Spurious correlation #2,518 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Master's degrees awarded in Mathematics and statistics and the second variable is Nuclear power generation in China. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Master's degrees awarded in Mathematics and statistics and the second variable is Nuclear power generation in China. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the number of Master's degrees awarded in Mathematics and statistics increased, so did the expertise in calculating complex equations. This led to a chain reaction of improved efficiency and precision in nuclear power generation in China. After all, when it comes to splitting atoms, it's all about having the right formula for success!

^{Show image generation prompt} Model: dalle-3
Prompt: [A photo-realistic image] In a bustling classroom, a group of students is gathered, each one holding their Master's degree in Mathematics and Statistics. The room is filled with excitement and positive energy as they celebrate their achievement. The atmosphere is vibrant, and they appear to exude a sense of accomplishment and determination. The students are shown fusing their expertise with a nuclear power plant, symbolizing the ignition of a chain reaction of innovation and advancement. They work in unison, collaborating to break down complex challenges into manageable tasks, leading to exponential progress in China's nuclear energy production. The surge of Master's degrees is depicted as the driving force behind the peak performance in the industry. The image captures the energy and potential of these graduates as they contribute to the advancement of nuclear energy technology. 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: Master's degrees awarded in Mathematics and statistics and Nuclear power generation in China. 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: Photorealism Oragami A photo-realistic image Low poly Scalar art
ChatGPT: A photo-realistic 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 Master's degrees awarded in Mathematics and statistics and Nuclear power generation in China, in the following situation: As more students pursued their Masters in Math and statistics, they became "positively charged" about applying their knowledge to nuclear power. This led to a "chain reaction" of new and improved techniques in the industry, ultimately "powering up" China's nuclear energy production. These students were able to "divide and conquer" complex problems, leading to "exponential growth" in efficiency and output. In essence, the surge in Master's degrees became the "integral" factor in driving China's nuclear power generation to new "peaks".. Keep the description simple.
ChatGPT: In a bustling classroom, students celebrate receiving their Master's degrees in Mathematics and Statistics, glowing with excitement and positive energy. They fuse their expertise with a nuclear power plant, igniting a chain reaction of innovation and advancement. They work in unison, breaking down complex challenges into manageable tasks, leading to exponential progress in China's nuclear energy production. The surge of Master's degrees becomes the driving force behind peak performance in the industry.

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 photo-realistic image ] and depicting this scene: In a bustling classroom, students celebrate receiving their Master's degrees in Mathematics and Statistics, glowing with excitement and positive energy. They fuse their expertise with a nuclear power plant, igniting a chain reaction of innovation and advancement. They work in unison, breaking down complex challenges into manageable tasks, leading to exponential progress in China's nuclear energy production. The surge of Master's degrees becomes the driving force behind peak performance in the industry. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [A photo-realistic image] In a bustling classroom, a group of students is gathered, each one holding their Master's degree in Mathematics and Statistics. The room is filled with excitement and positive energy as they celebrate their achievement. The atmosphere is vibrant, and they appear to exude a sense of accomplishment and determination. The students are shown fusing their expertise with a nuclear power plant, symbolizing the ignition of a chain reaction of innovation and advancement. They work in unison, collaborating to break down complex challenges into manageable tasks, leading to exponential progress in China's nuclear energy production. The surge of Master's degrees is depicted as the driving force behind the peak performance in the industry. The image captures the energy and potential of these graduates as they contribute to the advancement of nuclear energy technology.

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

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

AI academic paper

(Because p < 0.01)

Crunching Numbers and Atoms: A Statistical Analysis of the Relationship Between Master's Degrees in Mathematics and Nuclear Power Generation in China

Journal of Quantitative Nuclear Mathematics

Jan 2024

Reminder: This paper is AI-generated. Not real!

^{Show prompt used to generate this paper} Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft 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 Master's degrees awarded in Mathematics and statistics and Nuclear power generation in China. 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 National Center for Education Statistics and Energy Information Administration . You used data from 2012 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]]
"Crunching Numbers and Atoms: A Statistical Analysis of the Relationship Between Master's Degrees in Mathematics and Nuclear Power Generation in China"

[[ABSTRACT]]
In this paper, we undertake a whimsical yet rigorous analysis of the correlation between the number of Master's degrees awarded in Mathematics and statistics and nuclear power generation in China. With tongue firmly in cheek, we leveraged data from the National Center for Education Statistics and Energy Information Administration to shed light on this unlikely pairing. Our findings revealed a positively shocking correlation coefficient of 0.9950881 and p < 0.01, indicating a staggeringly strong association between these seemingly disparate fields. We delve into the mathematical intricacies and potential causative factors behind this unexpected relationship, while not forgetting to inject a healthy dose of nuance and a sprinkling of humor into our interpretations. Grab your calculators and Geiger counters, because you're in for a charmingly unconventional statistical journey!

[[INTRODUCTION]]
The intertwining of mathematics and nuclear power generation may seem as unlikely as finding a vegan at a barbeque, but the relationship between the two is not as far-fetched as it may appear. As we embark on our statistical expedition, it's worth noting that this study is certainly not your average "2+2=4" endeavor. Instead, we find ourselves knee-deep in the world of Master's degrees in Mathematics and statistics, wielding statistical methods with the precision of a surgeon and the wit of a stand-up comedian to uncover the mysterious dance between number crunching and atom splitting.
The conundrum before us is as follows: Do the number of Master's degrees awarded in Mathematics and statistics have any discernible impact on the nuclear power generation landscape in China? Pardon the pun, but the idea of diving into this correlation is like trying to decipher Schrödinger's equation in a room full of black cats – it's both daunting and hauntingly intriguing.
Our investigation takes us through the labyrinth of data, where we blend mathematical analysis with a dash of whimsy. The quest to uncover the unlikely bond between Master's degrees in Mathematics and the nuclear realm in China is akin to playing a game of Sudoku where each number represents a nuclear fission event – it's simultaneously thrilling and mind-boggling.
As we plunge into the depths of this peculiar combination, we are armed with the arsenal of statistical tools, ready to decode the hidden patterns with the precision of a cryptographer and the curiosity of a cosmic explorer. The goal is not simply to amass numbers and variables, but to understand the underlying forces that bring these seemingly disparate domains together.
So, buckle up and set your calculators to "stun," because we are about to embark on an adventure that's as enigmatic as it is enthralling. Get ready to witness the fusion of numbers and neutrons, as we unravel the statistical secrets behind the parallel evolution of Master's degrees in Mathematics and the atomic empires of China.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To unravel the peculiar connection between Master's degrees awarded in Mathematics and statistics and the enigmatic world of nuclear power generation in China, we embarked on a mission that would make even Sherlock Holmes raise an eyebrow. Our data collection process was as thorough as a germaphobe's cleaning routine, ensuring we left no statistical stone unturned. We focused our efforts on the years 2012 to 2021, mainly sourcing data from the National Center for Education Statistics and the Energy Information Administration, because who doesn't love a good government website?

Our first step involved donning our statistical Safari hats and delving into the National Center for Education Statistics to extract the number of Master's degrees awarded in the fields of Mathematics and statistics. We combed through the data like Indiana Jones in search of treasure, meticulously documenting the annual awards to create a comprehensive dataset that would make a librarian proud.

Next, we dived into the labyrinth of the Energy Information Administration, where we hunted for the ins and outs of nuclear power generation in China. Armed with our metaphorical Geiger counters and a map of nuclear plants, we navigated through the sea of kilowatt-hours and uranium fuel cycles to paint a vivid picture of China's atomic energy landscape.

With our datasets in hand, we summoned the powers of correlation analysis, regression models, and other statistical incantations to unravel the intertwined destinies of Master's degrees in Mathematics and the atomic nuclei of China. We calculated correlation coefficients with the fervor of a chef perfecting a soufflé, searching for any hint of a meaningful relationship between the variables.

Furthermore, we employed sophisticated time-series analyses to examine the temporal dynamics of Master's degree awards in Mathematics and statistics alongside the ebb and flow of nuclear power generation. Our methods were as precise as a Swiss watch, ensuring that every statistical tick and tock was accounted for.

To add layers of depth to our investigation, we conjured the mystical powers of multivariate analyses, exploring potential confounding factors and covariates that could influence the observed relationship. Like scientific sleuths, we sifted through potential variables with the discernment of a detective, separating the likely suspects from the innocent bystanders.

With our statistical toolbox in hand, we designed a meticulous framework to peel back the layers of this enigmatic correlation, maintaining a balance of scientific rigor and whimsical curiosity throughout our analytical journey.

So, as our adventure through the statistical trenches came to an end, we emerged with insights as illuminating as a well-constructed pun, shedding light on the unlikely kinship between Master's degrees in Mathematics and the atomic realms of China. Keep your calculators handy, for the revelations we unearth may just cause some figurative nuclear reactions among the scientific community!

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 Master's degrees awarded in Mathematics and statistics and Nuclear power generation in China. 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 National Center for Education Statistics and Energy Information Administration .

For the time period 2012 to 2021, you found a correlation 0.9950881, r-squared of 0.9902002, 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]]
"Crunching Numbers and Atoms: A Statistical Analysis of the Relationship Between Master's Degrees in Mathematics and Nuclear Power Generation in China"

[[ABSTRACT]]
In this paper, we undertake a whimsical yet rigorous analysis of the correlation between the number of Master's degrees awarded in Mathematics and statistics and nuclear power generation in China. With tongue firmly in cheek, we leveraged data from the National Center for Education Statistics and Energy Information Administration to shed light on this unlikely pairing. Our findings revealed a positively shocking correlation coefficient of 0.9950881 and p < 0.01, indicating a staggeringly strong association between these seemingly disparate fields. We delve into the mathematical intricacies and potential causative factors behind this unexpected relationship, while not forgetting to inject a healthy dose of nuance and a sprinkling of humor into our interpretations. Grab your calculators and Geiger counters, because you're in for a charmingly unconventional statistical journey!

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intertwining of mathematics and nuclear power generation may seem as unlikely as finding a vegan at a barbeque, but the relationship between the two is not as far-fetched as it may appear. As we embark on our statistical expedition, it's worth noting that this study is certainly not your average "2+2=4" endeavor. Instead, we find ourselves knee-deep in the world of Master's degrees in Mathematics and statistics, wielding statistical methods with the precision of a surgeon and the wit of a stand-up comedian to uncover the mysterious dance between number crunching and atom splitting.
The conundrum before us is as follows: Do the number of Master's degrees awarded in Mathematics and statistics have any discernible impact on the nuclear power generation landscape in China? Pardon the pun, but the idea of diving into this correlation is like trying to decipher Schrödinger's equation in a room full of black cats – it's both daunting and hauntingly intriguing.
Our investigation takes us through the labyrinth of data, where we blend mathematical analysis with a dash of whimsy. The quest to uncover the unlikely bond between Master's degrees in Mathematics and the nuclear realm in China is akin to playing a game of Sudoku where each number represents a nuclear fission event – it's simultaneously thrilling and mind-boggling.
As we plunge into the depths of this peculiar combination, we are armed with the arsenal of statistical tools, ready to decode the hidden patterns with the precision of a cryptographer and the curiosity of a cosmic explorer. The goal is not simply to amass numbers and variables, but to understand the underlying forces that bring these seemingly disparate domains together.
So, buckle up and set your calculators to "stun," because we are about to embark on an adventure that's as enigmatic as it is enthralling. Get ready to witness the fusion of numbers and neutrons, as we unravel the statistical secrets behind the parallel evolution of Master's degrees in Mathematics and the atomic empires of China.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The pursuit of uncovering the enigmatic connection between Master's degrees in Mathematics and statistics and nuclear power generation in China has yielded decidedly electrifying results. Our data analysis from 2012 to 2021 revealed a positively radiant correlation coefficient of 0.9950881, indicating a jarringly strong relationship between the two variables. With an r-squared value of 0.9902002, we can confidently assert that a staggering 99.02% of the variation in nuclear power generation can be explained by the number of Master's degrees awarded in Mathematics and statistics. This result is more solid than a neutron star and even harder to resist.

Our findings send shockwaves through the world of statistical analysis, illuminating a connection that is as unexpected as finding a square root in a field of imaginary numbers. Who would have thought that the rhythm of nuclear energy production in China could be so harmoniously choreographed with the cadence of advanced mathematical education?

Upon visualizing our data, we present Fig. 1, a scatterplot that unmistakably captures the robust correlation between Master's degrees in Mathematics and statistics and nuclear power generation in China. In this enthralling depiction, each data point acts as a tiny maestro, orchestrating a symphony of statistical trends and atomic phenomena that dance across the plot with the elegance of a mathematical waltz.

With a p-value of less than 0.01, our results stand as solid as a carbon atom, decisively rejecting the null hypothesis and affirming the compelling association between these seemingly disparate domains. It's as if the numbers themselves conspired with atomic particles to reveal their intimate connection, leaving us marveling at the cosmic dance that unfolds within our dataset.

In conclusion, our study unearths a delightfully unexpected alliance between the realms of mathematics and nuclear physics, demonstrating that the intersection of these disciplines is as real as the force of gravity – and twice as captivating. This research not only adds a new dimension to the understanding of academic pursuits and energy generation but also serves as a reminder that the universe of statistics and science is brimming with delightful surprises, waiting to be decrypted and celebrated. Cheers to the unyielding bond between numbers and nuclei, and the quirky correlations that make the world of research a never-ending source of wonder and amusement!

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 Master's degrees awarded in Mathematics and statistics and Nuclear power generation in China. 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]]
"Crunching Numbers and Atoms: A Statistical Analysis of the Relationship Between Master's Degrees in Mathematics and Nuclear Power Generation in China"

[[ABSTRACT]]
In this paper, we undertake a whimsical yet rigorous analysis of the correlation between the number of Master's degrees awarded in Mathematics and statistics and nuclear power generation in China. With tongue firmly in cheek, we leveraged data from the National Center for Education Statistics and Energy Information Administration to shed light on this unlikely pairing. Our findings revealed a positively shocking correlation coefficient of 0.9950881 and p < 0.01, indicating a staggeringly strong association between these seemingly disparate fields. We delve into the mathematical intricacies and potential causative factors behind this unexpected relationship, while not forgetting to inject a healthy dose of nuance and a sprinkling of humor into our interpretations. Grab your calculators and Geiger counters, because you're in for a charmingly unconventional statistical journey!

[[LITERATURE REVIEW]]
As we embark on this unconventional journey at the intersection of mathematics and nuclear power, it is prudent to first explore the existing literature that may shed light on the unexpected correlation we seek to unravel. Smith et al. (2017) established a foundational understanding of the mathematical underpinnings of nuclear reactor design, while Doe's (2015) work delved into the statistical modeling of nuclear energy production. Jones (2019) contributed to our understanding of higher education trends in China, though unfortunately failed to touch on the connection to nuclear power.
Expanding beyond the academic realm, "The Art of Nuclear Power Generation" by Dr. A. Reactor and "Mathemagical Musings" by Statisti Cleverton add a touch of whimsy to our scholarly pursuit. Though, it must be noted, while the former provides insightful technical details, the latter often strays into the territory of numerology and spellcasting.
Turning to the more imaginative side of literature, "The Nuclear Paradox" by R. Fiction explores the psychological implications of working in nuclear facilities, while "The Statistician's Dilemma" by M. Fiction whimsically describes a world in which statistical methods have unforeseen consequences – though, it bears little relevance to our inquiry.
In the realm of cinema, "The Matrix" and "Good Will Hunting" offer tangentially related narratives, reminding us that numbers and equations can hold immense power – though, regrettably, neither delve into the captivating juxtaposition of Master's degrees in Mathematics and the nuclear domain.
With these sources as our backdrop, we forge ahead with our own investigation into the unexpected union of number crunching and atom splitting, armed with determination, curiosity, and perhaps one too many puns.

[[RESULTS]]
The pursuit of uncovering the enigmatic connection between Master's degrees in Mathematics and statistics and nuclear power generation in China has yielded decidedly electrifying results. Our data analysis from 2012 to 2021 revealed a positively radiant correlation coefficient of 0.9950881, indicating a jarringly strong relationship between the two variables. With an r-squared value of 0.9902002, we can confidently assert that a staggering 99.02% of the variation in nuclear power generation can be explained by the number of Master's degrees awarded in Mathematics and statistics. This result is more solid than a neutron star and even harder to resist.
Our findings send shockwaves through the world of statistical analysis, illuminating a connection that is as unexpected as finding a square root in a field of imaginary numbers. Who would have thought that the rhythm of nuclear energy production in China could be so harmoniously choreographed with the cadence of advanced mathematical education?
Upon visualizing our data, we present Fig. 1, a scatterplot that unmistakably captures the robust correlation between Master's degrees in Mathematics and statistics and nuclear power generation in China. In this enthralling depiction, each data point acts as a tiny maestro, orchestrating a symphony of statistical trends and atomic phenomena that dance across the plot with the elegance of a mathematical waltz.
With a p-value of less than 0.01, our results stand as solid as a carbon atom, decisively rejecting the null hypothesis and affirming the compelling association between these seemingly disparate domains. It's as if the numbers themselves conspired with atomic particles to reveal their intimate connection, leaving us marveling at the cosmic dance that unfolds within our dataset.
In conclusion, our study unearths a delightfully unexpected alliance between the realms of mathematics and nuclear physics, demonstrating that the intersection of these disciplines is as real as the force of gravity – and twice as captivating. This research not only adds a new dimension to the understanding of academic pursuits and energy generation but also serves as a reminder that the universe of statistics and science is brimming with delightful surprises, waiting to be decrypted and celebrated. Cheers to the unyielding bond between numbers and nuclei, and the quirky correlations that make the world of research a never-ending source of wonder and amusement!

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The positively glowing correlation coefficient and R-squared value we've extracted from our data casts a light brighter than a billion suns on the intriguing relationship between Master's degrees in Mathematics and statistics and nuclear power generation in China. These results are more surprising than finding a quantum physicist at a stand-up comedy club, but they align with prior research that hinted at the interplay of numerical prowess and atomic exploits.

The work of Smith et al. (2017) laid the groundwork for understanding the mathematical complexities of nuclear reactor design, akin to unraveling the convoluted mechanics of a Rubik's Cube made of atoms. Our findings corroborate this by demonstrating the academic synergy between mathematical expertise and the controlled chaos of nuclear power production. It appears that these Master's programs may very well be nurturing the next generation of nuclear sorcerers – or, to use the more scholarly term, nuclear engineers.

Additionally, Doe's (2015) exploration of statistical modeling in nuclear energy production resembles a statistical tango with atomic particles, and our results boogie right alongside this fancy-footed notion. It seems that the swaying rhythms of statistical methods and the nuclear domain are not merely synchronized; they're engaged in an intricate, exhilarating dance that’s more captivating than a particle collider poetry slam.

Even the more whimsical sources in our literature review seem to have inadvertently stumbled upon the mystifying connection we've unearthed. The "Mathemagical Musings" of Statisti Cleverton may sound fanciful, but our results suggest that there’s more wizardry in the statistical realm than meets the eye – it's as if the numbers themselves conspired with atomic particles to reveal their intimate connection, leaving us feeling like mere muggles in the face of this mystical coupling.

The visual representation of our findings in Fig. 1 serves as a veritable art exhibition, showcasing the breathtaking interplay between the meticulous orchestration of statistical trends and the explosive energy of nuclear phenomena. Each data point acts as a tiny maestro, conducting a symphony that speaks to the interconnectedness of these domains, and reminds us that statistical relationships can hold as much power as splitting an atom – albeit in a metaphorical sense.

Our research, while entertaining, also provides tangible implications for academia and industry. It underscores the potential importance of fostering interdisciplinary expertise in both disciplines, positively impacting the development of nuclear technology and the training of future mathematicians and statisticians. Who knew that academic pursuits could be a catalyst for nuclear advancements – it's as unexpected as finding a quark in a haystack!

In summary, our findings lend credence to the notion that the nexus of Master's degrees in Mathematics and statistics and nuclear power generation in China is not just a statistical oddity, but a robust, thought-provoking alliance that demands further exploration. The universe of research never ceases to surprise us, and this study is a testament to the joy of unearthing unexpected connections amidst the serious business of academic inquiry. So, let’s raise a toast to the nuclear mathletes and statistical physicists – may their union continue to spark both curiosity and mirth in the hallowed halls of academia!

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 Master's degrees awarded in Mathematics and statistics and Nuclear power generation in China. 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]]
"Crunching Numbers and Atoms: A Statistical Analysis of the Relationship Between Master's Degrees in Mathematics and Nuclear Power Generation in China"

[[ABSTRACT]]
In this paper, we undertake a whimsical yet rigorous analysis of the correlation between the number of Master's degrees awarded in Mathematics and statistics and nuclear power generation in China. With tongue firmly in cheek, we leveraged data from the National Center for Education Statistics and Energy Information Administration to shed light on this unlikely pairing. Our findings revealed a positively shocking correlation coefficient of 0.9950881 and p < 0.01, indicating a staggeringly strong association between these seemingly disparate fields. We delve into the mathematical intricacies and potential causative factors behind this unexpected relationship, while not forgetting to inject a healthy dose of nuance and a sprinkling of humor into our interpretations. Grab your calculators and Geiger counters, because you're in for a charmingly unconventional statistical journey!

[[INTRDUCTION]]
The intertwining of mathematics and nuclear power generation may seem as unlikely as finding a vegan at a barbeque, but the relationship between the two is not as far-fetched as it may appear. As we embark on our statistical expedition, it's worth noting that this study is certainly not your average "2+2=4" endeavor. Instead, we find ourselves knee-deep in the world of Master's degrees in Mathematics and statistics, wielding statistical methods with the precision of a surgeon and the wit of a stand-up comedian to uncover the mysterious dance between number crunching and atom splitting.
The conundrum before us is as follows: Do the number of Master's degrees awarded in Mathematics and statistics have any discernible impact on the nuclear power generation landscape in China? Pardon the pun, but the idea of diving into this correlation is like trying to decipher Schrödinger's equation in a room full of black cats – it's both daunting and hauntingly intriguing.
Our investigation takes us through the labyrinth of data, where we blend mathematical analysis with a dash of whimsy. The quest to uncover the unlikely bond between Master's degrees in Mathematics and the nuclear realm in China is akin to playing a game of Sudoku where each number represents a nuclear fission event – it's simultaneously thrilling and mind-boggling.
As we plunge into the depths of this peculiar combination, we are armed with the arsenal of statistical tools, ready to decode the hidden patterns with the precision of a cryptographer and the curiosity of a cosmic explorer. The goal is not simply to amass numbers and variables, but to understand the underlying forces that bring these seemingly disparate domains together.
So, buckle up and set your calculators to "stun," because we are about to embark on an adventure that's as enigmatic as it is enthralling. Get ready to witness the fusion of numbers and neutrons, as we unravel the statistical secrets behind the parallel evolution of Master's degrees in Mathematics and the atomic empires of China.

[[RESULTS]]
The pursuit of uncovering the enigmatic connection between Master's degrees in Mathematics and statistics and nuclear power generation in China has yielded decidedly electrifying results. Our data analysis from 2012 to 2021 revealed a positively radiant correlation coefficient of 0.9950881, indicating a jarringly strong relationship between the two variables. With an r-squared value of 0.9902002, we can confidently assert that a staggering 99.02% of the variation in nuclear power generation can be explained by the number of Master's degrees awarded in Mathematics and statistics. This result is more solid than a neutron star and even harder to resist.
Our findings send shockwaves through the world of statistical analysis, illuminating a connection that is as unexpected as finding a square root in a field of imaginary numbers. Who would have thought that the rhythm of nuclear energy production in China could be so harmoniously choreographed with the cadence of advanced mathematical education?
Upon visualizing our data, we present Fig. 1, a scatterplot that unmistakably captures the robust correlation between Master's degrees in Mathematics and statistics and nuclear power generation in China. In this enthralling depiction, each data point acts as a tiny maestro, orchestrating a symphony of statistical trends and atomic phenomena that dance across the plot with the elegance of a mathematical waltz.
With a p-value of less than 0.01, our results stand as solid as a carbon atom, decisively rejecting the null hypothesis and affirming the compelling association between these seemingly disparate domains. It's as if the numbers themselves conspired with atomic particles to reveal their intimate connection, leaving us marveling at the cosmic dance that unfolds within our dataset.
In conclusion, our study unearths a delightfully unexpected alliance between the realms of mathematics and nuclear physics, demonstrating that the intersection of these disciplines is as real as the force of gravity – and twice as captivating. This research not only adds a new dimension to the understanding of academic pursuits and energy generation but also serves as a reminder that the universe of statistics and science is brimming with delightful surprises, waiting to be decrypted and celebrated. Cheers to the unyielding bond between numbers and nuclei, and the quirky correlations that make the world of research a never-ending source of wonder and amusement!

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In the grand symphony of the scientific world, our findings strike a chord that reverberates with the resonance of a freshly tuned Steinway piano. The unexpected tango between Master's degrees in Mathematics and statistics and the powerhouse of nuclear energy production in China is as dazzling as discovering a rainbow within the wavelengths of the electromagnetic spectrum. Our results speak volumes, shouting “Eureka!” loudly enough to send even Archimedes spinning in delight.

With a correlation coefficient as strong as the chemical bonds holding together a robust molecule, and an r-squared value so high it feels like the statistical equivalent of reaching the summit of Mount Everest, it's clear that this relationship is more than a chance encounter in the realm of research. The p-value, much like a strict bouncer at a nightclub, refuses entry to any doubts about the legitimacy of this cosmic connection.

The scatterplot, our Mona Lisa of mathematical artistry, paints a picture so vivid that it could make even the most stoic statistician crack a smile. Each data point pirouettes across the plot in a dance as mesmerizing as the orbits of electrons around a nucleus, harmonizing the intricate patterns of academic achievement with the pulsating rhythm of energy production.

In the end, our study not only unearths an unexpected bond between numbers and neutrons, but it also serves as a reminder that the world of science is as full of delightful surprises as a magician's hat. As we raise our beakers and calculators in celebration, let us toast to the mesmerizing allure of statistical exploration and the tantalizing mysteries that permeate the fabric of our existence.

In conclusion, we assert that no further research is needed in this area. After all, with numbers and atoms waltzing in harmony, the stage of scientific inquiry has been graced with a performance so thrilling that it would make even Einstein exclaim, "It's statistically bonkers, and I love it!"

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 Crunching Numbers and Atoms: A Statistical Analysis of the Relationship Between Master's Degrees in Mathematics and Nuclear Power Generation in China. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Quantitative Nuclear Mathematics

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 Crunching Numbers and Atoms: A Statistical Analysis of the Relationship Between Master's Degrees in Mathematics and Nuclear Power Generation in China. Respond only with the name of a fictional organization.

ChatGPT:

The Quantum Mathematics and Nuclear Energy Research Society

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 Crunching Numbers and Atoms: A Statistical Analysis of the Relationship Between Master's Degrees in Mathematics and Nuclear Power Generation in China with an abstract of In this paper, we undertake a whimsical yet rigorous analysis of the correlation between the number of Master's degrees awarded in Mathematics and statistics and nuclear power generation in China. With tongue firmly in cheek, we leveraged data from the National Center for Education Statistics and Energy Information Administration to shed light on this unlikely pairing. Our findings revealed a positively shocking correlation coefficient of 0.9950881 and p < 0.01, indicating a staggeringly strong association between these seemingly disparate fields. We delve into the mathematical intricacies and potential causative factors behind this unexpected relationship, while not forgetting to inject a healthy dose of nuance and a sprinkling of humor into our interpretations. Grab your calculators and Geiger counters, because you're in for a charmingly unconventional statistical journey!

ChatGPT:

Master's degrees in Mathematics, nuclear power generation, correlation analysis, statistical analysis, China, National Center for Education Statistics, Energy Information Administration, correlation coefficient, causative factors, quantitative analysis, graduate education, energy production, data analysis

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

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Data details

Master's degrees awarded in Mathematics and statistics
Detailed data title: Master's degrees conferred by postsecondary institutions in Mathematics and statistics
Source: National Center for Education Statistics
See what else correlates with Master's degrees awarded in Mathematics and statistics

Nuclear power generation in China
Detailed data title: Total nuclear power generation in China in billion kWh
Source: Energy Information Administration
See what else correlates with Nuclear power generation in China

Correlation r = 0.9950881 (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 = 0.9902002 (Coefficient of determination)
This means 99% of the change in the one variable (i.e., Nuclear power generation in China) is predictable based on the change in the other (i.e., Master's degrees awarded in Mathematics and statistics) over the 10 years from 2012 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.5E-9.^Show 0.0000000025318015210799110000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

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

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 1 in 2.5E-7% of random cases. Said differently, if you correlated 394,975,669 random variables^Note You don't actually need 394 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

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

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

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 9 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 9 because we have two variables measured over a period of 10 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.

[ 0.98, 1 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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

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

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Master's degrees awarded in Mathematics and statistics (Degrees awarded)	6246	6957	7273	7589	8451	9082	10443	11382	12041	12583
Nuclear power generation in China (Billion kWh)	97.39	111.61	132.54	170.79	213.29	248.07	294.36	348.35	366.25	407.52

Why this works

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

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([6246,6957,7273,7589,8451,9082,10443,11382,12041,12583,])
array_2 = np.array([97.39,111.61,132.54,170.79,213.29,248.07,294.36,348.35,366.25,407.52,])
array_1_name = "Master's degrees awarded in Mathematics and statistics"
array_2_name = "Nuclear power generation in China"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. ^Note Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

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

Full license details.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:

High resolution line chart ^Note The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.

If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only Master's degrees awarded in Mathematics and statistics
Line chart for only Nuclear power generation in China
AI-generated correlation image
The spurious research paper: Crunching Numbers and Atoms: A Statistical Analysis of the Relationship Between Master's Degrees in Mathematics and Nuclear Power Generation in China

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Correlation ID: 2518 · Black Variable ID: 1330 · Red Variable ID: 23587


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