Spurious correlation #1,473 · 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 and the second variable is Gender pay gap in the U.S.. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Bachelor's degrees awarded in Engineering and the second variable is Gender pay gap in the U.S.. The chart goes from 2012 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the number of Bachelor's degrees awarded in Engineering increased, so did the demand for engineers. With more engineers in the workforce, there was a sudden scarcity of pocket protectors, leading to a nationwide shortage. This, in turn, caused a spike in the retail price of pocket protectors, disproportionately affecting women who were often forced to pay more for the same item. And that, my friend, is how an increase in Bachelor's degrees awarded in Engineering led to a higher gender pay gap in the U.S.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a bustling graduation ceremony in a pixel art style. Depict a group of students in caps and gowns proudly holding their Bachelor's degrees in Engineering. Nearby, include a line graph that shows the gender pay gap in the U.S. Use simple and distinct bars to symbolize the widening space between the male and female pay rates. The overall scene should be vibrant and full of celebratory energy. 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 and Gender pay gap in the U.S.. 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: Macabre Voxel art Magic realism a Polaroid photo Cutsy graphics
ChatGPT: Voxel art

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 and Gender pay gap in the U.S. together. Keep the description simple.
ChatGPT: In a bustling graduation ceremony, a group of students in caps and gowns proudly holds their Bachelor's degrees in Engineering, while nearby, a line graph shows the gender pay gap in the U.S., with the gap symbolized by a widening space between two distinct bars.

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: [ Voxel art ] and depicting this scene: In a bustling graduation ceremony, a group of students in caps and gowns proudly holds their Bachelor's degrees in Engineering, while nearby, a line graph shows the gender pay gap in the U.S., with the gap symbolized by a widening space between two distinct bars. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a bustling graduation ceremony in a pixel art style. Depict a group of students in caps and gowns proudly holding their Bachelor's degrees in Engineering. Nearby, include a line graph that shows the gender pay gap in the U.S. Use simple and distinct bars to symbolize the widening space between the male and female pay rates. The overall scene should be vibrant and full of celebratory energy.

*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in Bachelor's degrees awarded in Engineering caused Gender pay gap in the U.S. to increase. Make it feel plausible.

AI academic paper

(Because p < 0.01)

The Engineer's Salary Equation: Bridging the Gender Gap

Journal of Workplace Equity and Engineering

Jan 2024

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

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

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft 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 and Gender pay gap in the U.S.. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name a few real TV shows that sound like they might be relevant to the topic that you watched as research.

Here is the title and abstract of the paper:
[[TITLE]]
"The Engineer's Salary Equation: Bridging the Gender Gap"

[[ABSTRACT]]
In this paper, we delve into the intersection of bachelor's degrees awarded in engineering and the gender pay gap in the United States. Through meticulous data analysis from the National Center for Education Statistics and Statista, our research team uncovered a striking correlation between the two variables. With a noteworthy correlation coefficient of 0.9731387 and a p < 0.01 for the years 2012 to 2021, our findings highlight the unmistakable link between the pursuit of engineering degrees and the enduring gender wage disparity. Our analysis provides compelling insights and prompts further investigation into the larger societal factors at play. Join us on this exploration as we navigate the complex terrain of academia, statistics, and the ever-persistent quest for gender equality.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The gender pay gap has been a persistent thorn in the side of workforce equality, much like a pesky mosquito buzzing around a campfire. While progress has been made in recent decades, the gap between the earnings of men and women stubbornly lingers, casting a shadow over the professional landscape like an ominous cloud on an otherwise sunny day. At the same time, the field of engineering has been a magnet for ambitious students seeking to build a career as sturdy as a well-constructed bridge. But what happens when we bring these two seemingly disparate elements together? Do they clash like mismatched puzzle pieces, or do they intertwine like a pair of star-crossed lovers? Our investigation aims to shed light on this intriguing intersection, uncovering the hidden patterns and correlations that lie beneath the surface.
The pursuit of bachelor's degrees in engineering has long been associated with promising career prospects, with graduates poised to enter the workforce with the confidence of a seasoned tightrope walker. Yet, does this promising outlook extend equally to all genders, or does it resemble a bumpy road, riddled with obstacles and detours for some? Our analysis seeks to untangle these strands, unraveling the intricate web of factors that contribute to the gender disparities within the engineering profession. Through the lens of rigorous statistical analysis and a dash of academic curiosity, we aim to uncover the underlying forces that shape the earnings trajectories of engineering graduates, male and female alike.
Join us on this intellectual adventure, as we navigate the maze of data and theory, armed with the tools of inquiry and a healthy dose of caffeine. Our journey promises to be an illuminating one, full of unexpected twists and turns, like a riveting novel that keeps readers eagerly turning the pages. By delving into the heart of this compelling correlation, we aim to not only advance our understanding of the gender pay gap within the context of engineering education but also to spark discussions that pave the way for meaningful change. So, buckle up and grab your thinking caps, as we set off to explore the enigmatic relationship between bachelor's degrees in engineering and the enduring saga of the gender wage gap.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The relationship between Bachelor's degrees awarded in Engineering and the gender pay gap in the United States has been the subject of numerous studies, offering a range of perspectives on this intriguing intersection. Smith et al. (2015) examined the potential impact of educational pursuits in engineering on future earnings, whereas Doe (2018) delved into the nuanced gender dynamics within the engineering profession. Additionally, Jones (2020) explored the influence of societal attitudes and norms on the career trajectories of engineering graduates, shedding light on the complex interplay between education and workforce realities.

In "The Gender Pay Gap: Challenging the Status Quo," lorem and ipsum (2017) provided a comprehensive overview of the historical and contemporary factors contributing to wage disparities, offering valuable insights into the larger societal context within which engineering education and careers unfold. This work laid the groundwork for understanding the broader implications of gender pay differentials and the potential role of educational pathways in mitigating or perpetuating these gaps.

Turning to the realm of non-fiction literature, "Lean In: Women, Work, and the Will to Lead" by Sheryl Sandberg and "Brotopia: Breaking Up the Boys' Club of Silicon Valley" by Emily Chang grapple with the nuanced complexities of gender dynamics in the professional sphere, including the tech and engineering sectors. While these texts offer diverse perspectives, they highlight the pervasive influence of gender norms and workplace culture on career opportunities and remuneration.

In the realm of fiction, "The Rosie Project" by Graeme Simsion and "Hidden Figures" by Margot Lee Shetterly, while not strictly academic in nature, present captivating narratives that touch on themes of ambition, perseverance, and the pursuit of professional success in domains traditionally dominated by men. It is worth noting the power of storytelling in reflecting and shaping societal attitudes toward gender equality in education and careers, providing a lens through which to explore the complex realities confronting aspiring engineers of all genders.

As part of our research endeavors, the investigative team dived into the captivating world of television, tuning in to shows such as "The Big Bang Theory" and "Grey's Anatomy." While seemingly unrelated on the surface, these cultural touchstones offer subtle yet telling portrayals of professional environments where the convergence of education, career aspirations, and gender dynamics unfolds, providing rich fodder for reflection and analysis.

With this diverse array of scholarly works, non-fiction literature, and popular media as our backdrop, we embark on our own empirical journey to unravel the perplexing connection between pursuing Bachelor's degrees in Engineering and the enduring gender pay gap, armed with statistical rigor and plenty of offbeat humor to enliven our expedition.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between Bachelor's degrees awarded in Engineering and Gender pay gap in the U.S.. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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 Statista . 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]]
"The Engineer's Salary Equation: Bridging the Gender Gap"

[[ABSTRACT]]
In this paper, we delve into the intersection of bachelor's degrees awarded in engineering and the gender pay gap in the United States. Through meticulous data analysis from the National Center for Education Statistics and Statista, our research team uncovered a striking correlation between the two variables. With a noteworthy correlation coefficient of 0.9731387 and a p < 0.01 for the years 2012 to 2021, our findings highlight the unmistakable link between the pursuit of engineering degrees and the enduring gender wage disparity. Our analysis provides compelling insights and prompts further investigation into the larger societal factors at play. Join us on this exploration as we navigate the complex terrain of academia, statistics, and the ever-persistent quest for gender equality.

[[INTRODUCTION]]
The gender pay gap has been a persistent thorn in the side of workforce equality, much like a pesky mosquito buzzing around a campfire. While progress has been made in recent decades, the gap between the earnings of men and women stubbornly lingers, casting a shadow over the professional landscape like an ominous cloud on an otherwise sunny day. At the same time, the field of engineering has been a magnet for ambitious students seeking to build a career as sturdy as a well-constructed bridge. But what happens when we bring these two seemingly disparate elements together? Do they clash like mismatched puzzle pieces, or do they intertwine like a pair of star-crossed lovers? Our investigation aims to shed light on this intriguing intersection, uncovering the hidden patterns and correlations that lie beneath the surface.
The pursuit of bachelor's degrees in engineering has long been associated with promising career prospects, with graduates poised to enter the workforce with the confidence of a seasoned tightrope walker. Yet, does this promising outlook extend equally to all genders, or does it resemble a bumpy road, riddled with obstacles and detours for some? Our analysis seeks to untangle these strands, unraveling the intricate web of factors that contribute to the gender disparities within the engineering profession. Through the lens of rigorous statistical analysis and a dash of academic curiosity, we aim to uncover the underlying forces that shape the earnings trajectories of engineering graduates, male and female alike.
Join us on this intellectual adventure, as we navigate the maze of data and theory, armed with the tools of inquiry and a healthy dose of caffeine. Our journey promises to be an illuminating one, full of unexpected twists and turns, like a riveting novel that keeps readers eagerly turning the pages. By delving into the heart of this compelling correlation, we aim to not only advance our understanding of the gender pay gap within the context of engineering education but also to spark discussions that pave the way for meaningful change. So, buckle up and grab your thinking caps, as we set off to explore the enigmatic relationship between bachelor's degrees in engineering and the enduring saga of the gender wage gap.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To uncover the intricate connection between the awarding of bachelor's degrees in engineering and the gender pay gap in the United States, our research team embarked on a data odyssey of epic proportions, akin to Odysseus navigating the treacherous waters of statistical analysis. Our journey began with the intrepid quest to gather data from the vast expanses of the internet, where information abounds like treasure waiting to be discovered. Channels such as the National Center for Education Statistics and Statista served as our lodestars, guiding us through the labyrinthine corridors of data collection.

Once the data was in our possession, like a trove of intellectual riches, we set about our analysis with the diligence of diligent ants constructing a meticulous data hill. We utilized a multivariate regression model to examine the relationship between the number of engineering bachelor's degrees awarded and the gender pay gap. Our model took into account a myriad of covariates, including but not limited to time period, geographical region, and fluctuations in the price of avocado toast.

In addition, we employed a time series analysis to capture the dynamic shifts in both engineering degree conferment and wage differentials over the years 2012 to 2021. This allowed us to discern patterns and trends that may have escaped casual observation, like hidden constellations waiting to be charted in the night sky of data.

Furthermore, we conducted subgroup analyses to explore variations in the gender pay gap across different engineering disciplines, from mechanical engineering to computer science. This approach shed light on the nuanced differences within the broader field of engineering, offering insights akin to uncovering the unique flavors in a box of assorted chocolates.

Lastly, to validate the robustness of our findings, we subjected our data to a battery of sensitivity analyses and robustness checks, ensuring that our conclusions stood firm against the gusts of statistical skepticism. With each methodological step, we strived to uphold the principles of empirical rigor while injecting a touch of whimsy into the world of academic inquiry.

In the following sections, we present the results of our intrepid journey—a journey that has unearthed not just statistical associations, but a newfound sense of appreciation for the complexities of gender dynamics within the realm of engineering education. So, hold onto your pocket protectors as we unravel the findings that promise to bring fresh insights into the grand tapestry of gender wage differentials.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between Bachelor's degrees awarded in Engineering and Gender pay gap in the U.S.. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
"The Engineer's Salary Equation: Bridging the Gender Gap"

[[ABSTRACT]]
In this paper, we delve into the intersection of bachelor's degrees awarded in engineering and the gender pay gap in the United States. Through meticulous data analysis from the National Center for Education Statistics and Statista, our research team uncovered a striking correlation between the two variables. With a noteworthy correlation coefficient of 0.9731387 and a p < 0.01 for the years 2012 to 2021, our findings highlight the unmistakable link between the pursuit of engineering degrees and the enduring gender wage disparity. Our analysis provides compelling insights and prompts further investigation into the larger societal factors at play. Join us on this exploration as we navigate the complex terrain of academia, statistics, and the ever-persistent quest for gender equality.

[[LITERATURE REVIEW]]
The relationship between Bachelor's degrees awarded in Engineering and the gender pay gap in the United States has been the subject of numerous studies, offering a range of perspectives on this intriguing intersection. Smith et al. (2015) examined the potential impact of educational pursuits in engineering on future earnings, whereas Doe (2018) delved into the nuanced gender dynamics within the engineering profession. Additionally, Jones (2020) explored the influence of societal attitudes and norms on the career trajectories of engineering graduates, shedding light on the complex interplay between education and workforce realities.
In "The Gender Pay Gap: Challenging the Status Quo," lorem and ipsum (2017) provided a comprehensive overview of the historical and contemporary factors contributing to wage disparities, offering valuable insights into the larger societal context within which engineering education and careers unfold. This work laid the groundwork for understanding the broader implications of gender pay differentials and the potential role of educational pathways in mitigating or perpetuating these gaps.
Turning to the realm of non-fiction literature, "Lean In: Women, Work, and the Will to Lead" by Sheryl Sandberg and "Brotopia: Breaking Up the Boys' Club of Silicon Valley" by Emily Chang grapple with the nuanced complexities of gender dynamics in the professional sphere, including the tech and engineering sectors. While these texts offer diverse perspectives, they highlight the pervasive influence of gender norms and workplace culture on career opportunities and remuneration.
In the realm of fiction, "The Rosie Project" by Graeme Simsion and "Hidden Figures" by Margot Lee Shetterly, while not strictly academic in nature, present captivating narratives that touch on themes of ambition, perseverance, and the pursuit of professional success in domains traditionally dominated by men. It is worth noting the power of storytelling in reflecting and shaping societal attitudes toward gender equality in education and careers, providing a lens through which to explore the complex realities confronting aspiring engineers of all genders.
As part of our research endeavors, the investigative team dived into the captivating world of television, tuning in to shows such as "The Big Bang Theory" and "Grey's Anatomy." While seemingly unrelated on the surface, these cultural touchstones offer subtle yet telling portrayals of professional environments where the convergence of education, career aspirations, and gender dynamics unfolds, providing rich fodder for reflection and analysis.
With this diverse array of scholarly works, non-fiction literature, and popular media as our backdrop, we embark on our own empirical journey to unravel the perplexing connection between pursuing Bachelor's degrees in Engineering and the enduring gender pay gap, armed with statistical rigor and plenty of offbeat humor to enliven our expedition.

[[RESULTS]]
The results of our analysis revealed a robust and eye-catching correlation between the number of Bachelor's degrees awarded in engineering and the gender pay gap in the United States. From 2012 to 2021, a correlation coefficient of 0.9731387 dazzled us, indicating an extremely strong positive relationship between these two variables. It's like finding out that peanut butter and jelly do indeed go hand in hand – a classic combination that never fails to satisfy.
Furthermore, the r-squared value of 0.9469989 highlighted the remarkable explanatory power of engineering degrees awarded on the gender pay gap. If we were to quantify it in terms of household appliances, this correlation would be the trusty microwave, heating up our understanding of the relationship between education and earnings.
The p-value of less than 0.01 served as the icing on the statistical cake, affirming the significance of our findings. It's as if the data were waving brightly colored flags, shouting, "Pay attention to us! We've got something important to say!"
Fig. 1, the visual representation of our data, presents a scatterplot that perfectly captures the strong relationship we uncovered. It's like seeing two synchronized swimmers move in perfect harmony – a sight to behold, indeed.
In conclusion, our findings not only confirm the substantial connection between Bachelor's degrees in engineering and the ongoing gender pay gap but also serve as a reminder that behind every number and statistic, there are intriguing stories waiting to be explored. As we move forward, the captivating dance between education and earnings will continue to unfold, inviting us to join in the pursuit of a more equitable and inclusive future.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our study has provided compelling evidence to support the longstanding association between the number of Bachelor's degrees awarded in engineering and the gender pay gap in the United States. Our findings confirm and extend the prior research, echoing the sentiments of Smith et al. (2015) and Doe (2018) while amplifying the significance of educational pathways in shaping economic disparities. It's like we've entered a scientific version of "The Twilight Zone," where the familiar becomes delightfully twisted in unexpected ways.

The robust correlation coefficient of 0.9731387 that we uncovered aligns with the previous literature, akin to discovering a treasure chest filled with gold doubloons. This striking correlation serves as a testament to the enduring influence of engineering education on the landscape of gender-based wage differentials. The r-squared value further reinforces this connection, akin to a trusty lighthouse guiding ships through treacherous waters, shedding light on the profound explanatory power of engineering degrees in shaping earnings disparities.

Moreover, our results, supported by the p-value of less than 0.01, leave little room for doubt – the link between pursuing Bachelor's degrees in engineering and the gender pay gap stands out like a neon sign in the night sky. It's as if the data were a team of cheerleaders, rallying support for the undeniable importance of our findings.

Fig. 1, our visual representation of the data, not only captures the essence of our analysis but also illustrates the harmonious relationship between engineering degrees and gender-based pay differentials. It's reminiscent of a perfectly choreographed dance performance, where each movement tells a compelling story of connection and influence.

In sum, our study not only adds a resounding voice to the existing academic chorus but also sheds light on the intricate interplay between education and earnings. As we navigate the complex dynamics of gender disparities, let us not forget that behind every meticulously calculated statistic lies a story waiting to be told – a reminder that in the pursuit of equity, the curious narrative of human experience remains at the heart of our scholarly pursuits. As we continue on this scholarly journey, may our findings serve as a call to action, prompting further exploration and reflection on the path toward a more equitable professional landscape.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Bachelor's degrees awarded in Engineering and Gender pay gap in the U.S.. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, 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]]
"The Engineer's Salary Equation: Bridging the Gender Gap"

[[ABSTRACT]]
In this paper, we delve into the intersection of bachelor's degrees awarded in engineering and the gender pay gap in the United States. Through meticulous data analysis from the National Center for Education Statistics and Statista, our research team uncovered a striking correlation between the two variables. With a noteworthy correlation coefficient of 0.9731387 and a p < 0.01 for the years 2012 to 2021, our findings highlight the unmistakable link between the pursuit of engineering degrees and the enduring gender wage disparity. Our analysis provides compelling insights and prompts further investigation into the larger societal factors at play. Join us on this exploration as we navigate the complex terrain of academia, statistics, and the ever-persistent quest for gender equality.

[[INTRDUCTION]]
The gender pay gap has been a persistent thorn in the side of workforce equality, much like a pesky mosquito buzzing around a campfire. While progress has been made in recent decades, the gap between the earnings of men and women stubbornly lingers, casting a shadow over the professional landscape like an ominous cloud on an otherwise sunny day. At the same time, the field of engineering has been a magnet for ambitious students seeking to build a career as sturdy as a well-constructed bridge. But what happens when we bring these two seemingly disparate elements together? Do they clash like mismatched puzzle pieces, or do they intertwine like a pair of star-crossed lovers? Our investigation aims to shed light on this intriguing intersection, uncovering the hidden patterns and correlations that lie beneath the surface.
The pursuit of bachelor's degrees in engineering has long been associated with promising career prospects, with graduates poised to enter the workforce with the confidence of a seasoned tightrope walker. Yet, does this promising outlook extend equally to all genders, or does it resemble a bumpy road, riddled with obstacles and detours for some? Our analysis seeks to untangle these strands, unraveling the intricate web of factors that contribute to the gender disparities within the engineering profession. Through the lens of rigorous statistical analysis and a dash of academic curiosity, we aim to uncover the underlying forces that shape the earnings trajectories of engineering graduates, male and female alike.
Join us on this intellectual adventure, as we navigate the maze of data and theory, armed with the tools of inquiry and a healthy dose of caffeine. Our journey promises to be an illuminating one, full of unexpected twists and turns, like a riveting novel that keeps readers eagerly turning the pages. By delving into the heart of this compelling correlation, we aim to not only advance our understanding of the gender pay gap within the context of engineering education but also to spark discussions that pave the way for meaningful change. So, buckle up and grab your thinking caps, as we set off to explore the enigmatic relationship between bachelor's degrees in engineering and the enduring saga of the gender wage gap.

[[RESULTS]]
The results of our analysis revealed a robust and eye-catching correlation between the number of Bachelor's degrees awarded in engineering and the gender pay gap in the United States. From 2012 to 2021, a correlation coefficient of 0.9731387 dazzled us, indicating an extremely strong positive relationship between these two variables. It's like finding out that peanut butter and jelly do indeed go hand in hand – a classic combination that never fails to satisfy.
Furthermore, the r-squared value of 0.9469989 highlighted the remarkable explanatory power of engineering degrees awarded on the gender pay gap. If we were to quantify it in terms of household appliances, this correlation would be the trusty microwave, heating up our understanding of the relationship between education and earnings.
The p-value of less than 0.01 served as the icing on the statistical cake, affirming the significance of our findings. It's as if the data were waving brightly colored flags, shouting, "Pay attention to us! We've got something important to say!"
Fig. 1, the visual representation of our data, presents a scatterplot that perfectly captures the strong relationship we uncovered. It's like seeing two synchronized swimmers move in perfect harmony – a sight to behold, indeed.
In conclusion, our findings not only confirm the substantial connection between Bachelor's degrees in engineering and the ongoing gender pay gap but also serve as a reminder that behind every number and statistic, there are intriguing stories waiting to be explored. As we move forward, the captivating dance between education and earnings will continue to unfold, inviting us to join in the pursuit of a more equitable and inclusive future.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In the illustrious words of Sir Isaac Newton, "We built too many walls and not enough bridges." Our research has done just the opposite, crafting a sturdy bridge between the engineering realm and the perplexing puzzle of gender pay disparity. Through our painstaking analysis, we've unveiled a correlation so strong, it's as if engineers and the gender wage gap were engaged in a lively tango – a dance of undeniable connection. The r-squared value spoke volumes, much like a chatterbox at a tea party, emphasizing the profound impact of engineering degrees on the wage gap. And let's not forget the p-value, which shouted its significance from the statistical mountaintop, akin to a high-decibel alarm clock demanding attention. Our findings reiterate the influential role of education on earnings, reminding us that behind every data point lies a captivating narrative, waiting to be brought to light.

As we dust off our academic capes and bid farewell to this captivating expedition, it's safe to say that our journey has provided invaluable insights into the intricate dynamics of gender, education, and income. So, let’s raise a hypothetical toast to this scholarly escapade, acknowledging that no further research in this area is needed. After all, we've meticulously paved the path for future scholars, leaving them with nothing more than to marvel at the robust connection we've unraveled. Onward to new frontiers of inquiry – for we, dear colleagues, have other academic fish to fry!

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 The Engineer's Salary Equation: Bridging the Gender Gap. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Workplace Equity and Engineering

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 The Engineer's Salary Equation: Bridging the Gender Gap. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Gender Equity in Engineering

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 The Engineer's Salary Equation: Bridging the Gender Gap with an abstract of In this paper, we delve into the intersection of bachelor's degrees awarded in engineering and the gender pay gap in the United States. Through meticulous data analysis from the National Center for Education Statistics and Statista, our research team uncovered a striking correlation between the two variables. With a noteworthy correlation coefficient of 0.9731387 and a p < 0.01 for the years 2012 to 2021, our findings highlight the unmistakable link between the pursuit of engineering degrees and the enduring gender wage disparity. Our analysis provides compelling insights and prompts further investigation into the larger societal factors at play. Join us on this exploration as we navigate the complex terrain of academia, statistics, and the ever-persistent quest for gender equality.

ChatGPT:

engineering degrees, gender pay gap, United States, National Center for Education Statistics, Statista, correlation coefficient, wage disparity, gender equality, academia, statistics

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

Gender pay gap in the U.S.
Source: Statista
See what else correlates with Gender pay gap in the U.S.

Correlation r = 0.9731387 (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 = 0.9469989 (Coefficient of determination)
This means 94.7% of the change in the one variable (i.e., Gender pay gap in the U.S.) is predictable based on the change in the other (i.e., Bachelor's degrees awarded in Engineering) over the 10 years from 2012 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.21E-6.^Show 0.0000022050518233998352000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

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

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.97 in 0.000221% of random cases. Said differently, if you correlated 453,504 random variables^Note You don't actually need 453 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 9 degrees of freedom, ^Note 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.

[ 0.89, 0.99 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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

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

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Bachelor's degrees awarded in Engineering (Degrees awarded)	81371	85987	92169	97852	106789	115671	121953	126692	128337	126037
Gender pay gap in the U.S. (Percentage)	76.5	78.3	78.6	79.6	80.5	81.7	81.6	82.3	83.1	83.7

Why this works

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

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([81371,85987,92169,97852,106789,115671,121953,126692,128337,126037,])
array_2 = np.array([76.5,78.3,78.6,79.6,80.5,81.7,81.6,82.3,83.1,83.7,])
array_1_name = "Bachelor's degrees awarded in Engineering"
array_2_name = "Gender pay gap in the U.S."

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

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

Reuseable content

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

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

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

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

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

Download images for these variables:

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

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

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only Bachelor's degrees awarded in Engineering
Line chart for only Gender pay gap in the U.S.
AI-generated correlation image
The spurious research paper: The Engineer's Salary Equation: Bridging the Gender Gap

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Correlation ID: 1473 · Black Variable ID: 1273 · Red Variable ID: 356


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