Spurious correlation #3,273 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of agricultural equipment operators in Pennsylvania and the second variable is Petroluem consumption in Saint Pierre and Miquelon. The chart goes from 2003 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of agricultural equipment operators in Pennsylvania and the second variable is Petroluem consumption in Saint Pierre and Miquelon. The chart goes from 2003 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the agricultural equipment operators in Pennsylvania revved up their engines, it sent out powerful tractor vibes that resonated all the way to Saint Pierre and Miquelon, fueling a sudden surge in petroluem consumption. It's like their farming prowess unknowingly awakened a deep-seated love for all things petroleum in the distant islands, creating a tractor-magnetic connection that even defies geographic logic. Who knew that farming in Pennsylvania could cultivate a whole new appreciation for petroleum in Saint Pierre and Miquelon!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image that visually represents a scene from 'Star Wars' but depicts the following scenario: In the background, the central focus is on an increasing number of agricultural equipment operators in Pennsylvania. These operators are shown operating various farming machinery in a rural setting. The machinery generates "cropdust" emissions, symbolized by billowing clouds of dust and smoggy air around the equipment. In response to the rising air pollution, the scene transitions to show the residents of Saint Pierre and Miquelon. They are depicted consuming more petroleum to power air purifiers, indicated by the presence of fuel containers and the operation of air purifying machines. The air purifiers are actively working to clean the smoggy air, resulting in a visually cleaner and healthier atmosphere in the scene. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

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

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: The number of agricultural equipment operators in Pennsylvania and Petroluem consumption in Saint Pierre and Miquelon. 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: 3D Pixar Style a funny image macro An image that looks like it is a scene from A Disney Movie Cutsy art illustration An image that looks like it is a scene from 'Star Wars'
ChatGPT: An image that looks like it is a scene from 'Star Wars'

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both The number of agricultural equipment operators in Pennsylvania and Petroluem consumption in Saint Pierre and Miquelon, in the following situation: As the number of agricultural equipment operators in Pennsylvania increased, so did their "cropdust" emissions. This led to a rise in air pollution, which caused Saint Pierre and Miquelon to consume more petroleum for air purifiers. It's a real gas!. Keep the description simple.
ChatGPT: In a visual scene, we see an increasing number of agricultural equipment operators in Pennsylvania, which leads to more "cropdust" emissions. This results in a rise in air pollution, represented by smoggy air. In response, we see the residents of Saint Pierre and Miquelon consuming more petroleum to power air purifiers, indicated by an increase in fuel usage and the operation of air purifying machines.

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: [ An image that looks like it is a scene from 'Star Wars' ] and depicting this scene: In a visual scene, we see an increasing number of agricultural equipment operators in Pennsylvania, which leads to more "cropdust" emissions. This results in a rise in air pollution, represented by smoggy air. In response, we see the residents of Saint Pierre and Miquelon consuming more petroleum to power air purifiers, indicated by an increase in fuel usage and the operation of air purifying machines. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that visually represents a scene from 'Star Wars' but depicts the following scenario: In the background, the central focus is on an increasing number of agricultural equipment operators in Pennsylvania. These operators are shown operating various farming machinery in a rural setting. The machinery generates "cropdust" emissions, symbolized by billowing clouds of dust and smoggy air around the equipment. In response to the rising air pollution, the scene transitions to show the residents of Saint Pierre and Miquelon. They are depicted consuming more petroleum to power air purifiers, indicated by the presence of fuel containers and the operation of air purifying machines. The air purifiers are actively working to clean the smoggy air, resulting in a visually cleaner and healthier atmosphere in the scene.

*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 increases in The number of agricultural equipment operators in Pennsylvania positively influenced Petroluem consumption in Saint Pierre and Miquelon.

AI academic paper

(Because p < 0.01)

Agriculture and Petroleum: A Fuelish Connection between Machinery Operators and Consumption Patterns

The Journal of Agro-Petrology

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between The number of agricultural equipment operators in Pennsylvania and Petroluem consumption in Saint Pierre and Miquelon. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name a couple popular internet memes that are related to one of the topics.

Here is the title and abstract of the paper:
[[TITLE]]
Agriculture and Petroleum: A Fuelish Connection between Machinery Operators and Consumption Patterns

[[ABSTRACT]]
This study examines the curious relationship between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. Using data from the Bureau of Labor Statistics and the Energy Information Administration, our research team conducted a thorough analysis from 2003 to 2021. The results revealed a surprisingly robust correlation coefficient of 0.7809882, with a p-value less than 0.01, despite the geographical separation of these regions. This unexpected correlation prompts further investigation into the potential interplay between agricultural practices and fuel consumption across borders. The findings may plow new ground for future studies in the field of agricultural economics and energy consumption, shedding light on the often-overlooked, yet pivotally interconnected, aspects of these seemingly distinct sectors.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intricate web of connections between seemingly disparate variables has long been a subject of fascination within the realm of research and statistics. In the realm of agricultural economics, the interplay between agricultural machinery operators and petroleum consumption may seem like a stretch to the untrained eye. However, as we delve into the data, a fertile ground of correlation and causation emerges, ripe for exploration.
The contemporary research landscape is rife with the cultivation of novel statistical methodologies and data sources. Against this backdrop, our study aims to plow through the soil of conventional wisdom and unearth the often overlooked relationships that lie beneath the surface of agricultural and energy consumption patterns. By harnessing the power of empirical data, we set out to sow the seeds of curiosity and reap the fruits of knowledge in the domain of interdisciplinary research.
With a nod to the renowned economist John Maynard Keynes, "The difficulty lies not so much in developing new ideas as in escaping from old ones." Our investigation seeks to untangle the roots of conventional thinking, and to illuminate the hidden interconnections that underlie the seemingly distinct realms of agricultural equipment operators in Pennsylvania and petroleum consumption in the overseas territory of Saint Pierre and Miquelon.
As we embark on this scientific journey, let us remember the immortal words of the physicist Albert Einstein: "The most beautiful thing we can experience is the mysterious. It is the source of all true art and science." With this spirit in mind, we embark on a quest to unravel the curious and, dare we say, fuelish connection between machinery operators in agriculture and consumption patterns of petroleum.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The existing literature on the correlation between the number of agricultural equipment operators in specific regions and petroleum consumption patterns in geographically distant areas provides valuable insights into the underexplored interplay between these seemingly disparate variables. Smith et al. (2010) observe a positive association between agricultural machinery use and energy consumption in rural regions, laying the groundwork for further investigations into the potential spillover effects across broader geographic scales. Similarly, Doe and Jones (2015) identify a significant linkage between the agricultural workforce and fuel demand, highlighting the intricate dynamics at play within the realm of energy economics.

Turning to non-fiction works, "The Omnivore's Dilemma: A Natural History of Four Meals" by Michael Pollan and "The Prize: The Epic Quest for Oil, Money, and Power" by Daniel Yergin provide valuable context regarding the intricate relationships between agriculture and energy consumption. Meanwhile, fictional accounts such as "The Grapes of Wrath" by John Steinbeck and "Oil!" by Upton Sinclair offer poignant narratives that underscore the multifaceted nature of humanity's reliance on both agricultural labor and petroleum products.

Further enriching the discourse, popular internet memes such as the "Ermahgerd" girl and "This is Fine" dog convey the often unexpected, yet undeniably interconnected, aspects of human behavior and resource utilization, serving as lighthearted reminders of the complex web of relationships that underlie the fuelish connection between machinery operators in agriculture and consumption patterns of petroleum.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between The number of agricultural equipment operators in Pennsylvania and Petroluem consumption in Saint Pierre and Miquelon. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 Bureau of Larbor Statistics and Energy Information Administration . You used data from 2003 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]]
Agriculture and Petroleum: A Fuelish Connection between Machinery Operators and Consumption Patterns

[[ABSTRACT]]
This study examines the curious relationship between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. Using data from the Bureau of Labor Statistics and the Energy Information Administration, our research team conducted a thorough analysis from 2003 to 2021. The results revealed a surprisingly robust correlation coefficient of 0.7809882, with a p-value less than 0.01, despite the geographical separation of these regions. This unexpected correlation prompts further investigation into the potential interplay between agricultural practices and fuel consumption across borders. The findings may plow new ground for future studies in the field of agricultural economics and energy consumption, shedding light on the often-overlooked, yet pivotally interconnected, aspects of these seemingly distinct sectors.

[[INTRODUCTION]]
The intricate web of connections between seemingly disparate variables has long been a subject of fascination within the realm of research and statistics. In the realm of agricultural economics, the interplay between agricultural machinery operators and petroleum consumption may seem like a stretch to the untrained eye. However, as we delve into the data, a fertile ground of correlation and causation emerges, ripe for exploration.
The contemporary research landscape is rife with the cultivation of novel statistical methodologies and data sources. Against this backdrop, our study aims to plow through the soil of conventional wisdom and unearth the often overlooked relationships that lie beneath the surface of agricultural and energy consumption patterns. By harnessing the power of empirical data, we set out to sow the seeds of curiosity and reap the fruits of knowledge in the domain of interdisciplinary research.
With a nod to the renowned economist John Maynard Keynes, "The difficulty lies not so much in developing new ideas as in escaping from old ones." Our investigation seeks to untangle the roots of conventional thinking, and to illuminate the hidden interconnections that underlie the seemingly distinct realms of agricultural equipment operators in Pennsylvania and petroleum consumption in the overseas territory of Saint Pierre and Miquelon.
As we embark on this scientific journey, let us remember the immortal words of the physicist Albert Einstein: "The most beautiful thing we can experience is the mysterious. It is the source of all true art and science." With this spirit in mind, we embark on a quest to unravel the curious and, dare we say, fuelish connection between machinery operators in agriculture and consumption patterns of petroleum.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
The data for this study was harvested from the Bureau of Labor Statistics and the Energy Information Administration, much like a dedicated farmer diligently tending to their fields. The information gathered spanned the years 2003 to 2021, allowing for a comprehensive analysis of the long-term trends and fluctuations in the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. Our team then cultivatED a dataset ripe for statistical analysis, plucking the relevant variables with the precision of an experienced wine taster selecting the finest grapes for vintage production.

Variable Selection:
To ensure a bountiful yield of insights, our research team pruned the dataset with care, selecting the key variables that would best capture the essence of the relationship between agricultural machinery operators and petroleum consumption. The number of agricultural equipment operators in Pennsylvania stood as a testament to the mechanical muscle powering the state's agricultural sector, while the petroleum consumption in Saint Pierre and Miquelon served as a proxy for energy demand in a distinct geographical context. The juxtaposition of these variables facilitated a comparison between the sweat of the land and the fuel powering distant shores.

Statistical Analysis:
Employing the tried and true tools of correlation analysis, our research team plowed through the data with diligence, examining the relationship between the aforementioned variables. The Pearson correlation coefficient emerged as the instrument of choice, allowing us to quantify the degree of association between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. Furthermore, a rigorous regression analysis was sown into the statistical framework, providing a fertile ground for exploring potential causal relationships and predictive insights.

Caveats and Assumptions:
It is important to note, however, that our study, like any scientific endeavor, is not immune to certain limitations. The observational nature of the data restricts the ability to establish definite causation, leaving some room for the seeds of uncertainty to take root. Furthermore, the reliance on publicly available data sources may introduce elements of measurement error, much like an unexpected weed disrupting the purity of a carefully tended field. Nonetheless, armed with statistical acumen and a spirit of scientific inquiry, these challenges were met with fortitude, and our analysis pressed onward in the pursuit of knowledge.

In summary, the methodology employed in this study reflects the dedication to rigour and thoroughness that underlies the field of empirical research, while also sprinkling a healthy dose of whimsy and levity in the spirit of scholarly exploration.

Model: gpt-3.5-turbo-1106
System prompt: You are a dry but witty academic researcher. You speak in the passive voice, and your writing sounds like it belongs in a medical or economics journal.You draft the 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 The number of agricultural equipment operators in Pennsylvania and Petroluem consumption in Saint Pierre and Miquelon. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 Bureau of Larbor Statistics and Energy Information Administration .

For the time period 2003 to 2021, you found a correlation 0.7809882, r-squared of 0.6099426, 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]]
Agriculture and Petroleum: A Fuelish Connection between Machinery Operators and Consumption Patterns

[[ABSTRACT]]
This study examines the curious relationship between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. Using data from the Bureau of Labor Statistics and the Energy Information Administration, our research team conducted a thorough analysis from 2003 to 2021. The results revealed a surprisingly robust correlation coefficient of 0.7809882, with a p-value less than 0.01, despite the geographical separation of these regions. This unexpected correlation prompts further investigation into the potential interplay between agricultural practices and fuel consumption across borders. The findings may plow new ground for future studies in the field of agricultural economics and energy consumption, shedding light on the often-overlooked, yet pivotally interconnected, aspects of these seemingly distinct sectors.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intricate web of connections between seemingly disparate variables has long been a subject of fascination within the realm of research and statistics. In the realm of agricultural economics, the interplay between agricultural machinery operators and petroleum consumption may seem like a stretch to the untrained eye. However, as we delve into the data, a fertile ground of correlation and causation emerges, ripe for exploration.
The contemporary research landscape is rife with the cultivation of novel statistical methodologies and data sources. Against this backdrop, our study aims to plow through the soil of conventional wisdom and unearth the often overlooked relationships that lie beneath the surface of agricultural and energy consumption patterns. By harnessing the power of empirical data, we set out to sow the seeds of curiosity and reap the fruits of knowledge in the domain of interdisciplinary research.
With a nod to the renowned economist John Maynard Keynes, "The difficulty lies not so much in developing new ideas as in escaping from old ones." Our investigation seeks to untangle the roots of conventional thinking, and to illuminate the hidden interconnections that underlie the seemingly distinct realms of agricultural equipment operators in Pennsylvania and petroleum consumption in the overseas territory of Saint Pierre and Miquelon.
As we embark on this scientific journey, let us remember the immortal words of the physicist Albert Einstein: "The most beautiful thing we can experience is the mysterious. It is the source of all true art and science." With this spirit in mind, we embark on a quest to unravel the curious and, dare we say, fuelish connection between machinery operators in agriculture and consumption patterns of petroleum.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The analysis of the data from 2003 to 2021 revealed a striking correlation coefficient of 0.7809882 between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. This unexpectedly strong positive correlation suggests a potential link between the agricultural practices in Pennsylvania and the fuel consumption patterns in the remote archipelago. The correlation is of considerable magnitude, indicating a substantial relationship between the variables.

Furthermore, the coefficient of determination (r-squared) of 0.6099426 underscores the significant influence of the number of agricultural equipment operators on petroleum consumption in Saint Pierre and Miquelon. The model accounts for approximately 60.99% of the variation in petroleum consumption, highlighting the robustness of the relationship.

The statistical significance of the correlation is confirmed by a p-value of less than 0.01, indicating an extremely low probability that the observed correlation is merely due to chance. This statistically significant result lends further credence to the notion of a meaningful association between the two variables, despite the geographical and geopolitical disparities between the regions under investigation.

The strength and statistical significance of the correlation are visually depicted in Fig. 1, which illustrates a clear and compelling scatterplot of the relationship between the number of agricultural equipment operators and petroleum consumption. The figure serves as a graphical testament to the unexpectedly rich and enduring bond between agricultural machinery and fuel consumption, encapsulating the essence of our findings with visual eloquence.

In conclusion, the results of our analysis unearth an intriguing and, one might say, "fuelish" connection between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. This unanticipated correlation prompts a deeper exploration of the intricate interplay between agricultural practices and fuel consumption across disparate regions, paving the way for future inquiries into these strangely intertwined domains.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between The number of agricultural equipment operators in Pennsylvania and Petroluem consumption in Saint Pierre and Miquelon. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
Agriculture and Petroleum: A Fuelish Connection between Machinery Operators and Consumption Patterns

[[ABSTRACT]]
This study examines the curious relationship between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. Using data from the Bureau of Labor Statistics and the Energy Information Administration, our research team conducted a thorough analysis from 2003 to 2021. The results revealed a surprisingly robust correlation coefficient of 0.7809882, with a p-value less than 0.01, despite the geographical separation of these regions. This unexpected correlation prompts further investigation into the potential interplay between agricultural practices and fuel consumption across borders. The findings may plow new ground for future studies in the field of agricultural economics and energy consumption, shedding light on the often-overlooked, yet pivotally interconnected, aspects of these seemingly distinct sectors.

[[LITERATURE REVIEW]]
The existing literature on the correlation between the number of agricultural equipment operators in specific regions and petroleum consumption patterns in geographically distant areas provides valuable insights into the underexplored interplay between these seemingly disparate variables. Smith et al. (2010) observe a positive association between agricultural machinery use and energy consumption in rural regions, laying the groundwork for further investigations into the potential spillover effects across broader geographic scales. Similarly, Doe and Jones (2015) identify a significant linkage between the agricultural workforce and fuel demand, highlighting the intricate dynamics at play within the realm of energy economics.
Turning to non-fiction works, "The Omnivore's Dilemma: A Natural History of Four Meals" by Michael Pollan and "The Prize: The Epic Quest for Oil, Money, and Power" by Daniel Yergin provide valuable context regarding the intricate relationships between agriculture and energy consumption. Meanwhile, fictional accounts such as "The Grapes of Wrath" by John Steinbeck and "Oil!" by Upton Sinclair offer poignant narratives that underscore the multifaceted nature of humanity's reliance on both agricultural labor and petroleum products.
Further enriching the discourse, popular internet memes such as the "Ermahgerd" girl and "This is Fine" dog convey the often unexpected, yet undeniably interconnected, aspects of human behavior and resource utilization, serving as lighthearted reminders of the complex web of relationships that underlie the fuelish connection between machinery operators in agriculture and consumption patterns of petroleum.

[[RESULTS]]
The analysis of the data from 2003 to 2021 revealed a striking correlation coefficient of 0.7809882 between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. This unexpectedly strong positive correlation suggests a potential link between the agricultural practices in Pennsylvania and the fuel consumption patterns in the remote archipelago. The correlation is of considerable magnitude, indicating a substantial relationship between the variables.
Furthermore, the coefficient of determination (r-squared) of 0.6099426 underscores the significant influence of the number of agricultural equipment operators on petroleum consumption in Saint Pierre and Miquelon. The model accounts for approximately 60.99% of the variation in petroleum consumption, highlighting the robustness of the relationship.
The statistical significance of the correlation is confirmed by a p-value of less than 0.01, indicating an extremely low probability that the observed correlation is merely due to chance. This statistically significant result lends further credence to the notion of a meaningful association between the two variables, despite the geographical and geopolitical disparities between the regions under investigation.
The strength and statistical significance of the correlation are visually depicted in Fig. 1, which illustrates a clear and compelling scatterplot of the relationship between the number of agricultural equipment operators and petroleum consumption. The figure serves as a graphical testament to the unexpectedly rich and enduring bond between agricultural machinery and fuel consumption, encapsulating the essence of our findings with visual eloquence.
In conclusion, the results of our analysis unearth an intriguing and, one might say, "fuelish" connection between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. This unanticipated correlation prompts a deeper exploration of the intricate interplay between agricultural practices and fuel consumption across disparate regions, paving the way for future inquiries into these strangely intertwined domains.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of this study demonstrate a remarkably robust positive correlation between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. These findings lend empirical support to the prior research that has hinted at the potential interdependence between agricultural activities and energy usage. It is quite remarkable to witness such a strong statistical relationship emerge from what may initially appear to be unrelated variables. The literature review, in all its seriousness, had indeed alluded to the hidden intricacies of this connection, substantiating the importance of further investigation into this peculiar phenomenon.

The unexpected strength of the correlation coefficient suggests a substantive influence of agricultural machinery operations on petroleum consumption in Saint Pierre and Miquelon. This illuminates the dense network of relationships that underlie the "fuelish" connection between these seemingly disparate regions. The statistical significance of the correlation, with a p-value of less than 0.01, reinforces the notion that this association is not merely a chance occurrence. It underscores the need to delve deeper into the factors driving this connection, despite the geographical and geopolitical differences between the two regions.

The substantial coefficient of determination indicates that the variations in petroleum consumption in Saint Pierre and Miquelon are notably influenced by the number of agricultural equipment operators in Pennsylvania. This finding not only adds weight to the correlation but also highlights the considerable explanatory power of this relationship. It is a captivating revelation that underscores the intricate dance between agricultural practices and fuel consumption patterns, one that has eluded scholarly attention until now.

In conclusion, this study illuminates a curious and, one might say, "fuelish" relationship between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. Our findings, while unexpected, open the door to a deeper understanding of the underlying mechanisms at play in these intertwined domains. The results not only support previous research but also pave the way for future investigations into this remarkably interconnected interplay between agriculture and energy consumption.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between The number of agricultural equipment operators in Pennsylvania and Petroluem consumption in Saint Pierre and Miquelon. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
Agriculture and Petroleum: A Fuelish Connection between Machinery Operators and Consumption Patterns

[[ABSTRACT]]
This study examines the curious relationship between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. Using data from the Bureau of Labor Statistics and the Energy Information Administration, our research team conducted a thorough analysis from 2003 to 2021. The results revealed a surprisingly robust correlation coefficient of 0.7809882, with a p-value less than 0.01, despite the geographical separation of these regions. This unexpected correlation prompts further investigation into the potential interplay between agricultural practices and fuel consumption across borders. The findings may plow new ground for future studies in the field of agricultural economics and energy consumption, shedding light on the often-overlooked, yet pivotally interconnected, aspects of these seemingly distinct sectors.

[[INTRDUCTION]]
The intricate web of connections between seemingly disparate variables has long been a subject of fascination within the realm of research and statistics. In the realm of agricultural economics, the interplay between agricultural machinery operators and petroleum consumption may seem like a stretch to the untrained eye. However, as we delve into the data, a fertile ground of correlation and causation emerges, ripe for exploration.
The contemporary research landscape is rife with the cultivation of novel statistical methodologies and data sources. Against this backdrop, our study aims to plow through the soil of conventional wisdom and unearth the often overlooked relationships that lie beneath the surface of agricultural and energy consumption patterns. By harnessing the power of empirical data, we set out to sow the seeds of curiosity and reap the fruits of knowledge in the domain of interdisciplinary research.
With a nod to the renowned economist John Maynard Keynes, "The difficulty lies not so much in developing new ideas as in escaping from old ones." Our investigation seeks to untangle the roots of conventional thinking, and to illuminate the hidden interconnections that underlie the seemingly distinct realms of agricultural equipment operators in Pennsylvania and petroleum consumption in the overseas territory of Saint Pierre and Miquelon.
As we embark on this scientific journey, let us remember the immortal words of the physicist Albert Einstein: "The most beautiful thing we can experience is the mysterious. It is the source of all true art and science." With this spirit in mind, we embark on a quest to unravel the curious and, dare we say, fuelish connection between machinery operators in agriculture and consumption patterns of petroleum.

[[RESULTS]]
The analysis of the data from 2003 to 2021 revealed a striking correlation coefficient of 0.7809882 between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. This unexpectedly strong positive correlation suggests a potential link between the agricultural practices in Pennsylvania and the fuel consumption patterns in the remote archipelago. The correlation is of considerable magnitude, indicating a substantial relationship between the variables.
Furthermore, the coefficient of determination (r-squared) of 0.6099426 underscores the significant influence of the number of agricultural equipment operators on petroleum consumption in Saint Pierre and Miquelon. The model accounts for approximately 60.99% of the variation in petroleum consumption, highlighting the robustness of the relationship.
The statistical significance of the correlation is confirmed by a p-value of less than 0.01, indicating an extremely low probability that the observed correlation is merely due to chance. This statistically significant result lends further credence to the notion of a meaningful association between the two variables, despite the geographical and geopolitical disparities between the regions under investigation.
The strength and statistical significance of the correlation are visually depicted in Fig. 1, which illustrates a clear and compelling scatterplot of the relationship between the number of agricultural equipment operators and petroleum consumption. The figure serves as a graphical testament to the unexpectedly rich and enduring bond between agricultural machinery and fuel consumption, encapsulating the essence of our findings with visual eloquence.
In conclusion, the results of our analysis unearth an intriguing and, one might say, "fuelish" connection between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. This unanticipated correlation prompts a deeper exploration of the intricate interplay between agricultural practices and fuel consumption across disparate regions, paving the way for future inquiries into these strangely intertwined domains.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In the culmination of our investigation, we have unmasked an unexpected and somewhat "fuelish" connection between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. This peculiar correlation, with a coefficient of 0.7809882 and a p-value less than 0.01, indicates a substantial association, despite the considerable geographic gulf between these regions.

The statistical significance of this correlation, accompanied by a coefficient of determination of 0.6099426, implies a noteworthy influence of agricultural equipment operators on petroleum consumption in Saint Pierre and Miquelon. As we contemplate the strength and robustness of this relationship, one cannot help but be drawn to the intriguing intersection of agricultural machinery and fuel consumption.

The visual depiction of this correlation in Fig. 1 serves as a compelling testament to the unexpected bond between these variables, encapsulating the essence of our findings with visual eloquence. This peculiar connection prompts further exploration and contemplation of the interwoven nature of agricultural practices and fuel consumption patterns, inviting future studies to till this "fuelish" soil for new insights and discoveries.

In the timeless words of Sir Arthur Conan Doyle's Sherlock Holmes, "It is a capital mistake to theorize before one has data." With the data in hand, our investigation has peeled back the layers of convention to reveal a captivating link between seemingly disparate domains. Therefore, one might venture to conclude that no further research is required in this odd, yet undeniably fascinating, field of inquiry.

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 Agriculture and Petroleum: A Fuelish Connection between Machinery Operators and Consumption Patterns. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Agro-Petrology

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 Agriculture and Petroleum: A Fuelish Connection between Machinery Operators and Consumption Patterns. Respond only with the name of a fictional organization.

ChatGPT:

Sustainable Agriculture and Energy Research Institute

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Agriculture and Petroleum: A Fuelish Connection between Machinery Operators and Consumption Patterns with an abstract of This study examines the curious relationship between the number of agricultural equipment operators in Pennsylvania and petroleum consumption in Saint Pierre and Miquelon. Using data from the Bureau of Labor Statistics and the Energy Information Administration, our research team conducted a thorough analysis from 2003 to 2021. The results revealed a surprisingly robust correlation coefficient of 0.7809882, with a p-value less than 0.01, despite the geographical separation of these regions. This unexpected correlation prompts further investigation into the potential interplay between agricultural practices and fuel consumption across borders. The findings may plow new ground for future studies in the field of agricultural economics and energy consumption, shedding light on the often-overlooked, yet pivotally interconnected, aspects of these seemingly distinct sectors.

ChatGPT:

agricultural equipment operators, petroleum consumption, Pennsylvania, Saint Pierre and Miquelon, correlation coefficient, agricultural practices, fuel consumption, agricultural economics, energy consumption, machinery operators, consumption patterns

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

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Data details

The number of agricultural equipment operators in Pennsylvania
Detailed data title: BLS estimate of agricultural equipment operators in Pennsylvania
Source: Bureau of Larbor Statistics
See what else correlates with The number of agricultural equipment operators in Pennsylvania

Petroluem consumption in Saint Pierre and Miquelon
Detailed data title: Volume of petroluem consumption consumed in Saint Pierre and Miquelon in millions of barrels per day
Source: Energy Information Administration
See what else correlates with Petroluem consumption in Saint Pierre and Miquelon

Correlation r = 0.7809882 (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 = 0.6099426 (Coefficient of determination)
This means 61% of the change in the one variable (i.e., Petroluem consumption in Saint Pierre and Miquelon) is predictable based on the change in the other (i.e., The number of agricultural equipment operators in Pennsylvania) over the 19 years from 2003 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 7.9E-5.^Show 0.0000791822487367219000000000
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 0.78 in 0.0079% of random cases. Said differently, if you correlated 12,629 random variables^Note Which I absolutely did.
with the same 18 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 18 because we have two variables measured over a period of 19 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.

[ 0.51, 0.91 ] 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.

	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
The number of agricultural equipment operators in Pennsylvania (Laborers)	270	60	170	210	320	220	260	260	180	170	170	410	340	450	320	450	270	240	240
Petroluem consumption in Saint Pierre and Miquelon (Million Barrels/Day)	0.563562	0.541639	0.563562	0.563562	0.584	0.582404	0.584	0.604438	0.608526	0.59871	0.614658	0.649989	0.649989	0.66	0.649989	0.704204	0.608722	0.556793	0.562176

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.
Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([270,60,170,210,320,220,260,260,180,170,170,410,340,450,320,450,270,240,240,])
array_2 = np.array([0.563562,0.541639,0.563562,0.563562,0.584,0.582404,0.584,0.604438,0.608526,0.59871,0.614658,0.649989,0.649989,0.66,0.649989,0.704204,0.608722,0.556793,0.562176,])
array_1_name = "The number of agricultural equipment operators in Pennsylvania"
array_2_name = "Petroluem consumption in Saint Pierre and Miquelon"

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

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

Reuseable content

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

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

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

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

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

Download images for these variables:

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

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

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only The number of agricultural equipment operators in Pennsylvania
Line chart for only Petroluem consumption in Saint Pierre and Miquelon
AI-generated correlation image
The spurious research paper: Agriculture and Petroleum: A Fuelish Connection between Machinery Operators and Consumption Patterns

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Correlation ID: 3273 · Black Variable ID: 13752 · Red Variable ID: 24861


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