akin to anchovies and pineapple on a pizza, but our findings beg the question: is there something deeper at play here, or is it just a coincidence as tasty as a perfectly-seasoned slice of pizza? It's a riddle wrapped in a mystery inside a pizza box, so join us as we venture into the delightfully cheesy world of politics-meets-pizza economics!

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To uncover the enigmatic link between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share, our research team embarked on a data-driven odyssey worthy of Odysseus himself. We amassed a trove of information from reputable sources such as the MIT Election Data and Science Lab, Harvard Dataverse, and Wikipedia, covering the period from 2003 to 2020. We assure you, our internet spelunking skills were as sharp as the edges of a Chicago-style deep dish.

To begin, we ventured into the electoral labyrinth, where we meticulously gathered the Republican votes for Kansas Senators in each election cycle. Our approach could be likened to a political scavenger hunt, and just like a good slice of pizza, we left no trail of breadcrumbs unexamined. We cross-referenced our findings with demographic data, political campaign rhetoric, and voter sentiment, leaving no political stone unturned in this quest for knowledge. We were determined to avoid making any half-baked assumptions in this process.

Next, turning our attention to Domino's Pizza Group's Earnings per Share, we delved into the delectable world of corporate finances, meticulously analyzing quarterly and annual reports with the precision of a world-class pizzaiolo crafting the perfect crust. Whether it was tracking sales figures through the feta-laden fields of finance or deciphering investment patterns with the agility of a mozzarella maestro, our approach endeavored to be as precise as measuring the circumference of a perfectly circular pizza.

With our hands metaphorically dusted with data flour, we then navigated the statistical minefield, employing the robust toolset of quantitative analysis. We calculated a correlation coefficient using bivariate analysis, indulging in some good old-fashioned number crunching without the added calories. This involved employing sophisticated statistical software that would make the most complex pizza topping combinations look like child's play. Our approach was methodically crafted, akin to the meticulous placement of pepperoni on a well-arranged pizza pie.

Furthermore, we conducted a hypothesis test to ascertain the significance of the relationship between the variables. Our p-value analysis was more scrutinized than a customer counting the number of pepperoni slices on their pizza, ensuring that our statistical inferences were as sharp as a well-sharpened pizza wheel cutter. We embraced the scientific rigor demanded in this research endeavor, acknowledging that our findings needed to be as robust as the crust of a New York-style pizza slice.

Lastly, we took measures to mitigate potential confounding variables, employing multi-variate analysis to tease out the true nature of the relationship between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. Our approach to controlling for extraneous factors was as meticulous as disentangling a knotted mess of cheese on a supreme pizza, ensuring that our findings represented the unadulterated essence of the phenomenon under investigation.

In summary, our methodological cocktail combined the savory ingredients of political data, corporate financials, and statistical analyses with a dash of academic rigor and a sprinkle of humor, resulting in a research amalgamation as tantalizing as a perfectly cooked pizza.


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

Please make up the results section of an academic research paper presenting the findings of the connection between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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 Wikipedia .

For the time period 2003 to 2020, you found a correlation -0.9501201, r-squared of 0.9027282, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
The Pizza and Politics Paradox: Exploring the Connection between Republican Votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share

[[ABSTRACT]]
In this paper, we delve into the unlikely link between political preferences and pizza profits by examining the correlation between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. Our research team embarked on a quest to unravel this mysterious association, blending the savory world of pizza with the nuanced realm of political decision-making. As we peppered our analysis with statistical rigor, we discovered a correlation coefficient of -0.9501201 and p < 0.01, provoking an incredulous "you had me at pizza!" response.


-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
Picture this: it's election night, and the political fervor in Kansas is as hot as a fresh slice of pepperoni pizza. Meanwhile, investors are on the edge of their seats, anxiously eyeing Domino's Pizza Group's Earnings per Share like it's the last slice in the box. As improbable as it sounds, our research sets out to unravel the curious connection between these two seemingly unrelated phenomena. It's a paradoxical puzzle that could rival the twistiest of pretzels.
Now, you might be thinking, "What's the deal with the correlation between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share? Who ordered that combo?" Well, we're here to serve up some answers fresher than a dough tossed in the air. You might say our hypothesis is as bold as extra-spicy jalapenos.
Research like this can sometimes be as elusive as catching a slippery greased pig, but after analyzing mountains of data - and, let's be honest, indulging in our fair share of pizza - we stumbled upon a correlation coefficient of -0.9501201, an unexpected finding that could cause even the most stoic statistician to do a double take. Our p-value of less than 0.01 resulted in a "statistically significant" stamp that even a pizza delivery driver would be proud to hand over.
Just when you thought you've seen it all - pizza, politics, and profit walk into a bar, and the bartender says, "What's the correlation coefficient here?" It's like a joke with a punchline that leaves you scratching your head, but ultimately makes you chuckle. And speaking of chuckles, why did the researcher go to art class? To brush up on his statistical modeling!
The intersection of public opinion and corporate earnings may seem like an unlikely pair, akin to anchovies and pineapple on a pizza, but our findings beg the question: is there something deeper at play here, or is it just a coincidence as tasty as a perfectly-seasoned slice of pizza? It's a riddle wrapped in a mystery inside a pizza box, so join us as we venture into the delightfully cheesy world of politics-meets-pizza economics!

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The analysis of the data revealed a striking correlation of -0.9501201 between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share for the time period 2003 to 2020. This finding was accompanied by an r-squared value of 0.9027282, indicating that a substantial 90% of the variation in Domino's Pizza Group's Earnings per Share can be explained by the variation in Republican votes for Senators in Kansas. In statistical terms, you could say that this correlation is as strong as the aroma of a freshly-baked pizza wafting through the air – it's hard to miss!

Now, as we presented our results to the research team, there was a unanimous sense of awe, akin to the surprise when a pizza arrives with the exact number of slices to satisfy everyone at the table. The p-value of less than 0.01 further reinforced the statistical robustness of our findings, much like a well-kneaded pizza dough that holds its shape in the oven.

Fig. 1 showcases the striking relationship between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. When we look at this graph, we can't help but think of it as the "ultimate supreme" of correlations – it has all the right ingredients perfectly mingled together.

As we basked in the limelight of this unexpected discovery, we couldn't help but ponder: why did the statistician go to therapy? To sort out their residual plots! While the correlation is undeniably compelling, we recognize that further research is needed to uncover the underlying mechanisms driving this curious relationship. In the world of pizza and politics, nothing is as simple as a cheese pizza – there are always layers of complexity waiting to be uncovered.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Kansas and Domino's Pizza Group's Earnings per Share. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
The Pizza and Politics Paradox: Exploring the Connection between Republican Votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share

[[ABSTRACT]]
In this paper, we delve into the unlikely link between political preferences and pizza profits by examining the correlation between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. Our research team embarked on a quest to unravel this mysterious association, blending the savory world of pizza with the nuanced realm of political decision-making. As we peppered our analysis with statistical rigor, we discovered a correlation coefficient of -0.9501201 and p < 0.01, provoking an incredulous "you had me at pizza!" response.


[[LITERATURE REVIEW]]
The study of political preferences and their impact on corporate success has been a topic of interest for many researchers. Smith et al. (2016) discovered correlations between political party alignment and consumer behavior, shedding light on the potential influence of political ideology on market dynamics. Similarly, Doe and Jones (2018) examined the intersection of public sentiment and corporate performance, revealing intriguing connections between state politics and business outcomes.
Now, let's take a slice of the literature that delves into the delectable world of pizzas. In "Extra Cheese: A Deep-Dish Dive into Pizza Economics" by Pizza Lover (2017), the author delves into the complex dynamics of the pizza industry, exploring the impact of consumer preferences and economic factors on pizza sales. In "The Art of Toppings: A Gourmet Guide to Pizza Innovation" by Pizza Aficionado (2019), the author uncovers the unique relationship between pizza flavor profiles and consumer satisfaction, offering a tantalizing perspective on the role of taste in market success.
Turning to fictional works that may offer tangential insights, "Cheesy Mysteries: A Novel Approach to Pizza Paradoxes" by Fictional Writer (2020) entraps readers in a web of intrigue as they unravel the enigmatic connections between pizzas and peculiar phenomena. In "The Politics of Pepperoni: A Pizza-Infused Political Thriller" by Imaginary Author (2015), the author spins a political tale intertwined with the tantalizing aroma of freshly baked pizzas, blurring the lines between electoral strategies and savory sensations.
In the realm of cinema, the movie "Pizza & Prosperity: A Culinary Conspiracy" explores the clandestine world of pizza magnates and political machinations, painting a visually captivating narrative that hints at the hidden influence of pizzas on public decisions. Meanwhile, in "Election Pie: A Political Feast" viewers are treated to a comedic exploration of political campaigning that unfolds against the backdrop of a bustling pizza parlor, serving up a generous helping of laughter alongside food for thought.
In "Dough and Donkeys: The Political Culinary Conundrum" by Silly Researcher (2021), the author stirs up a blend of humor and intrigue, utilizing unconventional correlations to shed light on the whimsical connections between political preferences and pizza profits. The study's findings may provoke a chuckle or two, but they also serve as a reminder that sometimes, the most unexpected pairings can yield fascinating insights.
And now for a quick joke! Why did the pizza maker go to therapy? He needed to get a pizza his mind!

[[RESULTS]]
The analysis of the data revealed a striking correlation of -0.9501201 between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share for the time period 2003 to 2020. This finding was accompanied by an r-squared value of 0.9027282, indicating that a substantial 90% of the variation in Domino's Pizza Group's Earnings per Share can be explained by the variation in Republican votes for Senators in Kansas. In statistical terms, you could say that this correlation is as strong as the aroma of a freshly-baked pizza wafting through the air – it's hard to miss!
Now, as we presented our results to the research team, there was a unanimous sense of awe, akin to the surprise when a pizza arrives with the exact number of slices to satisfy everyone at the table. The p-value of less than 0.01 further reinforced the statistical robustness of our findings, much like a well-kneaded pizza dough that holds its shape in the oven.
Fig. 1 showcases the striking relationship between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. When we look at this graph, we can't help but think of it as the "ultimate supreme" of correlations – it has all the right ingredients perfectly mingled together.
As we basked in the limelight of this unexpected discovery, we couldn't help but ponder: why did the statistician go to therapy? To sort out their residual plots! While the correlation is undeniably compelling, we recognize that further research is needed to uncover the underlying mechanisms driving this curious relationship. In the world of pizza and politics, nothing is as simple as a cheese pizza – there are always layers of complexity waiting to be uncovered.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

Our findings bring to light the unlikely, yet undeniably captivating correlation between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. The negative correlation coefficient of -0.9501201 suggests a strong relationship, akin to the steadfast bond between cheese and crust in a good old margherita pizza. The p-value of less than 0.01 reinforces the robustness of this connection, just like the sturdy crust of a deep-dish pizza.

Building on the prior research by Smith et al. (2016) and Doe and Jones (2018), our results provide empirical support for the notion that political alignments can influence market dynamics. Much like the careful assembly of toppings on a pizza, it appears that political preferences may indeed flavor corporate performance. Our findings echo Pizza Lover's (2017) exploration of economic factors in the pizza industry, revealing a savory dimension to the interplay between public sentiment and business outcomes. Just as Pizza Aficionado (2019) uncovered the impact of taste on consumer satisfaction, our study uncovers the tantalizing synergy between political votes and corporate earnings. Who would have thought that pizza could provide such a meaty subject for serious academic inquiry?

The spectacular correlation unveiled in our study was indeed met with the kind of astonishment one might experience upon discovering a free pizza delivery coupon tucked amidst a pile of bills. Our results serve as a testament to the oft-quoted wisdom that "where there's a will, there's a way," much like the determination of a pizza enthusiast committed to finding the perfect slice.

In light of our findings, it is clear that the world of pizza and politics is not as disparate as one might assume. Much like the intricate dance of scientific inquiry, it seems that the flavorful world of pizzas and the complex realm of political decisions are not immune to sharing the same statistical stage. As we continue to unravel the mysteries of these unexpected connections, may we approach our research with the same degree of curiosity and humor that we bring to our search for the finest slice of pizza. After all, the researcher who doesn't crack a smile in the face of a pizza-related correlation may just be missing out on the true joy of discovery.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 Kansas and Domino's Pizza Group's Earnings per Share. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. 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]]
The Pizza and Politics Paradox: Exploring the Connection between Republican Votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share

[[ABSTRACT]]
In this paper, we delve into the unlikely link between political preferences and pizza profits by examining the correlation between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. Our research team embarked on a quest to unravel this mysterious association, blending the savory world of pizza with the nuanced realm of political decision-making. As we peppered our analysis with statistical rigor, we discovered a correlation coefficient of -0.9501201 and p < 0.01, provoking an incredulous "you had me at pizza!" response.


[[INTRDUCTION]]
Picture this: it's election night, and the political fervor in Kansas is as hot as a fresh slice of pepperoni pizza. Meanwhile, investors are on the edge of their seats, anxiously eyeing Domino's Pizza Group's Earnings per Share like it's the last slice in the box. As improbable as it sounds, our research sets out to unravel the curious connection between these two seemingly unrelated phenomena. It's a paradoxical puzzle that could rival the twistiest of pretzels.
Now, you might be thinking, "What's the deal with the correlation between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share? Who ordered that combo?" Well, we're here to serve up some answers fresher than a dough tossed in the air. You might say our hypothesis is as bold as extra-spicy jalapenos.
Research like this can sometimes be as elusive as catching a slippery greased pig, but after analyzing mountains of data - and, let's be honest, indulging in our fair share of pizza - we stumbled upon a correlation coefficient of -0.9501201, an unexpected finding that could cause even the most stoic statistician to do a double take. Our p-value of less than 0.01 resulted in a "statistically significant" stamp that even a pizza delivery driver would be proud to hand over.
Just when you thought you've seen it all - pizza, politics, and profit walk into a bar, and the bartender says, "What's the correlation coefficient here?" It's like a joke with a punchline that leaves you scratching your head, but ultimately makes you chuckle. And speaking of chuckles, why did the researcher go to art class? To brush up on his statistical modeling!
The intersection of public opinion and corporate earnings may seem like an unlikely pair, akin to anchovies and pineapple on a pizza, but our findings beg the question: is there something deeper at play here, or is it just a coincidence as tasty as a perfectly-seasoned slice of pizza? It's a riddle wrapped in a mystery inside a pizza box, so join us as we venture into the delightfully cheesy world of politics-meets-pizza economics!

[[RESULTS]]
The analysis of the data revealed a striking correlation of -0.9501201 between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share for the time period 2003 to 2020. This finding was accompanied by an r-squared value of 0.9027282, indicating that a substantial 90% of the variation in Domino's Pizza Group's Earnings per Share can be explained by the variation in Republican votes for Senators in Kansas. In statistical terms, you could say that this correlation is as strong as the aroma of a freshly-baked pizza wafting through the air – it's hard to miss!
Now, as we presented our results to the research team, there was a unanimous sense of awe, akin to the surprise when a pizza arrives with the exact number of slices to satisfy everyone at the table. The p-value of less than 0.01 further reinforced the statistical robustness of our findings, much like a well-kneaded pizza dough that holds its shape in the oven.
Fig. 1 showcases the striking relationship between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. When we look at this graph, we can't help but think of it as the "ultimate supreme" of correlations – it has all the right ingredients perfectly mingled together.
As we basked in the limelight of this unexpected discovery, we couldn't help but ponder: why did the statistician go to therapy? To sort out their residual plots! While the correlation is undeniably compelling, we recognize that further research is needed to uncover the underlying mechanisms driving this curious relationship. In the world of pizza and politics, nothing is as simple as a cheese pizza – there are always layers of complexity waiting to be uncovered.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, our research has successfully unraveled the enigmatic connection between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share, showcasing a correlation coefficient so strong, it could almost hold its own as a supporting beam in a pizzeria. Our findings have opened a door to a world where political preferences and pizza profits intertwine like two perfectly entwined strands of mozzarella on a stretchy pizza.

As we wrap up our study, it's clear that the association between these seemingly unrelated variables is as real as the aroma of garlic breadsticks wafting through a crowded restaurant. While our results are as significant as a perfectly timed pizza delivery, it's important to remember that correlation does not imply causation – but it does imply a potential for some cheesy jokes!

So, why did the Republican voter order a pizza before heading to the polls? Because they wanted to ensure their tastes were reflected both in politics and in their choice of dinner! In the world of research, the unexpected findings are often the most delicious, much like the delightful surprise of finding a forgotten slice of pizza in the fridge.

In the grand tradition of academic inquiry, we must solemnly declare that further research in this area is not just unnecessary – it's like ordering pineapple on a pizza. It simply doesn't belong! This study has served up its slice of knowledge, and it's as satisfying as a perfectly golden crust – leaving us no desire to take another bite.


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 The Pizza and Politics Paradox: Exploring the Connection between Republican Votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Gastronomical Governance


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 The Pizza and Politics Paradox: Exploring the Connection between Republican Votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Culinary and Political 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 The Pizza and Politics Paradox: Exploring the Connection between Republican Votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share with an abstract of In this paper, we delve into the unlikely link between political preferences and pizza profits by examining the correlation between Republican votes for Senators in Kansas and Domino's Pizza Group's Earnings per Share. Our research team embarked on a quest to unravel this mysterious association, blending the savory world of pizza with the nuanced realm of political decision-making. As we peppered our analysis with statistical rigor, we discovered a correlation coefficient of -0.9501201 and p < 0.01, provoking an incredulous "you had me at pizza!" response.

ChatGPT:

Republican votes, Senators, Kansas, Domino's Pizza Group, earnings per share, correlation, political preferences, pizza profits, correlation coefficient, statistical 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 Kansas
Detailed data title: Total number of votes cast for Federal Republican Senate candidates in Kansas
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for Republican Senators in Kansas

Domino's Pizza Group's Earnings per Share
Source: Wikipedia
See what else correlates with Domino's Pizza Group's Earnings per Share

Correlation r = -0.9501201 (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 = 0.9027282 (Coefficient of determination)
This means 90.3% of the change in the one variable (i.e., Domino's Pizza Group's Earnings per Share) is predictable based on the change in the other (i.e., Votes for Republican Senators in Kansas) over the 6 years from 2003 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0037. 0.0036699565064428384000000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as -0.95 in 0.37% of random cases. Said differently, if you correlated 272 random variables Which I absolutely did.
with the same 5 degrees of freedom, 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.

[ -0.99, -0.61 ] 95% correlation confidence interval (using the Fisher z-transformation)
The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!


All values for the years included above: If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.
200420082010201420162020
Votes for Republican Senators in Kansas (Total votes)780863727121587175460350732376727962
Domino's Pizza Group's Earnings per Share (Percentage)4.110.1215.425.913.18.9




Why this works

  1. 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.
  2. Lack of causal connection: There is probably Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
    no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
  3. Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
    p-value calculation does not take this into account, so mathematically it appears less probable than it really is.
  4. 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.
  5. Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
    Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.
  6. Inverted Y-axis: I inverted the Y-axis on the chart above so that the lines would move together. This is visually pleasing, but not at all intuitive. Below is a line graph that does not invert the Y-axis.




Try it yourself

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

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

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

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

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

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

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

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

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


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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([780863,727121,587175,460350,732376,727962,])
array_2 = np.array([4.1,10.12,15.4,25.9,13.1,8.9,])
array_1_name = "Votes for Republican Senators in Kansas"
array_2_name = "Domino's Pizza Group's Earnings per Share"

# 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)



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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."

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Correlation ID: 4612 · Black Variable ID: 26242 · Red Variable ID: 263
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