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Spurious correlation #2,171 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of electricians in Mississippi and the second variable is Runs Scored by the losing team in the World Series.  The chart goes from 2003 to 2013, and the two variables track closely in value over that time. Small Image
Download png
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AI explanation

As the number of electricians in Mississippi dwindled, so did their ability to generate sparks. This led to a shortage of sparklers being sold at the World Series, ultimately dampening the losing team's post-game celebrations. Without the electrifying display, the losing team just couldn't muster up the same level of enthusiasm and motivation to score runs, putting a whole new spin on the term "power outage" in baseball.

Model: dalle-3
Prompt: Generate an image inspired by a classic TIME magazine cover, featuring a vibrant and bustling town in Mississippi. Show a group of skilled electricians diligently collaborating on wiring a new building while incorporating a sense of dynamism and energy in their interactions. In the same scene, depict a group of baseball fans experiencing disappointment as they watch the losing team score a few runs in the World Series. The contrast of focused work and disappointed spectators should be clear, with a focus on the electrical team at work and the deflated fans in the background. 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: The number of electricians in Mississippi and Runs Scored by the losing team in the World Series. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: Vaporwave Steampunk Sci-fi fantacy A TIME magazine cover image inspired by bauhaus
ChatGPT: A TIME magazine cover image

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 The number of electricians in Mississippi and Runs Scored by the losing team in the World Series together. Keep the description simple.
ChatGPT: In a bustling town in Mississippi, several electricians are working together on wiring a new building, while a group of baseball fans sadly watches as the losing team scores a few runs in the World Series.

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 TIME magazine cover image ] and depicting this scene: In a bustling town in Mississippi, several electricians are working together on wiring a new building, while a group of baseball fans sadly watches as the losing team scores a few runs in the World Series. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image inspired by a classic TIME magazine cover, featuring a vibrant and bustling town in Mississippi. Show a group of skilled electricians diligently collaborating on wiring a new building while incorporating a sense of dynamism and energy in their interactions. In the same scene, depict a group of baseball fans experiencing disappointment as they watch the losing team score a few runs in the World Series. The contrast of focused work and disappointed spectators should be clear, with a focus on the electrical team at work and the deflated fans in the background.

*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 a decrease in The number of electricians in Mississippi caused Runs Scored by the losing team in the World Series to decrease.

AI academic paper

(Because p < 0.01)
Sparking Joy: The Wattage of Electricians in Mississippi and the Wattage of the Losing World Series Team

The Journal of Illuminating Insights

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 The number of electricians in Mississippi and Runs Scored by the losing team in the World Series. Make lots of jokes, goofy observations, and puns.

Make the title a clever pun.

Your research team used data from Bureau of Larbor Statistics and Wikipedia to assess this nagging question. You found a correlation coefficient of 0.7718826 and p < 0.01 for 2003 to 2013.

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]]
Sparking Joy: The Wattage of Electricians in Mississippi and the Wattage of the Losing World Series Team

[[ABSTRACT]]
In this paper, we shed light on the electrifying connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series. Using data from the Bureau of Labor Statistics and Wikipedia, our research team conducted a voltage analysis and uncovered a shocking correlation coefficient of 0.7718826 with p < 0.01 for the years 2003 to 2013. We delve into the current state of affairs, offering a bright perspective on the intersection of electrical labor and major league baseball outcomes. Our findings illuminate a previously overlooked relationship, sparking joy and sparking curiosity in the realms of sports and economics.


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 The number of electricians in Mississippi and Runs Scored by the losing team in the World Series. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Here is the title and abstract of the paper:
[[TITLE]]
Sparking Joy: The Wattage of Electricians in Mississippi and the Wattage of the Losing World Series Team

[[ABSTRACT]]
In this paper, we shed light on the electrifying connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series. Using data from the Bureau of Labor Statistics and Wikipedia, our research team conducted a voltage analysis and uncovered a shocking correlation coefficient of 0.7718826 with p < 0.01 for the years 2003 to 2013. We delve into the current state of affairs, offering a bright perspective on the intersection of electrical labor and major league baseball outcomes. Our findings illuminate a previously overlooked relationship, sparking joy and sparking curiosity in the realms of sports and economics.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
INTRODUCTION

As Thomas Edison once said, "I have not failed. I've just found 10,000 ways that won't work." Well, in our case, we're hoping to find at least one way that does work. Our research aims to shed some light on the shocking connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series.

Now, you might be thinking, "What in the name of Ohm's Law does electrical labor have to do with baseball outcomes?" That's a shockingly good question, my dear reader, and one that sparked our curiosity as well. It's not every day that you stumble upon an electrifying correlation between seemingly unrelated entities, but fear not, we are here to illuminate this unexplored territory.

So, grab your voltage meters and baseball gloves because we are about to take a deep dive into the wattage of electricians and the wattage of the losing World Series team. We'll be conducting a statistical analysis that's guaranteed to be more electrifying than a lightning storm on a summer's day.

Our findings promise to be illuminating, and who knows, by the end of this paper, you might just be "amped" up about the potential for unexpected connections in the world of research. So, without further ado, let's flip the switch and shine a light on this captivating correlation.


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 The number of electricians in Mississippi and Runs Scored by the losing team in the World Series. 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]]
Sparking Joy: The Wattage of Electricians in Mississippi and the Wattage of the Losing World Series Team

[[ABSTRACT]]
In this paper, we shed light on the electrifying connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series. Using data from the Bureau of Labor Statistics and Wikipedia, our research team conducted a voltage analysis and uncovered a shocking correlation coefficient of 0.7718826 with p < 0.01 for the years 2003 to 2013. We delve into the current state of affairs, offering a bright perspective on the intersection of electrical labor and major league baseball outcomes. Our findings illuminate a previously overlooked relationship, sparking joy and sparking curiosity in the realms of sports and economics.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
As Thomas Edison once said, "I have not failed. I've just found 10,000 ways that won't work." Well, in our case, we're hoping to find at least one way that does work. Our research aims to shed some light on the shocking connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series.
Now, you might be thinking, "What in the name of Ohm's Law does electrical labor have to do with baseball outcomes?" That's a shockingly good question, my dear reader, and one that sparked our curiosity as well. It's not every day that you stumble upon an electrifying correlation between seemingly unrelated entities, but fear not, we are here to illuminate this unexplored territory.
So, grab your voltage meters and baseball gloves because we are about to take a deep dive into the wattage of electricians and the wattage of the losing World Series team. We'll be conducting a statistical analysis that's guaranteed to be more electrifying than a lightning storm on a summer's day.
Our findings promise to be illuminating, and who knows, by the end of this paper, you might just be "amped" up about the potential for unexpected connections in the world of research. So, without further ado, let's flip the switch and shine a light on this captivating correlation.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series has drawn the attention of researchers from diverse backgrounds. Smith et al. (2015) conducted an extensive study on the impact of state-level occupational distribution on regional sporting achievements. Their findings revealed a surprising positive correlation between electrical labor force density and suboptimal baseball performance, captivating the scientific community with a jolt of curiosity.

Furthermore, Doe (2019) delved into the economic implications of electrical workforce fluctuations on local communities, inadvertently stumbling upon a peculiar association with underwhelming World Series outcomes for the runner-up team. This unsuspected revelation prompted an electrifying discussion among scholars, illuminating the potential for unexplored linkages between labor dynamics and professional sports.

In "The Shocking Truth: A Comprehensive Guide to Electrical Wonders" (Copperfield, 2018), the author delves into the electrifying world of electrical marvels, providing insights that may spark new perspectives on the interactions between electricians and athletic events. Similarly, "The Curveball Connection: Unraveling the Mysteries of Baseball" (St. Claire, 2016) explores the intricate subtleties of baseball's unpredictable nature, encouraging readers to consider the possibility of unanticipated correlations with unrelated industries.

On a more speculative note, our research team embarked on an unconventional literature exploration, perusing an eclectic mix of sources, including but not limited to the back of cereal boxes, fortune cookie messages, and even the intricate details of CVS receipts. While these unconventional sources failed to yield scholarly merit, they did provide a delightful array of puns and quirky observations, sparking joy in unexpected ways.

As we plunge into the depths of this enigmatic correlation, it is essential to approach the subject matter with a sense of humor, electrical puns, and a keen eye for the unexpected. After all, the exploration of unusual connections between electrical labor and baseball outcomes has the potential to enlighten our understanding of the world in unexpected and amusing ways.


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 The number of electricians in Mississippi and Runs Scored by the losing team in the World Series. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Larbor Statistics and Wikipedia . You used data from 2003 to 2013

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]]
Sparking Joy: The Wattage of Electricians in Mississippi and the Wattage of the Losing World Series Team

[[ABSTRACT]]
In this paper, we shed light on the electrifying connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series. Using data from the Bureau of Labor Statistics and Wikipedia, our research team conducted a voltage analysis and uncovered a shocking correlation coefficient of 0.7718826 with p < 0.01 for the years 2003 to 2013. We delve into the current state of affairs, offering a bright perspective on the intersection of electrical labor and major league baseball outcomes. Our findings illuminate a previously overlooked relationship, sparking joy and sparking curiosity in the realms of sports and economics.

[[INTRODUCTION]]
INTRODUCTION
As Thomas Edison once said, "I have not failed. I've just found 10,000 ways that won't work." Well, in our case, we're hoping to find at least one way that does work. Our research aims to shed some light on the shocking connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series.
Now, you might be thinking, "What in the name of Ohm's Law does electrical labor have to do with baseball outcomes?" That's a shockingly good question, my dear reader, and one that sparked our curiosity as well. It's not every day that you stumble upon an electrifying correlation between seemingly unrelated entities, but fear not, we are here to illuminate this unexplored territory.
So, grab your voltage meters and baseball gloves because we are about to take a deep dive into the wattage of electricians and the wattage of the losing World Series team. We'll be conducting a statistical analysis that's guaranteed to be more electrifying than a lightning storm on a summer's day.
Our findings promise to be illuminating, and who knows, by the end of this paper, you might just be "amped" up about the potential for unexpected connections in the world of research. So, without further ado, let's flip the switch and shine a light on this captivating correlation.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To shed light on the enigmatic relationship between the number of electricians in Mississippi and the runs scored by the losing team in the World Series, our research team harnessed a combination of statistical sorcery and data divination. Our methodology involved conjuring information from the mysterious depths of the Bureau of Labor Statistics and entrancing webpages of Wikipedia, where we scoured and scrutinized data from the years 2003 to 2013.

Now, onto the electrifying specifics of our methodology! We began by utilizing a magical brew of Excel spreadsheet potions and Python incantations to wrangle the data into submission. Our enchanting brew allowed us to calculate the mystical correlation coefficient and perform a spellbinding regression analysis to uncover hidden patterns in the data.

In our pursuit of knowledge, we adhered to the strict rituals of statistical significance, ensuring that any apparent connections between electricians and baseball did not arise from mere chance. We even enlisted the help of a wizardly statistical software package to cast spells of p-values and hypothesis testing to ensure the validity of our findings.

Furthermore, we took great care to account for any confounding variables that might have tried to creep into our study like mischievous gremlins. While we focused on the number of electricians in Mississippi, we made sure to shield our analysis from the influence of other factors such as team performance, weather patterns, and the lunar phases, as we didn't want any extraneous forces casting a shadow over our results.

In the end, our methodology may seem like a concoction of mystical elements and statistical sleights of hand, but rest assured, our approach was grounded in the sacred principles of sound research and rigorous analysis. We dared to dance on the edge of uncertainty, for in the pursuit of knowledge, one must be willing to embrace the unknown and perhaps stumble upon a little magic along the way.


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 The number of electricians in Mississippi and Runs Scored by the losing team in the World Series. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Bureau of Larbor Statistics and Wikipedia .

For the time period 2003 to 2013, you found a correlation 0.7718826, r-squared of 0.5958028, 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]]
Sparking Joy: The Wattage of Electricians in Mississippi and the Wattage of the Losing World Series Team

[[ABSTRACT]]
In this paper, we shed light on the electrifying connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series. Using data from the Bureau of Labor Statistics and Wikipedia, our research team conducted a voltage analysis and uncovered a shocking correlation coefficient of 0.7718826 with p < 0.01 for the years 2003 to 2013. We delve into the current state of affairs, offering a bright perspective on the intersection of electrical labor and major league baseball outcomes. Our findings illuminate a previously overlooked relationship, sparking joy and sparking curiosity in the realms of sports and economics.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
INTRODUCTION
As Thomas Edison once said, "I have not failed. I've just found 10,000 ways that won't work." Well, in our case, we're hoping to find at least one way that does work. Our research aims to shed some light on the shocking connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series.
Now, you might be thinking, "What in the name of Ohm's Law does electrical labor have to do with baseball outcomes?" That's a shockingly good question, my dear reader, and one that sparked our curiosity as well. It's not every day that you stumble upon an electrifying correlation between seemingly unrelated entities, but fear not, we are here to illuminate this unexplored territory.
So, grab your voltage meters and baseball gloves because we are about to take a deep dive into the wattage of electricians and the wattage of the losing World Series team. We'll be conducting a statistical analysis that's guaranteed to be more electrifying than a lightning storm on a summer's day.
Our findings promise to be illuminating, and who knows, by the end of this paper, you might just be "amped" up about the potential for unexpected connections in the world of research. So, without further ado, let's flip the switch and shine a light on this captivating correlation.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

Our research team's analysis revealed a stunning correlation coefficient of 0.7718826 between the number of electricians in Mississippi and the runs scored by the losing team in the World Series for the years 2003 to 2013. This correlation exhibited an r-squared value of 0.5958028, signifying that a shocking 59.58% of the variation in runs scored by the losing team can be explained by the number of electricians in the Magnolia State.

The connection between electrical labor and the outcomes of the Fall Classic is positively electrifying, indicating a strong relationship between these seemingly unrelated variables. The p-value of less than 0.01 further underscores the significance of this correlation, demonstrating a strikingly low probability that our findings are merely a result of random chance.

To visually capture the magnitude of this unforeseen relationship, we present Fig. 1, a scatterplot showcasing the strong correlation between the number of electricians in Mississippi and the runs scored by the losing team in the World Series. The figure provides a powerful visual representation of the wattage of electricians and the wattage of the losing World Series team, illuminating the unexpected connection between these disparate factors.

Our results electrify the research landscape, shedding light on an unexplored junction between the realm of electrical labor and the world of baseball outcomes. The implications of this correlation are shocking, sparking joy and curiosity among both sports enthusiasts and economic analysts. As we continue to probe this electrifying relationship, we anticipate a surge of interest in exploring the unexpected connections that lie beneath the surface of seemingly unrelated variables.


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 The number of electricians in Mississippi and Runs Scored by the losing team in the World Series. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
Sparking Joy: The Wattage of Electricians in Mississippi and the Wattage of the Losing World Series Team

[[ABSTRACT]]
In this paper, we shed light on the electrifying connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series. Using data from the Bureau of Labor Statistics and Wikipedia, our research team conducted a voltage analysis and uncovered a shocking correlation coefficient of 0.7718826 with p < 0.01 for the years 2003 to 2013. We delve into the current state of affairs, offering a bright perspective on the intersection of electrical labor and major league baseball outcomes. Our findings illuminate a previously overlooked relationship, sparking joy and sparking curiosity in the realms of sports and economics.

[[LITERATURE REVIEW]]
The connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series has drawn the attention of researchers from diverse backgrounds. Smith et al. (2015) conducted an extensive study on the impact of state-level occupational distribution on regional sporting achievements. Their findings revealed a surprising positive correlation between electrical labor force density and suboptimal baseball performance, captivating the scientific community with a jolt of curiosity.
Furthermore, Doe (2019) delved into the economic implications of electrical workforce fluctuations on local communities, inadvertently stumbling upon a peculiar association with underwhelming World Series outcomes for the runner-up team. This unsuspected revelation prompted an electrifying discussion among scholars, illuminating the potential for unexplored linkages between labor dynamics and professional sports.
In "The Shocking Truth: A Comprehensive Guide to Electrical Wonders" (Copperfield, 2018), the author delves into the electrifying world of electrical marvels, providing insights that may spark new perspectives on the interactions between electricians and athletic events. Similarly, "The Curveball Connection: Unraveling the Mysteries of Baseball" (St. Claire, 2016) explores the intricate subtleties of baseball's unpredictable nature, encouraging readers to consider the possibility of unanticipated correlations with unrelated industries.
On a more speculative note, our research team embarked on an unconventional literature exploration, perusing an eclectic mix of sources, including but not limited to the back of cereal boxes, fortune cookie messages, and even the intricate details of CVS receipts. While these unconventional sources failed to yield scholarly merit, they did provide a delightful array of puns and quirky observations, sparking joy in unexpected ways.
As we plunge into the depths of this enigmatic correlation, it is essential to approach the subject matter with a sense of humor, electrical puns, and a keen eye for the unexpected. After all, the exploration of unusual connections between electrical labor and baseball outcomes has the potential to enlighten our understanding of the world in unexpected and amusing ways.

[[RESULTS]]
Our research team's analysis revealed a stunning correlation coefficient of 0.7718826 between the number of electricians in Mississippi and the runs scored by the losing team in the World Series for the years 2003 to 2013. This correlation exhibited an r-squared value of 0.5958028, signifying that a shocking 59.58% of the variation in runs scored by the losing team can be explained by the number of electricians in the Magnolia State.
The connection between electrical labor and the outcomes of the Fall Classic is positively electrifying, indicating a strong relationship between these seemingly unrelated variables. The p-value of less than 0.01 further underscores the significance of this correlation, demonstrating a strikingly low probability that our findings are merely a result of random chance.
To visually capture the magnitude of this unforeseen relationship, we present Fig. 1, a scatterplot showcasing the strong correlation between the number of electricians in Mississippi and the runs scored by the losing team in the World Series. The figure provides a powerful visual representation of the wattage of electricians and the wattage of the losing World Series team, illuminating the unexpected connection between these disparate factors.
Our results electrify the research landscape, shedding light on an unexplored junction between the realm of electrical labor and the world of baseball outcomes. The implications of this correlation are shocking, sparking joy and curiosity among both sports enthusiasts and economic analysts. As we continue to probe this electrifying relationship, we anticipate a surge of interest in exploring the unexpected connections that lie beneath the surface of seemingly unrelated variables.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

Our findings have sparked a surge of excitement as we marvel at the electrifying correlation between the number of electricians in Mississippi and the runs scored by the losing team in the World Series. The magnitude of our correlation coefficient, even greater than that sudden jolt of energy when you accidentally touch a light socket, has shocked and illuminated the scientific community, much like Edison’s bulb during its inaugural glow.

In line with the peculiar but fervently conducted studies of Smith et al. (2015), the positive correlation between electrical labor force density and the suboptimal performance of the baseball runner-up team has been gloriously illuminated. It's as though the runners-up were left in the dark, unable to harness the power necessary to shine on the grandest stage.

Similarly, the enlightening work of Doe (2019) on the economic implications of electrical workforce fluctuations had inadvertently highlighted faint sparks of association with underwhelming World Series outcomes for the second-best team, much like a refrigerator light that flickers unsure of whether to stay on or not.

Moving on from these thoroughly serious observations, the delightful array of puns and quirky observations gathered from our exploration into unconventional sources has left us positively charged for further research in this illuminating area. We have truly appreciated the sparks of amusement and joy these unexpected sources have ignited, much like a brilliantly timed one-liner in a deeply serious conversation - a jolt of unexpected delight.

Our results robustly support and even enhance prior research findings, showcasing an unanticipated but awe-inspiring relationship between electrical labor and baseball outcomes. The r-squared value of 0.5958028 indicates that an electrifying 59.58% of the variation in runs scored by the losing team can be explained by the number of electricians in Mississippi, leaving just under 41% unaccounted for, much like a dimly lit room where the last bulb refuses to turn on.

As we bask in the radiance of our findings, we echo the sentiments of Copperfield’s (2018) "The Shocking Truth: A Comprehensive Guide to Electrical Wonders," and St. Claire’s (2016) "The Curveball Connection: Unraveling the Mysteries of Baseball," reminding ourselves and our esteemed colleagues that science, research, and statistics can indeed be as thrilling, unpredictable, and full of delightful twists as a rollercoaster ride at an amusement park.

Our future endeavors hold the promise of even more electrifying revelations, igniting a firestorm of further inquiry and excitement among researchers. After all, who would have thought that the number of electricians in Mississippi could hold such illuminating insights into major league baseball outcomes? There's no doubt about it; our findings have truly sparked joy and a fervor for uncovering more unexpected connections, sparking a renewed and spirited pursuit of electrifying academic 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 The number of electricians in Mississippi and Runs Scored by the losing team in the World Series. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 300 tokens. At the very end, assert that no more research is needed in this area.

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Sparking Joy: The Wattage of Electricians in Mississippi and the Wattage of the Losing World Series Team

[[ABSTRACT]]
In this paper, we shed light on the electrifying connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series. Using data from the Bureau of Labor Statistics and Wikipedia, our research team conducted a voltage analysis and uncovered a shocking correlation coefficient of 0.7718826 with p < 0.01 for the years 2003 to 2013. We delve into the current state of affairs, offering a bright perspective on the intersection of electrical labor and major league baseball outcomes. Our findings illuminate a previously overlooked relationship, sparking joy and sparking curiosity in the realms of sports and economics.

[[INTRDUCTION]]
INTRODUCTION
As Thomas Edison once said, "I have not failed. I've just found 10,000 ways that won't work." Well, in our case, we're hoping to find at least one way that does work. Our research aims to shed some light on the shocking connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series.
Now, you might be thinking, "What in the name of Ohm's Law does electrical labor have to do with baseball outcomes?" That's a shockingly good question, my dear reader, and one that sparked our curiosity as well. It's not every day that you stumble upon an electrifying correlation between seemingly unrelated entities, but fear not, we are here to illuminate this unexplored territory.
So, grab your voltage meters and baseball gloves because we are about to take a deep dive into the wattage of electricians and the wattage of the losing World Series team. We'll be conducting a statistical analysis that's guaranteed to be more electrifying than a lightning storm on a summer's day.
Our findings promise to be illuminating, and who knows, by the end of this paper, you might just be "amped" up about the potential for unexpected connections in the world of research. So, without further ado, let's flip the switch and shine a light on this captivating correlation.

[[RESULTS]]
Our research team's analysis revealed a stunning correlation coefficient of 0.7718826 between the number of electricians in Mississippi and the runs scored by the losing team in the World Series for the years 2003 to 2013. This correlation exhibited an r-squared value of 0.5958028, signifying that a shocking 59.58% of the variation in runs scored by the losing team can be explained by the number of electricians in the Magnolia State.
The connection between electrical labor and the outcomes of the Fall Classic is positively electrifying, indicating a strong relationship between these seemingly unrelated variables. The p-value of less than 0.01 further underscores the significance of this correlation, demonstrating a strikingly low probability that our findings are merely a result of random chance.
To visually capture the magnitude of this unforeseen relationship, we present Fig. 1, a scatterplot showcasing the strong correlation between the number of electricians in Mississippi and the runs scored by the losing team in the World Series. The figure provides a powerful visual representation of the wattage of electricians and the wattage of the losing World Series team, illuminating the unexpected connection between these disparate factors.
Our results electrify the research landscape, shedding light on an unexplored junction between the realm of electrical labor and the world of baseball outcomes. The implications of this correlation are shocking, sparking joy and curiosity among both sports enthusiasts and economic analysts. As we continue to probe this electrifying relationship, we anticipate a surge of interest in exploring the unexpected connections that lie beneath the surface of seemingly unrelated variables.

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Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, our research has shed an illuminating light on the electrifying connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series. The wattage of our findings is truly shocking, with a correlation coefficient that's more electrifying than a Tesla coil at a science fair.

By uncovering this unexpected relationship, we have "harnessed" a jolt of curiosity in the realms of sports and economics. This association may seem like a bolt out of the blue, but our statistical analysis has grounded it in the realms of probability, offering a veritable "power surge" of insight into the intersection of electrical labor and baseball outcomes.

The implications of our findings are truly "shocking" and should electrify any skeptic into acknowledging the unexpected connections that can emerge from the depths of data analysis. Our results serve as a testament to the "current" potential for interdisciplinary research to uncover hidden correlations and shed light on surprising relationships.

In light of these electrifying findings, it is clear that no more research is needed in this area - unless, of course, someone manages to find a way to quantify the "wattage" of other professions and their peculiar connections to seemingly unrelated events. After all, in the world of research, you never quite know what "current" of discovery may lead to the next "shockingly" unexpected revelation!


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 Sparking Joy: The Wattage of Electricians in Mississippi and the Wattage of the Losing World Series Team. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Illuminating Insights


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 Sparking Joy: The Wattage of Electricians in Mississippi and the Wattage of the Losing World Series Team. Respond only with the name of a fictional organization.

ChatGPT:

The Association for Southern Electrical Studies and Baseball 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 Sparking Joy: The Wattage of Electricians in Mississippi and the Wattage of the Losing World Series Team with an abstract of In this paper, we shed light on the electrifying connection between the number of electricians in Mississippi and the runs scored by the losing team in the World Series. Using data from the Bureau of Labor Statistics and Wikipedia, our research team conducted a voltage analysis and uncovered a shocking correlation coefficient of 0.7718826 with p < 0.01 for the years 2003 to 2013. We delve into the current state of affairs, offering a bright perspective on the intersection of electrical labor and major league baseball outcomes. Our findings illuminate a previously overlooked relationship, sparking joy and sparking curiosity in the realms of sports and economics.

ChatGPT:

electricians in Mississippi, World Series losing team, correlation between electricians and World Series, voltage analysis, Bureau of Labor Statistics, major league baseball outcomes, economic impact of electrical labor, Mississippi labor statistics, electrical labor and sports outcomes

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



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Data details

The number of electricians in Mississippi
Detailed data title: BLS estimate of electricians in Mississippi
Source: Bureau of Larbor Statistics
See what else correlates with The number of electricians in Mississippi

Runs Scored by the losing team in the World Series
Source: Wikipedia
See what else correlates with Runs Scored by the losing team in the World Series

Correlation r = 0.7718826 (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 = 0.5958028 (Coefficient of determination)
This means 59.6% of the change in the one variable (i.e., Runs Scored by the losing team in the World Series) is predictable based on the change in the other (i.e., The number of electricians in Mississippi) over the 11 years from 2003 through 2013.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0054. 0.0053815812722358130000000000
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 0.77 in 0.54% of random cases. Said differently, if you correlated 186 random variables Which I absolutely did.
with the same 10 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 10 because we have two variables measured over a period of 11 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.32, 0.94 ] 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.
20032004200520062007200820092010201120122013
The number of electricians in Mississippi (Electricians)57505380526050805170496056605080553047705440
Runs Scored by the losing team in the World Series (Runs scored)211214111015271230614




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. 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.




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([5750,5380,5260,5080,5170,4960,5660,5080,5530,4770,5440,])
array_2 = np.array([21,12,14,11,10,15,27,12,30,6,14,])
array_1_name = "The number of electricians in Mississippi"
array_2_name = "Runs Scored by the losing team in the World Series"

# 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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When spoken, my last name is pronounced "vegan," like I don't eat meat.

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Correlation ID: 2171 · Black Variable ID: 10307 · Red Variable ID: 535
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