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Spurious correlation #2,653 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Bachelor's degrees awarded in Engineering technologies and the second variable is The number of probation officers in Arizona.  The chart goes from 2012 to 2021, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

As the number of students excelled in Engineering technologies, it led to a surge in designing and building pun-derful machines. With all these 'model' citizens, the demand for probation officers in Arizona rose as they had to 'field' any potential 'mechanical' troublemakers. It seems like these technology enthusiasts were really 'gearing' up the need for supervision!

Model: dalle-3
Prompt: Prompt: Generate a photorealistic image of a graduation ceremony taking place at a technical university, where students are receiving their Bachelor's degrees in Engineering technologies. In the background, depict probation officers in Arizona keeping a close eye on a group of inmates using elaborate engineering techniques to attempt an escape from the prison. Capture the contrast between the celebration of academic achievement and the tense situation of the prison break, while maintaining a realistic visual style. 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: Bachelor's degrees awarded in Engineering technologies and The number of probation officers in Arizona. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: Cute cartoon Magic realism Photorealism Cutsy art illustration a funny image macro
ChatGPT: Photorealism

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 Bachelor's degrees awarded in Engineering technologies and The number of probation officers in Arizona, in the following situation: Because as more people learned to engineer solutions, there was an uptick in elaborate escape attempts, requiring more probation officers to keep up with the crafty convicts.. Keep the description simple.
ChatGPT: A graduation ceremony at a technical university where students are receiving their Bachelor's degrees in Engineering technologies. In the background, probation officers in Arizona are seen keeping a close eye on a group of inmates who are using elaborate engineering techniques to attempt an escape from the prison.

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: [ Photorealism ] and depicting this scene: A graduation ceremony at a technical university where students are receiving their Bachelor's degrees in Engineering technologies. In the background, probation officers in Arizona are seen keeping a close eye on a group of inmates who are using elaborate engineering techniques to attempt an escape from the prison. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Prompt: Generate a photorealistic image of a graduation ceremony taking place at a technical university, where students are receiving their Bachelor's degrees in Engineering technologies. In the background, depict probation officers in Arizona keeping a close eye on a group of inmates using elaborate engineering techniques to attempt an escape from the prison. Capture the contrast between the celebration of academic achievement and the tense situation of the prison break, while maintaining a realistic visual style.

*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 Bachelor's degrees awarded in Engineering technologies caused The number of probation officers in Arizona to increase. Include one good pun.

AI academic paper

(Because p < 0.01)
Engineering a Probationary Connection: The Bachelor's Degrees and the Bureau in Arizona

Journal of Socio-Technical Architecture and Policy

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 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 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 Bachelor's degrees awarded in Engineering technologies and The number of probation officers in Arizona. Make lots of jokes, goofy observations, and puns.

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

Your research team used data from National Center for Education Statistics and Bureau of Larbor Statistics to assess this nagging question. You found a correlation coefficient of 0.9831819 and p < 0.01 for 2012 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]]
Engineering a Probationary Connection: The Bachelor's Degrees and the Bureau in Arizona

[[ABSTRACT]]
This paper investigates the peculiar correlation between the number of Bachelor's degrees awarded in engineering technologies and the employment of probation officers in the state of Arizona. Through the rigorous analysis of data obtained from the National Center for Education Statistics and the Bureau of Labor Statistics spanning from 2012 to 2021, a surprisingly robust correlation coefficient of 0.9831819 and p < 0.01 was uncovered. The findings suggest a strong, albeit inexplicable, association between these seemingly disparate domains. As we delve into this entangled web of statistics, we invite readers to ponder the humorous and confounding nature of this connection, and consider the inherent complexities and unexpected relationships that permeate our world.


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 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 Bachelor's degrees awarded in Engineering technologies and The number of probation officers in Arizona. Make lots of jokes, goofy observations, and puns.

Here is the title and abstract of the paper:
[[TITLE]]
Engineering a Probationary Connection: The Bachelor's Degrees and the Bureau in Arizona

[[ABSTRACT]]
This paper investigates the peculiar correlation between the number of Bachelor's degrees awarded in engineering technologies and the employment of probation officers in the state of Arizona. Through the rigorous analysis of data obtained from the National Center for Education Statistics and the Bureau of Labor Statistics spanning from 2012 to 2021, a surprisingly robust correlation coefficient of 0.9831819 and p < 0.01 was uncovered. The findings suggest a strong, albeit inexplicable, association between these seemingly disparate domains. As we delve into this entangled web of statistics, we invite readers to ponder the humorous and confounding nature of this connection, and consider the inherent complexities and unexpected relationships that permeate our world.

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

In the intricate tapestry of societal trends, there exist connections that are both fascinating and, at times, utterly perplexing. One such enigmatic correlation that piqued the curiosity of researchers is the link between the number of Bachelor's degrees awarded in engineering technologies and the prevalence of probation officers in the state of Arizona. At first glance, one might be inclined to exclaim, "What on earth do engineering degrees have to do with probation officers?" The answer, it seems, lies hidden amidst the labyrinthine pathways of sociological phenomena and statistical anomalies.

While the employment of probation officers may not be a topic commonly associated with engineering or technological pursuits, the emergence of an uncanny statistical relationship has raised eyebrows and inspired raucous debates among the research community. As we ponder this confounding connection, one can't help but marvel at the quirkiness of the human experience and the unexpected bedfellows that statistics can sometimes be.

The pursuit of knowledge often takes us down uncharted and, in this case, downright curious avenues. As we navigate this peculiar pairing of disciplines, we invite readers to fasten their seatbelts and embrace the whimsical side of academia. After all, who would have thought that the realms of engineering education and probationary matters could converge in such a statistically dramatic fashion? The plot thickens, the data intrigues, and the absurdities of statistical analysis never fail to astound. Let us embark on this analytical adventure with a sense of wonder and a touch of bemused contemplation.


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 Bachelor's degrees awarded in Engineering technologies and The number of probation officers in Arizona. Make lots of jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then cite some social media posts you read that seem to be related.

Here is the title and abstract of the paper:
[[TITLE]]
Engineering a Probationary Connection: The Bachelor's Degrees and the Bureau in Arizona

[[ABSTRACT]]
This paper investigates the peculiar correlation between the number of Bachelor's degrees awarded in engineering technologies and the employment of probation officers in the state of Arizona. Through the rigorous analysis of data obtained from the National Center for Education Statistics and the Bureau of Labor Statistics spanning from 2012 to 2021, a surprisingly robust correlation coefficient of 0.9831819 and p < 0.01 was uncovered. The findings suggest a strong, albeit inexplicable, association between these seemingly disparate domains. As we delve into this entangled web of statistics, we invite readers to ponder the humorous and confounding nature of this connection, and consider the inherent complexities and unexpected relationships that permeate our world.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
In the intricate tapestry of societal trends, there exist connections that are both fascinating and, at times, utterly perplexing. One such enigmatic correlation that piqued the curiosity of researchers is the link between the number of Bachelor's degrees awarded in engineering technologies and the prevalence of probation officers in the state of Arizona. At first glance, one might be inclined to exclaim, "What on earth do engineering degrees have to do with probation officers?" The answer, it seems, lies hidden amidst the labyrinthine pathways of sociological phenomena and statistical anomalies.
While the employment of probation officers may not be a topic commonly associated with engineering or technological pursuits, the emergence of an uncanny statistical relationship has raised eyebrows and inspired raucous debates among the research community. As we ponder this confounding connection, one can't help but marvel at the quirkiness of the human experience and the unexpected bedfellows that statistics can sometimes be.
The pursuit of knowledge often takes us down uncharted and, in this case, downright curious avenues. As we navigate this peculiar pairing of disciplines, we invite readers to fasten their seatbelts and embrace the whimsical side of academia. After all, who would have thought that the realms of engineering education and probationary matters could converge in such a statistically dramatic fashion? The plot thickens, the data intrigues, and the absurdities of statistical analysis never fail to astound. Let us embark on this analytical adventure with a sense of wonder and a touch of bemused contemplation.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The authors find that Bachelor's degrees awarded in engineering technologies exhibit a surprisingly robust correlation with the number of probation officers in Arizona. Smith (2015) notes that the employment of probation officers is typically associated with criminal justice and social work, making the connection to engineering technologies a perplexing anomaly. However, as the data unfolds, a confounding relationship emerges, leaving researchers scratching their heads in bemusement.

Further adding to the intrigue, Doe (2018) delves into the intricacies of educational trends in the field of engineering and technology, highlighting the increasing numbers of graduates in these disciplines. One cannot help but wonder how such seemingly distant domains find themselves entangled in a statistical waltz of unexpected proportions.

Jones (2020) approaches the correlation from a different angle, exploring the demographic and geographic factors that may influence the distribution of probation officers in Arizona. While the serious and scholarly tone of these researchers is duly noted, the whimsical nature of this statistical convergence begs for a touch of levity in the face of statistical absurdity.

Moving beyond the salient scholarly works, it is worth considering the broader literary landscape to grasp the depth and breadth of this peculiar correlation. "Engineering Ethics" by Harris (2019) and "Probationary Tales" by Miller (2017) may not be directly related to the statistical analysis at hand, yet their titles certainly pique the imagination when pondering the unlikely intersection of engineering and probationary matters.

Delving into the world of fiction, one cannot overlook the timeless classics that, at first glance, appear to hold no relevance to this matter. "Brave New World" by Aldous Huxley and "The Metamorphosis" by Franz Kafka may seem far removed from engineering education and probation officers, yet their themes of societal structure and individual agency offer a nuanced lens through which to view this enigmatic correlation.

In the realm of social media, one cannot ignore the anecdotal musings that occasionally shed light on unexpected connections. A tweet by @TechAndJustice reads, "Who would have thought that the future of engineering and the intricacies of probationary matters would intertwine in the statistical cosmos? #StrangerThanFiction #EngineeringProbation." While not a scholarly treatise by any means, the tweet encapsulates the astonishment and bewilderment that this statistical relationship has elicited from both researchers and laypersons alike.


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 Bachelor's degrees awarded in Engineering technologies and The number of probation officers in Arizona. Make lots of jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from National Center for Education Statistics and Bureau of Larbor Statistics . You used data from 2012 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]]
Engineering a Probationary Connection: The Bachelor's Degrees and the Bureau in Arizona

[[ABSTRACT]]
This paper investigates the peculiar correlation between the number of Bachelor's degrees awarded in engineering technologies and the employment of probation officers in the state of Arizona. Through the rigorous analysis of data obtained from the National Center for Education Statistics and the Bureau of Labor Statistics spanning from 2012 to 2021, a surprisingly robust correlation coefficient of 0.9831819 and p < 0.01 was uncovered. The findings suggest a strong, albeit inexplicable, association between these seemingly disparate domains. As we delve into this entangled web of statistics, we invite readers to ponder the humorous and confounding nature of this connection, and consider the inherent complexities and unexpected relationships that permeate our world.

[[INTRODUCTION]]
In the intricate tapestry of societal trends, there exist connections that are both fascinating and, at times, utterly perplexing. One such enigmatic correlation that piqued the curiosity of researchers is the link between the number of Bachelor's degrees awarded in engineering technologies and the prevalence of probation officers in the state of Arizona. At first glance, one might be inclined to exclaim, "What on earth do engineering degrees have to do with probation officers?" The answer, it seems, lies hidden amidst the labyrinthine pathways of sociological phenomena and statistical anomalies.
While the employment of probation officers may not be a topic commonly associated with engineering or technological pursuits, the emergence of an uncanny statistical relationship has raised eyebrows and inspired raucous debates among the research community. As we ponder this confounding connection, one can't help but marvel at the quirkiness of the human experience and the unexpected bedfellows that statistics can sometimes be.
The pursuit of knowledge often takes us down uncharted and, in this case, downright curious avenues. As we navigate this peculiar pairing of disciplines, we invite readers to fasten their seatbelts and embrace the whimsical side of academia. After all, who would have thought that the realms of engineering education and probationary matters could converge in such a statistically dramatic fashion? The plot thickens, the data intrigues, and the absurdities of statistical analysis never fail to astound. Let us embark on this analytical adventure with a sense of wonder and a touch of bemused contemplation.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

The data for this study was collected from the National Center for Education Statistics and the Bureau of Labor Statistics, utilizing information spanning from 2012 to 2021. The initial step involved sifting through a myriad of digital haystacks, much like seeking a needle in a cybernetic domain. While the internet can resemble a boundless information superhighway, our journey was more akin to traversing a convoluted data labyrinth, replete with dead ends and surprising treasure troves of statistical gems.

The research team embarked on a quest that necessitated harnessing the powers of technological sorcery, employing complex algorithms and abstruse coding incantations to procure the requisite datasets. Once secured, these datasets underwent a rigorous curation process, comparable to the meticulous examination of rare artifacts in an academic museum. Every data point was scrutinized with the precision of a forensic investigator, ensuring that no statistical anomalies eluded our keen scrutiny, for even the smallest oversight could have cataclysmic effects on the integrity of our results.

After curating the datasets, a series of quantitative analyses was performed to elucidate the underlying patterns and associations between the number of Bachelor's degrees awarded in engineering technologies and the presence of probation officers in Arizona. Through the labyrinthine corridors of statistical software, we navigated with the dexterity of a digital cartographer, mapping the terrain of correlation and regression with an unwavering resolve.

The statistical analyses were conducted with the uncompromising rigor expected of academic inquiry, as we sought to unveil the enigmatic relationship between these seemingly incongruous variables. The calculations were executed with the precision of a surgical operation, slicing through the layers of data to reveal the beating heart of correlation coefficients, p-values, and confidence intervals. The results of these analyses were then subjected to scrutiny as intense as a forensic interrogation, interrogating every inch of statistical significance with the tenacity of a scholarly bloodhound on the scent of empirical truth.

In summary, our methodology sought to unravel the entangled web of statistics that shrouded the relationship between the number of Bachelor's degrees in engineering technologies and the cadre of probation officers in the state of Arizona. It was a journey marked by the perils of data spelunking, the finesse of digital cartography, and the relentless pursuit of statistical truth. Through this analytical odyssey, we endeavored to shed light on the unexpected nexus between engineering education and the world of probationary matters, all while embracing the whimsical side of statistical inquiry.


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 Bachelor's degrees awarded in Engineering technologies and The number of probation officers in Arizona. Make lots of jokes, goofy observations, and puns.

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

For the time period 2012 to 2021, you found a correlation 0.9831819, r-squared of 0.9666466, 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]]
Engineering a Probationary Connection: The Bachelor's Degrees and the Bureau in Arizona

[[ABSTRACT]]
This paper investigates the peculiar correlation between the number of Bachelor's degrees awarded in engineering technologies and the employment of probation officers in the state of Arizona. Through the rigorous analysis of data obtained from the National Center for Education Statistics and the Bureau of Labor Statistics spanning from 2012 to 2021, a surprisingly robust correlation coefficient of 0.9831819 and p < 0.01 was uncovered. The findings suggest a strong, albeit inexplicable, association between these seemingly disparate domains. As we delve into this entangled web of statistics, we invite readers to ponder the humorous and confounding nature of this connection, and consider the inherent complexities and unexpected relationships that permeate our world.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
In the intricate tapestry of societal trends, there exist connections that are both fascinating and, at times, utterly perplexing. One such enigmatic correlation that piqued the curiosity of researchers is the link between the number of Bachelor's degrees awarded in engineering technologies and the prevalence of probation officers in the state of Arizona. At first glance, one might be inclined to exclaim, "What on earth do engineering degrees have to do with probation officers?" The answer, it seems, lies hidden amidst the labyrinthine pathways of sociological phenomena and statistical anomalies.
While the employment of probation officers may not be a topic commonly associated with engineering or technological pursuits, the emergence of an uncanny statistical relationship has raised eyebrows and inspired raucous debates among the research community. As we ponder this confounding connection, one can't help but marvel at the quirkiness of the human experience and the unexpected bedfellows that statistics can sometimes be.
The pursuit of knowledge often takes us down uncharted and, in this case, downright curious avenues. As we navigate this peculiar pairing of disciplines, we invite readers to fasten their seatbelts and embrace the whimsical side of academia. After all, who would have thought that the realms of engineering education and probationary matters could converge in such a statistically dramatic fashion? The plot thickens, the data intrigues, and the absurdities of statistical analysis never fail to astound. Let us embark on this analytical adventure with a sense of wonder and a touch of bemused contemplation.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The analysis revealed a remarkably strong correlation between the number of Bachelor's degrees awarded in engineering technologies and the employment of probation officers in the state of Arizona. The correlation coefficient of 0.9831819 suggests an almost inseparable connection between these two seemingly unrelated facets of society. This eyebrow-raising correlation prompts us to ponder the whimsical interplay of education and law enforcement, and to consider the uncharted territories of statistical anomalies.

To accentuate the strength of this connection, the r-squared value of 0.9666466 further solidifies the robustness of this improbable relationship. One might quip that these variables are as closely intertwined as a pair of socks fresh out of the dryer - inexplicably linked despite their apparent dissimilarities.

The significance level (p < 0.01) of this correlation underscores the statistical validity of the findings, leading us to conclude that the likelihood of this connection being due to random chance is exceedingly low. It seems that the marriage between engineering education and the realm of probation officers is a match made not in heaven, but in the perplexing playground of statistical oddities.

The scatterplot (Fig. 1) visually depicts the striking clustering of data points, providing a clear demonstration of the strong association between the number of Bachelor's degrees awarded in engineering technologies and the prevalence of probation officers in Arizona. The plot tells a tale of unexpected camaraderie among variables and invites the viewer to marvel at the capricious nature of statistical relationships.

In summary, the results of this analysis elicit a sense of both amusement and bewilderment, underscoring the delightful eccentricities that can arise within the realm of statistical research. What a wacky world of numbers and correlations we inhabit!


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 Bachelor's degrees awarded in Engineering technologies and The number of probation officers in Arizona. Make lots of jokes, goofy observations, and puns.

Limit your response to 500 tokens.

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

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

[[TITLE]]
Engineering a Probationary Connection: The Bachelor's Degrees and the Bureau in Arizona

[[ABSTRACT]]
This paper investigates the peculiar correlation between the number of Bachelor's degrees awarded in engineering technologies and the employment of probation officers in the state of Arizona. Through the rigorous analysis of data obtained from the National Center for Education Statistics and the Bureau of Labor Statistics spanning from 2012 to 2021, a surprisingly robust correlation coefficient of 0.9831819 and p < 0.01 was uncovered. The findings suggest a strong, albeit inexplicable, association between these seemingly disparate domains. As we delve into this entangled web of statistics, we invite readers to ponder the humorous and confounding nature of this connection, and consider the inherent complexities and unexpected relationships that permeate our world.

[[LITERATURE REVIEW]]
The authors find that Bachelor's degrees awarded in engineering technologies exhibit a surprisingly robust correlation with the number of probation officers in Arizona. Smith (2015) notes that the employment of probation officers is typically associated with criminal justice and social work, making the connection to engineering technologies a perplexing anomaly. However, as the data unfolds, a confounding relationship emerges, leaving researchers scratching their heads in bemusement.
Further adding to the intrigue, Doe (2018) delves into the intricacies of educational trends in the field of engineering and technology, highlighting the increasing numbers of graduates in these disciplines. One cannot help but wonder how such seemingly distant domains find themselves entangled in a statistical waltz of unexpected proportions.
Jones (2020) approaches the correlation from a different angle, exploring the demographic and geographic factors that may influence the distribution of probation officers in Arizona. While the serious and scholarly tone of these researchers is duly noted, the whimsical nature of this statistical convergence begs for a touch of levity in the face of statistical absurdity.
Moving beyond the salient scholarly works, it is worth considering the broader literary landscape to grasp the depth and breadth of this peculiar correlation. "Engineering Ethics" by Harris (2019) and "Probationary Tales" by Miller (2017) may not be directly related to the statistical analysis at hand, yet their titles certainly pique the imagination when pondering the unlikely intersection of engineering and probationary matters.
Delving into the world of fiction, one cannot overlook the timeless classics that, at first glance, appear to hold no relevance to this matter. "Brave New World" by Aldous Huxley and "The Metamorphosis" by Franz Kafka may seem far removed from engineering education and probation officers, yet their themes of societal structure and individual agency offer a nuanced lens through which to view this enigmatic correlation.
In the realm of social media, one cannot ignore the anecdotal musings that occasionally shed light on unexpected connections. A tweet by @TechAndJustice reads, "Who would have thought that the future of engineering and the intricacies of probationary matters would intertwine in the statistical cosmos? #StrangerThanFiction #EngineeringProbation." While not a scholarly treatise by any means, the tweet encapsulates the astonishment and bewilderment that this statistical relationship has elicited from both researchers and laypersons alike.

[[RESULTS]]
The analysis revealed a remarkably strong correlation between the number of Bachelor's degrees awarded in engineering technologies and the employment of probation officers in the state of Arizona. The correlation coefficient of 0.9831819 suggests an almost inseparable connection between these two seemingly unrelated facets of society. This eyebrow-raising correlation prompts us to ponder the whimsical interplay of education and law enforcement, and to consider the uncharted territories of statistical anomalies.
To accentuate the strength of this connection, the r-squared value of 0.9666466 further solidifies the robustness of this improbable relationship. One might quip that these variables are as closely intertwined as a pair of socks fresh out of the dryer - inexplicably linked despite their apparent dissimilarities.
The significance level (p < 0.01) of this correlation underscores the statistical validity of the findings, leading us to conclude that the likelihood of this connection being due to random chance is exceedingly low. It seems that the marriage between engineering education and the realm of probation officers is a match made not in heaven, but in the perplexing playground of statistical oddities.
The scatterplot (Fig. 1) visually depicts the striking clustering of data points, providing a clear demonstration of the strong association between the number of Bachelor's degrees awarded in engineering technologies and the prevalence of probation officers in Arizona. The plot tells a tale of unexpected camaraderie among variables and invites the viewer to marvel at the capricious nature of statistical relationships.
In summary, the results of this analysis elicit a sense of both amusement and bewilderment, underscoring the delightful eccentricities that can arise within the realm of statistical research. What a wacky world of numbers and correlations we inhabit!

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

The findings of this study corroborate the earlier research by Smith (2015) and Doe (2018) that suggested an unanticipated link between the number of Bachelor's degrees awarded in engineering technologies and the employment of probation officers in Arizona. The strong correlation coefficient from our analysis aligns with the perplexing anomaly noted by Smith (2015), adding weight to the confounding relationship between these ostensibly disparate domains. Indeed, as Jones (2020) highlighted the demographic and geographic factors influencing the distribution of probation officers, our results offer further support for the entangled statistical waltz of the engineering and probationary realms.

The robust correlation coefficient and the high degree of statistical significance lend credibility to the novel interplay between these variables. Our findings echo the absurdity highlighted in the tweet by @TechAndJustice, cementing the unexpected convergence of engineering education and probationary matters in the statistical cosmos. The r-squared value serves as a quantitative measure of the close association between these variables, akin to the surprising connectedness of mismatched socks fresh from the dryer.

The strength of this improbable relationship, as illustrated by the scatterplot (Fig. 1), evokes a sense of whimsy and disbelief, inviting observers to revel in the capricious nature of statistical oddities. The statistical marriage between engineering education and the realm of probation officers, as signified by the significance level, is indeed a match made not in heaven, but in the confounding playground of statistical anomalies. This study exemplifies the delightful eccentricities that can manifest within the realm of statistical research, paralleling the bemusement expressed by the scholarly community and laypersons alike.

The results of this study not only underscore the multifaceted and often confounding nature of statistical relationships, but they also prompt further discussion and inquiry into the underlying mechanisms driving this unprecedented connection. As researchers and scholars, we are left to marvel at the serendipitous interplay of education and law enforcement and to contemplate the deeper meaning behind this statistical conundrum. The humorous and confounding nature of this connection calls for continued exploration, challenging us to embrace the unexpected and revel in the delightful absurdities that pervade our statistical landscape.


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 Bachelor's degrees awarded in Engineering technologies and The number of probation officers in Arizona. Make lots of jokes, goofy observations, and puns.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Engineering a Probationary Connection: The Bachelor's Degrees and the Bureau in Arizona

[[ABSTRACT]]
This paper investigates the peculiar correlation between the number of Bachelor's degrees awarded in engineering technologies and the employment of probation officers in the state of Arizona. Through the rigorous analysis of data obtained from the National Center for Education Statistics and the Bureau of Labor Statistics spanning from 2012 to 2021, a surprisingly robust correlation coefficient of 0.9831819 and p < 0.01 was uncovered. The findings suggest a strong, albeit inexplicable, association between these seemingly disparate domains. As we delve into this entangled web of statistics, we invite readers to ponder the humorous and confounding nature of this connection, and consider the inherent complexities and unexpected relationships that permeate our world.

[[INTRDUCTION]]
In the intricate tapestry of societal trends, there exist connections that are both fascinating and, at times, utterly perplexing. One such enigmatic correlation that piqued the curiosity of researchers is the link between the number of Bachelor's degrees awarded in engineering technologies and the prevalence of probation officers in the state of Arizona. At first glance, one might be inclined to exclaim, "What on earth do engineering degrees have to do with probation officers?" The answer, it seems, lies hidden amidst the labyrinthine pathways of sociological phenomena and statistical anomalies.
While the employment of probation officers may not be a topic commonly associated with engineering or technological pursuits, the emergence of an uncanny statistical relationship has raised eyebrows and inspired raucous debates among the research community. As we ponder this confounding connection, one can't help but marvel at the quirkiness of the human experience and the unexpected bedfellows that statistics can sometimes be.
The pursuit of knowledge often takes us down uncharted and, in this case, downright curious avenues. As we navigate this peculiar pairing of disciplines, we invite readers to fasten their seatbelts and embrace the whimsical side of academia. After all, who would have thought that the realms of engineering education and probationary matters could converge in such a statistically dramatic fashion? The plot thickens, the data intrigues, and the absurdities of statistical analysis never fail to astound. Let us embark on this analytical adventure with a sense of wonder and a touch of bemused contemplation.

[[RESULTS]]
The analysis revealed a remarkably strong correlation between the number of Bachelor's degrees awarded in engineering technologies and the employment of probation officers in the state of Arizona. The correlation coefficient of 0.9831819 suggests an almost inseparable connection between these two seemingly unrelated facets of society. This eyebrow-raising correlation prompts us to ponder the whimsical interplay of education and law enforcement, and to consider the uncharted territories of statistical anomalies.
To accentuate the strength of this connection, the r-squared value of 0.9666466 further solidifies the robustness of this improbable relationship. One might quip that these variables are as closely intertwined as a pair of socks fresh out of the dryer - inexplicably linked despite their apparent dissimilarities.
The significance level (p < 0.01) of this correlation underscores the statistical validity of the findings, leading us to conclude that the likelihood of this connection being due to random chance is exceedingly low. It seems that the marriage between engineering education and the realm of probation officers is a match made not in heaven, but in the perplexing playground of statistical oddities.
The scatterplot (Fig. 1) visually depicts the striking clustering of data points, providing a clear demonstration of the strong association between the number of Bachelor's degrees awarded in engineering technologies and the prevalence of probation officers in Arizona. The plot tells a tale of unexpected camaraderie among variables and invites the viewer to marvel at the capricious nature of statistical relationships.
In summary, the results of this analysis elicit a sense of both amusement and bewilderment, underscoring the delightful eccentricities that can arise within the realm of statistical research. What a wacky world of numbers and correlations we inhabit!

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, the mind-boggling association between the number of Bachelor's degrees in engineering technologies and the employment of probation officers in Arizona has left us both scratching our heads and chuckling in disbelief. It's as if statistical analysis threw a masquerade ball, and these two variables showed up in matching costumes despite their seemingly incongruent identities.

The robust correlation coefficient and the proverbial R-squared value have conspired to reveal a connection as inexplicable as finding a kangaroo in an engineering classroom – unexpected, puzzling, and altogether whimsical. This correlation coefficient is as strong as a well-caffeinated barista crafting a perfect rosetta on your latte – you just can't ignore it!

The scatterplot, akin to a prodigious work of surrealist art, tells a tale of statistical absurdity that could rival Lewis Carroll's Wonderland. Who would have thought that the employment of probation officers could cozy up to the world of engineering education in such a statistically intimate embrace? It's a statistical odd couple, reminiscent of a penguin waltzing with a flamingo – an unlikely pair that captivates the imagination and befuddles the mind.

After this confounding yet delightful expedition into the world of improbable correlations, we can confidently assert that no further research is needed in this area. The data has spoken, and the enigmatic bond between engineering degrees and probation officers in Arizona shall remain a quirky, yet inexplicably robust, scholarly mystery.


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 Engineering a Probationary Connection: The Bachelor's Degrees and the Bureau in Arizona. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Socio-Technical Architecture and Policy


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 Engineering a Probationary Connection: The Bachelor's Degrees and the Bureau in Arizona. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Advancement of Academic Innovation and Research


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 Engineering a Probationary Connection: The Bachelor's Degrees and the Bureau in Arizona with an abstract of This paper investigates the peculiar correlation between the number of Bachelor's degrees awarded in engineering technologies and the employment of probation officers in the state of Arizona. Through the rigorous analysis of data obtained from the National Center for Education Statistics and the Bureau of Labor Statistics spanning from 2012 to 2021, a surprisingly robust correlation coefficient of 0.9831819 and p < 0.01 was uncovered. The findings suggest a strong, albeit inexplicable, association between these seemingly disparate domains. As we delve into this entangled web of statistics, we invite readers to ponder the humorous and confounding nature of this connection, and consider the inherent complexities and unexpected relationships that permeate our world.

ChatGPT:

engineering technology, bachelor's degrees, probation officers, employment, correlation, Arizona, National Center for Education Statistics, Bureau of Labor Statistics, association, statistics, data analysis

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



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Data details

Bachelor's degrees awarded in Engineering technologies
Detailed data title: Bachelor's degrees conferred by postsecondary institutions, in field of study: Engineering technologies
Source: National Center for Education Statistics
See what else correlates with Bachelor's degrees awarded in Engineering technologies

The number of probation officers in Arizona
Detailed data title: BLS estimate of probation officers and correctional treatment specialists in Arizona
Source: Bureau of Larbor Statistics
See what else correlates with The number of probation officers in Arizona

Correlation r = 0.9831819 (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 = 0.9666466 (Coefficient of determination)
This means 96.7% of the change in the one variable (i.e., The number of probation officers in Arizona) is predictable based on the change in the other (i.e., Bachelor's degrees awarded in Engineering technologies) over the 10 years from 2012 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 3.4E-7. 0.0000003430017579511922300000
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 0.98 in 3.4E-5% of random cases. Said differently, if you correlated 2,915,437 random variables You don't actually need 2 million 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 9 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 9 because we have two variables measured over a period of 10 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.

[ 0.93, 1 ] 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.
2012201320142015201620172018201920202021
Bachelor's degrees awarded in Engineering technologies (Degrees awarded)17283170101680717253171591811918728196201978819004
The number of probation officers in Arizona (Laborers)2000195017501980198021902320246025402350




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([17283,17010,16807,17253,17159,18119,18728,19620,19788,19004,])
array_2 = np.array([2000,1950,1750,1980,1980,2190,2320,2460,2540,2350,])
array_1_name = "Bachelor's degrees awarded in Engineering technologies"
array_2_name = "The number of probation officers in Arizona"

# 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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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: 2653 · Black Variable ID: 1274 · Red Variable ID: 4900
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