Spurious correlation #1,957 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Butter consumption and the second variable is Wind power generated in Latvia. The chart goes from 1996 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 Butter consumption and the second variable is Wind power generated in Latvia. The chart goes from 1996 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As butter consumption increased, people's gaseous emissions became more potent, creating a higher demand for wind power in Latvia to disperse the increased flatulence, leading to a simultaneous rise in wind power generation.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a sunny field in Latvia where a family is enjoying a picnic. The scene includes a checkered blanket spread out on the grass, on which there are loaves of fresh bread cut into slices. A small dish holds creamy yellow butter, which glistens in the sunlight. Close by, a row of wind turbines can be seen gracefully spinning and generating clean energy. The composition feels lively and wholesome, emphasizing the serene beauty of the countryside. 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: Butter consumption and Wind power generated in Latvia. 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: Realistic Cyber folk Cubism Psychedelic Photorealism
ChatGPT: Photorealism

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 Butter consumption and Wind power generated in Latvia together. Keep the description simple.
ChatGPT: In a sunny field in Latvia, a family enjoys a picnic with fresh bread spread with creamy yellow butter. Nearby, a row of wind turbines spins gracefully, generating clean energy from the gentle breeze.

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: [ Photorealism ] and depicting this scene: In a sunny field in Latvia, a family enjoys a picnic with fresh bread spread with creamy yellow butter. Nearby, a row of wind turbines spins gracefully, generating clean energy from the gentle breeze. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a sunny field in Latvia where a family is enjoying a picnic. The scene includes a checkered blanket spread out on the grass, on which there are loaves of fresh bread cut into slices. A small dish holds creamy yellow butter, which glistens in the sunlight. Close by, a row of wind turbines can be seen gracefully spinning and generating clean energy. The composition feels lively and wholesome, emphasizing the serene beauty of the countryside.

*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 Butter consumption caused Wind power generated in Latvia to increase.

AI academic paper

(Because p < 0.01)

Churning Wind: The Butter-Wind Power Connection in Latvia

The Journal of Renewable Energy and Dairy Science

Jan 2024

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

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

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between Butter consumption and Wind power generated in Latvia. Make lots of jokes, goofy observations, and puns.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
Churning Wind: The Butter-Wind Power Connection in Latvia

[[ABSTRACT]]
In this groundbreaking study, we explored the curious correlation between butter consumption and wind power generation in Latvia. While one might initially dismiss this as utterly ridiculous, our findings point to an unexpectedly strong relationship. Leveraging data from the USDA and the Energy Information Administration, we calculated a remarkable correlation coefficient of 0.9349282 with a significance level of p < 0.01 for the period spanning 1996 to 2021.

Our findings challenge conventional wisdom and spread a generous dollop of humor on the serious business of academic research. The implications of our study are both thought-provoking and, dare we say, whimsical. We invite fellow researchers to join us in churning through the implications of this unexpected link between butter and wind power, as we aim to butter up the world of renewable energy research.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The world of academic research often churns out groundbreaking discoveries and unexpected correlations. In this paper, we delve into the curious relationship between butter consumption and wind power generation in Latvia. At first glance, one might question the sanity of investigating such a seemingly unrelated pair, but as the saying goes, "where there's a whisk, there's a way."
While many may consider this topic as frivolous as a pat of butter on a hot pancake, our findings suggest otherwise. Our study takes a buttery smooth approach to exploring this correlation, aiming to spread light on a topic that is, quite frankly, in the realm of the absurd.
Now, let's address the elephant in the room – or should I say the cow? How on earth could butter consumption be related to wind power generation? Well, hold on to your dairy products, because the results of our analysis may just butter your bread and blow you away at the same time.
When it comes to research, it's crucial to not spread oneself too thin, but in this case, we are happy to report that our empirical analysis yielded surprisingly rich results. By harnessing data from the USDA and the Energy Information Administration, we uncovered a correlation coefficient that would make even the most seasoned statistician do a double take – a coefficient of 0.9349282, with a significance level of p < 0.01. It's safe to say that our findings were as clear as the skies over Latvia on a breezy day.
As we dive deeper into this uncharted territory of butter-infused wind, we invite our esteemed colleagues to join us in this quirky quest. We aim to churn through the implications of our findings and fathom the potential impact on the world of renewable energy research. After all, who knew that studying butter and wind power could be so gouda for the academic soul?
In the pages that follow, we'll peel back the layers of this unexpected correlation and butter up the field of energy research with a fresh perspective. So, fasten your seatbelts and hold on to your hats – this is going to be one whirlwind of a scientific journey.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In "The Dairy Dilemma: A Statistical Perspective," Smith et al. investigate the enigmatic relationship between dairy product consumption and unconventional sources of energy generation. While their study encompasses a wide array of dairy products, including milk, cheese, and yogurt, it provides crucial insights into the potential links between butter consumption and wind power generation. Their findings lay the foundation for understanding the unexplored realm of dairy-energy dynamics, setting the stage for our own investigation in the context of Latvia.

Doe and Jones, in "Renewable Butter: Unraveling the Mysteries of Energy," offer a comprehensive overview of alternative energy sources, delving into the intersections of renewable energy and unexpected culinary elements. While their focus is not specifically on wind power and butter, their work prompts us to consider the uncharted territories of unconventional energy relationships, inspiring our curiosity and dedication to uncovering the butter-wind power connection.

Taking a tangential approach, "The Windy World of Latvia" by Energy Enthusiast Magazine shines light on Latvia's ambitious wind power initiatives. While the publication does not overtly discuss butter, the metaphorical winds of change in the Latvian energy landscape serve as a captivating backdrop for our exploration of the butter-wind power correlation.

Transitioning to the realm of fiction, "Gone with the Wind" by Margaret Mitchell may seem like an unlikely source for insight into butter and wind power. However, the transcendent nature of the wind in the novel provides a metaphorical backdrop for contemplating the ephemeral yet influential aspects of energy sources. Additionally, "The Butter Battle Book" by Dr. Seuss playfully explores the contentious realm of butter preferences, serving as a lighthearted reminder of the diversity of perspectives within the scientific community.

Venturing into the realm of unconventional sources, the back labels of shampoo bottles, while not traditionally recognized as scholarly literature, cannot be overlooked. The concise yet captivating descriptions of botanical ingredients and promises of luscious locks offer a refreshing perspective on the interplay of natural elements – perhaps providing unexpected inspiration for our investigation.

In essence, while the existing literature presents a range of perspectives, it is clear that the link between butter consumption and wind power generation in Latvia remains an enthralling and understudied domain, ripe for further exploration. With this understanding, we embark on our own study, aiming to enrich the scholarly landscape with a newfound appreciation for the buttery winds of renewable energy research.

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 Butter consumption and Wind power generated in Latvia. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from USDA and Energy Information Administration . You used data from 1996 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]]
Churning Wind: The Butter-Wind Power Connection in Latvia

[[ABSTRACT]]
In this groundbreaking study, we explored the curious correlation between butter consumption and wind power generation in Latvia. While one might initially dismiss this as utterly ridiculous, our findings point to an unexpectedly strong relationship. Leveraging data from the USDA and the Energy Information Administration, we calculated a remarkable correlation coefficient of 0.9349282 with a significance level of p < 0.01 for the period spanning 1996 to 2021.

Our findings challenge conventional wisdom and spread a generous dollop of humor on the serious business of academic research. The implications of our study are both thought-provoking and, dare we say, whimsical. We invite fellow researchers to join us in churning through the implications of this unexpected link between butter and wind power, as we aim to butter up the world of renewable energy research.

[[INTRODUCTION]]
The world of academic research often churns out groundbreaking discoveries and unexpected correlations. In this paper, we delve into the curious relationship between butter consumption and wind power generation in Latvia. At first glance, one might question the sanity of investigating such a seemingly unrelated pair, but as the saying goes, "where there's a whisk, there's a way."
While many may consider this topic as frivolous as a pat of butter on a hot pancake, our findings suggest otherwise. Our study takes a buttery smooth approach to exploring this correlation, aiming to spread light on a topic that is, quite frankly, in the realm of the absurd.
Now, let's address the elephant in the room – or should I say the cow? How on earth could butter consumption be related to wind power generation? Well, hold on to your dairy products, because the results of our analysis may just butter your bread and blow you away at the same time.
When it comes to research, it's crucial to not spread oneself too thin, but in this case, we are happy to report that our empirical analysis yielded surprisingly rich results. By harnessing data from the USDA and the Energy Information Administration, we uncovered a correlation coefficient that would make even the most seasoned statistician do a double take – a coefficient of 0.9349282, with a significance level of p < 0.01. It's safe to say that our findings were as clear as the skies over Latvia on a breezy day.
As we dive deeper into this uncharted territory of butter-infused wind, we invite our esteemed colleagues to join us in this quirky quest. We aim to churn through the implications of our findings and fathom the potential impact on the world of renewable energy research. After all, who knew that studying butter and wind power could be so gouda for the academic soul?
In the pages that follow, we'll peel back the layers of this unexpected correlation and butter up the field of energy research with a fresh perspective. So, fasten your seatbelts and hold on to your hats – this is going to be one whirlwind of a scientific journey.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To tackle the perplexing puzzle of the butter-wind power connection in Latvia, our research team concocted a methodological brew that would make even the most seasoned scientist raise an eyebrow. We embarked upon a flavorful journey through the realms of data analysis and statistical inference, aiming to whip up meaningful insights from what appeared to be a rather eclectic mix of variables.

Data Collection:
Our first task was to churn through copious amounts of data from the USDA and the Energy Information Administration. We gathered information on butter consumption per capita and wind power generation in Latvia from 1996 to 2021. This involved navigating through an array of spreadsheets, wrangling with data formats, and occasionally pondering the dairy-liciousness of butter consumption statistics.

Normalization and Transformation:
Having assembled our dairy and wind-related data, we engaged in a bit of statistical alchemy. We normalized and transformed the data with the precision of a master butter sculptor, ensuring that our variables were in a palatable format for advanced analysis. This step was instrumental in ensuring that our statistical recipes would yield valid and reliable results, much like the perfect ratio of butter in a delectable pastry.

Correlation Analysis:
With our datasets primed and ready, we turned our attention to uncovering the potential link between butter consumption and wind power generation. Employing the robust tools of correlation analysis, we scrutinized the data with the sharpness of a cheese grater. Our analysis sought to ascertain whether there was a buttery smooth relationship between these seemingly incongruous variables.

Statistical Wizardry:
In the realm of statistical inference, we summoned the powers of regression analysis to delve into the nuances of the butter-wind power connection. By crafting intricate models and conducting hypothesis tests, we aimed to unravel the hidden flavors of association between butter consumption and wind power generation. This phase of our methodology brought a touch of magic to our research, reminiscent of a culinary maestro transforming humble ingredients into a gastronomic delight.

Sensitivity Analyses:
As any seasoned researcher knows, no analysis is complete without a pinch of sensitivity testing. We probed the robustness of our findings through various sensitivity analyses, ensuring that our conclusions held firm even in the face of potential confounders and statistical turbulence. This step was crucial in ensuring that our results were as unassailable as a fortified castle made of butter – a rather amusing mental image, to say the least.

In integrating these methodological ingredients, our research sought to churn through the seemingly whimsical and unravel the unexpected symmetry between butter consumption and wind power generation in Latvia. The following sections will unveil the creamy core of our findings and, quite possibly, shed light on a hitherto undiscovered slice of scientific curiosity. So, without further ado, let us dive into the rich and, dare I say, tangy depths of our empirical analysis.

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 Butter consumption and Wind power generated in Latvia. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 500 tokens.

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

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

[[TITLE]]
Churning Wind: The Butter-Wind Power Connection in Latvia

[[ABSTRACT]]
In this groundbreaking study, we explored the curious correlation between butter consumption and wind power generation in Latvia. While one might initially dismiss this as utterly ridiculous, our findings point to an unexpectedly strong relationship. Leveraging data from the USDA and the Energy Information Administration, we calculated a remarkable correlation coefficient of 0.9349282 with a significance level of p < 0.01 for the period spanning 1996 to 2021.

Our findings challenge conventional wisdom and spread a generous dollop of humor on the serious business of academic research. The implications of our study are both thought-provoking and, dare we say, whimsical. We invite fellow researchers to join us in churning through the implications of this unexpected link between butter and wind power, as we aim to butter up the world of renewable energy research.

[[LITERATURE REVIEW]]
In "The Dairy Dilemma: A Statistical Perspective," Smith et al. investigate the enigmatic relationship between dairy product consumption and unconventional sources of energy generation. While their study encompasses a wide array of dairy products, including milk, cheese, and yogurt, it provides crucial insights into the potential links between butter consumption and wind power generation. Their findings lay the foundation for understanding the unexplored realm of dairy-energy dynamics, setting the stage for our own investigation in the context of Latvia.
Doe and Jones, in "Renewable Butter: Unraveling the Mysteries of Energy," offer a comprehensive overview of alternative energy sources, delving into the intersections of renewable energy and unexpected culinary elements. While their focus is not specifically on wind power and butter, their work prompts us to consider the uncharted territories of unconventional energy relationships, inspiring our curiosity and dedication to uncovering the butter-wind power connection.
Taking a tangential approach, "The Windy World of Latvia" by Energy Enthusiast Magazine shines light on Latvia's ambitious wind power initiatives. While the publication does not overtly discuss butter, the metaphorical winds of change in the Latvian energy landscape serve as a captivating backdrop for our exploration of the butter-wind power correlation.
Transitioning to the realm of fiction, "Gone with the Wind" by Margaret Mitchell may seem like an unlikely source for insight into butter and wind power. However, the transcendent nature of the wind in the novel provides a metaphorical backdrop for contemplating the ephemeral yet influential aspects of energy sources. Additionally, "The Butter Battle Book" by Dr. Seuss playfully explores the contentious realm of butter preferences, serving as a lighthearted reminder of the diversity of perspectives within the scientific community.
Venturing into the realm of unconventional sources, the back labels of shampoo bottles, while not traditionally recognized as scholarly literature, cannot be overlooked. The concise yet captivating descriptions of botanical ingredients and promises of luscious locks offer a refreshing perspective on the interplay of natural elements – perhaps providing unexpected inspiration for our investigation.
In essence, while the existing literature presents a range of perspectives, it is clear that the link between butter consumption and wind power generation in Latvia remains an enthralling and understudied domain, ripe for further exploration. With this understanding, we embark on our own study, aiming to enrich the scholarly landscape with a newfound appreciation for the buttery winds of renewable energy research.

[[RESULTS]]
Our analysis revealed a remarkably strong correlation between butter consumption and wind power generation in Latvia. The correlation coefficient of 0.9349282 suggests a robust positive relationship between these seemingly unrelated variables. It's as if the winds of fate were blowing through the dairy aisle, whispering secrets to the turbines.
With an r-squared value of 0.8740908, we found that a substantial proportion of the variation in wind power generation can be explained by changes in butter consumption. It seems that the spread of butter across toast might be accompanied by an equally spread of wind power across the Latvian landscape.
The p-value of less than 0.01 further attests to the significance of this correlation, indicating that the likelihood of observing such a strong relationship by chance alone is quite low. It's almost as if our findings were meant to be churned out of the data, a delightful surprise waiting to be savored.
Behold, in Fig. 1, the scatterplot that captures this striking correlation, a visual representation of the breezy kinship shared by butter consumption and wind power generation. It's a sight to behold – a testament to the unexpected connections that can be uncovered through rigorous analysis and a dash of whimsy.
Our results push the boundaries of conventional research, shedding light on an unlikely relationship that begs further exploration. As we ponder the implications of this buttery breeze of a correlation, we invite our esteemed colleagues to join us in churning through the potential impact on the world of renewable energy research. After all, who knew that studying butter and wind power could be so uplifting?

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our findings provide compelling evidence for the hitherto overlooked association between butter consumption and wind power generation in Latvia. This unexpected correlation, with a remarkably high correlation coefficient of 0.9349282, challenges traditional perceptions of energy dynamics and dietary influences. Much like the intricate layers of a flaky pastry, our study has revealed the interwoven relationship between these seemingly disparate variables.

The literature review set the stage for our investigation, drawing attention to the captivating parallels between butter consumption and unconventional energy sources. While some may have initially dismissed these works as whimsical diversions, our results lend credence to the notion that hidden within the dairy-rich pastures of Latvia lies a gusty, butter-scented secret to wind power abundance.

In assembling our findings, we reveled in the delightful surprises unveiled by our data analysis. The substantial r-squared value of 0.8740908 underscores the substantial proportion of wind power variation explicable by fluctuations in butter consumption. It appears that the ebb and flow of butter usage may hold the key to the undulating currents of wind energy production in Latvia.

The p-value, sparkling at less than 0.01, unequivocally accentuates the statistical significance of this association. It's as if our results were simply waiting to be churned out of the data, like a well-aged batch of artisanal butter, ready to enrich the palate of renewable energy research with its nuanced flavors.

While the implications of our study may seem as whimsical as a flighty zephyr, they bear substantial importance for the trajectory of renewable energy research. The visual manifestation of our findings in the form of the evocative scatterplot in Fig. 1 serves as a testament to the uncharted territories of scientific inquiry, urging us to embrace the unexpected twists and flutters that underpin the fabric of our natural world.

It is our sincere hope that our pioneering investigation sparks a gust of curiosity among our peers, encouraging them to embrace the winds of change in energy research. As we collectively marinate in the reverberations of our findings, we stand poised at the cusp of an extraordinary journey, ready to unravel the enigmatic ties that bind butter consumption and wind power generation. After all, as the saying goes, it's not easy to see the wind, but when butter and wind power intertwine, their dance among the Latvian turbines becomes a spectacle worth savoring.

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 Butter consumption and Wind power generated in Latvia. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Churning Wind: The Butter-Wind Power Connection in Latvia

[[ABSTRACT]]
In this groundbreaking study, we explored the curious correlation between butter consumption and wind power generation in Latvia. While one might initially dismiss this as utterly ridiculous, our findings point to an unexpectedly strong relationship. Leveraging data from the USDA and the Energy Information Administration, we calculated a remarkable correlation coefficient of 0.9349282 with a significance level of p < 0.01 for the period spanning 1996 to 2021.

Our findings challenge conventional wisdom and spread a generous dollop of humor on the serious business of academic research. The implications of our study are both thought-provoking and, dare we say, whimsical. We invite fellow researchers to join us in churning through the implications of this unexpected link between butter and wind power, as we aim to butter up the world of renewable energy research.

[[INTRDUCTION]]
The world of academic research often churns out groundbreaking discoveries and unexpected correlations. In this paper, we delve into the curious relationship between butter consumption and wind power generation in Latvia. At first glance, one might question the sanity of investigating such a seemingly unrelated pair, but as the saying goes, "where there's a whisk, there's a way."
While many may consider this topic as frivolous as a pat of butter on a hot pancake, our findings suggest otherwise. Our study takes a buttery smooth approach to exploring this correlation, aiming to spread light on a topic that is, quite frankly, in the realm of the absurd.
Now, let's address the elephant in the room – or should I say the cow? How on earth could butter consumption be related to wind power generation? Well, hold on to your dairy products, because the results of our analysis may just butter your bread and blow you away at the same time.
When it comes to research, it's crucial to not spread oneself too thin, but in this case, we are happy to report that our empirical analysis yielded surprisingly rich results. By harnessing data from the USDA and the Energy Information Administration, we uncovered a correlation coefficient that would make even the most seasoned statistician do a double take – a coefficient of 0.9349282, with a significance level of p < 0.01. It's safe to say that our findings were as clear as the skies over Latvia on a breezy day.
As we dive deeper into this uncharted territory of butter-infused wind, we invite our esteemed colleagues to join us in this quirky quest. We aim to churn through the implications of our findings and fathom the potential impact on the world of renewable energy research. After all, who knew that studying butter and wind power could be so gouda for the academic soul?
In the pages that follow, we'll peel back the layers of this unexpected correlation and butter up the field of energy research with a fresh perspective. So, fasten your seatbelts and hold on to your hats – this is going to be one whirlwind of a scientific journey.

[[RESULTS]]
Our analysis revealed a remarkably strong correlation between butter consumption and wind power generation in Latvia. The correlation coefficient of 0.9349282 suggests a robust positive relationship between these seemingly unrelated variables. It's as if the winds of fate were blowing through the dairy aisle, whispering secrets to the turbines.
With an r-squared value of 0.8740908, we found that a substantial proportion of the variation in wind power generation can be explained by changes in butter consumption. It seems that the spread of butter across toast might be accompanied by an equally spread of wind power across the Latvian landscape.
The p-value of less than 0.01 further attests to the significance of this correlation, indicating that the likelihood of observing such a strong relationship by chance alone is quite low. It's almost as if our findings were meant to be churned out of the data, a delightful surprise waiting to be savored.
Behold, in Fig. 1, the scatterplot that captures this striking correlation, a visual representation of the breezy kinship shared by butter consumption and wind power generation. It's a sight to behold – a testament to the unexpected connections that can be uncovered through rigorous analysis and a dash of whimsy.
Our results push the boundaries of conventional research, shedding light on an unlikely relationship that begs further exploration. As we ponder the implications of this buttery breeze of a correlation, we invite our esteemed colleagues to join us in churning through the potential impact on the world of renewable energy research. After all, who knew that studying butter and wind power could be so uplifting?

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]
In the illustrious words of Julia Child, "With enough butter, anything is good." Our study has not only lent a dollop of whimsy to the realm of academic research but has also churned out compelling evidence of the surprising connection between butter consumption and wind power generation in Latvia.

Our findings, with a correlation coefficient that could make even the creamiest of mashed potatoes jealous, have buttered our understanding of renewable energy dynamics in an unexpected way. It appears that the winds of change are not merely blowing through the turbines but also through the dairy aisles of Latvia.

The implications of this correlation are as rich as a double butter pecan ice cream on a hot summer day. Who could have fathomed that the spread of butter and the spread of wind power would be so intimately entwined? It's a pairing more surprising than a peanut butter and pickles sandwich – but one that has undeniably blown us away.

While our study stands as a beacon of light in the realm of quirky correlations, it beckons no further investigation. We assert, with a firmness matching that of chilled butter, that no more research is needed in this area. Our findings, like a carefully crafted soufflé, have risen to the occasion, and it's time to savor the delight without any additional churning.

In conclusion, our study has proven that when it comes to butter and wind power in Latvia, there's more than meets the eye. With a twirl and a gust, we bid adieu to this delightful journey through the winds of fate and the spreads of sustenance. Let our findings stand as a testament to the unexpected connections that await those who dare to churn through the data with a sprinkle of humor and a whip of curiosity.

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 Churning Wind: The Butter-Wind Power Connection in Latvia. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Renewable Energy and Dairy Science

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 Churning Wind: The Butter-Wind Power Connection in Latvia. Respond only with the name of a fictional organization.

ChatGPT:

The International Institute for Dairy Energy Research (IIDER)

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 Churning Wind: The Butter-Wind Power Connection in Latvia with an abstract of In this groundbreaking study, we explored the curious correlation between butter consumption and wind power generation in Latvia. While one might initially dismiss this as utterly ridiculous, our findings point to an unexpectedly strong relationship. Leveraging data from the USDA and the Energy Information Administration, we calculated a remarkable correlation coefficient of 0.9349282 with a significance level of p < 0.01 for the period spanning 1996 to 2021.
Our findings challenge conventional wisdom and spread a generous dollop of humor on the serious business of academic research. The implications of our study are both thought-provoking and, dare we say, whimsical. We invite fellow researchers to join us in churning through the implications of this unexpected link between butter and wind power, as we aim to butter up the world of renewable energy research.

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butter consumption, wind power generation, Latvia, correlation coefficient, USDA, Energy Information Administration, renewable energy research

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Data details

Butter consumption
Detailed data title: Per capita consumption of Butter in the US
Source: USDA
See what else correlates with Butter consumption

Wind power generated in Latvia
Detailed data title: Total wind power generated in Latvia in billion kWh
Source: Energy Information Administration
See what else correlates with Wind power generated in Latvia

Correlation r = 0.9349282 (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 = 0.8740908 (Coefficient of determination)
This means 87.4% of the change in the one variable (i.e., Wind power generated in Latvia) is predictable based on the change in the other (i.e., Butter consumption) over the 26 years from 1996 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.7E-12.^Show 0.0000000000027217449250011030
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

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

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.93 in 2.7E-10% of random cases. Said differently, if you correlated 367,411,358,358 random variables^Note You don't actually need 367 billion 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 25 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 25 because we have two variables measured over a period of 26 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.

[ 0.86, 0.97 ] 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.

	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Butter consumption (Pounds per person)	4.3	4.2	4.3	4.6	4.5	4.3	4.4	4.5	4.5	4.5	4.7	4.7	5	5	4.9	5.4	5.5	5.5	5.5	5.6	5.7	5.7	6	6.2	6.3	6.5
Wind power generated in Latvia (Billion kWh)	0.001	0.002	0.002	0.002	0.004	0.003	0.011	0.048	0.049	0.047	0.046	0.053	0.059	0.05	0.048703	0.07007	0.113093	0.11889	0.138	0.14557	0.126761	0.148522	0.12084	0.15249	0.175084	0.137282

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.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. ^{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([4.3,4.2,4.3,4.6,4.5,4.3,4.4,4.5,4.5,4.5,4.7,4.7,5,5,4.9,5.4,5.5,5.5,5.5,5.6,5.7,5.7,6,6.2,6.3,6.5,])
array_2 = np.array([0.001,0.002,0.002,0.002,0.004,0.003,0.011,0.048,0.049,0.047,0.046,0.053,0.059,0.05,0.048703,0.07007,0.113093,0.11889,0.138,0.14557,0.126761,0.148522,0.12084,0.15249,0.175084,0.137282,])
array_1_name = "Butter consumption"
array_2_name = "Wind power generated in Latvia"

# 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 Butter consumption
Line chart for only Wind power generated in Latvia
AI-generated correlation image
The spurious research paper: Churning Wind: The Butter-Wind Power Connection in Latvia

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Correlation ID: 1957 · Black Variable ID: 557 · Red Variable ID: 23783


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