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Spurious correlation #2,526 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Hydopower energy generated in Togo and the second variable is Robberies in Alaska.  The chart goes from 1985 to 2021, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

The surge in hydroelectric power in Togo has led to a corresponding increase in the local rodent population. This has disrupted the delicate ecosystem in Alaska, leading to a shortage of food for the native Alaskan robbers, I mean, residents. Desperate for sustenance, the Alaskan robbers have resorted to more frequent thievery, causing a spike in robbery rates. It's a classic case of Togo's hydroelectric success sparking a chain reaction that's making Alaskan robbers work harder for their loot. Remember, a well-fed robber is a happy robber, and Togo's hydropower is inadvertently keeping the thieves up north in business!

Model: dalle-3
Prompt: Generate an image that combines the stark, geometric shapes and bold primary colors inspired by Bauhaus with the contrasting scenes of environmental decline and criminal activity. In the foreground, depict the imposing form of the hydroelectric dam in Togo, its angular structure reflecting the Bauhaus aesthetic. The dam stands still, surrounded by diminishing water flow, symbolizing the decline in hydropower energy. In the background, against a minimalist Alaskan landscape, portray the silhouettes of robbers sneaking under the midnight sun, their movements and presence hinted at through use of bold shapes and shadows. The resulting image should capture the tension between the stagnation of sustainable energy and the illicit activity of the thieves in a visually striking and thought-provoking way. 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: Hydopower energy generated in Togo and Robberies in Alaska. 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: inspired by bauhaus Whimsical illustration Brutalism Vaporwave a funny image macro
ChatGPT: inspired by bauhaus

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 Hydopower energy generated in Togo and Robberies in Alaska, in the following situation: The decrease in Hydropower energy in Togo led to a cascade of events. As the energy flow dried up, Togo couldn't keep its cool, and the whole situation became un-hydro-stable. This tipped the balance of power, leaving the country feeling like it was all washed up. Desperate for a jolt of electricity, Togo turned to a life of crime, unleashing a current of theft and plunder. This sparked a chain reaction that traveled across continents, reaching Alaska like a shockwave. In the land of the midnight sun, the robbers saw an opportunity to steal some 'cool' loot, creating a polarizing situation. It's a high-voltage tale of how a Togo's energy crisis flowed all the way to Alaska, shocking everyone involved.. Keep the description simple.
ChatGPT: In Togo, a once-powerful hydroelectric dam stands still as the river flow diminishes, symbolizing the decrease in hydropower energy. Meanwhile, in Alaska, a group of robbers sneaks under the cover of the midnight sun, attempting to steal valuable loot, depicting the incidents of robberies.

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: [ inspired by bauhaus ] and depicting this scene: In Togo, a once-powerful hydroelectric dam stands still as the river flow diminishes, symbolizing the decrease in hydropower energy. Meanwhile, in Alaska, a group of robbers sneaks under the cover of the midnight sun, attempting to steal valuable loot, depicting the incidents of robberies. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that combines the stark, geometric shapes and bold primary colors inspired by Bauhaus with the contrasting scenes of environmental decline and criminal activity. In the foreground, depict the imposing form of the hydroelectric dam in Togo, its angular structure reflecting the Bauhaus aesthetic. The dam stands still, surrounded by diminishing water flow, symbolizing the decline in hydropower energy. In the background, against a minimalist Alaskan landscape, portray the silhouettes of robbers sneaking under the midnight sun, their movements and presence hinted at through use of bold shapes and shadows. The resulting image should capture the tension between the stagnation of sustainable energy and the illicit activity of the thieves in a visually striking and thought-provoking way.

*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.

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 Hydopower energy generated in Togo caused Robberies in Alaska to increase.\Make it sound serious until the end.

AI academic paper

(Because p < 0.01)
Powering Up Crime: Can Hydopower in Togo Spark A Surge in Robberies in Alaska?

Journal of Global Energy and Socioeconomic Trends

Jan 2024

Reminder: This paper is AI-generated. Not real!
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 witty academic researcher. Your writing sounds like it belongs in a statistics journal.You write titles and abstracts of goofy academic research papers.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, and add unexpected twists in your writing.

Please draft the title and abstract of an academic research paper presenting the findings of the connection between Hydopower energy generated in Togo and Robberies in Alaska. Make lots of jokes, goofy observations, and puns.

Include a pun in the title.

Your research team used data from Energy Information Administration and FBI Criminal Justice Information Services to assess this nagging question. You found a correlation coefficient of 0.7009736 and p < 0.01 for 1985 to 2021.

Please respond in this format (replace "Lorem ipsum dolor sit" with your title and abstract)

[[TITLE]]
Lorem ipsum dolor sit

[[ABSTRACT]]
Lorem ipsum dolor sit



ChatGPT:
[[TITLE]]
"Powering Up Crime: Can Hydopower in Togo Spark A Surge in Robberies in Alaska?"

[[ABSTRACT]]
Energy generation in Togo and its potential impact on remote crime hotspots in Alaska has remained an intriguing puzzle for researchers and policymakers. In this study, we tackle this electrifying topic by conducting a statistical analysis of hydropower production in Togo and its association with robberies in Alaska. Our research yields a shocking correlation coefficient of 0.7009736 and p < 0.01, revealing a current of strong connection between these seemingly unrelated phenomena. The results not only shed light on the power dynamics at play but also energize discussions on the far-reaching effects of global energy production. We harness the tide of data from the Energy Information Administration and FBI Criminal Justice Information Services to illuminate this zappy relationship, sparking a surge of interest in exploring the current-cy between hydropower and crime.


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

Please make up the introduction section of an academic research paper presenting the findings of the connection between Hydopower energy generated in Togo and Robberies in Alaska. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Here is the title and abstract of the paper:
[[TITLE]]
"Powering Up Crime: Can Hydopower in Togo Spark A Surge in Robberies in Alaska?"

[[ABSTRACT]]
Energy generation in Togo and its potential impact on remote crime hotspots in Alaska has remained an intriguing puzzle for researchers and policymakers. In this study, we tackle this electrifying topic by conducting a statistical analysis of hydropower production in Togo and its association with robberies in Alaska. Our research yields a shocking correlation coefficient of 0.7009736 and p < 0.01, revealing a current of strong connection between these seemingly unrelated phenomena. The results not only shed light on the power dynamics at play but also energize discussions on the far-reaching effects of global energy production. We harness the tide of data from the Energy Information Administration and FBI Criminal Justice Information Services to illuminate this zappy relationship, sparking a surge of interest in exploring the current-cy between hydropower and crime.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

As we dive into the intriguing world of energy generation and crime statistics, we are faced with a shocking puzzle that seems to defy the laws of causality and geography. The unlikely duo of hydropower in Togo and robberies in Alaska has sparked a surge of curiosity among researchers and policymakers alike. It's no small feat to navigate the currents of statistical analysis and harness the power of data to illuminate the connection between these seemingly disparate phenomena.

In this electrifying study, we set out to unravel the enigmatic relationship between hydropower production in Togo and the incidence of robberies in remote crime hotspots in Alaska. It's a bit like trying to connect the positive and negative poles of a battery, but with a twist of global consequences and statistical wizardry. We decided to take a plunge into the deep waters of data to see if we could shock the scientific community with our findings.

Our mission was clear: to unleash the power of statistical analysis and shed light on the electrifying dynamics at play. With the help of the Energy Information Administration and FBI Criminal Justice Information Services, we harnessed the tide of data to electrify our understanding of this zappy relationship. The results we obtained not only sparked a surge of interest but also lit up new pathways for exploring the current-cy between hydropower and crime.

So, fasten your seatbelts, grab a cup of coffee, and prepare to be shocked by the amped-up adventure that lies ahead. Don't worry; we promise to ground our discussion in solid statistical principles, even if the topic feels like a jolt from a wayward lightning bolt. Let's ride the wave of data and see where the current will take us in this electrifying journey through the world of hydropower and crime statistics.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Hydopower energy generated in Togo and Robberies in Alaska. Make lots of jokes, goofy observations, and puns.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
"Powering Up Crime: Can Hydopower in Togo Spark A Surge in Robberies in Alaska?"

[[ABSTRACT]]
Energy generation in Togo and its potential impact on remote crime hotspots in Alaska has remained an intriguing puzzle for researchers and policymakers. In this study, we tackle this electrifying topic by conducting a statistical analysis of hydropower production in Togo and its association with robberies in Alaska. Our research yields a shocking correlation coefficient of 0.7009736 and p < 0.01, revealing a current of strong connection between these seemingly unrelated phenomena. The results not only shed light on the power dynamics at play but also energize discussions on the far-reaching effects of global energy production. We harness the tide of data from the Energy Information Administration and FBI Criminal Justice Information Services to illuminate this zappy relationship, sparking a surge of interest in exploring the current-cy between hydropower and crime.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
As we dive into the intriguing world of energy generation and crime statistics, we are faced with a shocking puzzle that seems to defy the laws of causality and geography. The unlikely duo of hydropower in Togo and robberies in Alaska has sparked a surge of curiosity among researchers and policymakers alike. It's no small feat to navigate the currents of statistical analysis and harness the power of data to illuminate the connection between these seemingly disparate phenomena.
In this electrifying study, we set out to unravel the enigmatic relationship between hydropower production in Togo and the incidence of robberies in remote crime hotspots in Alaska. It's a bit like trying to connect the positive and negative poles of a battery, but with a twist of global consequences and statistical wizardry. We decided to take a plunge into the deep waters of data to see if we could shock the scientific community with our findings.
Our mission was clear: to unleash the power of statistical analysis and shed light on the electrifying dynamics at play. With the help of the Energy Information Administration and FBI Criminal Justice Information Services, we harnessed the tide of data to electrify our understanding of this zappy relationship. The results we obtained not only sparked a surge of interest but also lit up new pathways for exploring the current-cy between hydropower and crime.
So, fasten your seatbelts, grab a cup of coffee, and prepare to be shocked by the amped-up adventure that lies ahead. Don't worry; we promise to ground our discussion in solid statistical principles, even if the topic feels like a jolt from a wayward lightning bolt. Let's ride the wave of data and see where the current will take us in this electrifying journey through the world of hydropower and crime statistics.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The existing literature on the relationship between hydropower energy generation in Togo and robberies in Alaska is surprisingly limited, given the electrifying nature of this connection. Smith, in "Hydropower Dynamics and Crime: A Comparative Analysis," provides an initial exploration of this topic, highlighting the potential for energy production to spark an increase in criminal activities. Similarly, Doe's work in "Currents of Crime: A Statistical Investigation" delves into the nuanced associations between energy sources and criminal behavior, suggesting a potential current of influence between hydropower generation and the occurrence of theft in remote locations.

Moving beyond the realm of standard academic publications, we encounter a surge of interest in related non-fiction works. "The Shocking Truth: Crime Waves and Energy Currents" by Jones offers an in-depth analysis of the global impact of energy production on criminal activities, shedding light on the potential link between Togo's hydropower and remote crimes in Alaska. Additionally, "Watt's Up with That: The Crime-Energy Connection" by Brown provides a comprehensive overview of the complex dynamics at play when considering the influence of energy sources on crime rates in distant locations.

Venturing into the realm of fiction, we encounter works such as "The Power Heist Chronicles" by J.K. Rolling and "Watts and Robbers: A Tale of Electrifying Escapades" by Stephen King. While these imaginative narratives may not contribute directly to the academic discourse, they serve as a poignant reminder of the electrifying allure of this research topic and its potential for sparking creative interpretations.

In our quest for comprehensive understanding, we cast a wide net in search of relevant literature. This led to the unexpected discovery of empirical insights hidden within seemingly mundane sources. Remarkably, a thorough examination of CVS receipts from remote Alaskan towns revealed a surprising correlation between the purchase of energy drinks and the incidence of theft, offering a tangentially related yet undeniably quirky perspective on our research topic.

The scarce existing literature coupled with the diverse range of sources consulted emphasizes the urgent need for an electrifying exploration of the link between hydropower energy generation in Togo and robberies in Alaska. As we embark on this high-voltage journey, our research aims to harness the power of statistical analysis to shed light on this shocking connection and energize discussions within the academic community and beyond.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Hydopower energy generated in Togo and Robberies in Alaska. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Energy Information Administration and FBI Criminal Justice Information Services . You used data from 1985 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]]
"Powering Up Crime: Can Hydopower in Togo Spark A Surge in Robberies in Alaska?"

[[ABSTRACT]]
Energy generation in Togo and its potential impact on remote crime hotspots in Alaska has remained an intriguing puzzle for researchers and policymakers. In this study, we tackle this electrifying topic by conducting a statistical analysis of hydropower production in Togo and its association with robberies in Alaska. Our research yields a shocking correlation coefficient of 0.7009736 and p < 0.01, revealing a current of strong connection between these seemingly unrelated phenomena. The results not only shed light on the power dynamics at play but also energize discussions on the far-reaching effects of global energy production. We harness the tide of data from the Energy Information Administration and FBI Criminal Justice Information Services to illuminate this zappy relationship, sparking a surge of interest in exploring the current-cy between hydropower and crime.

[[INTRODUCTION]]
As we dive into the intriguing world of energy generation and crime statistics, we are faced with a shocking puzzle that seems to defy the laws of causality and geography. The unlikely duo of hydropower in Togo and robberies in Alaska has sparked a surge of curiosity among researchers and policymakers alike. It's no small feat to navigate the currents of statistical analysis and harness the power of data to illuminate the connection between these seemingly disparate phenomena.
In this electrifying study, we set out to unravel the enigmatic relationship between hydropower production in Togo and the incidence of robberies in remote crime hotspots in Alaska. It's a bit like trying to connect the positive and negative poles of a battery, but with a twist of global consequences and statistical wizardry. We decided to take a plunge into the deep waters of data to see if we could shock the scientific community with our findings.
Our mission was clear: to unleash the power of statistical analysis and shed light on the electrifying dynamics at play. With the help of the Energy Information Administration and FBI Criminal Justice Information Services, we harnessed the tide of data to electrify our understanding of this zappy relationship. The results we obtained not only sparked a surge of interest but also lit up new pathways for exploring the current-cy between hydropower and crime.
So, fasten your seatbelts, grab a cup of coffee, and prepare to be shocked by the amped-up adventure that lies ahead. Don't worry; we promise to ground our discussion in solid statistical principles, even if the topic feels like a jolt from a wayward lightning bolt. Let's ride the wave of data and see where the current will take us in this electrifying journey through the world of hydropower and crime statistics.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]
To unveil the mysterious connection between hydropower in Togo and robberies in Alaska, we embarked on a scientific odyssey that would make even Odysseus envious. Our approach involved tapping into the rich reservoir of data from the Energy Information Administration and FBI Criminal Justice Information Services, casting our net wide to ensnare information from the depths of 1985 to 2021. This temporal expanse allowed us to navigate the ebb and flow of hydropower production in Togo and the undulating tides of crime in the far-off lands of Alaska.

In order to harness the full potential of this thunderous data, we employed a multi-tiered analysis strategy that was as intricate as assembling a Rubik's Cube blindfolded. Our first step involved conjuring up the statistical sorcery known as correlation analysis. We calculated the Pearson correlation coefficient to measure the strength and direction of the relationship between hydropower production in Togo and the number of robberies in Alaska. This process made us feel a bit like modern-day alchemists, attempting to transmute raw data into illuminating insights.

Furthermore, we took a plunge into the ocean of regression analysis to explore the predictive power of hydropower production in Togo on the occurrence of robberies in Alaska. This endeavor unfolded like navigating a ship through a stormy sea, as we sought to chart the course of potential causality amidst the turbulent waves of statistical significance.

To ensure the integrity of our analysis, we constructed a robust framework that balanced the delicate equilibrium between control variables and the electrifying variables under scrutiny. Our extensive data wrangling and model specification leveraged the power of meticulous detail, akin to threading the eye of a statistical needle while being buffeted by zephyrs of uncertainty.

After assembling this impressive arsenal of statistical instruments, we then conducted sensitivity analyses akin to tuning an orchestra before a symphonic performance. These analyses allowed us to test the resilience of our findings to variations in model specifications and data subsets, ensuring that our results resonated with the harmonious melody of scientific rigor.

Throughout this process, we remained vigilant against the siren song of spurious correlations and the lurking dangers of confounding variables, steadfastly steering our research ship towards the shores of empirical truth. The confluence of these methodological currents provided us with a robust framework to unravel the electrifying link between hydropower production in Togo and the incidence of robberies in Alaska.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics journal.You draft the results section of an academic research paper. You speak in the past tense (you found...).
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the results section of an academic research paper presenting the findings of the connection between Hydopower energy generated in Togo and Robberies in Alaska. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from Energy Information Administration and FBI Criminal Justice Information Services .

For the time period 1985 to 2021, you found a correlation 0.7009736, r-squared of 0.4913639, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
"Powering Up Crime: Can Hydopower in Togo Spark A Surge in Robberies in Alaska?"

[[ABSTRACT]]
Energy generation in Togo and its potential impact on remote crime hotspots in Alaska has remained an intriguing puzzle for researchers and policymakers. In this study, we tackle this electrifying topic by conducting a statistical analysis of hydropower production in Togo and its association with robberies in Alaska. Our research yields a shocking correlation coefficient of 0.7009736 and p < 0.01, revealing a current of strong connection between these seemingly unrelated phenomena. The results not only shed light on the power dynamics at play but also energize discussions on the far-reaching effects of global energy production. We harness the tide of data from the Energy Information Administration and FBI Criminal Justice Information Services to illuminate this zappy relationship, sparking a surge of interest in exploring the current-cy between hydropower and crime.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
As we dive into the intriguing world of energy generation and crime statistics, we are faced with a shocking puzzle that seems to defy the laws of causality and geography. The unlikely duo of hydropower in Togo and robberies in Alaska has sparked a surge of curiosity among researchers and policymakers alike. It's no small feat to navigate the currents of statistical analysis and harness the power of data to illuminate the connection between these seemingly disparate phenomena.
In this electrifying study, we set out to unravel the enigmatic relationship between hydropower production in Togo and the incidence of robberies in remote crime hotspots in Alaska. It's a bit like trying to connect the positive and negative poles of a battery, but with a twist of global consequences and statistical wizardry. We decided to take a plunge into the deep waters of data to see if we could shock the scientific community with our findings.
Our mission was clear: to unleash the power of statistical analysis and shed light on the electrifying dynamics at play. With the help of the Energy Information Administration and FBI Criminal Justice Information Services, we harnessed the tide of data to electrify our understanding of this zappy relationship. The results we obtained not only sparked a surge of interest but also lit up new pathways for exploring the current-cy between hydropower and crime.
So, fasten your seatbelts, grab a cup of coffee, and prepare to be shocked by the amped-up adventure that lies ahead. Don't worry; we promise to ground our discussion in solid statistical principles, even if the topic feels like a jolt from a wayward lightning bolt. Let's ride the wave of data and see where the current will take us in this electrifying journey through the world of hydropower and crime statistics.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

Our hair-raising research into the connection between hydropower energy generated in Togo and robberies in Alaska has left us positively electrified with the shockingly strong correlation we uncovered. From 1985 to 2021, our statistical analysis revealed a correlation coefficient of 0.7009736 and an r-squared of 0.4913639, with p < 0.01, suggesting a powerful association between these two seemingly unrelated variables.

As we anticipated, our findings are truly electrifying, much like witnessing a bolt of lightning on a stormy night. Fig. 1, our scatterplot, visually illustrates the strong relationship we uncovered, sparking excitement and intrigue among the scientific community.

Our results not only shed light on the power dynamics at play between hydropower production in Togo and the surge of robberies in remote crime hotspots in Alaska, but they also generate a surge of interest in exploring the current-cy between energy production and crime on a global scale.

This zappy relationship we've uncovered is truly a shocking twist in the world of statistical analysis, and we hope it energizes discussions on the far-reaching effects of global energy production. We can't help but feel a surge of excitement as we illuminate this electrifying connection and spark new pathways for research in this electrifying journey through the world of hydropower and crime statistics!


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Hydopower energy generated in Togo and Robberies in Alaska. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

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

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

[[TITLE]]
"Powering Up Crime: Can Hydopower in Togo Spark A Surge in Robberies in Alaska?"

[[ABSTRACT]]
Energy generation in Togo and its potential impact on remote crime hotspots in Alaska has remained an intriguing puzzle for researchers and policymakers. In this study, we tackle this electrifying topic by conducting a statistical analysis of hydropower production in Togo and its association with robberies in Alaska. Our research yields a shocking correlation coefficient of 0.7009736 and p < 0.01, revealing a current of strong connection between these seemingly unrelated phenomena. The results not only shed light on the power dynamics at play but also energize discussions on the far-reaching effects of global energy production. We harness the tide of data from the Energy Information Administration and FBI Criminal Justice Information Services to illuminate this zappy relationship, sparking a surge of interest in exploring the current-cy between hydropower and crime.

[[LITERATURE REVIEW]]
The existing literature on the relationship between hydropower energy generation in Togo and robberies in Alaska is surprisingly limited, given the electrifying nature of this connection. Smith, in "Hydropower Dynamics and Crime: A Comparative Analysis," provides an initial exploration of this topic, highlighting the potential for energy production to spark an increase in criminal activities. Similarly, Doe's work in "Currents of Crime: A Statistical Investigation" delves into the nuanced associations between energy sources and criminal behavior, suggesting a potential current of influence between hydropower generation and the occurrence of theft in remote locations.
Moving beyond the realm of standard academic publications, we encounter a surge of interest in related non-fiction works. "The Shocking Truth: Crime Waves and Energy Currents" by Jones offers an in-depth analysis of the global impact of energy production on criminal activities, shedding light on the potential link between Togo's hydropower and remote crimes in Alaska. Additionally, "Watt's Up with That: The Crime-Energy Connection" by Brown provides a comprehensive overview of the complex dynamics at play when considering the influence of energy sources on crime rates in distant locations.
Venturing into the realm of fiction, we encounter works such as "The Power Heist Chronicles" by J.K. Rolling and "Watts and Robbers: A Tale of Electrifying Escapades" by Stephen King. While these imaginative narratives may not contribute directly to the academic discourse, they serve as a poignant reminder of the electrifying allure of this research topic and its potential for sparking creative interpretations.
In our quest for comprehensive understanding, we cast a wide net in search of relevant literature. This led to the unexpected discovery of empirical insights hidden within seemingly mundane sources. Remarkably, a thorough examination of CVS receipts from remote Alaskan towns revealed a surprising correlation between the purchase of energy drinks and the incidence of theft, offering a tangentially related yet undeniably quirky perspective on our research topic.
The scarce existing literature coupled with the diverse range of sources consulted emphasizes the urgent need for an electrifying exploration of the link between hydropower energy generation in Togo and robberies in Alaska. As we embark on this high-voltage journey, our research aims to harness the power of statistical analysis to shed light on this shocking connection and energize discussions within the academic community and beyond.

[[RESULTS]]
Our hair-raising research into the connection between hydropower energy generated in Togo and robberies in Alaska has left us positively electrified with the shockingly strong correlation we uncovered. From 1985 to 2021, our statistical analysis revealed a correlation coefficient of 0.7009736 and an r-squared of 0.4913639, with p < 0.01, suggesting a powerful association between these two seemingly unrelated variables.
As we anticipated, our findings are truly electrifying, much like witnessing a bolt of lightning on a stormy night. Fig. 1, our scatterplot, visually illustrates the strong relationship we uncovered, sparking excitement and intrigue among the scientific community.
Our results not only shed light on the power dynamics at play between hydropower production in Togo and the surge of robberies in remote crime hotspots in Alaska, but they also generate a surge of interest in exploring the current-cy between energy production and crime on a global scale.
This zappy relationship we've uncovered is truly a shocking twist in the world of statistical analysis, and we hope it energizes discussions on the far-reaching effects of global energy production. We can't help but feel a surge of excitement as we illuminate this electrifying connection and spark new pathways for research in this electrifying journey through the world of hydropower and crime statistics!

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

Our hair-raising results have certainly sent shockwaves through the scientific community, illuminating an electrifying connection between hydropower energy generation in Togo and the surge of robberies in remote crime hotspots in Alaska. As we eagerly discussed in our literature review, the existing research on this topic left much to be desired, with only a flicker of attention given to this zappy association. However, our findings not only support but also invigorate the claims made in previous works, demonstrating a powerful current of influence between energy production and criminal activities.

In the field of statistical research, uncovering such a strong correlation is like discovering a hidden electrical circuit - it simply sparks excitement! Our results align with Smith's initial exploration, where the potential for energy production to spark an increase in criminal activities was suggested. We have not only confirmed this potential but also illuminated the voltage of this relationship with a correlation coefficient that electrifies the academic discourse.

Doe's work in "Currents of Crime: A Statistical Investigation" also foreshadowed the nuanced association we have unveiled, highlighting the potential current of influence between hydropower generation and the occurrence of theft in remote locations. Our results not only confirm Doe's suspicions but also bring an unprecedented surge of statistical evidence to support this intriguing current-cy.

Venturing into unconventional territory, we recall the tangentially related yet undeniably quirky perspective revealed through the examination of CVS receipts from remote Alaskan towns. While the purchase of energy drinks proved to have a shocking correlation with the incidence of theft, our research harnessed a more direct and robust current, establishing a powerful link between hydropower and crime that offers a new pathway for understanding the dynamics at play in remote locations.

The true power of our findings lies in their potential to energize discussions not only within the academic community but also in the realm of policy and resource allocation. This electrifying connection between hydropower and crime rates in distant locations serves as a reminder of the far-reaching effects of global energy production, adding a current of urgency to the need for further research and attention. Our statistical analysis has certainly sparked a surge of interest in exploring the complex power dynamics at play and illuminated the potential for electrifying findings in the domains of energy production and crime statistics.


Model: gpt-3.5-turbo-1106
System prompt: You are a witty academic researcher. Your writing sounds like it belongs in a statistics 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 Hydopower energy generated in Togo and Robberies in Alaska. Make lots of jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"Powering Up Crime: Can Hydopower in Togo Spark A Surge in Robberies in Alaska?"

[[ABSTRACT]]
Energy generation in Togo and its potential impact on remote crime hotspots in Alaska has remained an intriguing puzzle for researchers and policymakers. In this study, we tackle this electrifying topic by conducting a statistical analysis of hydropower production in Togo and its association with robberies in Alaska. Our research yields a shocking correlation coefficient of 0.7009736 and p < 0.01, revealing a current of strong connection between these seemingly unrelated phenomena. The results not only shed light on the power dynamics at play but also energize discussions on the far-reaching effects of global energy production. We harness the tide of data from the Energy Information Administration and FBI Criminal Justice Information Services to illuminate this zappy relationship, sparking a surge of interest in exploring the current-cy between hydropower and crime.

[[INTRDUCTION]]
As we dive into the intriguing world of energy generation and crime statistics, we are faced with a shocking puzzle that seems to defy the laws of causality and geography. The unlikely duo of hydropower in Togo and robberies in Alaska has sparked a surge of curiosity among researchers and policymakers alike. It's no small feat to navigate the currents of statistical analysis and harness the power of data to illuminate the connection between these seemingly disparate phenomena.
In this electrifying study, we set out to unravel the enigmatic relationship between hydropower production in Togo and the incidence of robberies in remote crime hotspots in Alaska. It's a bit like trying to connect the positive and negative poles of a battery, but with a twist of global consequences and statistical wizardry. We decided to take a plunge into the deep waters of data to see if we could shock the scientific community with our findings.
Our mission was clear: to unleash the power of statistical analysis and shed light on the electrifying dynamics at play. With the help of the Energy Information Administration and FBI Criminal Justice Information Services, we harnessed the tide of data to electrify our understanding of this zappy relationship. The results we obtained not only sparked a surge of interest but also lit up new pathways for exploring the current-cy between hydropower and crime.
So, fasten your seatbelts, grab a cup of coffee, and prepare to be shocked by the amped-up adventure that lies ahead. Don't worry; we promise to ground our discussion in solid statistical principles, even if the topic feels like a jolt from a wayward lightning bolt. Let's ride the wave of data and see where the current will take us in this electrifying journey through the world of hydropower and crime statistics.

[[RESULTS]]
Our hair-raising research into the connection between hydropower energy generated in Togo and robberies in Alaska has left us positively electrified with the shockingly strong correlation we uncovered. From 1985 to 2021, our statistical analysis revealed a correlation coefficient of 0.7009736 and an r-squared of 0.4913639, with p < 0.01, suggesting a powerful association between these two seemingly unrelated variables.
As we anticipated, our findings are truly electrifying, much like witnessing a bolt of lightning on a stormy night. Fig. 1, our scatterplot, visually illustrates the strong relationship we uncovered, sparking excitement and intrigue among the scientific community.
Our results not only shed light on the power dynamics at play between hydropower production in Togo and the surge of robberies in remote crime hotspots in Alaska, but they also generate a surge of interest in exploring the current-cy between energy production and crime on a global scale.
This zappy relationship we've uncovered is truly a shocking twist in the world of statistical analysis, and we hope it energizes discussions on the far-reaching effects of global energy production. We can't help but feel a surge of excitement as we illuminate this electrifying connection and spark new pathways for research in this electrifying journey through the world of hydropower and crime statistics!

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, our study has shed light on a truly electrifying relationship between hydropower energy in Togo and the surge of robberies in remote crime hotspots in Alaska. We've uncovered a current of statistical shock, illuminating the zappy connection between these seemingly unrelated variables. It's as if we've stumbled upon the scientific equivalent of a power surge – a shocking twist that has left us positively electrified.

Our findings not only spark new conversations but also energize discussions about the global impact of energy production on crime. It's like discovering that crime and energy have been secretly conducting a clandestine affair behind our backs – a liaison that now has been revealed through the power of statistical analysis.

Our results have certainly amped up the discussion, much like a lightning bolt on a stormy night sparking both excitement and caution. However, as we look ahead, it's clear that no more research is needed in this area. We've already lit up the scientific community with our findings, and any further investigation would just be beating a dead battery. So, let's power down this line of inquiry and switch our focus to new, less shockingly charged research endeavors.


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 Powering Up Crime: Can Hydopower in Togo Spark A Surge in Robberies in Alaska?. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Global Energy and Socioeconomic Trends


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 Powering Up Crime: Can Hydopower in Togo Spark A Surge in Robberies in Alaska?. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Global Energy and Societal Impact (IGESI)


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 Powering Up Crime: Can Hydopower in Togo Spark A Surge in Robberies in Alaska? with an abstract of Energy generation in Togo and its potential impact on remote crime hotspots in Alaska has remained an intriguing puzzle for researchers and policymakers. In this study, we tackle this electrifying topic by conducting a statistical analysis of hydropower production in Togo and its association with robberies in Alaska. Our research yields a shocking correlation coefficient of 0.7009736 and p < 0.01, revealing a current of strong connection between these seemingly unrelated phenomena. The results not only shed light on the power dynamics at play but also energize discussions on the far-reaching effects of global energy production. We harness the tide of data from the Energy Information Administration and FBI Criminal Justice Information Services to illuminate this zappy relationship, sparking a surge of interest in exploring the current-cy between hydropower and crime.

ChatGPT:

Hydropower, Togo, Alaska, crime, robberies, energy generation, statistical analysis, correlation coefficient, energy production, global energy, Energy Information Administration, FBI Criminal Justice Information Services

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



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Data details

Hydopower energy generated in Togo
Detailed data title: Total hydopower energy generated in Togo in billion kWh
Source: Energy Information Administration
See what else correlates with Hydopower energy generated in Togo

Robberies in Alaska
Detailed data title: The robbery rate per 100,000 residents in Alaska
Source: FBI Criminal Justice Information Services
See what else correlates with Robberies in Alaska

Correlation r = 0.7009736 (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 = 0.4913639 (Coefficient of determination)
This means 49.1% of the change in the one variable (i.e., Robberies in Alaska) is predictable based on the change in the other (i.e., Hydopower energy generated in Togo) over the 37 years from 1985 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.4E-6. 0.0000013545375052728650000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

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

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.7 in 0.00014% of random cases. Said differently, if you correlated 738,259 random variables You don't actually need 738 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 36 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 36 because we have two variables measured over a period of 37 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.

[ 0.49, 0.84 ] 95% correlation confidence interval (using the Fisher z-transformation)
The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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


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

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.
1985198619871988198919901991199219931994199519961997199819992000200120022003200420052006200720082009201020112012201320142015201620172018201920202021
Hydopower energy generated in Togo (Billion kWh)0.0760.0760.0760.0760.0950.0950.1930.1280.230.1480.2550.190.0930.1720.2030.0990.0490.0850.1210.080.0730.090.0910.090.1090.0920.1050.0880.036630.11880.055440.201960.2070.19610.236420.143860.15
Robberies in Alaska (Robbery rate)92.98873.172.967.676.7113.2109122.4146.2155.1117106.486.691.478.281.176.268.8688189.5859493.683.279.686.384.585.3103.2114.6128.7121.9112.697.473.6




Why this works

  1. 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.
  2. Lack of causal connection: There is probably Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
    no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
  3. Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
    p-value calculation does not take this into account, so mathematically it appears less probable than it really is.
  4. Outlandish outliers: There are "outliers" in this data. 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. 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([0.076,0.076,0.076,0.076,0.095,0.095,0.193,0.128,0.23,0.148,0.255,0.19,0.093,0.172,0.203,0.099,0.049,0.085,0.121,0.08,0.073,0.09,0.091,0.09,0.109,0.092,0.105,0.088,0.03663,0.1188,0.05544,0.20196,0.207,0.1961,0.23642,0.14386,0.15,])
array_2 = np.array([92.9,88,73.1,72.9,67.6,76.7,113.2,109,122.4,146.2,155.1,117,106.4,86.6,91.4,78.2,81.1,76.2,68.8,68,81,89.5,85,94,93.6,83.2,79.6,86.3,84.5,85.3,103.2,114.6,128.7,121.9,112.6,97.4,73.6,])
array_1_name = "Hydopower energy generated in Togo"
array_2_name = "Robberies in Alaska"

# 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)



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Correlation ID: 2526 · Black Variable ID: 24038 · Red Variable ID: 20181
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