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AI explanation
The widespread use of the 'crying michael jordan' meme led to an increase in internet traffic. This surge in data usage put a strain on internet infrastructure, including the underground cables. As a result, there was a higher demand for maintenance, which included the repair of underground rail car systems in Mississippi. The meme literally caused people to 'cable' together, creating more work for the rail car repairers in the state.
Create a meme inspired by the crying michael jordan meme that in some way references The number of rail car repairers in Mississippi. Try to emulate the style of the meme - don’t violate the copyright, just make it look and feel like the original meme.
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 Popularity of the 'crying michael jordan' meme positively influenced The number of rail car repairers in Mississippi. Include one good pun.
AI academic paper
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From Crying Jordan to Crying Train Repairs: An Unlikely Connection Journal of Unexpected Connections Jan 2024 Reminder: This paper is AI-generated. Not real! |
Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You 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 Popularity of the 'crying michael jordan' meme and The number of rail car repairers in Mississippi. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.
Make the title seem clever.
Your research team used data from Google Trends and Bureau of Larbor Statistics to assess this nagging question. You found a correlation coefficient of 0.8014994 and p < 0.01 for 2006 to 2021.
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]]
"From Crying Jordan to Crying Train Repairs: An Unlikely Connection"
[[ABSTRACT]]
In this paper, we investigate the surprising, and somewhat comical, relationship between the popularity of the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. Using Google Trends data for the meme's search popularity and Bureau of Labor Statistics data for the number of rail car repairers, we analyzed the correlation between these seemingly unrelated variables. Our findings revealed a correlation coefficient of 0.8014994 and a p-value less than 0.01 for the period spanning from 2006 to 2021.
Our research team was initially skeptical, thinking "What's the connection between our beloved Crying Jordan and the railways? Is it a train joke waiting to happen?" Yet, to our surprise, our analysis provided compelling evidence of a noteworthy relationship. It seems that as the Crying Jordan meme gained traction on the internet, the number of rail car repairers in Mississippi also saw an increase. It's almost as if the tears shed in the meme were magically turning into jobs in the rail industry. We couldn't help but wonder if the meme was crying all the way to the bank, or in this case, the rail yard.
While we initially approached this research with a sense of skepticism, we were struck by the robustness of the correlation and the potential implications for understanding the whimsical dynamics of internet memes and labor statistics. Our findings may pave the way for a new field of "memeconomics," where economists are left pondering jokes like "Why did the meme economist break up with their partner? They didn't understand their sense of humormetrics." In conclusion, this unexpected connection sheds light on the intertwined nature of online culture and labor dynamics, serving as a reminder that sometimes, the most unlikely pairings can lead to meaningful insights.
Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the 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 Popularity of the 'crying michael jordan' meme and The number of rail car repairers in Mississippi. 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]]
"From Crying Jordan to Crying Train Repairs: An Unlikely Connection"
[[ABSTRACT]]
In this paper, we investigate the surprising, and somewhat comical, relationship between the popularity of the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. Using Google Trends data for the meme's search popularity and Bureau of Labor Statistics data for the number of rail car repairers, we analyzed the correlation between these seemingly unrelated variables. Our findings revealed a correlation coefficient of 0.8014994 and a p-value less than 0.01 for the period spanning from 2006 to 2021.
Our research team was initially skeptical, thinking "What's the connection between our beloved Crying Jordan and the railways? Is it a train joke waiting to happen?" Yet, to our surprise, our analysis provided compelling evidence of a noteworthy relationship. It seems that as the Crying Jordan meme gained traction on the internet, the number of rail car repairers in Mississippi also saw an increase. It's almost as if the tears shed in the meme were magically turning into jobs in the rail industry. We couldn't help but wonder if the meme was crying all the way to the bank, or in this case, the rail yard.
While we initially approached this research with a sense of skepticism, we were struck by the robustness of the correlation and the potential implications for understanding the whimsical dynamics of internet memes and labor statistics. Our findings may pave the way for a new field of "memeconomics," where economists are left pondering jokes like "Why did the meme economist break up with their partner? They didn't understand their sense of humormetrics." In conclusion, this unexpected connection sheds light on the intertwined nature of online culture and labor dynamics, serving as a reminder that sometimes, the most unlikely pairings can lead to meaningful insights.
-------
Your response should start like this:
[[INTRODUCTION]]
ChatGPT:
**INTRODUCTION**
As the famous adage goes, "All aboard the meme train!" In recent years, internet memes have become a ubiquitous form of visual expression, serving as the whimsical heartbeat of online culture. From the classic 'Grumpy Cat' to the more recent 'Woman Yelling at a Cat,' these memes have captivated the digital landscape, eliciting laughter and bewilderment in equal measure. However, amidst this sea of viral sensations, one meme has risen above the rest, capturing the essence of despair and defeat in its poignant imagery - the 'crying michael jordan' meme. And what better place to explore the unexpected connections related to this iconic meme than the heart of the Deep South and the railway hub of the state of Mississippi?
To put it mildly, our research journey began with a dash of skepticism and a sprinkle of humor, as we pondered the potential link between the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. One might even say we were tempted to exclaim, "What's the 'rail' connection between a famous basketball player's tears and railroad maintenance?" It was a mystery that begged to be unraveled, much like a dad joke waiting at the end of a long train ride.
Yet, much to our surprise, our investigation uncovered a statistically significant correlation between the rise and fall of the meme's popularity and the number of rail car repairers in Mississippi. It's almost as if the meme was signalling all aboard the job train, inviting enthusiasts to hop on the express route to gainful employment. One can't help but picture the meme donning a conductor's hat and hollering, "Choo-choo-choose a career in rail car repair!"
We found ourselves immersed in the unexplored terrain at the intersection of internet culture and labor statistics, pondering the implications and potential applications of our findings. In a way, our research became a journey akin to riding a rollercoaster, filled with unexpected twists and turns, leaving us with an exhilarating blend of scientific inquiry and internet absurdity. And we couldn't help but wonder, "Is this the birth of a new field - memenomics? If economists specialize in the invisible hand, will memenomists study the visible impact of internet humor on labor markets?"
In the following sections, we present our methodology, detailed analysis, and the implications of our findings, inviting readers to join us on this comical yet insightful expedition into the unlikely connection between the 'crying michael jordan' meme and the labor dynamics of the railway industry in Mississippi. All aboard, and remember – keep an eye out for unexpected connections, because as we've learned, sometimes the most unlikely pairings can lead to meaningful insights, or at least a good chuckle.
Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up a literature review section of an academic research paper presenting the findings of the connection between Popularity of the 'crying michael jordan' meme and The number of rail car repairers in Mississippi. 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 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]]
"From Crying Jordan to Crying Train Repairs: An Unlikely Connection"
[[ABSTRACT]]
In this paper, we investigate the surprising, and somewhat comical, relationship between the popularity of the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. Using Google Trends data for the meme's search popularity and Bureau of Labor Statistics data for the number of rail car repairers, we analyzed the correlation between these seemingly unrelated variables. Our findings revealed a correlation coefficient of 0.8014994 and a p-value less than 0.01 for the period spanning from 2006 to 2021.
Our research team was initially skeptical, thinking "What's the connection between our beloved Crying Jordan and the railways? Is it a train joke waiting to happen?" Yet, to our surprise, our analysis provided compelling evidence of a noteworthy relationship. It seems that as the Crying Jordan meme gained traction on the internet, the number of rail car repairers in Mississippi also saw an increase. It's almost as if the tears shed in the meme were magically turning into jobs in the rail industry. We couldn't help but wonder if the meme was crying all the way to the bank, or in this case, the rail yard.
While we initially approached this research with a sense of skepticism, we were struck by the robustness of the correlation and the potential implications for understanding the whimsical dynamics of internet memes and labor statistics. Our findings may pave the way for a new field of "memeconomics," where economists are left pondering jokes like "Why did the meme economist break up with their partner? They didn't understand their sense of humormetrics." In conclusion, this unexpected connection sheds light on the intertwined nature of online culture and labor dynamics, serving as a reminder that sometimes, the most unlikely pairings can lead to meaningful insights.
-------
And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
As the famous adage goes, "All aboard the meme train!" In recent years, internet memes have become a ubiquitous form of visual expression, serving as the whimsical heartbeat of online culture. From the classic 'Grumpy Cat' to the more recent 'Woman Yelling at a Cat,' these memes have captivated the digital landscape, eliciting laughter and bewilderment in equal measure. However, amidst this sea of viral sensations, one meme has risen above the rest, capturing the essence of despair and defeat in its poignant imagery - the 'crying michael jordan' meme. And what better place to explore the unexpected connections related to this iconic meme than the heart of the Deep South and the railway hub of the state of Mississippi?
To put it mildly, our research journey began with a dash of skepticism and a sprinkle of humor, as we pondered the potential link between the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. One might even say we were tempted to exclaim, "What's the 'rail' connection between a famous basketball player's tears and railroad maintenance?" It was a mystery that begged to be unraveled, much like a dad joke waiting at the end of a long train ride.
Yet, much to our surprise, our investigation uncovered a statistically significant correlation between the rise and fall of the meme's popularity and the number of rail car repairers in Mississippi. It's almost as if the meme was signalling all aboard the job train, inviting enthusiasts to hop on the express route to gainful employment. One can't help but picture the meme donning a conductor's hat and hollering, "Choo-choo-choose a career in rail car repair!"
We found ourselves immersed in the unexplored terrain at the intersection of internet culture and labor statistics, pondering the implications and potential applications of our findings. In a way, our research became a journey akin to riding a rollercoaster, filled with unexpected twists and turns, leaving us with an exhilarating blend of scientific inquiry and internet absurdity. And we couldn't help but wonder, "Is this the birth of a new field - memenomics? If economists specialize in the invisible hand, will memenomists study the visible impact of internet humor on labor markets?"
In the following sections, we present our methodology, detailed analysis, and the implications of our findings, inviting readers to join us on this comical yet insightful expedition into the unlikely connection between the 'crying michael jordan' meme and the labor dynamics of the railway industry in Mississippi. All aboard, and remember – keep an eye out for unexpected connections, because as we've learned, sometimes the most unlikely pairings can lead to meaningful insights, or at least a good chuckle.
-------
Your response should start like this:
[[LITERATURE REVIEW]]
ChatGPT:
[[LITERATURE REVIEW]]
As we delve into the perplexing realm of internet memes and their unforeseen influence on labor dynamics, it is imperative to consider the existing literature that traverses these unconventional terrains. In "Memes and Labor: A Statistical Analysis," Smith et al. provide a comprehensive overview of the correlation between meme virality and regional employment patterns. Their analysis, though serious in nature, lays the groundwork for uncovering the unexpected intersections between online humor and labor statistics. However, it is "Dank Memes and Economic Trends" by Doe that truly captivates the reader with its exploration of the economic implications of meme culture, leaving us pondering questions like "If a meme falls on the internet and no one is around to share it, does it make a sound?"
In the non-fiction arena, "Railways and Southern Labor" by Jones examines the historical and contemporary dynamics of railway employment in the Southern United States. This provides a crucial backdrop for our investigation into the correlation between the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. However, one cannot overlook the fictional works that have subtly played into our understanding of seemingly unrelated phenomena. "The Whistlestop Chronicles" by Jane Austen (not really, but wouldn't that be wild?) offers a whimsical exploration of railway culture, opening our minds to the unexpected connections that unfold along the tracks. And who could forget the classic "Murder on the Orient Express" by Agatha Christie, where the journey on the railway leads to a thrilling revelation, much like our own journey in uncovering the 'crying michael jordan' meme's impact on rail car repairs in Mississippi.
But, in the spirit of embracing the unexpected and the absurd, our literature review extends beyond traditional sources. The aisles of the CVS pharmacy provided a treasure trove of wisdom, as we analyzed the meticulously long receipts in search of hidden insights. Alas, the only revelation we found was a coupon for cat food and a reminder to refill our prescription for Dad Joke Enthusiasts monthly magazine. And speaking of dad jokes, why don't we ever tell secrets on a train?
Because the loco-motive!
Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the methodology section of an academic research paper presenting the findings of the connection between Popularity of the 'crying michael jordan' meme and The number of rail car repairers in Mississippi. 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 Google Trends and Bureau of Larbor Statistics . You used data from 2006 to 2021
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]]
"From Crying Jordan to Crying Train Repairs: An Unlikely Connection"
[[ABSTRACT]]
In this paper, we investigate the surprising, and somewhat comical, relationship between the popularity of the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. Using Google Trends data for the meme's search popularity and Bureau of Labor Statistics data for the number of rail car repairers, we analyzed the correlation between these seemingly unrelated variables. Our findings revealed a correlation coefficient of 0.8014994 and a p-value less than 0.01 for the period spanning from 2006 to 2021.
Our research team was initially skeptical, thinking "What's the connection between our beloved Crying Jordan and the railways? Is it a train joke waiting to happen?" Yet, to our surprise, our analysis provided compelling evidence of a noteworthy relationship. It seems that as the Crying Jordan meme gained traction on the internet, the number of rail car repairers in Mississippi also saw an increase. It's almost as if the tears shed in the meme were magically turning into jobs in the rail industry. We couldn't help but wonder if the meme was crying all the way to the bank, or in this case, the rail yard.
While we initially approached this research with a sense of skepticism, we were struck by the robustness of the correlation and the potential implications for understanding the whimsical dynamics of internet memes and labor statistics. Our findings may pave the way for a new field of "memeconomics," where economists are left pondering jokes like "Why did the meme economist break up with their partner? They didn't understand their sense of humormetrics." In conclusion, this unexpected connection sheds light on the intertwined nature of online culture and labor dynamics, serving as a reminder that sometimes, the most unlikely pairings can lead to meaningful insights.
[[INTRODUCTION]]
As the famous adage goes, "All aboard the meme train!" In recent years, internet memes have become a ubiquitous form of visual expression, serving as the whimsical heartbeat of online culture. From the classic 'Grumpy Cat' to the more recent 'Woman Yelling at a Cat,' these memes have captivated the digital landscape, eliciting laughter and bewilderment in equal measure. However, amidst this sea of viral sensations, one meme has risen above the rest, capturing the essence of despair and defeat in its poignant imagery - the 'crying michael jordan' meme. And what better place to explore the unexpected connections related to this iconic meme than the heart of the Deep South and the railway hub of the state of Mississippi?
To put it mildly, our research journey began with a dash of skepticism and a sprinkle of humor, as we pondered the potential link between the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. One might even say we were tempted to exclaim, "What's the 'rail' connection between a famous basketball player's tears and railroad maintenance?" It was a mystery that begged to be unraveled, much like a dad joke waiting at the end of a long train ride.
Yet, much to our surprise, our investigation uncovered a statistically significant correlation between the rise and fall of the meme's popularity and the number of rail car repairers in Mississippi. It's almost as if the meme was signalling all aboard the job train, inviting enthusiasts to hop on the express route to gainful employment. One can't help but picture the meme donning a conductor's hat and hollering, "Choo-choo-choose a career in rail car repair!"
We found ourselves immersed in the unexplored terrain at the intersection of internet culture and labor statistics, pondering the implications and potential applications of our findings. In a way, our research became a journey akin to riding a rollercoaster, filled with unexpected twists and turns, leaving us with an exhilarating blend of scientific inquiry and internet absurdity. And we couldn't help but wonder, "Is this the birth of a new field - memenomics? If economists specialize in the invisible hand, will memenomists study the visible impact of internet humor on labor markets?"
In the following sections, we present our methodology, detailed analysis, and the implications of our findings, inviting readers to join us on this comical yet insightful expedition into the unlikely connection between the 'crying michael jordan' meme and the labor dynamics of the railway industry in Mississippi. All aboard, and remember – keep an eye out for unexpected connections, because as we've learned, sometimes the most unlikely pairings can lead to meaningful insights, or at least a good chuckle.
-------
Your response should start like this:
[[METHODOLOGY]]
ChatGPT:
[[METHODOLOGY]]
To embark upon our investigation of the peculiar relationship between the popularity of the 'crying michael jordan' meme and the number of rail car repairers in Mississippi, we employed a hybrid approach that combined statistical analysis with a sprinkle of internet whimsy. It was a bit like mixing a serious lab coat with a pair of clown shoes - a fusion of the solemn and the silly in the name of scientific discovery.
First, we gathered data from Google Trends to track the search interest over time for the 'crying michael jordan' meme, spanning from 2006 to 2021. This involved diving deep into the vast ocean of internet culture, much like a marine biologist studying the ebb and flow of meme tides. We then employed rigorous statistical techniques to wrangle this seemingly capricious data, all the while reminding ourselves that even memes must adhere to the laws of statistics - after all, humor without standard deviations is simply non-standard humor.
Simultaneously, we turned our attention to the Bureau of Labor Statistics to procure the data on the number of rail car repairers in the state of Mississippi during the same timeframe. This step required a delicate dance between the digital and the tangible - a bit like trying to coordinate synchronized swimming in both the internet meme and labor statistics pools.
The most intensive part of our methodology involved aligning these datasets, employing advanced mathematical models to uncover potential patterns and correlations. It was like trying to find the punchline in a complex joke - many variables had to be considered, and just like in comedy, timing was crucial. We took our time, ensuring that our statistical t's were crossed and our memeological i's dotted.
Next, we utilized time series analysis and regression models to delve into the dynamics between the meme's popularity and the number of rail car repairers in Mississippi. It was a bit like trying to decipher the punchline of a particularly cryptic dad joke - requiring careful analysis and a keen eye for patterns, even in the most unexpected places.
Lastly, we performed a thorough sensitivity analysis to test the robustness of our findings, ensuring that our results could withstand the academic equivalent of a good-natured ribbing. We wanted to be certain that our statistical 'punchline' held up under scrutiny and wasn't just a statistical 'dad joke' looking for a laugh.
In the end, our methodology was as rigorous as a seasoned comedian perfecting their set, blending the precision of statistical analysis with the light-hearted spirit of internet culture. As they say, “Statistics are like a bikini. What they reveal is suggestive, but what they conceal is vital.” This was the approach we took, peeling back the layers of humor and data to reveal the unexpected connection between the 'crying michael jordan' meme and the labor dynamics of the railway industry in Mississippi.
Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the 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 Popularity of the 'crying michael jordan' meme and The number of rail car repairers in Mississippi. 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 Google Trends and Bureau of Larbor Statistics .
For the time period 2006 to 2021, you found a correlation 0.8014994, r-squared of 0.6424012, 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]]
"From Crying Jordan to Crying Train Repairs: An Unlikely Connection"
[[ABSTRACT]]
In this paper, we investigate the surprising, and somewhat comical, relationship between the popularity of the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. Using Google Trends data for the meme's search popularity and Bureau of Labor Statistics data for the number of rail car repairers, we analyzed the correlation between these seemingly unrelated variables. Our findings revealed a correlation coefficient of 0.8014994 and a p-value less than 0.01 for the period spanning from 2006 to 2021.
Our research team was initially skeptical, thinking "What's the connection between our beloved Crying Jordan and the railways? Is it a train joke waiting to happen?" Yet, to our surprise, our analysis provided compelling evidence of a noteworthy relationship. It seems that as the Crying Jordan meme gained traction on the internet, the number of rail car repairers in Mississippi also saw an increase. It's almost as if the tears shed in the meme were magically turning into jobs in the rail industry. We couldn't help but wonder if the meme was crying all the way to the bank, or in this case, the rail yard.
While we initially approached this research with a sense of skepticism, we were struck by the robustness of the correlation and the potential implications for understanding the whimsical dynamics of internet memes and labor statistics. Our findings may pave the way for a new field of "memeconomics," where economists are left pondering jokes like "Why did the meme economist break up with their partner? They didn't understand their sense of humormetrics." In conclusion, this unexpected connection sheds light on the intertwined nature of online culture and labor dynamics, serving as a reminder that sometimes, the most unlikely pairings can lead to meaningful insights.
-------
And here is the methodology section that led to this result:
[[METHODOLOGY]]
As the famous adage goes, "All aboard the meme train!" In recent years, internet memes have become a ubiquitous form of visual expression, serving as the whimsical heartbeat of online culture. From the classic 'Grumpy Cat' to the more recent 'Woman Yelling at a Cat,' these memes have captivated the digital landscape, eliciting laughter and bewilderment in equal measure. However, amidst this sea of viral sensations, one meme has risen above the rest, capturing the essence of despair and defeat in its poignant imagery - the 'crying michael jordan' meme. And what better place to explore the unexpected connections related to this iconic meme than the heart of the Deep South and the railway hub of the state of Mississippi?
To put it mildly, our research journey began with a dash of skepticism and a sprinkle of humor, as we pondered the potential link between the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. One might even say we were tempted to exclaim, "What's the 'rail' connection between a famous basketball player's tears and railroad maintenance?" It was a mystery that begged to be unraveled, much like a dad joke waiting at the end of a long train ride.
Yet, much to our surprise, our investigation uncovered a statistically significant correlation between the rise and fall of the meme's popularity and the number of rail car repairers in Mississippi. It's almost as if the meme was signalling all aboard the job train, inviting enthusiasts to hop on the express route to gainful employment. One can't help but picture the meme donning a conductor's hat and hollering, "Choo-choo-choose a career in rail car repair!"
We found ourselves immersed in the unexplored terrain at the intersection of internet culture and labor statistics, pondering the implications and potential applications of our findings. In a way, our research became a journey akin to riding a rollercoaster, filled with unexpected twists and turns, leaving us with an exhilarating blend of scientific inquiry and internet absurdity. And we couldn't help but wonder, "Is this the birth of a new field - memenomics? If economists specialize in the invisible hand, will memenomists study the visible impact of internet humor on labor markets?"
In the following sections, we present our methodology, detailed analysis, and the implications of our findings, inviting readers to join us on this comical yet insightful expedition into the unlikely connection between the 'crying michael jordan' meme and the labor dynamics of the railway industry in Mississippi. All aboard, and remember – keep an eye out for unexpected connections, because as we've learned, sometimes the most unlikely pairings can lead to meaningful insights, or at least a good chuckle.
-------
Your response should start like this:
[[RESULTS]]
ChatGPT:
[[RESULTS]]
Upon conducting our analysis, we found a strong positive correlation between the popularity of the 'crying michael jordan' meme and the number of rail car repairers in Mississippi for the period of 2006 to 2021. The correlation coefficient was calculated to be 0.8014994, indicating a robust relationship between these two seemingly disparate variables. In other words, as the visibility of the meme increased, so did the employment levels in the rail car repair sector. It's almost like the meme was shouting, "All abroad the employment express!"
The r-squared value of 0.6424012 further reinforced the strength of this association, explaining a significant proportion of the variability in the number of rail car repairers based on the fluctuations in the meme's popularity. It's as if the meme's tears were watering the seeds of labor demand, sprouting new opportunities in the rail industry. One might even say the meme was 'training' individuals for a career in an unexpected, tear-jerking way.
The probability value (p-value) of less than 0.01 indicated that the observed correlation was statistically significant. This means that it is highly unlikely that this relationship is due to random chance, affirming the legitimacy of our findings and leaving little room for doubt that there is indeed a tangible connection between the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. You could almost say the meme and rail industry are "track"ing each other's success.
Notably, our results are visualized in Fig. 1, depicting a clear and compelling scatterplot that demonstrates the strong positive correlation between the two variables. It's as if the laughter induced by the meme was echoing through the employment charts, creating a harmonious melody of statistical significance. You might say the results were both compelling and loco-motivating!
In conclusion, our findings provide empirical evidence of a surprising and noteworthy relationship between an internet meme and labor dynamics, adding a touch of whimsy and peculiarity to the realms of statistical analysis and economic trends. Who knew that a meme could have such a "rail" impact on employment? We hope our research leaves a lasting impression, along with a few chuckles, and inspires others to explore the uncharted territories where internet culture meets labor statistics. After all, as we've learned, sometimes the most unexpected connections lead to the most rewarding insights, and in this case, perhaps a few well-deserved dad jokes.
Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up the discussion section of an academic research paper presenting the findings of the connection between Popularity of the 'crying michael jordan' meme and The number of rail car repairers in Mississippi. 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]]
"From Crying Jordan to Crying Train Repairs: An Unlikely Connection"
[[ABSTRACT]]
In this paper, we investigate the surprising, and somewhat comical, relationship between the popularity of the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. Using Google Trends data for the meme's search popularity and Bureau of Labor Statistics data for the number of rail car repairers, we analyzed the correlation between these seemingly unrelated variables. Our findings revealed a correlation coefficient of 0.8014994 and a p-value less than 0.01 for the period spanning from 2006 to 2021.
Our research team was initially skeptical, thinking "What's the connection between our beloved Crying Jordan and the railways? Is it a train joke waiting to happen?" Yet, to our surprise, our analysis provided compelling evidence of a noteworthy relationship. It seems that as the Crying Jordan meme gained traction on the internet, the number of rail car repairers in Mississippi also saw an increase. It's almost as if the tears shed in the meme were magically turning into jobs in the rail industry. We couldn't help but wonder if the meme was crying all the way to the bank, or in this case, the rail yard.
While we initially approached this research with a sense of skepticism, we were struck by the robustness of the correlation and the potential implications for understanding the whimsical dynamics of internet memes and labor statistics. Our findings may pave the way for a new field of "memeconomics," where economists are left pondering jokes like "Why did the meme economist break up with their partner? They didn't understand their sense of humormetrics." In conclusion, this unexpected connection sheds light on the intertwined nature of online culture and labor dynamics, serving as a reminder that sometimes, the most unlikely pairings can lead to meaningful insights.
[[LITERATURE REVIEW]]
As we delve into the perplexing realm of internet memes and their unforeseen influence on labor dynamics, it is imperative to consider the existing literature that traverses these unconventional terrains. In "Memes and Labor: A Statistical Analysis," Smith et al. provide a comprehensive overview of the correlation between meme virality and regional employment patterns. Their analysis, though serious in nature, lays the groundwork for uncovering the unexpected intersections between online humor and labor statistics. However, it is "Dank Memes and Economic Trends" by Doe that truly captivates the reader with its exploration of the economic implications of meme culture, leaving us pondering questions like "If a meme falls on the internet and no one is around to share it, does it make a sound?"
In the non-fiction arena, "Railways and Southern Labor" by Jones examines the historical and contemporary dynamics of railway employment in the Southern United States. This provides a crucial backdrop for our investigation into the correlation between the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. However, one cannot overlook the fictional works that have subtly played into our understanding of seemingly unrelated phenomena. "The Whistlestop Chronicles" by Jane Austen (not really, but wouldn't that be wild?) offers a whimsical exploration of railway culture, opening our minds to the unexpected connections that unfold along the tracks. And who could forget the classic "Murder on the Orient Express" by Agatha Christie, where the journey on the railway leads to a thrilling revelation, much like our own journey in uncovering the 'crying michael jordan' meme's impact on rail car repairs in Mississippi.
But, in the spirit of embracing the unexpected and the absurd, our literature review extends beyond traditional sources. The aisles of the CVS pharmacy provided a treasure trove of wisdom, as we analyzed the meticulously long receipts in search of hidden insights. Alas, the only revelation we found was a coupon for cat food and a reminder to refill our prescription for Dad Joke Enthusiasts monthly magazine. And speaking of dad jokes, why don't we ever tell secrets on a train?
Because the loco-motive!
[[RESULTS]]
Upon conducting our analysis, we found a strong positive correlation between the popularity of the 'crying michael jordan' meme and the number of rail car repairers in Mississippi for the period of 2006 to 2021. The correlation coefficient was calculated to be 0.8014994, indicating a robust relationship between these two seemingly disparate variables. In other words, as the visibility of the meme increased, so did the employment levels in the rail car repair sector. It's almost like the meme was shouting, "All abroad the employment express!"
The r-squared value of 0.6424012 further reinforced the strength of this association, explaining a significant proportion of the variability in the number of rail car repairers based on the fluctuations in the meme's popularity. It's as if the meme's tears were watering the seeds of labor demand, sprouting new opportunities in the rail industry. One might even say the meme was 'training' individuals for a career in an unexpected, tear-jerking way.
The probability value (p-value) of less than 0.01 indicated that the observed correlation was statistically significant. This means that it is highly unlikely that this relationship is due to random chance, affirming the legitimacy of our findings and leaving little room for doubt that there is indeed a tangible connection between the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. You could almost say the meme and rail industry are "track"ing each other's success.
Notably, our results are visualized in Fig. 1, depicting a clear and compelling scatterplot that demonstrates the strong positive correlation between the two variables. It's as if the laughter induced by the meme was echoing through the employment charts, creating a harmonious melody of statistical significance. You might say the results were both compelling and loco-motivating!
In conclusion, our findings provide empirical evidence of a surprising and noteworthy relationship between an internet meme and labor dynamics, adding a touch of whimsy and peculiarity to the realms of statistical analysis and economic trends. Who knew that a meme could have such a "rail" impact on employment? We hope our research leaves a lasting impression, along with a few chuckles, and inspires others to explore the uncharted territories where internet culture meets labor statistics. After all, as we've learned, sometimes the most unexpected connections lead to the most rewarding insights, and in this case, perhaps a few well-deserved dad jokes.
-------
Your response should start like this:
[[DISCUSSION]]
ChatGPT:
[[DISCUSSION]]
Our findings have brought to light a correlation so unexpected, it's almost as surprising as finding out that our data was sponsored by the International Association of Dad Joke Enthusiasts. It seems that the popularity of the 'crying michael jordan' meme and the number of rail car repairers in Mississippi are inextricably linked, almost like a father and his puns - you just can't have one without the other.
Echoing the sentiments of Smith et al. and Doe in their serious exploration of memeconomics, our results not only supported but amplified the notion of the profound impact that internet memes can have on regional employment dynamics. It's almost as if every time someone shared the Crying Jordan meme, a little whistle blew and a new job popped up in the rail yard. If this trend continues, we might just need a new economic index for meme-related job creation. Just imagine the NASDAQ: LOL, FTSE Meme-erald, or the S&P 500 (Snickers and Puns).
Drawing on the historical and literary backdrop provided by Jones, Austen (allegedly), and Christie, our findings paint a picture reminiscent of a mystery novel, where the unexpected connection between a timeless meme and the niche labor market unveils itself as the ultimate plot twist. It's almost as if we stumbled upon the "Lol-caboose" that ties these unconventional players together!
Our results have significant implications for the fields of both internet culture and labor economics. It's like the meme was the conductor of a statistical orchestra, harmonizing with the employment data to produce a surprising symphony of correlation. In the spirit of Austen's purported "Whistlestop Chronicles," the 'crying michael jordan' meme has added a lighthearted, albeit tear-filled, chapter to the ongoing story of labor trends in Mississippi.
In summary, the connection between the 'crying michael jordan' meme and the number of rail car repairers in Mississippi might just be the start of a new era in economic research, where the laughter-inducing influence of internet culture is no longer ignored. Oh, and speaking of trains, why don't they play poker on trains?
Too many cheetahs!
Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.
Please make up a short conclusion of an academic research paper presenting the findings of the connection between Popularity of the 'crying michael jordan' meme and The number of rail car repairers in Mississippi. 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]]
"From Crying Jordan to Crying Train Repairs: An Unlikely Connection"
[[ABSTRACT]]
In this paper, we investigate the surprising, and somewhat comical, relationship between the popularity of the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. Using Google Trends data for the meme's search popularity and Bureau of Labor Statistics data for the number of rail car repairers, we analyzed the correlation between these seemingly unrelated variables. Our findings revealed a correlation coefficient of 0.8014994 and a p-value less than 0.01 for the period spanning from 2006 to 2021.
Our research team was initially skeptical, thinking "What's the connection between our beloved Crying Jordan and the railways? Is it a train joke waiting to happen?" Yet, to our surprise, our analysis provided compelling evidence of a noteworthy relationship. It seems that as the Crying Jordan meme gained traction on the internet, the number of rail car repairers in Mississippi also saw an increase. It's almost as if the tears shed in the meme were magically turning into jobs in the rail industry. We couldn't help but wonder if the meme was crying all the way to the bank, or in this case, the rail yard.
While we initially approached this research with a sense of skepticism, we were struck by the robustness of the correlation and the potential implications for understanding the whimsical dynamics of internet memes and labor statistics. Our findings may pave the way for a new field of "memeconomics," where economists are left pondering jokes like "Why did the meme economist break up with their partner? They didn't understand their sense of humormetrics." In conclusion, this unexpected connection sheds light on the intertwined nature of online culture and labor dynamics, serving as a reminder that sometimes, the most unlikely pairings can lead to meaningful insights.
[[INTRDUCTION]]
As the famous adage goes, "All aboard the meme train!" In recent years, internet memes have become a ubiquitous form of visual expression, serving as the whimsical heartbeat of online culture. From the classic 'Grumpy Cat' to the more recent 'Woman Yelling at a Cat,' these memes have captivated the digital landscape, eliciting laughter and bewilderment in equal measure. However, amidst this sea of viral sensations, one meme has risen above the rest, capturing the essence of despair and defeat in its poignant imagery - the 'crying michael jordan' meme. And what better place to explore the unexpected connections related to this iconic meme than the heart of the Deep South and the railway hub of the state of Mississippi?
To put it mildly, our research journey began with a dash of skepticism and a sprinkle of humor, as we pondered the potential link between the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. One might even say we were tempted to exclaim, "What's the 'rail' connection between a famous basketball player's tears and railroad maintenance?" It was a mystery that begged to be unraveled, much like a dad joke waiting at the end of a long train ride.
Yet, much to our surprise, our investigation uncovered a statistically significant correlation between the rise and fall of the meme's popularity and the number of rail car repairers in Mississippi. It's almost as if the meme was signalling all aboard the job train, inviting enthusiasts to hop on the express route to gainful employment. One can't help but picture the meme donning a conductor's hat and hollering, "Choo-choo-choose a career in rail car repair!"
We found ourselves immersed in the unexplored terrain at the intersection of internet culture and labor statistics, pondering the implications and potential applications of our findings. In a way, our research became a journey akin to riding a rollercoaster, filled with unexpected twists and turns, leaving us with an exhilarating blend of scientific inquiry and internet absurdity. And we couldn't help but wonder, "Is this the birth of a new field - memenomics? If economists specialize in the invisible hand, will memenomists study the visible impact of internet humor on labor markets?"
In the following sections, we present our methodology, detailed analysis, and the implications of our findings, inviting readers to join us on this comical yet insightful expedition into the unlikely connection between the 'crying michael jordan' meme and the labor dynamics of the railway industry in Mississippi. All aboard, and remember – keep an eye out for unexpected connections, because as we've learned, sometimes the most unlikely pairings can lead to meaningful insights, or at least a good chuckle.
[[RESULTS]]
Upon conducting our analysis, we found a strong positive correlation between the popularity of the 'crying michael jordan' meme and the number of rail car repairers in Mississippi for the period of 2006 to 2021. The correlation coefficient was calculated to be 0.8014994, indicating a robust relationship between these two seemingly disparate variables. In other words, as the visibility of the meme increased, so did the employment levels in the rail car repair sector. It's almost like the meme was shouting, "All abroad the employment express!"
The r-squared value of 0.6424012 further reinforced the strength of this association, explaining a significant proportion of the variability in the number of rail car repairers based on the fluctuations in the meme's popularity. It's as if the meme's tears were watering the seeds of labor demand, sprouting new opportunities in the rail industry. One might even say the meme was 'training' individuals for a career in an unexpected, tear-jerking way.
The probability value (p-value) of less than 0.01 indicated that the observed correlation was statistically significant. This means that it is highly unlikely that this relationship is due to random chance, affirming the legitimacy of our findings and leaving little room for doubt that there is indeed a tangible connection between the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. You could almost say the meme and rail industry are "track"ing each other's success.
Notably, our results are visualized in Fig. 1, depicting a clear and compelling scatterplot that demonstrates the strong positive correlation between the two variables. It's as if the laughter induced by the meme was echoing through the employment charts, creating a harmonious melody of statistical significance. You might say the results were both compelling and loco-motivating!
In conclusion, our findings provide empirical evidence of a surprising and noteworthy relationship between an internet meme and labor dynamics, adding a touch of whimsy and peculiarity to the realms of statistical analysis and economic trends. Who knew that a meme could have such a "rail" impact on employment? We hope our research leaves a lasting impression, along with a few chuckles, and inspires others to explore the uncharted territories where internet culture meets labor statistics. After all, as we've learned, sometimes the most unexpected connections lead to the most rewarding insights, and in this case, perhaps a few well-deserved dad jokes.
-------
Your response should start like this:
[[CONCLUSION]]
ChatGPT:
[[CONCLUSION]]
In summary, our research has successfully unveiled the intriguing relationship between the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. Who would have thought that a basketball legend's tearful expression could have a locomotive effect on the labor market? It seems the meme was not only scoring points on the internet but also chugging along to create jobs in an unexpected arena. You might say it was a slam dunk in the world of statistical surprises!
As we wrap up, let's not forget to "train" our sights on the implications of our findings. The robust correlation coefficient and statistically significant p-value point to a tangible connection, leaving us no choice but to say, "All aboard the meme train to labor market insights!" It's almost as if the meme was taunting us with its employment magic, daring us to solve the riddle of its impact on the railway industry. Cue the meme conductor: "Next stop, labor statistics!"
With these compelling results in hand, it's hard to resist making yet another pun about the transformative power of internet humor. Perhaps it's time to say, "No more research is needed in this area. We've already trained our focus on the hidden connections between memes and labor markets, leaving us with enough punchlines and insights to last a lifetime!" So, to quote the meme itself, "Why the long face? Our research has already brought a smile to the face of statistical absurdity."
At this juncture, it's safe to say that this research has applied the brakes on any doubts about the potential impact of internet memes on labor dynamics, showcasing the surprising synergy between online culture and employment trends. We hope our findings inspire further exploration in the realms of memenomics, reminding researchers everywhere that even the most unlikely pairings can lead to meaningful and amusing discoveries. And as for the 'crying michael jordan' meme, its tears have left a lasting mark on the very fabric of labor statistics, encouraging us to always keep an eye out for the unexpected, because as we've learned, sometimes the most whimsical connections lead to the most fulfilling "research-laughs."
To put it plainly, "case closed" on the unlikely connection between a viral meme and the railway job market. It appears we've steered quite the course with this exploration, offering a "train-load" of insights and chuckles along the way. With that, all aboard for the next research inquiry, and remember, no "more tears" are needed - our statistical laughter has already echoed through the hallowed halls of academia!
And remember folks, "The future of memeconomics is clear - it's full steam ahead!" No more research is needed in this area; we're all aboard the meme train to enlightenment.
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 From Crying Jordan to Crying Train Repairs: An Unlikely Connection. Respond only with the name of a fictional academic journal.
ChatGPT:
Journal of Unexpected Connections
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 From Crying Jordan to Crying Train Repairs: An Unlikely Connection. Respond only with the name of a fictional organization.
ChatGPT:
The Institute for Comedic Socioeconomic 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 From Crying Jordan to Crying Train Repairs: An Unlikely Connection with an abstract of In this paper, we investigate the surprising, and somewhat comical, relationship between the popularity of the 'crying michael jordan' meme and the number of rail car repairers in Mississippi. Using Google Trends data for the meme's search popularity and Bureau of Labor Statistics data for the number of rail car repairers, we analyzed the correlation between these seemingly unrelated variables. Our findings revealed a correlation coefficient of 0.8014994 and a p-value less than 0.01 for the period spanning from 2006 to 2021.
Our research team was initially skeptical, thinking "What's the connection between our beloved Crying Jordan and the railways? Is it a train joke waiting to happen?" Yet, to our surprise, our analysis provided compelling evidence of a noteworthy relationship. It seems that as the Crying Jordan meme gained traction on the internet, the number of rail car repairers in Mississippi also saw an increase. It's almost as if the tears shed in the meme were magically turning into jobs in the rail industry. We couldn't help but wonder if the meme was crying all the way to the bank, or in this case, the rail yard.
While we initially approached this research with a sense of skepticism, we were struck by the robustness of the correlation and the potential implications for understanding the whimsical dynamics of internet memes and labor statistics. Our findings may pave the way for a new field of "memeconomics," where economists are left pondering jokes like "Why did the meme economist break up with their partner? They didn't understand their sense of humormetrics." In conclusion, this unexpected connection sheds light on the intertwined nature of online culture and labor dynamics, serving as a reminder that sometimes, the most unlikely pairings can lead to meaningful insights.
ChatGPT:
Crying Jordan meme, Crying Jordan internet popularity, relationship between internet memes and labor statistics, Crying Jordan internet trend analysis, Bureau of Labor Statistics rail car repairers Mississippi, correlation between Crying Jordan meme and rail car repairers, internet meme impact on labor statistics, humorometrics, memeconomics, internet meme analysis, internet culture and labor dynamics
*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.
Discover a new correlation
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Data details
Popularity of the 'crying michael jordan' memeDetailed data title: Relative volume of Google searches for 'crying michael jordan meme' (without quotes, in the United States)
Source: Google Trends
Additional Info: Relative search volume is a unique Google thing; the shape of the chart is accurate but the actual numbers are meaningless.
See what else correlates with Popularity of the 'crying michael jordan' meme
The number of rail car repairers in Mississippi
Detailed data title: BLS estimate of rail car repairers in Mississippi
Source: Bureau of Larbor Statistics
See what else correlates with The number of rail car repairers in Mississippi
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 = 0.6424012 (Coefficient of determination)
This means 64.2% of the change in the one variable (i.e., The number of rail car repairers in Mississippi) is predictable based on the change in the other (i.e., Popularity of the 'crying michael jordan' meme) over the 16 years from 2006 through 2021.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00019. 0.0001891889542182226300000000
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 0.8 in 0.019% of random cases. Said differently, if you correlated 5,286 random variables Which I absolutely did.
with the same 15 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 15 because we have two variables measured over a period of 16 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.
[ 0.51, 0.93 ] 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.
| 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | |
| Popularity of the 'crying michael jordan' meme (Relative popularity) | 0.285714 | 0.555556 | 0.571429 | 0 | 0 | 0.125 | 0 | 0.285714 | 0.2 | 1.8 | 8.5 | 1.91667 | 1.33333 | 1.5 | 11.9167 | 2 |
| The number of rail car repairers in Mississippi (Rail Car Repairers) | 250 | 230 | 150 | 140 | 120 | 210 | 210 | 240 | 140 | 190 | 260 | 210 | 270 | 270 | 520 | 270 |
Why this works
- Data dredging: I have 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
- Lack of causal connection: There is probably
Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied. - Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple
Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is. - Confounding variable: 2020 is particularly different from the other years on this graph. Confounding variables (like global pandemics) will cause two variables to look connected when in fact a "sneaky third" variable is influencing both of them behind the scenes.
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([0.285714,0.555556,0.571429,0,0,0.125,0,0.285714,0.2,1.8,8.5,1.91667,1.33333,1.5,11.9167,2,])
array_2 = np.array([250,230,150,140,120,210,210,240,140,190,260,210,270,270,520,270,])
array_1_name = "Popularity of the 'crying michael jordan' meme"
array_2_name = "The number of rail car repairers in Mississippi"
# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)
# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)Reuseable content
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If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.
Alternatively you can use a tool like Inkscape. - High resolution line chart, optimized for mobile
- Alternative high resolution line chart
- Scatterplot
- Portable line chart (png)
- Portable line chart (png), optimized for mobile
- Line chart for only Popularity of the 'crying michael jordan' meme
- Line chart for only The number of rail car repairers in Mississippi
- AI-generated correlation image
- The spurious research paper: From Crying Jordan to Crying Train Repairs: An Unlikely Connection
Kudos for rating!
Correlation ID: 5432 · Black Variable ID: 25154 · Red Variable ID: 10324
