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Spurious correlation #3,144 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is GMO use in corn and the second variable is The number of judges in Indiana.  The chart goes from 2003 to 2021, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

As GMO use in corn increased, the corn production also increased significantly. This led to a surplus of corn, driving the demand for corn-based products. With more corn available, the production of cornbread, a beloved staple in Indiana, skyrocketed. This sudden boom in cornbread production created a need for more judges in various cornbread baking competitions across the state. It's safe to say that the judges were definitely earning their daily bread, and the cornnection between GMO corn and the influx of judges in Indiana is truly a-maize-ing!

Model: dalle-3
Prompt: [ A photo-realistic image ] A bucolic scene is depicted, featuring a vast Indiana cornfield bathed in warm sunlight. The sprawling field is filled with rows of vibrant GMO corn, reaching out toward the distant horizon. At the center of the field, a lively corn maze comprised of twisting paths and towering stalks beckons visitors to explore its intricacies. Inside the maze, a group of judges is diligently observing and scoring participants as they maneuver through the challenge. The image captures the fusion of rural beauty and agricultural innovation, with the proliferation of GMO corn leading to the surge of judges overseeing these popular mazes at local fall festivals. 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: GMO use in corn and The number of judges in Indiana. 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: Surrealism Concept art Voxel art A photo-realistic image A TIME magazine cover image
ChatGPT: A photo-realistic image

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 GMO use in corn and The number of judges in Indiana, in the following situation: As GMO corn yields increased, so did the number of corn mazes in Indiana, leading to a surge in demand for judges at local fall festivals.. Keep the description simple.
ChatGPT: A sprawling cornfield in Indiana showcases rows of vibrant GMO corn stretching into the distance. In the center, a lively corn maze with twisting paths and towering stalks entices visitors. Inside the maze, a group of judges busily observe and score participants as they navigate the challenge. The increasing yield of GMO corn has coincided with the proliferation of these mazes, resulting in a surge of judges at local fall festivals.

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: [ A photo-realistic image ] and depicting this scene: A sprawling cornfield in Indiana showcases rows of vibrant GMO corn stretching into the distance. In the center, a lively corn maze with twisting paths and towering stalks entices visitors. Inside the maze, a group of judges busily observe and score participants as they navigate the challenge. The increasing yield of GMO corn has coincided with the proliferation of these mazes, resulting in a surge of judges at local fall festivals. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ A photo-realistic image ] A bucolic scene is depicted, featuring a vast Indiana cornfield bathed in warm sunlight. The sprawling field is filled with rows of vibrant GMO corn, reaching out toward the distant horizon. At the center of the field, a lively corn maze comprised of twisting paths and towering stalks beckons visitors to explore its intricacies. Inside the maze, a group of judges is diligently observing and scoring participants as they maneuver through the challenge. The image captures the fusion of rural beauty and agricultural innovation, with the proliferation of GMO corn leading to the surge of judges overseeing these popular mazes at local fall festivals.

*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 GMO use in corn caused The number of judges in Indiana to increase. Include one good pun.

AI academic paper

(Because p < 0.01)
Stalk-ing the Connection: Corn GMO Usage and the Judicial Headcount in Indiana

The Journal of Agricultural Anecdotes

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 silly researcher writing an academic paper.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 GMO use in corn and The number of judges in Indiana. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Make the title punny if you can come up with clever pun. If not, make it sound serious.

Your research team used data from USDA and Bureau of Larbor Statistics to assess this nagging question. You found a correlation coefficient of 0.8390258 and p < 0.01 for 2003 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]]
"Stalk-ing the Connection: Corn GMO Usage and the Judicial Headcount in Indiana"

[[ABSTRACT]]
The debate over the impact of GMO use in corn continues to be a hot topic, just like the dad jokes at a family dinner. This study delves into the uncharted territory of exploring the relationship between the prevalence of genetically modified organisms (GMOs) in corn and the number of judges in the state of Indiana. Utilizing data from the USDA and Bureau of Labor Statistics from 2003 to 2021, we conducted a comprehensive analysis to unearth any possible correlations. Our findings revealed a surprisingly strong correlation coefficient of 0.8390258 and a statistically significant p-value of less than 0.01. It seems that when it comes to GMOs and judiciary, there's more to this corn-y connection than meets the eye!


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 GMO use in corn and The number of judges in Indiana. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Here is the title and abstract of the paper:
[[TITLE]]
"Stalk-ing the Connection: Corn GMO Usage and the Judicial Headcount in Indiana"

[[ABSTRACT]]
The debate over the impact of GMO use in corn continues to be a hot topic, just like the dad jokes at a family dinner. This study delves into the uncharted territory of exploring the relationship between the prevalence of genetically modified organisms (GMOs) in corn and the number of judges in the state of Indiana. Utilizing data from the USDA and Bureau of Labor Statistics from 2003 to 2021, we conducted a comprehensive analysis to unearth any possible correlations. Our findings revealed a surprisingly strong correlation coefficient of 0.8390258 and a statistically significant p-value of less than 0.01. It seems that when it comes to GMOs and judiciary, there's more to this corn-y connection than meets the eye!

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

The connection between genetically modified organisms (GMOs) and the number of judges in Indiana may seem about as coherent as a dad joke after a long day, but the potential relationship between these seemingly unrelated factors has piqued the curiosity of researchers and analysts. As we delve into this unlikely pairing, it's worth unraveling the layers of this corn-y conundrum to determine if there's more than just harmless cob-ligation. (Don't worry; I promise to keep the corny jokes to a minimum, or at least I'll try.)

The use of GMOs in crops, particularly corn, has been a contentious issue in the agricultural industry, with debates ranging from agricultural sustainability to food safety. On the other hand, the allocation and count of judges in a state are often regarded as complex administrative matters, seemingly worlds apart from the sprawling cornfields. And yet, could there be a kernel of truth in the belief that these two seemingly unrelated aspects are intertwined? (If you can't handle the corny puns, you may want to brace yourself for what's to come.)

Our study seeks to explore the potential correlations between the prevalence of GMOs in corn and the judicial headcount in Indiana from 2003 to 2021. The aim is to shed light on any unsuspected ties between these phenomena, making sure we don't just "ear" our assumptions without careful investigation. After all, much like a corn maze, the connections in this study may lead us down unexpected and twisty paths. (Alright, I'll stop with the corn-related puns now. I promise.)

The implications of such a correlation, if it exists, could have ramifications not just for the agricultural and legal spheres, but for broader economic and societal trends. Cue the suspenseful music, because we're about to embark on a journey through the maize of data analysis and statistical rigor to determine if this conjecture holds any "ear-resistible" truth. (Okay, I couldn't resist one last corny pun. I regret nothing!)

So, without further ado, let's dig deep into the stalk-ing connection between GMO usage in corn and the judicial headcount in Indiana, and see if we can "un-ear-th" something truly groundbreaking. (Alright, that's really the last one. I promise.)


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 GMO use in corn and The number of judges in Indiana. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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 some cartoons and childrens' shows that you watched that are related to the topic.

Here is the title and abstract of the paper:
[[TITLE]]
"Stalk-ing the Connection: Corn GMO Usage and the Judicial Headcount in Indiana"

[[ABSTRACT]]
The debate over the impact of GMO use in corn continues to be a hot topic, just like the dad jokes at a family dinner. This study delves into the uncharted territory of exploring the relationship between the prevalence of genetically modified organisms (GMOs) in corn and the number of judges in the state of Indiana. Utilizing data from the USDA and Bureau of Labor Statistics from 2003 to 2021, we conducted a comprehensive analysis to unearth any possible correlations. Our findings revealed a surprisingly strong correlation coefficient of 0.8390258 and a statistically significant p-value of less than 0.01. It seems that when it comes to GMOs and judiciary, there's more to this corn-y connection than meets the eye!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The connection between genetically modified organisms (GMOs) and the number of judges in Indiana may seem about as coherent as a dad joke after a long day, but the potential relationship between these seemingly unrelated factors has piqued the curiosity of researchers and analysts. As we delve into this unlikely pairing, it's worth unraveling the layers of this corn-y conundrum to determine if there's more than just harmless cob-ligation. (Don't worry; I promise to keep the corny jokes to a minimum, or at least I'll try.)
The use of GMOs in crops, particularly corn, has been a contentious issue in the agricultural industry, with debates ranging from agricultural sustainability to food safety. On the other hand, the allocation and count of judges in a state are often regarded as complex administrative matters, seemingly worlds apart from the sprawling cornfields. And yet, could there be a kernel of truth in the belief that these two seemingly unrelated aspects are intertwined? (If you can't handle the corny puns, you may want to brace yourself for what's to come.)
Our study seeks to explore the potential correlations between the prevalence of GMOs in corn and the judicial headcount in Indiana from 2003 to 2021. The aim is to shed light on any unsuspected ties between these phenomena, making sure we don't just "ear" our assumptions without careful investigation. After all, much like a corn maze, the connections in this study may lead us down unexpected and twisty paths. (Alright, I'll stop with the corn-related puns now. I promise.)
The implications of such a correlation, if it exists, could have ramifications not just for the agricultural and legal spheres, but for broader economic and societal trends. Cue the suspenseful music, because we're about to embark on a journey through the maize of data analysis and statistical rigor to determine if this conjecture holds any "ear-resistible" truth. (Okay, I couldn't resist one last corny pun. I regret nothing!)
So, without further ado, let's dig deep into the stalk-ing connection between GMO usage in corn and the judicial headcount in Indiana, and see if we can "un-ear-th" something truly groundbreaking. (Alright, that's really the last one. I promise.)

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

Previous research on the impact of genetically modified organisms (GMOs) in agricultural settings has primarily focused on factors such as crop yield, environmental sustainability, and food safety. Smith, Jones, and Doe (2015) examined the economic implications of GMO usage in corn, finding that while there were perceived benefits in terms of increased productivity, there were also concerns about long-term environmental effects. If we're talking about long-term effects, let's hope it doesn't lead to a "corn-y" situation! (Sorry, I couldn't resist.)

In "Corn and the Law: A Judicial Perspective," authors Green and Brown (2018) analyzed the legal frameworks surrounding the cultivation and distribution of corn, shedding light on the intricate web of regulations that govern the agricultural industry. Perhaps they might've stumbled upon the kernel of truth about the curious correlation with judiciary headcount!

Turning to non-fiction literature, "The Omnivore's Dilemma" by Michael Pollan delves into the complexities of the modern food industry, including the prevalence of GMOs in our diets, and "The Breeder's Dilemma" by Greg Jaffe explores the ethical and regulatory considerations related to genetic engineering in agriculture. Now, those are some books that really kernel-pile the information! (I promise that's the last one.)

In the world of fiction, "Children of the Corn" by Stephen King and "Corn Fields and Courtrooms" by Legal L. Eagle may not provide direct insights into our research topic, but they certainly add a touch of suspense and drama to the corn-related discourse. Who knew that cornfields could be so dramatically "ear-resistible"! (Okay, okay, I promise I'll stop now...maybe.)

Furthermore, growing up, shows like "Corn & Friends" and "The Corny Chronicles" were staples of childhood entertainment, embedding the cultural significance of corn in our minds from an early age. Who knew that our innocent childhood shows would have deeply-rooted connections to adult research? It's almost as if our research is as unpredictable as a "corny" cartoon plot twist! (I really can't help myself with these corn-related puns.)

In conclusion, the current literature, regardless of its seriousness or fictional nature, showcases the pervasiveness of corn in various aspects of our lives, including law, agriculture, and even popular culture. It appears that the connection between GMO use in corn and the judicial headcount in Indiana is not as far-fetched as one might initially think. As we uncover the mysterious link between these seemingly unrelated fields, we must embrace the humor in our research journey, just like a well-timed dad joke.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 GMO use in corn and The number of judges in Indiana. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from USDA and Bureau of Larbor Statistics . 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]]
"Stalk-ing the Connection: Corn GMO Usage and the Judicial Headcount in Indiana"

[[ABSTRACT]]
The debate over the impact of GMO use in corn continues to be a hot topic, just like the dad jokes at a family dinner. This study delves into the uncharted territory of exploring the relationship between the prevalence of genetically modified organisms (GMOs) in corn and the number of judges in the state of Indiana. Utilizing data from the USDA and Bureau of Labor Statistics from 2003 to 2021, we conducted a comprehensive analysis to unearth any possible correlations. Our findings revealed a surprisingly strong correlation coefficient of 0.8390258 and a statistically significant p-value of less than 0.01. It seems that when it comes to GMOs and judiciary, there's more to this corn-y connection than meets the eye!

[[INTRODUCTION]]
The connection between genetically modified organisms (GMOs) and the number of judges in Indiana may seem about as coherent as a dad joke after a long day, but the potential relationship between these seemingly unrelated factors has piqued the curiosity of researchers and analysts. As we delve into this unlikely pairing, it's worth unraveling the layers of this corn-y conundrum to determine if there's more than just harmless cob-ligation. (Don't worry; I promise to keep the corny jokes to a minimum, or at least I'll try.)
The use of GMOs in crops, particularly corn, has been a contentious issue in the agricultural industry, with debates ranging from agricultural sustainability to food safety. On the other hand, the allocation and count of judges in a state are often regarded as complex administrative matters, seemingly worlds apart from the sprawling cornfields. And yet, could there be a kernel of truth in the belief that these two seemingly unrelated aspects are intertwined? (If you can't handle the corny puns, you may want to brace yourself for what's to come.)
Our study seeks to explore the potential correlations between the prevalence of GMOs in corn and the judicial headcount in Indiana from 2003 to 2021. The aim is to shed light on any unsuspected ties between these phenomena, making sure we don't just "ear" our assumptions without careful investigation. After all, much like a corn maze, the connections in this study may lead us down unexpected and twisty paths. (Alright, I'll stop with the corn-related puns now. I promise.)
The implications of such a correlation, if it exists, could have ramifications not just for the agricultural and legal spheres, but for broader economic and societal trends. Cue the suspenseful music, because we're about to embark on a journey through the maize of data analysis and statistical rigor to determine if this conjecture holds any "ear-resistible" truth. (Okay, I couldn't resist one last corny pun. I regret nothing!)
So, without further ado, let's dig deep into the stalk-ing connection between GMO usage in corn and the judicial headcount in Indiana, and see if we can "un-ear-th" something truly groundbreaking. (Alright, that's really the last one. I promise.)

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To uncover the potential correlation between GMO use in corn and the number of judges in Indiana, we employed a multifaceted approach that would impress even the most discerning ear of corn. Our data was sourced from reputable databases such as the United States Department of Agriculture (USDA) and the Bureau of Labor Statistics, ensuring an a-maize-ing level of data integrity. (See what I did there? A-maize-ing!)

First, we conducted a thorough review of historical records spanning from 2003 to 2021, "ear"-marking relevant data points and ensuring a kernel of inclusivity in our selection process. (You didn't think I was done with the corn puns, did you?)

After carefully harvesting the data, we utilized advanced statistical methods, including Pearson's correlation coefficient and multiple regression analysis, to plow through the numbers and uncover any hidden connections. We employed these methods to husk out any spurious correlations and to ensure that our findings were as sweet as an ear of fresh, non-GMO corn. (Okay, okay, I'll stop with the corn puns now. Let's get serious for a moment.)

Moreover, to control for potential confounding variables such as population growth, legal system reforms, and economic fluctuations, we implemented a sophisticated matching algorithm that would make even the most complex cob green with envy. This algorithm allowed us to emulate a virtual cornfield—sowing the seeds of precision and accuracy in our analysis.

Furthermore, to enhance the robustness of our findings, we also employed time-series analysis to account for any temporal trends in both GMO usage in corn and the judicial headcount. This approach enabled us to glean insights from the data that would otherwise have remained buried beneath the soil of statistical ambiguity.

Lastly, and this is no corny joke, we subjected our data to stringent sensitivity tests and validation procedures to ensure that our results were as sturdy as a stalk of genetically modified corn in a gusty wind. With a rigorous methodology in place, we were able to break new ground in uncovering the potential relationship between GMO usage in corn and the number of judges in Indiana.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 GMO use in corn and The number of judges in Indiana. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from USDA and Bureau of Larbor Statistics .

For the time period 2003 to 2021, you found a correlation 0.8390258, r-squared of 0.7039642, 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]]
"Stalk-ing the Connection: Corn GMO Usage and the Judicial Headcount in Indiana"

[[ABSTRACT]]
The debate over the impact of GMO use in corn continues to be a hot topic, just like the dad jokes at a family dinner. This study delves into the uncharted territory of exploring the relationship between the prevalence of genetically modified organisms (GMOs) in corn and the number of judges in the state of Indiana. Utilizing data from the USDA and Bureau of Labor Statistics from 2003 to 2021, we conducted a comprehensive analysis to unearth any possible correlations. Our findings revealed a surprisingly strong correlation coefficient of 0.8390258 and a statistically significant p-value of less than 0.01. It seems that when it comes to GMOs and judiciary, there's more to this corn-y connection than meets the eye!

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The connection between genetically modified organisms (GMOs) and the number of judges in Indiana may seem about as coherent as a dad joke after a long day, but the potential relationship between these seemingly unrelated factors has piqued the curiosity of researchers and analysts. As we delve into this unlikely pairing, it's worth unraveling the layers of this corn-y conundrum to determine if there's more than just harmless cob-ligation. (Don't worry; I promise to keep the corny jokes to a minimum, or at least I'll try.)
The use of GMOs in crops, particularly corn, has been a contentious issue in the agricultural industry, with debates ranging from agricultural sustainability to food safety. On the other hand, the allocation and count of judges in a state are often regarded as complex administrative matters, seemingly worlds apart from the sprawling cornfields. And yet, could there be a kernel of truth in the belief that these two seemingly unrelated aspects are intertwined? (If you can't handle the corny puns, you may want to brace yourself for what's to come.)
Our study seeks to explore the potential correlations between the prevalence of GMOs in corn and the judicial headcount in Indiana from 2003 to 2021. The aim is to shed light on any unsuspected ties between these phenomena, making sure we don't just "ear" our assumptions without careful investigation. After all, much like a corn maze, the connections in this study may lead us down unexpected and twisty paths. (Alright, I'll stop with the corn-related puns now. I promise.)
The implications of such a correlation, if it exists, could have ramifications not just for the agricultural and legal spheres, but for broader economic and societal trends. Cue the suspenseful music, because we're about to embark on a journey through the maize of data analysis and statistical rigor to determine if this conjecture holds any "ear-resistible" truth. (Okay, I couldn't resist one last corny pun. I regret nothing!)
So, without further ado, let's dig deep into the stalk-ing connection between GMO usage in corn and the judicial headcount in Indiana, and see if we can "un-ear-th" something truly groundbreaking. (Alright, that's really the last one. I promise.)

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The analysis of the relationship between GMO usage in corn and the number of judges in Indiana revealed a remarkably robust correlation coefficient of 0.8390258, indicating a strong positive correlation between these variables. This finding suggests that as the adoption of GMOs in corn increased over the years, so did the number of judges in Indiana. It seems that the 'ear-reversible' link between these two seemingly disparate factors is not just a-maize-ing, but statistically significant as well.

Fig. 1 presents a scatterplot illustrating the positive linear relationship between GMO use in corn and the judicial headcount in Indiana. The scatterplot accentuates the clear trend, reinforcing the strength of the correlation and providing visual evidence of this unexpected connection. It's almost as surprising as finding a kernel of truth at the center of a corn maze!

The calculated r-squared value of 0.7039642 further reinforces the robustness of this association. This indicates that approximately 70.4% of the variability in the number of judges in Indiana can be explained by the variations in GMO usage in corn. It is safe to say that this correlation is not merely an accidental 'ear-iginality' but truly captures a substantial relationship between the variables.

The obtained p-value of less than 0.01 underscores the statistical significance of the correlation, providing strong evidence against the null hypothesis. In other words, it's about as likely as finding a kernel of unpopped corn in a freshly popped batch – highly improbable!

In conclusion, the results of this study demonstrate a striking and statistically significant correlation between the prevalence of GMOs in corn and the judicial headcount in Indiana. This unexpected connection invites further investigation into the underlying mechanisms that may explain this relationship. Our findings challenge conventional assumptions and open the door to further exploration of the intricate and unexpected threads that weave through the fabric of our agricultural, legal, and economic landscapes. It seems that when it comes to GMOs and the judiciary, there's no escaping the 'ear-resistible' truth – the correlation is as real as a freshly buttered cob at a summer barbecue!


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 GMO use in corn and The number of judges in Indiana. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Limit your response to 500 tokens.

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

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

[[TITLE]]
"Stalk-ing the Connection: Corn GMO Usage and the Judicial Headcount in Indiana"

[[ABSTRACT]]
The debate over the impact of GMO use in corn continues to be a hot topic, just like the dad jokes at a family dinner. This study delves into the uncharted territory of exploring the relationship between the prevalence of genetically modified organisms (GMOs) in corn and the number of judges in the state of Indiana. Utilizing data from the USDA and Bureau of Labor Statistics from 2003 to 2021, we conducted a comprehensive analysis to unearth any possible correlations. Our findings revealed a surprisingly strong correlation coefficient of 0.8390258 and a statistically significant p-value of less than 0.01. It seems that when it comes to GMOs and judiciary, there's more to this corn-y connection than meets the eye!

[[LITERATURE REVIEW]]
Previous research on the impact of genetically modified organisms (GMOs) in agricultural settings has primarily focused on factors such as crop yield, environmental sustainability, and food safety. Smith, Jones, and Doe (2015) examined the economic implications of GMO usage in corn, finding that while there were perceived benefits in terms of increased productivity, there were also concerns about long-term environmental effects. If we're talking about long-term effects, let's hope it doesn't lead to a "corn-y" situation! (Sorry, I couldn't resist.)
In "Corn and the Law: A Judicial Perspective," authors Green and Brown (2018) analyzed the legal frameworks surrounding the cultivation and distribution of corn, shedding light on the intricate web of regulations that govern the agricultural industry. Perhaps they might've stumbled upon the kernel of truth about the curious correlation with judiciary headcount!
Turning to non-fiction literature, "The Omnivore's Dilemma" by Michael Pollan delves into the complexities of the modern food industry, including the prevalence of GMOs in our diets, and "The Breeder's Dilemma" by Greg Jaffe explores the ethical and regulatory considerations related to genetic engineering in agriculture. Now, those are some books that really kernel-pile the information! (I promise that's the last one.)
In the world of fiction, "Children of the Corn" by Stephen King and "Corn Fields and Courtrooms" by Legal L. Eagle may not provide direct insights into our research topic, but they certainly add a touch of suspense and drama to the corn-related discourse. Who knew that cornfields could be so dramatically "ear-resistible"! (Okay, okay, I promise I'll stop now...maybe.)
Furthermore, growing up, shows like "Corn & Friends" and "The Corny Chronicles" were staples of childhood entertainment, embedding the cultural significance of corn in our minds from an early age. Who knew that our innocent childhood shows would have deeply-rooted connections to adult research? It's almost as if our research is as unpredictable as a "corny" cartoon plot twist! (I really can't help myself with these corn-related puns.)
In conclusion, the current literature, regardless of its seriousness or fictional nature, showcases the pervasiveness of corn in various aspects of our lives, including law, agriculture, and even popular culture. It appears that the connection between GMO use in corn and the judicial headcount in Indiana is not as far-fetched as one might initially think. As we uncover the mysterious link between these seemingly unrelated fields, we must embrace the humor in our research journey, just like a well-timed dad joke.

[[RESULTS]]
The analysis of the relationship between GMO usage in corn and the number of judges in Indiana revealed a remarkably robust correlation coefficient of 0.8390258, indicating a strong positive correlation between these variables. This finding suggests that as the adoption of GMOs in corn increased over the years, so did the number of judges in Indiana. It seems that the 'ear-reversible' link between these two seemingly disparate factors is not just a-maize-ing, but statistically significant as well.
Fig. 1 presents a scatterplot illustrating the positive linear relationship between GMO use in corn and the judicial headcount in Indiana. The scatterplot accentuates the clear trend, reinforcing the strength of the correlation and providing visual evidence of this unexpected connection. It's almost as surprising as finding a kernel of truth at the center of a corn maze!
The calculated r-squared value of 0.7039642 further reinforces the robustness of this association. This indicates that approximately 70.4% of the variability in the number of judges in Indiana can be explained by the variations in GMO usage in corn. It is safe to say that this correlation is not merely an accidental 'ear-iginality' but truly captures a substantial relationship between the variables.
The obtained p-value of less than 0.01 underscores the statistical significance of the correlation, providing strong evidence against the null hypothesis. In other words, it's about as likely as finding a kernel of unpopped corn in a freshly popped batch – highly improbable!
In conclusion, the results of this study demonstrate a striking and statistically significant correlation between the prevalence of GMOs in corn and the judicial headcount in Indiana. This unexpected connection invites further investigation into the underlying mechanisms that may explain this relationship. Our findings challenge conventional assumptions and open the door to further exploration of the intricate and unexpected threads that weave through the fabric of our agricultural, legal, and economic landscapes. It seems that when it comes to GMOs and the judiciary, there's no escaping the 'ear-resistible' truth – the correlation is as real as a freshly buttered cob at a summer barbecue!

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

The findings of this study support and expand upon prior research that has explored the multifaceted impacts of genetically modified organisms (GMOs) in agriculture. Smith, Jones, and Doe (2015) raised concerns regarding the long-term environmental effects of GMO usage in corn, and our results provide an unexpected "kernel" of insight by demonstrating a robust positive correlation with the number of judges in Indiana. It seems that when it comes to GMOs, the impact reaches beyond the fields and into the judicial chambers – a-maize-ing, isn't it?

Similarly, the work of Green and Brown (2018) shed light on the legal frameworks surrounding corn cultivation, inadvertently hinting at the intricate connection with the judicial headcount. This study breathes life into the kernel of truth about this unlikely correlation, highlighting that sometimes, the most fascinating discoveries are hidden in plain sight, much like a well-placed dad joke.

The statistically significant and strong correlation coefficient of 0.8390258 observed in our analysis echoes the surprising nature of the relationship between GMO use in corn and the number of judges in Indiana. It's as if the correlation was hiding in the "corn-ers" of the data, waiting to "pop" out and surprise us all.

The calculated r-squared value of 0.7039642 further corroborates the substantial impact of GMO usage on the judicial headcount, reminding us that this association is not just a mere "ear-relevant" coincidence, but rather a significant and influential connection.

The obtained p-value of less than 0.01 adds another layer of validation, dismissing any skepticism about the legitimacy of this relationship. This statistical significance compels us to take this correlation seriously, much like the necessity of butter on corn – it's crucial.

In essence, our results have peeled back the husk of an unexpected nexus between GMO use in corn and the number of judges in Indiana, baring a captivating connection that invites further exploration. It seems that when it comes to the intersection of agricultural practices and legal infrastructure, there's no escaping the "ear-resistible" and, dare we say, "ear-reversible" truth.


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.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 GMO use in corn and The number of judges in Indiana. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
"Stalk-ing the Connection: Corn GMO Usage and the Judicial Headcount in Indiana"

[[ABSTRACT]]
The debate over the impact of GMO use in corn continues to be a hot topic, just like the dad jokes at a family dinner. This study delves into the uncharted territory of exploring the relationship between the prevalence of genetically modified organisms (GMOs) in corn and the number of judges in the state of Indiana. Utilizing data from the USDA and Bureau of Labor Statistics from 2003 to 2021, we conducted a comprehensive analysis to unearth any possible correlations. Our findings revealed a surprisingly strong correlation coefficient of 0.8390258 and a statistically significant p-value of less than 0.01. It seems that when it comes to GMOs and judiciary, there's more to this corn-y connection than meets the eye!

[[INTRDUCTION]]
The connection between genetically modified organisms (GMOs) and the number of judges in Indiana may seem about as coherent as a dad joke after a long day, but the potential relationship between these seemingly unrelated factors has piqued the curiosity of researchers and analysts. As we delve into this unlikely pairing, it's worth unraveling the layers of this corn-y conundrum to determine if there's more than just harmless cob-ligation. (Don't worry; I promise to keep the corny jokes to a minimum, or at least I'll try.)
The use of GMOs in crops, particularly corn, has been a contentious issue in the agricultural industry, with debates ranging from agricultural sustainability to food safety. On the other hand, the allocation and count of judges in a state are often regarded as complex administrative matters, seemingly worlds apart from the sprawling cornfields. And yet, could there be a kernel of truth in the belief that these two seemingly unrelated aspects are intertwined? (If you can't handle the corny puns, you may want to brace yourself for what's to come.)
Our study seeks to explore the potential correlations between the prevalence of GMOs in corn and the judicial headcount in Indiana from 2003 to 2021. The aim is to shed light on any unsuspected ties between these phenomena, making sure we don't just "ear" our assumptions without careful investigation. After all, much like a corn maze, the connections in this study may lead us down unexpected and twisty paths. (Alright, I'll stop with the corn-related puns now. I promise.)
The implications of such a correlation, if it exists, could have ramifications not just for the agricultural and legal spheres, but for broader economic and societal trends. Cue the suspenseful music, because we're about to embark on a journey through the maize of data analysis and statistical rigor to determine if this conjecture holds any "ear-resistible" truth. (Okay, I couldn't resist one last corny pun. I regret nothing!)
So, without further ado, let's dig deep into the stalk-ing connection between GMO usage in corn and the judicial headcount in Indiana, and see if we can "un-ear-th" something truly groundbreaking. (Alright, that's really the last one. I promise.)

[[RESULTS]]
The analysis of the relationship between GMO usage in corn and the number of judges in Indiana revealed a remarkably robust correlation coefficient of 0.8390258, indicating a strong positive correlation between these variables. This finding suggests that as the adoption of GMOs in corn increased over the years, so did the number of judges in Indiana. It seems that the 'ear-reversible' link between these two seemingly disparate factors is not just a-maize-ing, but statistically significant as well.
Fig. 1 presents a scatterplot illustrating the positive linear relationship between GMO use in corn and the judicial headcount in Indiana. The scatterplot accentuates the clear trend, reinforcing the strength of the correlation and providing visual evidence of this unexpected connection. It's almost as surprising as finding a kernel of truth at the center of a corn maze!
The calculated r-squared value of 0.7039642 further reinforces the robustness of this association. This indicates that approximately 70.4% of the variability in the number of judges in Indiana can be explained by the variations in GMO usage in corn. It is safe to say that this correlation is not merely an accidental 'ear-iginality' but truly captures a substantial relationship between the variables.
The obtained p-value of less than 0.01 underscores the statistical significance of the correlation, providing strong evidence against the null hypothesis. In other words, it's about as likely as finding a kernel of unpopped corn in a freshly popped batch – highly improbable!
In conclusion, the results of this study demonstrate a striking and statistically significant correlation between the prevalence of GMOs in corn and the judicial headcount in Indiana. This unexpected connection invites further investigation into the underlying mechanisms that may explain this relationship. Our findings challenge conventional assumptions and open the door to further exploration of the intricate and unexpected threads that weave through the fabric of our agricultural, legal, and economic landscapes. It seems that when it comes to GMOs and the judiciary, there's no escaping the 'ear-resistible' truth – the correlation is as real as a freshly buttered cob at a summer barbecue!

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In wrapping up our study, it's clear that we've unearthed a robust and statistically significant correlation between GMO usage in corn and the number of judges in Indiana. It's almost as surprising as finding a corny joke at the bottom of a crackerjack box! This unexpected connection challenges conventional wisdom, much like realizing that a 'cornbread' is neither corn nor bread but a delightful combination of both! Our findings not only suggest a quantitative link between these seemingly unrelated variables but also raise intriguing questions about the potential mechanisms and implications of this association. It's as thought-provoking as contemplating whether a 'cob-spiracy' could be at play in the heartland!

The strength of the correlation coefficient and the statistical significance of the p-value leave little room to 'ear-gue' against the existence of this intriguing relationship. As convincing as a cob-shaped maze leading straight to the heart of Indiana, our results support the notion that the prevalence of GMOs in corn is intricately intertwined with the judicial landscape of the state, much like perfectly buttered corn complements a summer BBQ spread!

In a 'corn-clusive' fashion, we assert that our findings call for further exploration and analysis. However, one thing we're certain about is that there's no need for further research into the correlation between GMO usage in corn and the number of judges in Indiana. We've 'corn-firmed' the connection, and it's as solid as a husk protecting a golden ear of corn!


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 Stalk-ing the Connection: Corn GMO Usage and the Judicial Headcount in Indiana. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Agricultural Anecdotes


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 Stalk-ing the Connection: Corn GMO Usage and the Judicial Headcount in Indiana. Respond only with the name of a fictional organization.

ChatGPT:

The Association for Agricultural Advancement and Analysis


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 Stalk-ing the Connection: Corn GMO Usage and the Judicial Headcount in Indiana with an abstract of The debate over the impact of GMO use in corn continues to be a hot topic, just like the dad jokes at a family dinner. This study delves into the uncharted territory of exploring the relationship between the prevalence of genetically modified organisms (GMOs) in corn and the number of judges in the state of Indiana. Utilizing data from the USDA and Bureau of Labor Statistics from 2003 to 2021, we conducted a comprehensive analysis to unearth any possible correlations. Our findings revealed a surprisingly strong correlation coefficient of 0.8390258 and a statistically significant p-value of less than 0.01. It seems that when it comes to GMOs and judiciary, there's more to this corn-y connection than meets the eye!

ChatGPT:

GMO corn, judicial headcount, Indiana, genetically modified organisms, USDA data, Bureau of Labor Statistics, GMO usage, corn, correlation coefficient, statistical significance, GMO impact, corn production, Indiana judiciary

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



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Data details

GMO use in corn
Detailed data title: Percent of all corn planted in the United States that is genetically modified
Source: USDA
See what else correlates with GMO use in corn

The number of judges in Indiana
Detailed data title: BLS estimate of judges, magistrate judges, and magistrates in Indiana
Source: Bureau of Larbor Statistics
See what else correlates with The number of judges in Indiana

Correlation r = 0.8390258 (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 = 0.7039642 (Coefficient of determination)
This means 70.4% of the change in the one variable (i.e., The number of judges in Indiana) is predictable based on the change in the other (i.e., GMO use in corn) over the 19 years from 2003 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 7.1E-6. 0.0000071417478230948070000000
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 0.84 in 0.00071% of random cases. Said differently, if you correlated 140,022 random variables You don't actually need 140 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 18 degrees of freedom, 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.

[ 0.62, 0.94 ] 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.
2003200420052006200720082009201020112012201320142015201620172018201920202021
GMO use in corn (GMO corn %)40475261738085868888909392929292929293
The number of judges in Indiana (Laborers)260590230720550650630710730760770770790770750760730760790




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.




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([40,47,52,61,73,80,85,86,88,88,90,93,92,92,92,92,92,92,93,])
array_2 = np.array([260,590,230,720,550,650,630,710,730,760,770,770,790,770,750,760,730,760,790,])
array_1_name = "GMO use in corn"
array_2_name = "The number of judges in Indiana"

# 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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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.

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Correlation ID: 3144 · Black Variable ID: 766 · Red Variable ID: 7626
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