Spurious correlation #4,135 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of Breweries in the United States and the second variable is Solar power generated in Australia. The chart goes from 1992 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 The number of Breweries in the United States and the second variable is Solar power generated in Australia. The chart goes from 1992 to 2021, and the two variables track closely in value over that time.

AI explanation

As the breweries in the United States multiplied, so did their production of beer foam. This foam, when exported to Australia, was discovered to have light-reflecting properties, inadvertently boosting solar power generation. Talk about tapping into a renewable source of energy!

^{Show image generation prompt} Model: dalle-3
Prompt: Create an image of a vibrant scene where the landscape alternates between the US and Australia. In the foreground, portray a lively brewery district in the United States, with copper brewing tanks emitting billowing clusters of hyper-realistic beer foam. This foam should be depicted as effervescent and voluminous, signifying the energetic craft beer industry. Moving toward the background, transition to an Australian setting featuring an expanse of solar panels basking in bright, intense sunlight. The sunlight should cast dynamic, vivid reflections off the solar panels, showcasing the surplus of solar power being generated. As the scene unfolds, illustrate the foam from the American breweries intermingling with the sunlight in a transformative manner, amplifying the brilliance and efficiency of the solar power generation. This portrayal should convey a harmonious fusion of industry and nature, blending the essence of two distinct environments in a visually stunning composition. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: The number of Breweries in the United States and Solar power generated in Australia. 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: Concept art Scalar art Cubism Low poly Hyper-realistic
ChatGPT: Hyper-realistic

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both The number of Breweries in the United States and Solar power generated in Australia, in the following situation: As the breweries in the United States multiplied, so did their production of beer foam. This foam, when exported to Australia, was discovered to have light-reflecting properties, inadvertently boosting solar power generation. Talk about tapping into a renewable source of energy!. Keep the description simple.
ChatGPT: The United States is bustling with breweries, with clusters of beer foam bubbling up from their tanks. Across the Pacific in Australia, solar panels are reflecting bright sunlight, generating a surplus of solar power. The foam from the American breweries, when exported to Australia, is found to enhance the reflection of sunlight, amplifying the efficiency of solar power generation.

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: [ Hyper-realistic ] and depicting this scene: The United States is bustling with breweries, with clusters of beer foam bubbling up from their tanks. Across the Pacific in Australia, solar panels are reflecting bright sunlight, generating a surplus of solar power. The foam from the American breweries, when exported to Australia, is found to enhance the reflection of sunlight, amplifying the efficiency of solar power generation. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create an image of a vibrant scene where the landscape alternates between the US and Australia. In the foreground, portray a lively brewery district in the United States, with copper brewing tanks emitting billowing clusters of hyper-realistic beer foam. This foam should be depicted as effervescent and voluminous, signifying the energetic craft beer industry. Moving toward the background, transition to an Australian setting featuring an expanse of solar panels basking in bright, intense sunlight. The sunlight should cast dynamic, vivid reflections off the solar panels, showcasing the surplus of solar power being generated. As the scene unfolds, illustrate the foam from the American breweries intermingling with the sunlight in a transformative manner, amplifying the brilliance and efficiency of the solar power generation. This portrayal should convey a harmonious fusion of industry and nature, blending the essence of two distinct environments in a visually stunning composition.

*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 The number of Breweries in the United States caused Solar power generated in Australia to increase. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

Pouring Sunlight: A Sudsy Connection Between Breweries in the United States and Solar Power Generation in Australia

Journal of Eclectic Brewing and Solar Studies

Jan 2024

^{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 dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between The number of Breweries in the United States and Solar power generated in Australia. 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 name a few real TV shows that sound like they might be relevant to the topic that you watched as research.

Here is the title and abstract of the paper:
[[TITLE]]
Pouring Sunlight: A Sudsy Connection Between Breweries in the United States and Solar Power Generation in Australia

[[ABSTRACT]]
This paper investigates the intriguing relationship between the number of breweries in the United States and the solar power generated in Australia. Utilizing data from the Brewers Association and the Energy Information Administration, a robust correlation coefficient of 0.9214715 and p < 0.01 was observed for the period from 1992 to 2021. Our findings suggest a remarkably strong association between the proliferation of breweries in the US and the solar power output in Australia, raising questions about the global beer-sunshine nexus. We delve into potential explanations for this unexpected linkage, considering aspects such as international trade, climate change, and the shared cultural adoration of beer and sunshine. The results of this study highlight the need for further interdisciplinary exploration into the interplay between seemingly disparate industries and environmental factors.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
INTRODUCTION
The interplay between seemingly unrelated industries and environmental factors often yields unexpected and perplexing connections. In this paper, we explore the peculiar relationship between the number of breweries in the United States and the solar power generated in Australia. It's a tale of suds and sunshine, hops and photovoltaics, revealing a link that transcends geographical boundaries and industry sectors. While one might expect such an investigation to yield mere froth and frivolity, the robust correlation coefficient of 0.9214715 and p < 0.01 between these two variables demands serious scholarly attention.
As we embark on this scholarly journey, it becomes evident that the confluence of beer production and solar energy generation offers a refreshing perspective on the interconnectedness of global industries and environmental dynamics. Our findings beg the question: what is driving this unexpected correlation, and how might it inform our understanding of international trade, climate change, and the shared cultural appreciation for both craft brews and golden sunshine?
With an ample supply of data from the Brewers Association and the Energy Information Administration at our disposal, we delve into this heady brew of statistical analysis and theoretical speculation. Our investigation aims to shed light on this curious nexus, steering clear of any overly hopped-up conclusions as we traverse the barrels of data and hypotheses lying ahead.
So, join us as we tap into the intriguing relationship between beer and solar power, and prepare to be delighted, surprised, and perhaps even a little buzzed by the revelations that await.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The relationship between the number of breweries in the United States and solar power generation in Australia has garnered surprisingly little attention in scholarly literature. However, recent studies have shed light on this uncommon association. Smith et al. (2018) conducted a comprehensive analysis of beer production trends in correlation with renewable energy utilization in various countries, revealing initial hints of a potential linkage. Similarly, Doe and Jones (2020) investigated patterns of solar power generation across global regions, prompting further inquiry into the interrelation between beer and sunshine dynamics.

In "The Economics of Beer" by Johan Swinnen and Devin Briski, the authors discuss the economic and cultural significance of beer production, providing valuable insights into the driving forces behind the proliferation of breweries in different countries. Furthermore, "Solar Power Generation: Technology, New Concepts & Policy" by Dilip K. Bhowmik delves into the technological advancements and policy frameworks shaping solar energy utilization, laying the groundwork for understanding the solar power landscape in Australia.

Expanding the scope to fictional works, "The Sun Also Rises" by Ernest Hemingway presents a compelling narrative that, while not explicitly related to solar energy, offers a nuanced portrayal of human connections against a backdrop of radiant landscapes. In a similar imaginative vein, "Brewster's Millions" by George Barr McCutcheon humorously explores the challenges of unexpected windfalls, providing an allegorical reflection on the surprising outcomes of industrial proliferation.

As a researcher, I found that viewing "Brew Dogs," a TV show featuring craft beer enthusiasts embarking on brewing adventures, offered amusing parallels to our investigation. Conversely, "The Solar Opposites," an animated series following extraterrestrial beings grappling with suburban life, provided an unexpected yet strangely relevant perspective on solar power dynamics and their potential interplay with earthly industries.

In summary, while the literature on the correlation between breweries in the United States and solar power generation in Australia may be sparse, diverse sources offer intriguing insights and unexpected connections that merit further exploration.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between The number of Breweries in the United States and Solar power generated in Australia. Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from Brewers Association and Energy Information Administration . You used data from 1992 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]]
Pouring Sunlight: A Sudsy Connection Between Breweries in the United States and Solar Power Generation in Australia

[[ABSTRACT]]
This paper investigates the intriguing relationship between the number of breweries in the United States and the solar power generated in Australia. Utilizing data from the Brewers Association and the Energy Information Administration, a robust correlation coefficient of 0.9214715 and p < 0.01 was observed for the period from 1992 to 2021. Our findings suggest a remarkably strong association between the proliferation of breweries in the US and the solar power output in Australia, raising questions about the global beer-sunshine nexus. We delve into potential explanations for this unexpected linkage, considering aspects such as international trade, climate change, and the shared cultural adoration of beer and sunshine. The results of this study highlight the need for further interdisciplinary exploration into the interplay between seemingly disparate industries and environmental factors.

[[INTRODUCTION]]
INTRODUCTION
The interplay between seemingly unrelated industries and environmental factors often yields unexpected and perplexing connections. In this paper, we explore the peculiar relationship between the number of breweries in the United States and the solar power generated in Australia. It's a tale of suds and sunshine, hops and photovoltaics, revealing a link that transcends geographical boundaries and industry sectors. While one might expect such an investigation to yield mere froth and frivolity, the robust correlation coefficient of 0.9214715 and p < 0.01 between these two variables demands serious scholarly attention.
As we embark on this scholarly journey, it becomes evident that the confluence of beer production and solar energy generation offers a refreshing perspective on the interconnectedness of global industries and environmental dynamics. Our findings beg the question: what is driving this unexpected correlation, and how might it inform our understanding of international trade, climate change, and the shared cultural appreciation for both craft brews and golden sunshine?
With an ample supply of data from the Brewers Association and the Energy Information Administration at our disposal, we delve into this heady brew of statistical analysis and theoretical speculation. Our investigation aims to shed light on this curious nexus, steering clear of any overly hopped-up conclusions as we traverse the barrels of data and hypotheses lying ahead.
So, join us as we tap into the intriguing relationship between beer and solar power, and prepare to be delighted, surprised, and perhaps even a little buzzed by the revelations that await.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The data for this study was acquired from the Brewers Association, which provided comprehensive information on the number of breweries in the United States over the period from 1992 to 2021. Concurrently, the Energy Information Administration furnished data on solar power generation in Australia during the same time frame. The research team demonstrated an impressive capacity to navigate the labyrinthine corridors of the internet, carefully selecting these reputable sources from among a sea of less savory websites. It is important to note that while we did consider employing a fleet of carrier pigeons to transport the data, we ultimately chose the less avian-inclined route of digital data retrieval for reasons of practicality and reduced risk of feather-related mishaps.

Data Analysis:
Statistical analysis of the collected data was conducted with the precision of a brewmaster meticulously crafting a new ale recipe. A robust correlation coefficient of 0.9214715 was computed using the time-tested wizardry of correlation analysis, revealing a strikingly strong association between the proliferation of breweries in the US and solar power output in Australia. The p-value of less than 0.01 elicited much nodding and eyebrow-raising among the research team, prompting the conclusion that this connection was not a mere statistical fluke but a golden nugget of empirical evidence awaiting further exploration.

Multivariate Regression Models:
In an effort to untangle the intricacies of this beer-sunshine conundrum, the researchers employed multivariate regression models to sift through the tangled web of potential confounding variables. This process involved delicately teasing apart the influences of factors such as international trade dynamics, global climate patterns, and the cultural affinity for both frothy beverages and radiant rays. As the models took shape, it was impossible for the research team to resist the temptation to imbibe in a bit of wordplay, dubbing their endeavor the "brew-solar bivariate dance" as a lighthearted nod to the analytical tango of variable relationships.

Sensitivity Analysis:
To further fortify the credibility of the findings, sensitivity analysis was conducted to assess the robustness of the observed relationship between breweries in the US and solar power generation in Australia. This involved subjecting the data to a battery of hypothetical scenarios, akin to testing the resilience of a beer recipe against variations in fermentation temperature and hop additions. Through this analytical strategy, the researchers confirmed the steadfastness of the association, reinforcing the notion that the beer-sunshine nexus was not just a passing fad, but rather a stout connection worthy of scholarly scrutiny.

In conclusion, the methodology employed in this study epitomizes the blend of rigor and whimsy that characterizes the quest for knowledge in the academic arena. From the careful curation of data sources to the playful monikers bestowed upon statistical analyses, the research team navigated the terrain of empirical inquiry with a sprightly spirit and a scholarly gravitas, setting the stage for the revelatory findings to come.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between The number of Breweries in the United States and Solar power generated in Australia. Make lots of jokes, goofy observations, and puns.

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]]
Pouring Sunlight: A Sudsy Connection Between Breweries in the United States and Solar Power Generation in Australia

[[ABSTRACT]]
This paper investigates the intriguing relationship between the number of breweries in the United States and the solar power generated in Australia. Utilizing data from the Brewers Association and the Energy Information Administration, a robust correlation coefficient of 0.9214715 and p < 0.01 was observed for the period from 1992 to 2021. Our findings suggest a remarkably strong association between the proliferation of breweries in the US and the solar power output in Australia, raising questions about the global beer-sunshine nexus. We delve into potential explanations for this unexpected linkage, considering aspects such as international trade, climate change, and the shared cultural adoration of beer and sunshine. The results of this study highlight the need for further interdisciplinary exploration into the interplay between seemingly disparate industries and environmental factors.

[[LITERATURE REVIEW]]
The relationship between the number of breweries in the United States and solar power generation in Australia has garnered surprisingly little attention in scholarly literature. However, recent studies have shed light on this uncommon association. Smith et al. (2018) conducted a comprehensive analysis of beer production trends in correlation with renewable energy utilization in various countries, revealing initial hints of a potential linkage. Similarly, Doe and Jones (2020) investigated patterns of solar power generation across global regions, prompting further inquiry into the interrelation between beer and sunshine dynamics.
In "The Economics of Beer" by Johan Swinnen and Devin Briski, the authors discuss the economic and cultural significance of beer production, providing valuable insights into the driving forces behind the proliferation of breweries in different countries. Furthermore, "Solar Power Generation: Technology, New Concepts & Policy" by Dilip K. Bhowmik delves into the technological advancements and policy frameworks shaping solar energy utilization, laying the groundwork for understanding the solar power landscape in Australia.
Expanding the scope to fictional works, "The Sun Also Rises" by Ernest Hemingway presents a compelling narrative that, while not explicitly related to solar energy, offers a nuanced portrayal of human connections against a backdrop of radiant landscapes. In a similar imaginative vein, "Brewster's Millions" by George Barr McCutcheon humorously explores the challenges of unexpected windfalls, providing an allegorical reflection on the surprising outcomes of industrial proliferation.
As a researcher, I found that viewing "Brew Dogs," a TV show featuring craft beer enthusiasts embarking on brewing adventures, offered amusing parallels to our investigation. Conversely, "The Solar Opposites," an animated series following extraterrestrial beings grappling with suburban life, provided an unexpected yet strangely relevant perspective on solar power dynamics and their potential interplay with earthly industries.
In summary, while the literature on the correlation between breweries in the United States and solar power generation in Australia may be sparse, diverse sources offer intriguing insights and unexpected connections that merit further exploration.

[[RESULTS]]
The results of our investigation revealed a striking correlation between the number of breweries in the United States and the solar power generated in Australia. The correlation coefficient of 0.9214715 indicates a very strong positive relationship between these two seemingly unrelated variables. This suggests that as the number of breweries in the US increased, the solar power output in Australia also showed a synchronous rise, leaving us pondering the delightful mystery of a sudsy connection that transcends geographical boundaries and industry sectors.
Further reinforcing this finding, the r-squared value of 0.8491097 indicates that a substantial proportion of the variability in solar power generation in Australia can be explained by the number of breweries in the United States. This unexpected correlation between beer production and solar energy generation begs the question of what hidden factors might be at play or whether there is, in fact, a direct causation that we have yet to uncover. Perhaps the power of solar energy is enhanced by the jovial energy of beer enthusiasts raising their glasses in the US, providing an unintentional boost across the Pacific.
The statistical significance, with a p-value of less than 0.01, further strengthens the veracity of this relationship. It suggests that the likelihood of this correlation occurring by chance is extremely low, leaving us with no other option but to grapple with the implications of a connection as complex and effervescent as a freshly poured beer.
To visually capture the essence of this unexpected relationship, we present Fig. 1, a scatterplot displaying the strong positive correlation between the number of breweries in the US and the solar power generated in Australia. As you can see, the data points form a convincing upward trend, symbolizing the harmonious dance between beer and sunshine across the oceans.
In conclusion, our research underscores the need for interdisciplinary exploration into the interplay between seemingly disparate industries and environmental factors. It challenges conventional wisdom and invites scholars to contemplate the profound implications of a bubbly association that challenges the notion of cause and effect, leaving us tantalizingly close to uncovering the underlying mechanisms driving the enigmatic beer-sunshine nexus.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our study have illuminated an unexpected and whimsical connection between the number of breweries in the United States and solar power generation in Australia. The remarkably strong correlation we observed not only supports the prior research by Smith et al. and Doe and Jones but also adds a frothy layer of intrigue to the existing discourse. The robust correlation coefficient and statistical significance underscore the need to take this seemingly unusual relationship seriously, raising a toast to the possibility that the beer-sunshine nexus is more than just a sudsy coincidence.

Drawing on the literature review, the interpretation of "The Sun Also Rises" and "Brewster's Millions" takes on newfound relevance in the context of our findings. Hemingway's portrayal of radiant landscapes and McCutcheon's exploration of industrial proliferation now mirror the unexpected alignment of beer and sunshine dynamics that we have uncovered. Additionally, the TV show "Brew Dogs" suddenly appears as a prescient foreshadowing of our investigation, offering both amusement and insight into the whimsical interplay between earthly industries and the power of solar energy.

Furthermore, the r-squared value suggests that a substantial proportion of the variability in solar power generation in Australia can indeed be attributed to the number of breweries in the United States. This finding not only underscores the nuanced relationship between these two variables but also prompts us to ponder the potential mechanisms driving this correlation. Could it be the infectious energy of beer enthusiasts in the US unknowingly amplifying the power of solar energy across the Pacific, akin to a convivial cheer echoing through the cosmos? This possibility presents a charming conundrum that inspires further scholarly inquiry and whimsical contemplation.

In light of our results, it is evident that the unexpected correlation between beer production and solar energy generation challenges conventional understanding and invites us to unravel the effervescent mysteries of this unlikely partnership. The interplay between seemingly disparate industries and environmental factors requires continued interdisciplinary exploration, as the global beer-sunshine nexus beckons scholars to savor the implications of a connection as complex and delightful as a well-crafted brew.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the discussion section of goofy academic research papers that sounds like it belongs based on the title and abstract provided.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a short conclusion of an academic research paper presenting the findings of the connection between The number of Breweries in the United States and Solar power generated in Australia. Make lots of jokes, goofy observations, and puns.

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]]
Pouring Sunlight: A Sudsy Connection Between Breweries in the United States and Solar Power Generation in Australia

[[ABSTRACT]]
This paper investigates the intriguing relationship between the number of breweries in the United States and the solar power generated in Australia. Utilizing data from the Brewers Association and the Energy Information Administration, a robust correlation coefficient of 0.9214715 and p < 0.01 was observed for the period from 1992 to 2021. Our findings suggest a remarkably strong association between the proliferation of breweries in the US and the solar power output in Australia, raising questions about the global beer-sunshine nexus. We delve into potential explanations for this unexpected linkage, considering aspects such as international trade, climate change, and the shared cultural adoration of beer and sunshine. The results of this study highlight the need for further interdisciplinary exploration into the interplay between seemingly disparate industries and environmental factors.

[[INTRDUCTION]]
INTRODUCTION
The interplay between seemingly unrelated industries and environmental factors often yields unexpected and perplexing connections. In this paper, we explore the peculiar relationship between the number of breweries in the United States and the solar power generated in Australia. It's a tale of suds and sunshine, hops and photovoltaics, revealing a link that transcends geographical boundaries and industry sectors. While one might expect such an investigation to yield mere froth and frivolity, the robust correlation coefficient of 0.9214715 and p < 0.01 between these two variables demands serious scholarly attention.
As we embark on this scholarly journey, it becomes evident that the confluence of beer production and solar energy generation offers a refreshing perspective on the interconnectedness of global industries and environmental dynamics. Our findings beg the question: what is driving this unexpected correlation, and how might it inform our understanding of international trade, climate change, and the shared cultural appreciation for both craft brews and golden sunshine?
With an ample supply of data from the Brewers Association and the Energy Information Administration at our disposal, we delve into this heady brew of statistical analysis and theoretical speculation. Our investigation aims to shed light on this curious nexus, steering clear of any overly hopped-up conclusions as we traverse the barrels of data and hypotheses lying ahead.
So, join us as we tap into the intriguing relationship between beer and solar power, and prepare to be delighted, surprised, and perhaps even a little buzzed by the revelations that await.

[[RESULTS]]
The results of our investigation revealed a striking correlation between the number of breweries in the United States and the solar power generated in Australia. The correlation coefficient of 0.9214715 indicates a very strong positive relationship between these two seemingly unrelated variables. This suggests that as the number of breweries in the US increased, the solar power output in Australia also showed a synchronous rise, leaving us pondering the delightful mystery of a sudsy connection that transcends geographical boundaries and industry sectors.
Further reinforcing this finding, the r-squared value of 0.8491097 indicates that a substantial proportion of the variability in solar power generation in Australia can be explained by the number of breweries in the United States. This unexpected correlation between beer production and solar energy generation begs the question of what hidden factors might be at play or whether there is, in fact, a direct causation that we have yet to uncover. Perhaps the power of solar energy is enhanced by the jovial energy of beer enthusiasts raising their glasses in the US, providing an unintentional boost across the Pacific.
The statistical significance, with a p-value of less than 0.01, further strengthens the veracity of this relationship. It suggests that the likelihood of this correlation occurring by chance is extremely low, leaving us with no other option but to grapple with the implications of a connection as complex and effervescent as a freshly poured beer.
To visually capture the essence of this unexpected relationship, we present Fig. 1, a scatterplot displaying the strong positive correlation between the number of breweries in the US and the solar power generated in Australia. As you can see, the data points form a convincing upward trend, symbolizing the harmonious dance between beer and sunshine across the oceans.
In conclusion, our research underscores the need for interdisciplinary exploration into the interplay between seemingly disparate industries and environmental factors. It challenges conventional wisdom and invites scholars to contemplate the profound implications of a bubbly association that challenges the notion of cause and effect, leaving us tantalizingly close to uncovering the underlying mechanisms driving the enigmatic beer-sunshine nexus.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

CONCLUSION

In conclusion, our investigation into the connection between the number of breweries in the United States and the solar power generated in Australia has yielded results as refreshing as an ice-cold pint on a hot summer day. The remarkably strong positive correlation we observed between these two seemingly unrelated variables has left us in a state of pleasant perplexity, reminiscent of the effects of one too many beers.

As we reflect on the robust correlation coefficient of 0.9214715 and the p-value of less than 0.01, we can't help but raise a metaphorical toast to the unexpected bond between the boozy enthusiasm of American brewers and the radiant allure of Australian solar power. It appears that as the number of breweries in the US increased, the solar power output in Australia also experienced a synchronous rise, painting a picture as captivating as a mesmerizing sunset over a wheat field.

While this sudsy connection may seem as whimsical as a daydream in a beer garden, the statistical significance and the substantial proportion of variability explained by our findings invite us to take this surprising relationship seriously. The delightful mystery of a correlation so potent demands a deeper understanding, lest we leave this topic as unexplained as why the chicken crossed the road.

It is evident that the implications of this link go beyond just a frothy dalliance. This intriguing association challenges conventional wisdom and beckons researchers to consider the profound implications of this beer-sunshine nexus. What drives this unlikely correlation between fermentation and photovoltaics? Is the glow of solar energy enhanced by the exuberance of beer aficionados in the US, or is there a more complex interplay at work here, as intricate as the flavors in a craft brew?

In light of our findings, we assert that no further research is needed in this area, as we've truly tapped into the frothy depths of this curious connection. It's time to savor these results and raise a glass to the expansive and effervescent world of interdisciplinary research, where even the most unexpected connections can yield enlightening insights.

Cheers to the serendipitous harmony of beer and sunshine, and may our scholarly escapade leave you both delighted and slightly bemused by the whimsical interplay of seemingly disparate elements.

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 Pouring Sunlight: A Sudsy Connection Between Breweries in the United States and Solar Power Generation in Australia. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Eclectic Brewing and Solar Studies

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 Pouring Sunlight: A Sudsy Connection Between Breweries in the United States and Solar Power Generation in Australia. Respond only with the name of a fictional organization.

ChatGPT:

The Global BrewTech Institute

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 Pouring Sunlight: A Sudsy Connection Between Breweries in the United States and Solar Power Generation in Australia with an abstract of This paper investigates the intriguing relationship between the number of breweries in the United States and the solar power generated in Australia. Utilizing data from the Brewers Association and the Energy Information Administration, a robust correlation coefficient of 0.9214715 and p < 0.01 was observed for the period from 1992 to 2021. Our findings suggest a remarkably strong association between the proliferation of breweries in the US and the solar power output in Australia, raising questions about the global beer-sunshine nexus. We delve into potential explanations for this unexpected linkage, considering aspects such as international trade, climate change, and the shared cultural adoration of beer and sunshine. The results of this study highlight the need for further interdisciplinary exploration into the interplay between seemingly disparate industries and environmental factors.

ChatGPT:

breweries, solar power, United States, Australia, correlation, association, brewers association, energy information administration, global beer-sunshine nexus, international trade, climate change, interdisciplinary exploration

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

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Data details

The number of Breweries in the United States
Detailed data title: Number of Breweries in the United States
Source: Brewers Association
See what else correlates with The number of Breweries in the United States

Solar power generated in Australia
Detailed data title: Total solar power generated in Australia in billion kWh
Source: Energy Information Administration
See what else correlates with Solar power generated in Australia

Correlation r = 0.9214715 (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 = 0.8491097 (Coefficient of determination)
This means 84.9% of the change in the one variable (i.e., Solar power generated in Australia) is predictable based on the change in the other (i.e., The number of Breweries in the United States) over the 30 years from 1992 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 5.1E-13.^Show 0.0000000000005113567496301989
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 0.92 in 5.1E-11% of random cases. Said differently, if you correlated 1,955,581,892,139 random variables^Note You don't actually need 1 trillion 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 29 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 29 because we have two variables measured over a period of 30 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.

[ 0.84, 0.96 ] 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.

	1992	1993	1994	1995	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
The number of Breweries in the United States (Number of breweries)	405	490	634	1006	1305	1475	1651	1577	1493	1499	1575	1629	1635	1612	1741	1805	1896	1933	2131	2525	2670	3162	4014	4847	5780	6767	7722	8557	9092	9384
Solar power generated in Australia (Billion kWh)	0.0104	0.011	0.013	0.016	0.019	0.023	0.028	0.034	0.038	0.044	0.05	0.059	0.069	0.079	0.091	0.109	0.127	0.16	0.389	1.392	2.325	3.476	4.01	5.023	6.209	8.071	9.929	14.845	23.8532	28.0352

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.
Confounding variable: 2020 is particularly different from the other years on this graph. Confounding variables (like global pandemics) will cause two variables to look connected when in fact a "sneaky third" variable is influencing both of them behind the scenes.
Outlandish outliers: There are "outliers" in this data.^Note In concept, "outlier" just means "way different than the rest of your dataset." When calculating a correlation like this, they are particularly impactful because a single outlier can substantially increase your correlation.

For the purposes of this project, I counted a point as an outlier if it the residual was two standard deviations from the mean.

(This bullet point only shows up in the details page on charts that do, in fact, have outliers.)
They stand out on the scatterplot above: notice the dots that are far away from any other dots. I intentionally mishandeled outliers, which makes the correlation look extra strong.

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([405,490,634,1006,1305,1475,1651,1577,1493,1499,1575,1629,1635,1612,1741,1805,1896,1933,2131,2525,2670,3162,4014,4847,5780,6767,7722,8557,9092,9384,])
array_2 = np.array([0.0104,0.011,0.013,0.016,0.019,0.023,0.028,0.034,0.038,0.044,0.05,0.059,0.069,0.079,0.091,0.109,0.127,0.16,0.389,1.392,2.325,3.476,4.01,5.023,6.209,8.071,9.929,14.845,23.8532,28.0352,])
array_1_name = "The number of Breweries in the United States"
array_2_name = "Solar power generated in Australia"

# 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 The number of Breweries in the United States
Line chart for only Solar power generated in Australia
AI-generated correlation image
The spurious research paper: Pouring Sunlight: A Sudsy Connection Between Breweries in the United States and Solar Power Generation in Australia

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Correlation ID: 4135 · Black Variable ID: 34 · Red Variable ID: 23473


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