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Spurious correlation #6,659 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Master's degrees awarded in journalism 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. Small Image

AI explanation

As the number of journalism Master's degree holders rose, so did the demand for editorial positions. This led to a surplus of editorials, ultimately devaluing the editor-in-chief position. Hence, men ended up in higher-paying executive editor roles, while women were stuck writing "10 ways to organize your closet" articles. It seems in this case, a Master's in journalism only boosted the headlines, not the bottom lines!

Model: dalle-3
Prompt: Image Generation Prompt: Generate an image with the style of a Discovery magazine cover, set in a journalism school graduation scene. In the foreground, numerous excited women are receiving their Master's degrees and tossing their graduation caps into the air. As the caps are mid-air, the scene transitions to a bustling newsroom where female journalists are seen writing lifestyle and fluff pieces, while their male counterparts hold positions of executive editors, with higher pay and authority. The stark contrast between the joyous graduation and the gender imbalance in the newsroom should be clear to see. The visual should depict this transition in a simple and striking manner, emphasizing the disparity in the journalism field without including any words or speech bubbles. 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: Master's degrees awarded in journalism 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: A Discovery magazine cover image A scene from Skyrim Scalar art High-tech Magic realism
ChatGPT: A Discovery magazine cover image

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Master's degrees awarded in journalism and Gender pay gap in the U.S., in the following situation: As the number of journalism Master's degree holders rose, so did the demand for editorial positions. This led to a surplus of editorials, ultimately devaluing the editor-in-chief position. Hence, men ended up in higher-paying executive editor roles, while women were stuck writing "10 ways to organize your closet" articles. It seems in this case, a Master's in journalism only boosted the headlines, not the bottom lines!. Keep the description simple.
ChatGPT: A graduation ceremony at a journalism school where many women are receiving their Master's degrees, lined up to receive their diplomas. However, as they excitedly toss their graduation caps into the air, the scene transitions to a bustling newsroom. Here, female journalists are seen writing lifestyle and fluff pieces while their male counterparts hold the prestigious position of executive editors, enjoying higher pay and authority. The imbalance is striking, with the contrast between the joyous graduation and the stark reality of the gender pay gap in journalism clear to see.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A Discovery magazine cover image ] and depicting this scene: A graduation ceremony at a journalism school where many women are receiving their Master's degrees, lined up to receive their diplomas. However, as they excitedly toss their graduation caps into the air, the scene transitions to a bustling newsroom. Here, female journalists are seen writing lifestyle and fluff pieces while their male counterparts hold the prestigious position of executive editors, enjoying higher pay and authority. The imbalance is striking, with the contrast between the joyous graduation and the stark reality of the gender pay gap in journalism clear to see. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Image Generation Prompt: Generate an image with the style of a Discovery magazine cover, set in a journalism school graduation scene. In the foreground, numerous excited women are receiving their Master's degrees and tossing their graduation caps into the air. As the caps are mid-air, the scene transitions to a bustling newsroom where female journalists are seen writing lifestyle and fluff pieces, while their male counterparts hold positions of executive editors, with higher pay and authority. The stark contrast between the joyous graduation and the gender imbalance in the newsroom should be clear to see. The visual should depict this transition in a simple and striking manner, emphasizing the disparity in the journalism field without including any words or speech bubbles.

*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 Master's degrees awarded in journalism caused Gender pay gap in the U.S. to increase. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)
Breaking News: Master's Degrees in Journalism and the Gender Pay Gap in the U.S.: A Story of Inequality

The Journal of Media Equity and Social Justice

Jan 2024
Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.




Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.You write titles and abstracts of goofy academic research papers.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, and add unexpected twists in your writing.

Please draft the title and abstract of an academic research paper presenting the findings of the connection between Master's degrees awarded in journalism and Gender pay gap in the U.S.. Make lots of jokes, goofy observations, and puns.

Make the title seem clever.

Your research team used data from National Center for Education Statistics and Statista to assess this nagging question. You found a correlation coefficient of 0.9580270 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]]
Breaking News: Master's Degrees in Journalism and the Gender Pay Gap in the U.S.: A Story of Inequality

[[ABSTRACT]]
Extra, extra, read all about it! This research delves into the intriguing connection between the number of Master's degrees awarded in journalism and the persistent gender pay gap in the United States. Our team, armed with statistical tools and an arsenal of puns, mined data from the National Center for Education Statistics and Statista to shed light on this captivating topic. We present a correlation coefficient of 0.9580270 and a p-value of less than 0.01 for the period spanning from 2012 to 2021, unveiling a strong link between these phenomena. Join us as we uncover the headlines, analyze the newsroom, and explore the stories behind each data point. Get ready to navigate through the twists and turns of this gender pay gap mystery in the world of journalism, where the truth is stranger than fiction!


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

Please make up the introduction section of an academic research paper presenting the findings of the connection between Master's degrees awarded in journalism and Gender pay gap in the U.S.. Make lots of jokes, goofy observations, and puns.

Here is the title and abstract of the paper:
[[TITLE]]
Breaking News: Master's Degrees in Journalism and the Gender Pay Gap in the U.S.: A Story of Inequality

[[ABSTRACT]]
Extra, extra, read all about it! This research delves into the intriguing connection between the number of Master's degrees awarded in journalism and the persistent gender pay gap in the United States. Our team, armed with statistical tools and an arsenal of puns, mined data from the National Center for Education Statistics and Statista to shed light on this captivating topic. We present a correlation coefficient of 0.9580270 and a p-value of less than 0.01 for the period spanning from 2012 to 2021, unveiling a strong link between these phenomena. Join us as we uncover the headlines, analyze the newsroom, and explore the stories behind each data point. Get ready to navigate through the twists and turns of this gender pay gap mystery in the world of journalism, where the truth is stranger than fiction!

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

Good journalism is like a well-crafted joke - it grabs attention, sparks curiosity, and often comes with a punchline that leaves an impression. Similarly, the gender pay gap in the United States has been a persistent punchline in the story of economic inequality, with women often receiving the short end of the financial stick. But what's the connection between this ongoing narrative and the number of Master's degrees awarded in journalism? Are we witnessing a news flash of correlation, or is it just statistical noise in the background?

In this paper, we embark on a journalistic journey to investigate the intertwined relationship between the gender pay gap and the awarding of Master's degrees in journalism. Our research aims to shine the spotlight on the stories behind the stark numbers, to dig deeper into the data, and to unravel the narrative threads that may explain the perplexing pattern of inequality.

Cue the breaking news music - it's time to flip the pages of this investigation and dive into the tangled web of gender, education, and pay gaps. So grab your press pass, don your investigative hat, and join us as we chase down the leads and unravel the twists and turns of this captivating storyline!


Model: gpt-3.5-turbo-1106
System prompt: You are a silly researcher writing an academic paper.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between Master's degrees awarded in journalism and Gender pay gap in the U.S.. 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. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
Breaking News: Master's Degrees in Journalism and the Gender Pay Gap in the U.S.: A Story of Inequality

[[ABSTRACT]]
Extra, extra, read all about it! This research delves into the intriguing connection between the number of Master's degrees awarded in journalism and the persistent gender pay gap in the United States. Our team, armed with statistical tools and an arsenal of puns, mined data from the National Center for Education Statistics and Statista to shed light on this captivating topic. We present a correlation coefficient of 0.9580270 and a p-value of less than 0.01 for the period spanning from 2012 to 2021, unveiling a strong link between these phenomena. Join us as we uncover the headlines, analyze the newsroom, and explore the stories behind each data point. Get ready to navigate through the twists and turns of this gender pay gap mystery in the world of journalism, where the truth is stranger than fiction!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Good journalism is like a well-crafted joke - it grabs attention, sparks curiosity, and often comes with a punchline that leaves an impression. Similarly, the gender pay gap in the United States has been a persistent punchline in the story of economic inequality, with women often receiving the short end of the financial stick. But what's the connection between this ongoing narrative and the number of Master's degrees awarded in journalism? Are we witnessing a news flash of correlation, or is it just statistical noise in the background?
In this paper, we embark on a journalistic journey to investigate the intertwined relationship between the gender pay gap and the awarding of Master's degrees in journalism. Our research aims to shine the spotlight on the stories behind the stark numbers, to dig deeper into the data, and to unravel the narrative threads that may explain the perplexing pattern of inequality.
Cue the breaking news music - it's time to flip the pages of this investigation and dive into the tangled web of gender, education, and pay gaps. So grab your press pass, don your investigative hat, and join us as we chase down the leads and unravel the twists and turns of this captivating storyline!

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The relationship between the number of Master's degrees awarded in journalism and the gender pay gap in the United States has been a topic of growing interest in recent years. This section provides an overview of the existing literature on this subject, exploring the various studies and findings that have contributed to our understanding of the complex dynamics at play.

Smith et al. (2017) conducted a comprehensive analysis of gender pay disparities in the journalism industry, highlighting the persistent wage gap between male and female journalists. Their study revealed a nuanced interplay of factors, including career progression, job roles, and organizational structures, that contribute to the unequal distribution of pay.

Doe's (2019) research on educational attainment and earnings in the media sector emphasized the role of advanced degrees in shaping income disparities. The study found a positive association between higher levels of education, such as Master's degrees, and increased earning potential, albeit with notable variations across gender lines.

Jones (2020) delved into the societal and cultural influences on women in journalism, uncovering the systemic barriers and biases that impact their career trajectories and financial outcomes. The study underscored the need for holistic approaches to address gender inequality in the field, encompassing education, workplace policies, and broader societal attitudes.

Turning to non-fiction books, "Lean In: Women, Work, and the Will to Lead" by Sheryl Sandberg and "Good and Mad: The Revolutionary Power of Women's Anger" by Rebecca Traister offer insightful perspectives on gender dynamics in the workplace, providing context for the broader societal forces that shape the gender pay gap.

In a more fiction-oriented realm, "The Devil Wears Prada" by Lauren Weisberger and "The Girl with the Dragon Tattoo" by Stieg Larsson present vivid portrayals of the media industry, offering fictional yet compelling narratives that reflect elements of gender disparity and professional ambition.

Drawing inspiration from the world of board games, "Pandemic: The Cure" serves as an apt analogy for the infectious nature of gender-based inequalities, with its emphasis on collaborative problem-solving and systemic challenges. Meanwhile, "Clue" offers a playful reminder that unraveling the mysteries of the gender pay gap requires a keen eye for detail and a willingness to follow the trail of evidence, no matter where it may lead.

As we navigate through this literature review, it becomes clear that the issues of Master's degrees in journalism and the gender pay gap are rich with complexity and intrigue, much like a captivating news story that keeps readers on the edge of their seats. Our research aims to build upon these foundations and contribute fresh insights to this compelling narrative.


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

Please make up the methodology section of an academic research paper presenting the findings of the connection between Master's degrees awarded in journalism and Gender pay gap in the U.S.. 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 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]]
Breaking News: Master's Degrees in Journalism and the Gender Pay Gap in the U.S.: A Story of Inequality

[[ABSTRACT]]
Extra, extra, read all about it! This research delves into the intriguing connection between the number of Master's degrees awarded in journalism and the persistent gender pay gap in the United States. Our team, armed with statistical tools and an arsenal of puns, mined data from the National Center for Education Statistics and Statista to shed light on this captivating topic. We present a correlation coefficient of 0.9580270 and a p-value of less than 0.01 for the period spanning from 2012 to 2021, unveiling a strong link between these phenomena. Join us as we uncover the headlines, analyze the newsroom, and explore the stories behind each data point. Get ready to navigate through the twists and turns of this gender pay gap mystery in the world of journalism, where the truth is stranger than fiction!

[[INTRODUCTION]]
Good journalism is like a well-crafted joke - it grabs attention, sparks curiosity, and often comes with a punchline that leaves an impression. Similarly, the gender pay gap in the United States has been a persistent punchline in the story of economic inequality, with women often receiving the short end of the financial stick. But what's the connection between this ongoing narrative and the number of Master's degrees awarded in journalism? Are we witnessing a news flash of correlation, or is it just statistical noise in the background?
In this paper, we embark on a journalistic journey to investigate the intertwined relationship between the gender pay gap and the awarding of Master's degrees in journalism. Our research aims to shine the spotlight on the stories behind the stark numbers, to dig deeper into the data, and to unravel the narrative threads that may explain the perplexing pattern of inequality.
Cue the breaking news music - it's time to flip the pages of this investigation and dive into the tangled web of gender, education, and pay gaps. So grab your press pass, don your investigative hat, and join us as we chase down the leads and unravel the twists and turns of this captivating storyline!

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To get to the bottom of this "newsworthy" topic, our research team embarked on a quest through the digital newsroom, scouring the archives of the National Center for Education Statistics and Statista. Armed with spreadsheets and a thirst for uncovering the truth, we meticulously collected data from the years 2012 to 2021, attempting to piece together the puzzle of Master's degrees in journalism and the gender pay gap.

Our first order of business was to don our metaphorical Sherlock Holmes hats and gather data on the number of Master's degrees awarded in journalism. We combed through the internet, sifting through university websites, and even conducted a few seances with old LinkedIn profiles to obtain this crucial information. We then cross-referenced the data with reputable sources like the National Center for Education Statistics, keeping a close eye out for any suspicious characters in our dataset.

Next, we shifted our focus to the gender pay gap, an elusive figure that seems to play hide-and-seek with economists and policymakers alike. With a trusty magnifying glass in hand, we scoured through labor statistics, wage reports, and industry surveys, making sure to double-check for any sneaky statistical anomalies lurking in the shadows.

Once we had gathered all the evidence, we employed a top-secret statistical analysis technique that involved a magical concoction of regression models, correlation coefficients, and fancy p-values. We then threw in a pinch of probability theory and a dash of hypothesis testing, stirring the mixture with the precision of a seasoned chef crafting a soufflé. As the numbers simmered and sizzled, we watched with bated breath as the results materialized, revealing the tantalizing connection between Master's degrees in journalism and the gender pay gap.

With our methodology as quirky and intricate as a crossword puzzle in the Sunday paper, we set out to unravel the mystery of these intertwined phenomena, turning the pages of data and statistics to uncover the intricate web of inequality in the world of journalism. While our methods may have been unconventional, we believe they have led us to unrivaled insights into this captivating storyline.


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

Please make up the results section of an academic research paper presenting the findings of the connection between Master's degrees awarded in journalism and Gender pay gap in the U.S.. 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 Statista .

For the time period 2012 to 2021, you found a correlation 0.9580270, r-squared of 0.9178158, 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]]
Breaking News: Master's Degrees in Journalism and the Gender Pay Gap in the U.S.: A Story of Inequality

[[ABSTRACT]]
Extra, extra, read all about it! This research delves into the intriguing connection between the number of Master's degrees awarded in journalism and the persistent gender pay gap in the United States. Our team, armed with statistical tools and an arsenal of puns, mined data from the National Center for Education Statistics and Statista to shed light on this captivating topic. We present a correlation coefficient of 0.9580270 and a p-value of less than 0.01 for the period spanning from 2012 to 2021, unveiling a strong link between these phenomena. Join us as we uncover the headlines, analyze the newsroom, and explore the stories behind each data point. Get ready to navigate through the twists and turns of this gender pay gap mystery in the world of journalism, where the truth is stranger than fiction!

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
Good journalism is like a well-crafted joke - it grabs attention, sparks curiosity, and often comes with a punchline that leaves an impression. Similarly, the gender pay gap in the United States has been a persistent punchline in the story of economic inequality, with women often receiving the short end of the financial stick. But what's the connection between this ongoing narrative and the number of Master's degrees awarded in journalism? Are we witnessing a news flash of correlation, or is it just statistical noise in the background?
In this paper, we embark on a journalistic journey to investigate the intertwined relationship between the gender pay gap and the awarding of Master's degrees in journalism. Our research aims to shine the spotlight on the stories behind the stark numbers, to dig deeper into the data, and to unravel the narrative threads that may explain the perplexing pattern of inequality.
Cue the breaking news music - it's time to flip the pages of this investigation and dive into the tangled web of gender, education, and pay gaps. So grab your press pass, don your investigative hat, and join us as we chase down the leads and unravel the twists and turns of this captivating storyline!

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The ink-stained journey of analyzing the correlation between Master's degrees awarded in journalism and the gender pay gap in the U.S. has landed us at an eye-opening destination. With a correlation coefficient of 0.9580270 and an r-squared of 0.9178158 for the timeframe of 2012 to 2021, our statistical analysis has revealed a narrative that even Woodward and Bernstein would find compelling. The p-value of less than 0.01 further solidifies the newsworthy nature of our findings, leaving little room for doubt about the link between these two variables. It's as though our data points are delivering a headline-grabbing scoop that demands attention!

In Fig. 1, the scatterplot vividly illustrates the strong correlation between the number of Master's degrees awarded in journalism and the gender pay gap. The plot resembles a gripping plot twist, with each data point uncovering a riveting tale of inequality. It's a chart that speaks volumes, echoing the sentiments of those seeking equal pay and opportunities in the field. The story it weaves is not just a momentary blip on the radar; it's a front-page news update that demands to be acknowledged and tackled head-on.

As we sifted through the information from the National Center for Education Statistics and Statista, our team couldn't help but marvel at the richness of the patterns and relationships we uncovered. The numbers, much like the lead of a captivating news story, drew readers in with their undeniable significance and impact. The connection between the number of Master's degrees awarded in journalism and the gender pay gap is a standout feature in the chaotic newsroom of data, a persistent thread in the fabric of inequality that demands closer scrutiny and unrelenting investigation.

In essence, our results serve as a poignant reminder that behind every statistic lies a human story waiting to be told. What we've discovered is more than just a correlation; it's a narrative that calls for action, change, and a reimagining of the newsroom to ensure that everyone's story is valued and fairly compensated. This correlation isn't just a blip on the radar; it's a persistent signal that demands the attention of policymakers, educators, and industry leaders, prompting them to craft a new story - one of equality, progress, and a narrative where every voice commands the front page.


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

Please make up the discussion section of an academic research paper presenting the findings of the connection between Master's degrees awarded in journalism and Gender pay gap in the U.S.. 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]]
Breaking News: Master's Degrees in Journalism and the Gender Pay Gap in the U.S.: A Story of Inequality

[[ABSTRACT]]
Extra, extra, read all about it! This research delves into the intriguing connection between the number of Master's degrees awarded in journalism and the persistent gender pay gap in the United States. Our team, armed with statistical tools and an arsenal of puns, mined data from the National Center for Education Statistics and Statista to shed light on this captivating topic. We present a correlation coefficient of 0.9580270 and a p-value of less than 0.01 for the period spanning from 2012 to 2021, unveiling a strong link between these phenomena. Join us as we uncover the headlines, analyze the newsroom, and explore the stories behind each data point. Get ready to navigate through the twists and turns of this gender pay gap mystery in the world of journalism, where the truth is stranger than fiction!

[[LITERATURE REVIEW]]
The relationship between the number of Master's degrees awarded in journalism and the gender pay gap in the United States has been a topic of growing interest in recent years. This section provides an overview of the existing literature on this subject, exploring the various studies and findings that have contributed to our understanding of the complex dynamics at play.
Smith et al. (2017) conducted a comprehensive analysis of gender pay disparities in the journalism industry, highlighting the persistent wage gap between male and female journalists. Their study revealed a nuanced interplay of factors, including career progression, job roles, and organizational structures, that contribute to the unequal distribution of pay.
Doe's (2019) research on educational attainment and earnings in the media sector emphasized the role of advanced degrees in shaping income disparities. The study found a positive association between higher levels of education, such as Master's degrees, and increased earning potential, albeit with notable variations across gender lines.
Jones (2020) delved into the societal and cultural influences on women in journalism, uncovering the systemic barriers and biases that impact their career trajectories and financial outcomes. The study underscored the need for holistic approaches to address gender inequality in the field, encompassing education, workplace policies, and broader societal attitudes.
Turning to non-fiction books, "Lean In: Women, Work, and the Will to Lead" by Sheryl Sandberg and "Good and Mad: The Revolutionary Power of Women's Anger" by Rebecca Traister offer insightful perspectives on gender dynamics in the workplace, providing context for the broader societal forces that shape the gender pay gap.
In a more fiction-oriented realm, "The Devil Wears Prada" by Lauren Weisberger and "The Girl with the Dragon Tattoo" by Stieg Larsson present vivid portrayals of the media industry, offering fictional yet compelling narratives that reflect elements of gender disparity and professional ambition.
Drawing inspiration from the world of board games, "Pandemic: The Cure" serves as an apt analogy for the infectious nature of gender-based inequalities, with its emphasis on collaborative problem-solving and systemic challenges. Meanwhile, "Clue" offers a playful reminder that unraveling the mysteries of the gender pay gap requires a keen eye for detail and a willingness to follow the trail of evidence, no matter where it may lead.
As we navigate through this literature review, it becomes clear that the issues of Master's degrees in journalism and the gender pay gap are rich with complexity and intrigue, much like a captivating news story that keeps readers on the edge of their seats. Our research aims to build upon these foundations and contribute fresh insights to this compelling narrative.

[[RESULTS]]
The ink-stained journey of analyzing the correlation between Master's degrees awarded in journalism and the gender pay gap in the U.S. has landed us at an eye-opening destination. With a correlation coefficient of 0.9580270 and an r-squared of 0.9178158 for the timeframe of 2012 to 2021, our statistical analysis has revealed a narrative that even Woodward and Bernstein would find compelling. The p-value of less than 0.01 further solidifies the newsworthy nature of our findings, leaving little room for doubt about the link between these two variables. It's as though our data points are delivering a headline-grabbing scoop that demands attention!
In Fig. 1, the scatterplot vividly illustrates the strong correlation between the number of Master's degrees awarded in journalism and the gender pay gap. The plot resembles a gripping plot twist, with each data point uncovering a riveting tale of inequality. It's a chart that speaks volumes, echoing the sentiments of those seeking equal pay and opportunities in the field. The story it weaves is not just a momentary blip on the radar; it's a front-page news update that demands to be acknowledged and tackled head-on.
As we sifted through the information from the National Center for Education Statistics and Statista, our team couldn't help but marvel at the richness of the patterns and relationships we uncovered. The numbers, much like the lead of a captivating news story, drew readers in with their undeniable significance and impact. The connection between the number of Master's degrees awarded in journalism and the gender pay gap is a standout feature in the chaotic newsroom of data, a persistent thread in the fabric of inequality that demands closer scrutiny and unrelenting investigation.
In essence, our results serve as a poignant reminder that behind every statistic lies a human story waiting to be told. What we've discovered is more than just a correlation; it's a narrative that calls for action, change, and a reimagining of the newsroom to ensure that everyone's story is valued and fairly compensated. This correlation isn't just a blip on the radar; it's a persistent signal that demands the attention of policymakers, educators, and industry leaders, prompting them to craft a new story - one of equality, progress, and a narrative where every voice commands the front page.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

In this section, we dive deep into the gender pay gap in journalism, armed with our trusty protractor of correlation and the precision of p-values. Our findings not only lend support to the prior literature but also add a spicy dash of statistical oomph to the storytelling mix.

Let's rewind back to our literature review, where we playfully drew inspiration from board games and novels to illustrate the intricate web of gender inequality. We took the seemingly jestful references to "Clue" and "Pandemic: The Cure" and did a little sleuthing of our own. Lo and behold, our results indeed reflect the investigative fervor and collaborative effort these games embody. The patterns we've uncovered are not mere red herrings – they're the real deal.

Smith et al. (2017) spoke of the nuanced interplay of factors in journalism's gender pay gap. Well, our results have certainly added another layer to this narrative, with a staggering correlation coefficient of 0.9580270. It's as though the characters in their study have leaped off the pages and onto our scatterplot, driving home the gravity of this issue with more force than a sensational headline.

Doe's (2019) emphasis on advanced degrees' impact on earnings now takes on a whole new dimension, much like a pop-up book suddenly sprouting three-dimensional bar charts. Our robust statistical analysis reiterates the positive association between higher education and earning potential, but with a twist – the variations across gender lines can no longer be brushed off as mere plot devices.

And Jones (2020) painted a portrait of systemic barriers in journalism, laying bare the biases that shape career trajectories. Our data, like a photorealistic painting, amplifies the need for holistic approaches to tackle these ingrained inequalities. It's time to rewrite this storyline to ensure everyone gets top billing.

In the world of non-fiction and fiction, Sandberg and Traister's insights and Weisberger and Larsson's portrayals of the media industry now blend with our findings, creating a kind of literary universe where statistical significance and glaring disparities collide. As for "The Devil Wears Prada" and "The Girl with the Dragon Tattoo," it turns out the gender pay gap is less of a mystery and more of a documented exposé; it's not fiction when the numbers speak for themselves.

Our results are no mere footnote in this ongoing saga. They demand attention, action, and perhaps a sequel that features equitable pay and opportunity for all. Like a gripping news story, our research leaves readers on the edge of their seats, eagerly awaiting the next chapter in the fight against gender inequality in journalism.


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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Master's degrees awarded in journalism and Gender pay gap in the U.S.. 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]]
Breaking News: Master's Degrees in Journalism and the Gender Pay Gap in the U.S.: A Story of Inequality

[[ABSTRACT]]
Extra, extra, read all about it! This research delves into the intriguing connection between the number of Master's degrees awarded in journalism and the persistent gender pay gap in the United States. Our team, armed with statistical tools and an arsenal of puns, mined data from the National Center for Education Statistics and Statista to shed light on this captivating topic. We present a correlation coefficient of 0.9580270 and a p-value of less than 0.01 for the period spanning from 2012 to 2021, unveiling a strong link between these phenomena. Join us as we uncover the headlines, analyze the newsroom, and explore the stories behind each data point. Get ready to navigate through the twists and turns of this gender pay gap mystery in the world of journalism, where the truth is stranger than fiction!

[[INTRDUCTION]]
Good journalism is like a well-crafted joke - it grabs attention, sparks curiosity, and often comes with a punchline that leaves an impression. Similarly, the gender pay gap in the United States has been a persistent punchline in the story of economic inequality, with women often receiving the short end of the financial stick. But what's the connection between this ongoing narrative and the number of Master's degrees awarded in journalism? Are we witnessing a news flash of correlation, or is it just statistical noise in the background?
In this paper, we embark on a journalistic journey to investigate the intertwined relationship between the gender pay gap and the awarding of Master's degrees in journalism. Our research aims to shine the spotlight on the stories behind the stark numbers, to dig deeper into the data, and to unravel the narrative threads that may explain the perplexing pattern of inequality.
Cue the breaking news music - it's time to flip the pages of this investigation and dive into the tangled web of gender, education, and pay gaps. So grab your press pass, don your investigative hat, and join us as we chase down the leads and unravel the twists and turns of this captivating storyline!

[[RESULTS]]
The ink-stained journey of analyzing the correlation between Master's degrees awarded in journalism and the gender pay gap in the U.S. has landed us at an eye-opening destination. With a correlation coefficient of 0.9580270 and an r-squared of 0.9178158 for the timeframe of 2012 to 2021, our statistical analysis has revealed a narrative that even Woodward and Bernstein would find compelling. The p-value of less than 0.01 further solidifies the newsworthy nature of our findings, leaving little room for doubt about the link between these two variables. It's as though our data points are delivering a headline-grabbing scoop that demands attention!
In Fig. 1, the scatterplot vividly illustrates the strong correlation between the number of Master's degrees awarded in journalism and the gender pay gap. The plot resembles a gripping plot twist, with each data point uncovering a riveting tale of inequality. It's a chart that speaks volumes, echoing the sentiments of those seeking equal pay and opportunities in the field. The story it weaves is not just a momentary blip on the radar; it's a front-page news update that demands to be acknowledged and tackled head-on.
As we sifted through the information from the National Center for Education Statistics and Statista, our team couldn't help but marvel at the richness of the patterns and relationships we uncovered. The numbers, much like the lead of a captivating news story, drew readers in with their undeniable significance and impact. The connection between the number of Master's degrees awarded in journalism and the gender pay gap is a standout feature in the chaotic newsroom of data, a persistent thread in the fabric of inequality that demands closer scrutiny and unrelenting investigation.
In essence, our results serve as a poignant reminder that behind every statistic lies a human story waiting to be told. What we've discovered is more than just a correlation; it's a narrative that calls for action, change, and a reimagining of the newsroom to ensure that everyone's story is valued and fairly compensated. This correlation isn't just a blip on the radar; it's a persistent signal that demands the attention of policymakers, educators, and industry leaders, prompting them to craft a new story - one of equality, progress, and a narrative where every voice commands the front page.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

As we wrap up this riveting investigation into the connection between Master's degrees in journalism and the gender pay gap in the U.S., it's clear that this correlation isn't just a footnote in the annals of statistical analysis; it's a headline-worthy revelation. Our findings have painted a picture that even the most seasoned reporters would find captivating, akin to a story that unfolds with unexpected twists and turns.

The correlation coefficient and r-squared values have not only raised eyebrows but also sparked conversations about the role of education in rewriting the plot of gender inequality. It's as though each data point is a character in a dramatic news story, pulling readers deeper into the narrative of disparity and striving for a denouement that promises equity and fairness for all.

We've witnessed the scatterplot unravel a tale of injustice, akin to a cliffhanger that demands a sequel where every protagonist receives their rightful due. It's a plot thick with implications, setting the stage for an exposé on the need for systemic change in the newsroom of pay equity and opportunity.

In essence, our research illuminates the fact that this correlation is more than just a statistic; it is a call to action, a storyline clamoring for resolution. It's time to rewrite the headlines, not with a quick edit but with a comprehensive overhaul that ensures every journalist - regardless of gender - sees their narrative valued and compensated fairly.

And finally, as we close this chapter, it is evident that no more research is needed in this area. It's time for policy changes, educational reforms, and newsroom overhauls to take the lead in transforming this correlation into a byline of equality and empowerment. The end.


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 Breaking News: Master's Degrees in Journalism and the Gender Pay Gap in the U.S.: A Story of Inequality. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Media Equity and Social Justice


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 Breaking News: Master's Degrees in Journalism and the Gender Pay Gap in the U.S.: A Story of Inequality. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Gender Equality in Media (SGEM)


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 Breaking News: Master's Degrees in Journalism and the Gender Pay Gap in the U.S.: A Story of Inequality with an abstract of Extra, extra, read all about it! This research delves into the intriguing connection between the number of Master's degrees awarded in journalism and the persistent gender pay gap in the United States. Our team, armed with statistical tools and an arsenal of puns, mined data from the National Center for Education Statistics and Statista to shed light on this captivating topic. We present a correlation coefficient of 0.9580270 and a p-value of less than 0.01 for the period spanning from 2012 to 2021, unveiling a strong link between these phenomena. Join us as we uncover the headlines, analyze the newsroom, and explore the stories behind each data point. Get ready to navigate through the twists and turns of this gender pay gap mystery in the world of journalism, where the truth is stranger than fiction!

ChatGPT:

Master's degrees in journalism, gender pay gap, United States, correlation coefficient, p-value, National Center for Education Statistics, Statista, journalism education, gender inequality, newsroom diversity, women in journalism

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



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Data details

Master's degrees awarded in journalism
Detailed data title: Master's degrees conferred by postsecondary institutions in Communication, journalism, and related programs
Source: National Center for Education Statistics
See what else correlates with Master's degrees awarded in journalism

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

Correlation r = 0.9580270 (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 = 0.9178158 (Coefficient of determination)
This means 91.8% 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., Master's degrees awarded in journalism) over the 10 years from 2012 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 1.3E-5. 0.0000129066460174125120000000
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 0.96 in 0.0013% of random cases. Said differently, if you correlated 77,479 random variables You don't actually need 77 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, 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.

[ 0.83, 0.99 ] 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
Master's degrees awarded in journalism (Degrees awarded)900587609353958196761011910241104631058810805
Gender pay gap in the U.S. (Percentage)76.578.378.679.680.581.781.682.383.183.7




Why this works

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

    For the purposes of this project, I counted a point as an outlier if it the residual was two standard deviations from the mean.

    (This bullet point only shows up in the details page on charts that do, in fact, have outliers.)
    They stand out on the scatterplot above: notice the dots that are far away from any other dots. I intentionally mishandeled outliers, which makes the correlation look extra strong.




Try it yourself

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

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

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

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

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

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

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

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

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


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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([9005,8760,9353,9581,9676,10119,10241,10463,10588,10805,])
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 = "Master's degrees awarded in journalism"
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)



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

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

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Correlation ID: 6659 · Black Variable ID: 1316 · Red Variable ID: 356
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