Spurious correlation #1,749 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Renewable energy production in U.S. Virgin Islands and the second variable is The number of private detectives in Louisiana. The chart goes from 2011 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 Renewable energy production in U.S. Virgin Islands and the second variable is The number of private detectives in Louisiana. The chart goes from 2011 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the U.S. Virgin Islands embraced renewable energy, it inadvertently led to an influx of new, high-tech equipment. This surplus of tech found its way to Louisiana, where private detectives, always on the cutting edge of surveillance, eagerly adopted and repurposed the renewable energy technology for their sleuthing needs. Suddenly, solar-powered stakeouts and wind turbine hidden cameras became the latest craze in the world of private investigation, creating an unprecedented demand for private detectives in Louisiana. Who knew that the sun and wind would become the new best friends of gumshoes in the Bayou state? It's a renewable energy revolution that's truly a-mystery to behold!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a futuristic landscape where solar panels in the U.S. Virgin Islands glisten under the sun, powering homes and businesses. The panels should reflect the sunlight in a dazzling display. Meanwhile, in Louisiana, private detectives are depicted conducting investigations under the shade of a massive oak tree. The setting should have a sleek, modern aesthetic with elements of advanced technology mixed with serene natural scenery. 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: Renewable energy production in U.S. Virgin Islands and The number of private detectives in Louisiana. 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: Cute 2D animation Vaporwave An image that looks like it is a scene from 'Star Wars' Cybernetic art Futuristic
ChatGPT: Futuristic

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 Renewable energy production in U.S. Virgin Islands and The number of private detectives in Louisiana together. Keep the description simple.
ChatGPT: In the U.S. Virgin Islands, solar panels glisten in the sun as they power homes and businesses, while in Louisiana, private detectives are seen gathering information and conducting investigations under the shade of a massive oak tree.

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: [ Futuristic ] and depicting this scene: In the U.S. Virgin Islands, solar panels glisten in the sun as they power homes and businesses, while in Louisiana, private detectives are seen gathering information and conducting investigations under the shade of a massive oak tree. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a futuristic landscape where solar panels in the U.S. Virgin Islands glisten under the sun, powering homes and businesses. The panels should reflect the sunlight in a dazzling display. Meanwhile, in Louisiana, private detectives are depicted conducting investigations under the shade of a massive oak tree. The setting should have a sleek, modern aesthetic with elements of advanced technology mixed with serene natural scenery.

*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 Renewable energy production in U.S. Virgin Islands caused The number of private detectives in Louisiana to increase.

AI academic paper

(Because p < 0.01)

Shocking Connections: Renewable Energy in U.S. Virgin Islands and the Sleuth Surge in Louisiana

The Journal of Ecological Linkages

Jan 2024

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

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

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between Renewable energy production in U.S. Virgin Islands and The number of private detectives in Louisiana. Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from Energy Information Administration and Bureau of Larbor Statistics . You used data from 2011 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]]
Shocking Connections: Renewable Energy in U.S. Virgin Islands and the Sleuth Surge in Louisiana

[[ABSTRACT]]
This study investigates the unexpected correlation between renewable energy production in the U.S. Virgin Islands and the number of private detectives in Louisiana. With the use of data from the Energy Information Administration and the Bureau of Labor Statistics, our research team uncovered a striking correlation coefficient of 0.9496086 and p < 0.01 for the period spanning 2011 to 2021. Our findings not only suggest a potential link between the two seemingly unrelated factors but also shed light on the electrifying world of interregional dynamics. The implications of our findings are absolutely electrifying, proving that in the world of economic correlations, nothing is ever truly detective-proof!

[[INTRODUCTION]]
Electricity, energy, and detective work may not seem like they have much in common, but as our research delves into the shockingly intriguing connection between renewable energy production in the U.S. Virgin Islands and the surge in private detective numbers in Louisiana, it becomes clear that there's more wattage to this relationship than meets the eye. While one might expect renewable energy and private investigators to be as different as night and day, our investigation has uncovered an unexpectedly strong correlation that is truly electrifying.
As we delve into this perplexing puzzle, it's important to note that the field of economic research often leads us down unexpected paths. Much like a detective following the trail of clues, we found ourselves drawn into the world of energy production and the sleuthing industry, uncovering a link that could very well be the "shock" factor driving these two seemingly unrelated variables towards each other.
The academic landscape is often rife with scholarly findings that are, let's face it, quite dry, but today we aim to inject some sparks of humor and curiosity into our exploration. After all, we believe that research doesn't have to be as serious as a detective on a stakeout – we're all about shedding light on the unconventional and finding the "electric" in unexpected correlations.
So, grab your magnifying glass and prepare to be captivated by our findings, as we unravel the mystery behind the connection between renewable energy production in the U.S. Virgin Islands and the bubbling increase in private detective numbers in the bustling state of Louisiana. It's a tale that promises to be a real "whodunit" of the economic world, with plenty of sparks and surprises along the way.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Before embarking on our investigation into the electrifying connection between renewable energy production in the U.S. Virgin Islands and the surge in private detective numbers in Louisiana, our research team meticulously crafted a methodology designed to shed light on this unexpected correlation. Our research endeavors can be likened to a carefully planned stakeout, where each step was designed to capture the "current" of this perplexing economic puzzle.

Data Collection:
To capture the essence of this electrifying correlation, we scoured the digital landscape like a diligent sleuth, employing data from the Energy Information Administration and the Bureau of Labor Statistics. We meticulously extracted information spanning from 2011 to 2021, ensuring that no "amp" of data was left unturned in our pursuit of answers. While most of our data was sourced from these reputable institutions, it's worth noting that our research team also indulged in the occasional late-night internet trawl, navigating the tangled "web" of online databases like true digital detectives on the case.

Quantitative Analysis:
With our data in hand, we meticulously sifted through the numbers like detectives examining clues at a crime scene. Our quantitative analysis involved employing advanced statistical techniques to calculate correlation coefficients and significance levels, illuminating the strength and significance of the connection between renewable energy production and private detective numbers. Much like a detective measuring the angles of a crime scene, we meticulously examined the numerical evidence, uncovering a correlation that was positively "charged" with significance.

Qualitative Investigation:
Beyond the numbers, our investigation delved into qualitative insights that could shed light on the deeper narrative behind this surprising correlation. We conducted interviews and engaged in discussions with industry experts, probing into the dynamics of energy production and the private investigation sector, seeking to unravel the human story behind the statistical "current" we had uncovered. This qualitative component of our research allowed us to add a human touch to our findings, bringing the narrative to life in a way that numbers alone could never achieve.

The "Tesla" Test of Significance:
In a nod to the electrifying nature of our investigation, we devised the "Tesla" test of significance – an innovative approach to assessing the robustness of the correlation. This involved subjecting our findings to a series of hypothetical scenarios, asking ourselves, "Would Nikola Tesla himself be shocked by this correlation?" If the answer was a resounding "yes," then we deemed the correlation to be truly remarkable and worthy of further investigation.

Ethical Considerations:
Just as a detective upholds the law in the pursuit of justice, our research team maintained the highest ethical standards throughout our investigation. We ensured the confidentiality of all data sources and adhered to ethical guidelines in our engagement with industry experts. Our commitment to ethical conduct was unwavering, ensuring that our research was conducted with integrity and respect for all involved parties.

In summary, our methodology was a carefully concocted blend of quantitative scrutiny, qualitative exploration, and a touch of imaginative "Tesla" testing, all aimed at unraveling the enigmatic connection between renewable energy production in the U.S. Virgin Islands and the surge in private detective numbers in Louisiana. With our investigatory framework in place, we were ready to embark on a journey that promised to illuminate the world of economic correlations like never before.

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 Renewable energy production in U.S. Virgin Islands and The number of private detectives in Louisiana. 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]]
Shocking Connections: Renewable Energy in U.S. Virgin Islands and the Sleuth Surge in Louisiana

[[ABSTRACT]]
This study investigates the unexpected correlation between renewable energy production in the U.S. Virgin Islands and the number of private detectives in Louisiana. With the use of data from the Energy Information Administration and the Bureau of Labor Statistics, our research team uncovered a striking correlation coefficient of 0.9496086 and p < 0.01 for the period spanning 2011 to 2021. Our findings not only suggest a potential link between the two seemingly unrelated factors but also shed light on the electrifying world of interregional dynamics. The implications of our findings are absolutely electrifying, proving that in the world of economic correlations, nothing is ever truly detective-proof!

[[LITERATURE REVIEW]]
The unexpected correlation between renewable energy production in the U.S. Virgin Islands and the surge in private detective numbers in Louisiana has puzzled many researchers, leading to a fervent search for answers within the existing literature. Our exploration begins with serious investigations by Smith, Doe, and Jones, but soon ventures into a whimsical world of puns and unexpected connections.
In "Renewable Energy and Economic Development," Smith et al. delve into the impact of renewable energy on economic growth, providing a comprehensive overview of the potential benefits and challenges associated with sustainable energy sources. Meanwhile, Doe's work in "Private Investigator Insights" offers a detailed analysis of the investigative industry, shedding light on the trends and factors driving the demand for sleuth services in various regions.
As we traverse the academic landscape, we encounter books that bridge the gap between fact and fiction, offering intriguing insights into the world of energy production and detective work. "The Energy Detective's Handbook" by Ima Watt and "Gumshoes and Green Energy" by Drew Clueless provide a quirky perspective on the intersection of these two seemingly disparate fields, offering pun-filled anecdotes and electrifying revelations.
Venturing further into the realm of make-believe, we stumble upon fictional works that, at first glance, seem completely unrelated to our research topic. However, upon closer inspection, these books offer surprising parallels to our investigation. "Solar Sleuths: Mysteries of the Caribbean" by Agatha Solaris and "The Voltage Vendetta" by Sherlock Watts present gripping tales of intrigue and energy, weaving together elements of renewable power and investigative prowess in a compelling narrative.
But the fun doesn't stop there! Childhood cartoons and television shows also make an appearance in our literature review, offering a nostalgic twist to our exploration. "Inspector Gadget" and "Captain Planet and the Planeteers" may seem like lighthearted entertainment, but upon reflection, they showcase themes of energy conservation and problem-solving, mirroring the complex dynamics at play in our research.
As we navigate this unconventional journey through the literature, it becomes clear that the world of economic correlations is not devoid of humor and unexpected connections. From scholarly analyses to whimsical narratives, the literature surrounding renewable energy and private detection offers a diverse tapestry of insights, proving that even the most serious of topics can be infused with a dash of levity and intrigue.

[[RESULTS]]
The results of our investigation into the peculiar pairing of renewable energy production in the U.S. Virgin Islands and the surge in private detective numbers in Louisiana have left us feeling a bit like Sherlock Holmes stumbling upon a well-lit mystery. The correlation coefficient of 0.9496086 that we uncovered suggests an astonishingly strong relationship between these two seemingly unrelated variables. It's as if renewable energy and detective work have entered into an electrifying partnership, leaving us with more questions than answers.
When we looked at the data spanning from 2011 to 2021, we found an r-squared value of 0.9017565, indicating that a whopping 90% of the variation in private detective numbers in Louisiana can be explained by changes in renewable energy production in the U.S. Virgin Islands. Talk about shedding some light on the situation! And with a p-value of less than 0.01, we can confidently say that this relationship is not just a coincidence—it's the real deal!
As for our figure (Fig. 1), the scatterplot speaks for itself, showcasing a linear relationship that's as tight as handcuffs. The points on the plot form a pattern so clear, it's almost as if they've left us a trail of breadcrumbs through the forest of economic data. We're left marveling at the sheer magnitude of this correlation, wondering if there's some sort of renewable energy signal being transmitted directly to gumshoes in the Bayou State.
In the face of such remarkable findings, it's hard not to crack a smile and appreciate the sheer unpredictability of economic research. Who would've thought that the sunny shores of the U.S. Virgin Islands could be casting such a large shadow across the world of detective work in Louisiana? These results not only challenge our assumptions but also underscore the captivating and, dare we say, electrifying nature of economic correlations. After all, in the world of statistical analysis, it seems that everything truly is connected—even when we least expect it!

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

In this investigation, we set out to untangle the mystifying relationship between renewable energy production in the U.S. Virgin Islands and the surge in private detective numbers in Louisiana. And let me tell you, the results have us feeling like we've stumbled into a real-life episode of "The X-Files," only instead of extraterrestrial phenomena, we're dealing with the electrifying enigma of economic correlations. But before we dive into the electrifying implications of our findings, let's revisit some of the whimsical elements from our literature review that are surprisingly relevant in light of our results.

First, let's address the pun-filled anecdotes and electrifying revelations in "The Energy Detective's Handbook" by Ima Watt and "Gumshoes and Green Energy" by Drew Clueless. While these titles may have initially elicited some chuckles, their lighthearted approach to the intersection of renewable energy and detective work has taken on a whole new significance. Our findings align with the spirit of these works, highlighting the unexpected synergy between sustainable power and investigative prowess. It's as if our research has cracked open a treasure chest of insights, revealing that beneath the surface of economic data lies a world rife with humor and curious connections.

Additionally, the fictional works such as "Solar Sleuths: Mysteries of the Caribbean" by Agatha Solaris and "The Voltage Vendetta" by Sherlock Watts served as more than just amusing diversions. These narratives, which initially seemed tangential to our research, now bear a striking resemblance to our empirical findings. Much like the protagonists in these stories, our data points have uncovered a gripping tale of intrigue and energy, weaving together the threads of renewable power and investigative acumen.

As we turn our attention back to our results, we find ourselves marveling at the metaphorical handcuffs binding renewable energy and detective work in Louisiana. Our correlation coefficient of 0.9496086 serves as a testament to the captivating nature of this unexpected relationship. It's as if renewable energy has donned a cloak of mystery, casting a long-reaching shadow that extends all the way to the world of gumshoes and sleuths in the Bayou State.

Moreover, the substantial r-squared value of 0.9017565 highlights the robustness of this connection, indicating that a staggering 90% of the variation in private detective numbers in Louisiana can be attributed to changes in renewable energy production in the U.S. Virgin Islands. This result, along with a p-value of less than 0.01, firmly cements the validity of our findings, quashing any doubts about the reality of this correlation.

In conclusion, our investigation has not only uncovered an astonishing link between renewable energy and detective work but has also demonstrated that the world of economic correlations is a realm teeming with surprises and wit. Perhaps, in the grand tapestry of statistical analyses, there's a place for quirky connections and unexpected parallels. After all, who would have thought that the sun-soaked Caribbean could cast such a dazzling light on the world of investigation in Louisiana? Our results reinforce the notion that in the realm of economic research, even the most improbable correlations can hold a spark of truth.

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 Renewable energy production in U.S. Virgin Islands and The number of private detectives in Louisiana. 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]]
Shocking Connections: Renewable Energy in U.S. Virgin Islands and the Sleuth Surge in Louisiana

[[ABSTRACT]]
This study investigates the unexpected correlation between renewable energy production in the U.S. Virgin Islands and the number of private detectives in Louisiana. With the use of data from the Energy Information Administration and the Bureau of Labor Statistics, our research team uncovered a striking correlation coefficient of 0.9496086 and p < 0.01 for the period spanning 2011 to 2021. Our findings not only suggest a potential link between the two seemingly unrelated factors but also shed light on the electrifying world of interregional dynamics. The implications of our findings are absolutely electrifying, proving that in the world of economic correlations, nothing is ever truly detective-proof!

[[INTRDUCTION]]
Electricity, energy, and detective work may not seem like they have much in common, but as our research delves into the shockingly intriguing connection between renewable energy production in the U.S. Virgin Islands and the surge in private detective numbers in Louisiana, it becomes clear that there's more wattage to this relationship than meets the eye. While one might expect renewable energy and private investigators to be as different as night and day, our investigation has uncovered an unexpectedly strong correlation that is truly electrifying.
As we delve into this perplexing puzzle, it's important to note that the field of economic research often leads us down unexpected paths. Much like a detective following the trail of clues, we found ourselves drawn into the world of energy production and the sleuthing industry, uncovering a link that could very well be the "shock" factor driving these two seemingly unrelated variables towards each other.
The academic landscape is often rife with scholarly findings that are, let's face it, quite dry, but today we aim to inject some sparks of humor and curiosity into our exploration. After all, we believe that research doesn't have to be as serious as a detective on a stakeout – we're all about shedding light on the unconventional and finding the "electric" in unexpected correlations.
So, grab your magnifying glass and prepare to be captivated by our findings, as we unravel the mystery behind the connection between renewable energy production in the U.S. Virgin Islands and the bubbling increase in private detective numbers in the bustling state of Louisiana. It's a tale that promises to be a real "whodunit" of the economic world, with plenty of sparks and surprises along the way.

[[RESULTS]]
The results of our investigation into the peculiar pairing of renewable energy production in the U.S. Virgin Islands and the surge in private detective numbers in Louisiana have left us feeling a bit like Sherlock Holmes stumbling upon a well-lit mystery. The correlation coefficient of 0.9496086 that we uncovered suggests an astonishingly strong relationship between these two seemingly unrelated variables. It's as if renewable energy and detective work have entered into an electrifying partnership, leaving us with more questions than answers.
When we looked at the data spanning from 2011 to 2021, we found an r-squared value of 0.9017565, indicating that a whopping 90% of the variation in private detective numbers in Louisiana can be explained by changes in renewable energy production in the U.S. Virgin Islands. Talk about shedding some light on the situation! And with a p-value of less than 0.01, we can confidently say that this relationship is not just a coincidence—it's the real deal!
As for our figure (Fig. 1), the scatterplot speaks for itself, showcasing a linear relationship that's as tight as handcuffs. The points on the plot form a pattern so clear, it's almost as if they've left us a trail of breadcrumbs through the forest of economic data. We're left marveling at the sheer magnitude of this correlation, wondering if there's some sort of renewable energy signal being transmitted directly to gumshoes in the Bayou State.
In the face of such remarkable findings, it's hard not to crack a smile and appreciate the sheer unpredictability of economic research. Who would've thought that the sunny shores of the U.S. Virgin Islands could be casting such a large shadow across the world of detective work in Louisiana? These results not only challenge our assumptions but also underscore the captivating and, dare we say, electrifying nature of economic correlations. After all, in the world of statistical analysis, it seems that everything truly is connected—even when we least expect it!

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has illuminated an electrifying correlation between renewable energy production in the U.S. Virgin Islands and the surge in private detective numbers in Louisiana. It seems that the energy waves from the sunny shores of the Virgin Islands are sending shockwaves through the sleuthing industry in the Bayou State. Our findings not only shed light on the unexpected interconnectedness of seemingly disparate variables but also add a spark of humor and curiosity to the world of economic correlations.

The strength of the correlation coefficient and the r-squared value leaves little room for doubt – it's as clear as daybreak over the Caribbean Sea that there's something more than just a casual acquaintance between renewable energy and the world of detective work. As for the scatterplot, it's certainly got a grip on our attention, showcasing a relationship as tight as a handcuff clasp on a detective's wrist.

Now, while we've had a blast uncovering this enigmatic connection, we must acknowledge the limitations of our study. After all, correlation does not necessarily imply causation, and in the world of economic mysteries, there may be other hidden variables at play, lurking in the shadows like a stealthy private eye. Furthermore, the specific mechanisms through which renewable energy production and detective numbers might be linked remain a puzzle yet to be solved.

But fear not, dear readers, for we stand resolute in our assertion that further investigative efforts into this correlation may not yield significant returns. This juxtaposition has already proven to be one of the most unexpected and eyebrow-raising finds in the world of economic research. To put it bluntly, it's the kind of connection you'd expect to find in a plot twist of a detective novel – and in the spirit of both renewable energy and mystery solving, we declare that the case of the shocking correlation between these two variables is closed. There's no need for further research in this electrifying area – the verdict is in, the case is solved!

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 Shocking Connections: Renewable Energy in U.S. Virgin Islands and the Sleuth Surge in Louisiana. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Ecological Linkages

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 Shocking Connections: Renewable Energy in U.S. Virgin Islands and the Sleuth Surge in Louisiana. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Tropical Energy Solutions

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 Shocking Connections: Renewable Energy in U.S. Virgin Islands and the Sleuth Surge in Louisiana with an abstract of This study investigates the unexpected correlation between renewable energy production in the U.S. Virgin Islands and the number of private detectives in Louisiana. With the use of data from the Energy Information Administration and the Bureau of Labor Statistics, our research team uncovered a striking correlation coefficient of 0.9496086 and p < 0.01 for the period spanning 2011 to 2021. Our findings not only suggest a potential link between the two seemingly unrelated factors but also shed light on the electrifying world of interregional dynamics. The implications of our findings are absolutely electrifying, proving that in the world of economic correlations, nothing is ever truly detective-proof!

ChatGPT:

renewable energy, U.S. Virgin Islands, private detectives, Louisiana, correlation, Energy Information Administration, Bureau of Labor Statistics, interregional dynamics, economic correlations

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

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Data details

Renewable energy production in U.S. Virgin Islands
Detailed data title: Total renewable energy production in U.S. Virgin Islands in billion kWh
Source: Energy Information Administration
See what else correlates with Renewable energy production in U.S. Virgin Islands

The number of private detectives in Louisiana
Detailed data title: BLS estimate of private detectives and investigators in Louisiana
Source: Bureau of Larbor Statistics
See what else correlates with The number of private detectives in Louisiana

Correlation r = 0.9496086 (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 = 0.9017565 (Coefficient of determination)
This means 90.2% of the change in the one variable (i.e., The number of private detectives in Louisiana) is predictable based on the change in the other (i.e., Renewable energy production in U.S. Virgin Islands) over the 11 years from 2011 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 7.9E-6.^Show 0.0000078774501635534750000000
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 0.95 in 0.00079% of random cases. Said differently, if you correlated 126,945 random variables^Note You don't actually need 126 thousand 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 10 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 10 because we have two variables measured over a period of 11 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.81, 0.99 ] 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.

	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Renewable energy production in U.S. Virgin Islands (Billion kWh)	0.00069	0.000885	0.001248	0.007179	0.013274	0.01333	0.00724	0.014	0.014	0.014	0.01708
The number of private detectives in Louisiana (Laborers)	160	70	130	310	380	370	310	520	480	450	660

Why this works

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

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([0.00069,0.000885,0.001248,0.007179,0.013274,0.01333,0.00724,0.014,0.014,0.014,0.01708,])
array_2 = np.array([160,70,130,310,380,370,310,520,480,450,660,])
array_1_name = "Renewable energy production in U.S. Virgin Islands"
array_2_name = "The number of private detectives in Louisiana"

# 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 Renewable energy production in U.S. Virgin Islands
Line chart for only The number of private detectives in Louisiana
AI-generated correlation image
The spurious research paper: Shocking Connections: Renewable Energy in U.S. Virgin Islands and the Sleuth Surge in Louisiana

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Correlation ID: 1749 · Black Variable ID: 24055 · Red Variable ID: 8716


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