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Spurious correlation #5,393 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Votes for Democratic Senators in Florida and the second variable is The number of insurance claims and policy processing clerks in Florida.  The chart goes from 2003 to 2018, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

The more Senators the Democrats have, the more they can "claim" their policies on insurance, leading to a higher demand for insurance claims and policy processing clerks in Florida. It's a real political "coverage" operation!

Model: dalle-3
Prompt: [A photo-realistic image] Create an image of a bustling Florida office where a group of Democratic Senators celebrate their victory, displaying a large banner that reads "Votes for Democrats." Meanwhile, a line of eager applicants forms outside the office of an insurance company, where a sign reads "Now Hiring: Insurance Claims and Policy Processing Clerks." The Senators' victory has sparked a surge in insurance claims and policy processing, resulting in the need for more clerks to handle the increased demand. Ensure that the scene captures the energy and movement of the office environment, with the Senators and applicants interacting in a lively and dynamic manner. 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: Votes for Democratic Senators in Florida and The number of insurance claims and policy processing clerks in Florida. 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 photo-realistic image An image that looks like it is a scene from A Disney Movie Cute cartoon A scene from Skyrim Cybernetic art
ChatGPT: A photo-realistic image

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Votes for Democratic Senators in Florida and The number of insurance claims and policy processing clerks in Florida, in the following situation: The more Senators the Democrats have, the more they can "claim" their policies on insurance, leading to a higher demand for insurance claims and policy processing clerks in Florida. It's a real political "coverage" operation!. Keep the description simple.
ChatGPT: In a bustling Florida office, a group of Democratic Senators celebrate their victory, displaying a large banner that reads "Votes for Democrats." Meanwhile, a line of eager applicants forms outside the office of an insurance company, where a sign reads "Now Hiring: Insurance Claims and Policy Processing Clerks." The Senators' victory has sparked a surge in insurance claims and policy processing, resulting in the need for more clerks to handle the increased demand.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A photo-realistic image ] and depicting this scene: In a bustling Florida office, a group of Democratic Senators celebrate their victory, displaying a large banner that reads "Votes for Democrats." Meanwhile, a line of eager applicants forms outside the office of an insurance company, where a sign reads "Now Hiring: Insurance Claims and Policy Processing Clerks." The Senators' victory has sparked a surge in insurance claims and policy processing, resulting in the need for more clerks to handle the increased demand. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [A photo-realistic image] Create an image of a bustling Florida office where a group of Democratic Senators celebrate their victory, displaying a large banner that reads "Votes for Democrats." Meanwhile, a line of eager applicants forms outside the office of an insurance company, where a sign reads "Now Hiring: Insurance Claims and Policy Processing Clerks." The Senators' victory has sparked a surge in insurance claims and policy processing, resulting in the need for more clerks to handle the increased demand. Ensure that the scene captures the energy and movement of the office environment, with the Senators and applicants interacting in a lively and dynamic manner.

*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 increases in Democrat votes for Senators in Florida positively influenced The number of insurance claims and policy processing clerks in Florida. Include one good pun.

AI academic paper

(Because p < 0.01)
Fate of Florida's Senators: Democrat Votes and the Clerks' Insurance Claims Rates

The Journal of Political Prognostication and Actuarial Analysis

Jan 2024

Reminder: This paper is AI-generated. Not real!
Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.




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

Please draft the title and abstract of an academic research paper presenting the findings of the connection between Democrat votes for Senators in Florida and The number of insurance claims and policy processing clerks in Florida. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Rhyme words in the title.

Your research team used data from MIT Election Data and Science Lab, Harvard Dataverse and Bureau of Larbor Statistics to assess this nagging question. You found a correlation coefficient of 0.9669320 and p < 0.01 for 2003 to 2018.

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]]
Fate of Florida's Senators: Democrat Votes and the Clerks' Insurance Claims Rates

[[ABSTRACT]]
This study delves into the intriguing relationship between Democrat votes for Senators in Florida and the employment levels of insurance claims and policy processing clerks. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics, we evaluated this peculiar association. The findings revealed a remarkably high correlation coefficient of 0.9669320 and a p-value of less than 0.01 for the years 2003 to 2018.

Our analysis suggests that the fate of Florida's senators may indeed be related to the workload of insurance clerks, as surprising as it sounds. The correlation between these variables is striking and may prompt additional investigations into the underlying mechanisms at play. It appears that political preferences might have an unexpected impact on administrative roles within the insurance industry, stirring the curiosity of researchers and policymakers alike.

A Democrat-loving hurricane walks into the Senate, and the clerks can't stop making waves with their claims - a true tale of political winds shaping the workday grind!


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

Please make up the introduction section of an academic research paper presenting the findings of the connection between Democrat votes for Senators in Florida and The number of insurance claims and policy processing clerks in Florida. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Here is the title and abstract of the paper:
[[TITLE]]
Fate of Florida's Senators: Democrat Votes and the Clerks' Insurance Claims Rates

[[ABSTRACT]]
This study delves into the intriguing relationship between Democrat votes for Senators in Florida and the employment levels of insurance claims and policy processing clerks. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics, we evaluated this peculiar association. The findings revealed a remarkably high correlation coefficient of 0.9669320 and a p-value of less than 0.01 for the years 2003 to 2018.

Our analysis suggests that the fate of Florida's senators may indeed be related to the workload of insurance clerks, as surprising as it sounds. The correlation between these variables is striking and may prompt additional investigations into the underlying mechanisms at play. It appears that political preferences might have an unexpected impact on administrative roles within the insurance industry, stirring the curiosity of researchers and policymakers alike.

A Democrat-loving hurricane walks into the Senate, and the clerks can't stop making waves with their claims - a true tale of political winds shaping the workday grind!

-------

Your response should start like this:

[[INTRODUCTION]]



ChatGPT:
[[INTRODUCTION]]

The intersection of politics and labor market dynamics has long been a subject of fascination for researchers and pundits alike. While most studies have scrutinized the relationship between political preferences and broader economic indicators, such as unemployment rates and income levels, few have ventured into the peculiar realm of insurance clerks' workloads. This study aims to unravel the mysterious connection between Democrat votes for Senators in Florida and the number of insurance claims and policy processing clerks, shedding light on an unexplored aspect of political influence.

It's not every day that politicians and insurance clerks find themselves in the same sentence, let alone the same statistical analysis. Much like the unexpected plot twists in a mystery novel, the correlation between these seemingly disparate variables piques our intellectual curiosity and prompts us to investigate further.

Now, imagine a scenario where a Democrat votes surge and the insurance clerks' desks become a whirlwind of activity. It's not just a political whirlwind; it's a statistical storm brewing with intriguing implications for labor market dynamics.

The empirical analysis presented in this study draws on rich datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics, allowing us to embark on a journey through the labyrinth of statistics and research methods to uncover the link between political preferences and the insurance industry. As we navigate this territory, we are reminded of the sage advice that correlation does not imply causation, but it sure does make for a fascinating hypothesis.

Imagine the insurance clerks sifting through a pile of claims, much like researchers sifting through mountains of data, seeking patterns and connections that illuminate the complex web of political winds and occupational demands.

We embark on this scientific exploration with a twinkle in our eyes and a healthy dose of humor, for as every seasoned researcher knows, a good pun about correlation is always a significant finding.


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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Democrat votes for Senators in Florida and The number of insurance claims and policy processing clerks in Florida. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then you might move on to cartoons and children's shows that you watched for research.

Here is the title and abstract of the paper:
[[TITLE]]
Fate of Florida's Senators: Democrat Votes and the Clerks' Insurance Claims Rates

[[ABSTRACT]]
This study delves into the intriguing relationship between Democrat votes for Senators in Florida and the employment levels of insurance claims and policy processing clerks. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics, we evaluated this peculiar association. The findings revealed a remarkably high correlation coefficient of 0.9669320 and a p-value of less than 0.01 for the years 2003 to 2018.

Our analysis suggests that the fate of Florida's senators may indeed be related to the workload of insurance clerks, as surprising as it sounds. The correlation between these variables is striking and may prompt additional investigations into the underlying mechanisms at play. It appears that political preferences might have an unexpected impact on administrative roles within the insurance industry, stirring the curiosity of researchers and policymakers alike.

A Democrat-loving hurricane walks into the Senate, and the clerks can't stop making waves with their claims - a true tale of political winds shaping the workday grind!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of politics and labor market dynamics has long been a subject of fascination for researchers and pundits alike. While most studies have scrutinized the relationship between political preferences and broader economic indicators, such as unemployment rates and income levels, few have ventured into the peculiar realm of insurance clerks' workloads. This study aims to unravel the mysterious connection between Democrat votes for Senators in Florida and the number of insurance claims and policy processing clerks, shedding light on an unexplored aspect of political influence.
It's not every day that politicians and insurance clerks find themselves in the same sentence, let alone the same statistical analysis. Much like the unexpected plot twists in a mystery novel, the correlation between these seemingly disparate variables piques our intellectual curiosity and prompts us to investigate further.
Now, imagine a scenario where a Democrat votes surge and the insurance clerks' desks become a whirlwind of activity. It's not just a political whirlwind; it's a statistical storm brewing with intriguing implications for labor market dynamics.
The empirical analysis presented in this study draws on rich datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics, allowing us to embark on a journey through the labyrinth of statistics and research methods to uncover the link between political preferences and the insurance industry. As we navigate this territory, we are reminded of the sage advice that correlation does not imply causation, but it sure does make for a fascinating hypothesis.
Imagine the insurance clerks sifting through a pile of claims, much like researchers sifting through mountains of data, seeking patterns and connections that illuminate the complex web of political winds and occupational demands.
We embark on this scientific exploration with a twinkle in our eyes and a healthy dose of humor, for as every seasoned researcher knows, a good pun about correlation is always a significant finding.

-------

Your response should start like this:

[[LITERATURE REVIEW]]



ChatGPT:
[[LITERATURE REVIEW]]

The connection between political elections and occupational dynamics has been a subject of interest for academics and policymakers. Smith et al. (2016) examined the influence of political shifts on labor market outcomes, focusing on traditional economic measures such as unemployment and wage growth. However, little attention has been given to the peculiar relationship between political votes and the staffing levels of insurance claims and policy processing clerks.

In "Insurance Industry Trends and Analysis," Doe and Johnson (2018) discussed the impact of demographic and economic fluctuations on insurance labor demand, providing valuable insights into the industry's workforce trends. Meanwhile, Jones and Smith (2017) explored the role of political ideologies in shaping bureaucratic functions, offering a theoretical framework for understanding the potential interplay between political leanings and administrative occupations.

Nevertheless, the literature on the specific nexus of Democrat votes for Senators in Florida and the employment patterns of insurance clerks remains conspicuously sparse, akin to a desert island devoid of statistical insights. Amidst the serious scholarly inquiries, it seems that this enigmatic connection has eluded the academic limelight – until now.

In the realm of non-fiction, "Insurance and Politics: Unraveling the Underwriting of Democracy" by Anderson (2019) offers a thought-provoking exploration of the intersections between the insurance industry and political landscapes, although its focus lies outside the purview of our investigation. On the fictional front, "The Clerk Chronicles: Tales of Triumph and Tribulation in Bureaucratic Realms" by Roberts (2015) presents allegorical stories set in bureaucratic settings, evoking themes of resilience and humor amidst administrative challenges.

As we expand our horizons and seek unconventional inspiration, it is worth noting the educational value of children's cartoons and shows. While seemingly unrelated to our research, the antics of "SpongeBob SquarePants" and the organizational feats of "Paw Patrol" may offer unexpected parallels to the clerical intricacies and political undercurrents within the insurance sector. After all, a good laugh and a dash of creativity can invigorate scholarly pursuits and help navigate the quirks of statistical analysis.

A Democrat walks into an insurance office, and the clerks exclaim, "Looks like we've got a policy change coming!" It's evident that the whimsy of statistics permeates even the most unlikely scenarios, prompting us to embrace the unexpected with open arms and a healthy dose of statistical rigor.


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

Please make up the methodology section of an academic research paper presenting the findings of the connection between Democrat votes for Senators in Florida and The number of insurance claims and policy processing clerks in Florida. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from MIT Election Data and Science Lab, Harvard Dataverse and Bureau of Larbor Statistics . You used data from 2003 to 2018

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]]
Fate of Florida's Senators: Democrat Votes and the Clerks' Insurance Claims Rates

[[ABSTRACT]]
This study delves into the intriguing relationship between Democrat votes for Senators in Florida and the employment levels of insurance claims and policy processing clerks. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics, we evaluated this peculiar association. The findings revealed a remarkably high correlation coefficient of 0.9669320 and a p-value of less than 0.01 for the years 2003 to 2018.

Our analysis suggests that the fate of Florida's senators may indeed be related to the workload of insurance clerks, as surprising as it sounds. The correlation between these variables is striking and may prompt additional investigations into the underlying mechanisms at play. It appears that political preferences might have an unexpected impact on administrative roles within the insurance industry, stirring the curiosity of researchers and policymakers alike.

A Democrat-loving hurricane walks into the Senate, and the clerks can't stop making waves with their claims - a true tale of political winds shaping the workday grind!

[[INTRODUCTION]]
The intersection of politics and labor market dynamics has long been a subject of fascination for researchers and pundits alike. While most studies have scrutinized the relationship between political preferences and broader economic indicators, such as unemployment rates and income levels, few have ventured into the peculiar realm of insurance clerks' workloads. This study aims to unravel the mysterious connection between Democrat votes for Senators in Florida and the number of insurance claims and policy processing clerks, shedding light on an unexplored aspect of political influence.
It's not every day that politicians and insurance clerks find themselves in the same sentence, let alone the same statistical analysis. Much like the unexpected plot twists in a mystery novel, the correlation between these seemingly disparate variables piques our intellectual curiosity and prompts us to investigate further.
Now, imagine a scenario where a Democrat votes surge and the insurance clerks' desks become a whirlwind of activity. It's not just a political whirlwind; it's a statistical storm brewing with intriguing implications for labor market dynamics.
The empirical analysis presented in this study draws on rich datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics, allowing us to embark on a journey through the labyrinth of statistics and research methods to uncover the link between political preferences and the insurance industry. As we navigate this territory, we are reminded of the sage advice that correlation does not imply causation, but it sure does make for a fascinating hypothesis.
Imagine the insurance clerks sifting through a pile of claims, much like researchers sifting through mountains of data, seeking patterns and connections that illuminate the complex web of political winds and occupational demands.
We embark on this scientific exploration with a twinkle in our eyes and a healthy dose of humor, for as every seasoned researcher knows, a good pun about correlation is always a significant finding.

-------

Your response should start like this:

[[METHODOLOGY]]



ChatGPT:

[[METHODOLOGY]]

To disentangle the enigmatic relationship between Democrat votes for Senators in Florida and the employment levels of insurance claims and policy processing clerks, we embarked on an analytical journey that combined elements of statistical wizardry and methodological mirth. Our dataset spanned the years 2003 to 2018, sourced primarily from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics.

First, we employed a rigorous quantitative approach to explore the correlation between Democrat votes in Florida's Senate elections and the number of insurance claims and policy processing clerks. Using advanced statistical software, we conducted a thorough analysis to unravel the intricate dance between political preferences and occupational demands.

With the precision of a skilled surgeon, we adeptly sliced through the data to uncover patterns and connections, much like a magician revealing the secrets of a mesmerizing illusion. Our arsenal of statistical tools included Pearson's correlation coefficient, t-tests, and regression analyses, each serving as a sleight of hand in the scientific spectacle of unveiling the unexpected relationship between these seemingly unrelated variables.

As we delved deeper into the statistical maze, we also accounted for potential confounding variables, such as demographic shifts and economic fluctuations. It was crucial to ensure that our findings were not obscured by lurking covariates, akin to spotting a hidden object in a vibrant mosaic.

Of course, no statistical escapade would be complete without a touch of humor amid the seriousness of research. After all, as the old adage goes, "A good joke about covariance is the key to a robust statistical model."

In a clever twist, we engaged in a bit of statistical matchmaking, using propensity score matching to pair Democrat votes with the employment levels of insurance clerks, creating a harmonious union of political sentiments and professional pursuits. Think of it as eHarmony for variables, where compatibility is based on correlation rather than hobbies and interests.

Throughout this methodological odyssey, we maintained a keen sense of curiosity and an unwavering commitment to scientific rigor, all while sprinkling in the occasional pun to keep the spirits high. After all, in the realm of research, a well-placed pun is not just a statistical tool; it's a valuable instrument of levity and intellectual camaraderie.

In conducting this study, we embraced the challenges of untangling a statistical puzzle as perplexing as a riddle wrapped in an enigma, knowing that our findings would shed light on a captivating confluence of political and occupational dynamics.

With each statistical test and methodological maneuver, we navigated the labyrinth of data with the determination of explorers charting new territories, ready to uncover the unexpected correlations and unveil the mysteries of political winds and clerical duties.

In summary, our methodology involved a blend of statistical sophistication, methodological creativity, and an unwavering dedication to uncovering the unusual connections that shape the intricate tapestry of political and labor market influences. And as every diligent researcher knows, a good methodological section is like a good joke: it's all about the delivery.


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

Please make up the results section of an academic research paper presenting the findings of the connection between Democrat votes for Senators in Florida and The number of insurance claims and policy processing clerks in Florida. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from MIT Election Data and Science Lab, Harvard Dataverse and Bureau of Larbor Statistics .

For the time period 2003 to 2018, you found a correlation 0.9669320, r-squared of 0.9349574, 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]]
Fate of Florida's Senators: Democrat Votes and the Clerks' Insurance Claims Rates

[[ABSTRACT]]
This study delves into the intriguing relationship between Democrat votes for Senators in Florida and the employment levels of insurance claims and policy processing clerks. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics, we evaluated this peculiar association. The findings revealed a remarkably high correlation coefficient of 0.9669320 and a p-value of less than 0.01 for the years 2003 to 2018.

Our analysis suggests that the fate of Florida's senators may indeed be related to the workload of insurance clerks, as surprising as it sounds. The correlation between these variables is striking and may prompt additional investigations into the underlying mechanisms at play. It appears that political preferences might have an unexpected impact on administrative roles within the insurance industry, stirring the curiosity of researchers and policymakers alike.

A Democrat-loving hurricane walks into the Senate, and the clerks can't stop making waves with their claims - a true tale of political winds shaping the workday grind!

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intersection of politics and labor market dynamics has long been a subject of fascination for researchers and pundits alike. While most studies have scrutinized the relationship between political preferences and broader economic indicators, such as unemployment rates and income levels, few have ventured into the peculiar realm of insurance clerks' workloads. This study aims to unravel the mysterious connection between Democrat votes for Senators in Florida and the number of insurance claims and policy processing clerks, shedding light on an unexplored aspect of political influence.
It's not every day that politicians and insurance clerks find themselves in the same sentence, let alone the same statistical analysis. Much like the unexpected plot twists in a mystery novel, the correlation between these seemingly disparate variables piques our intellectual curiosity and prompts us to investigate further.
Now, imagine a scenario where a Democrat votes surge and the insurance clerks' desks become a whirlwind of activity. It's not just a political whirlwind; it's a statistical storm brewing with intriguing implications for labor market dynamics.
The empirical analysis presented in this study draws on rich datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics, allowing us to embark on a journey through the labyrinth of statistics and research methods to uncover the link between political preferences and the insurance industry. As we navigate this territory, we are reminded of the sage advice that correlation does not imply causation, but it sure does make for a fascinating hypothesis.
Imagine the insurance clerks sifting through a pile of claims, much like researchers sifting through mountains of data, seeking patterns and connections that illuminate the complex web of political winds and occupational demands.
We embark on this scientific exploration with a twinkle in our eyes and a healthy dose of humor, for as every seasoned researcher knows, a good pun about correlation is always a significant finding.

-------

Your response should start like this:

[[RESULTS]]



ChatGPT:

[[RESULTS]]

The results of our analysis unveiled a remarkably strong correlation between Democrat votes for Senators in Florida and the employment levels of insurance claims and policy processing clerks. The correlation coefficient of 0.9669320 indicates a robust positive relationship between these seemingly unrelated variables. This finding suggests that as Democrat votes increased, so did the number of insurance clerks processing claims.

It seems that Democratic support in Florida created quite the stir, not just in the political arena, but also on the desks of insurance clerks. It's as if a hurricane of Democrat votes swept through the state, leaving a whirlwind of paperwork for the insurance clerks to handle.

The r-squared value of 0.9349574 further indicates that approximately 93.5% of the variation in the employment levels of insurance clerks can be explained by the variation in Democrat votes for Senators in Florida. The remaining 6.5% can perhaps be attributed to unforeseen factors – maybe a stray alligator in the data pond!

The p-value less than 0.01 lends credence to the significance of this correlation, indicating that the observed relationship is very unlikely to be a result of random chance. It's as unlikely as finding a statistical analyst who doesn't enjoy a good pun about data!

In Fig. 1, the scatterplot illustrates the striking relationship between Democrat votes and the number of insurance claims and policy processing clerks in Florida. The data points align themselves in a neat, upward-sloping pattern, akin to the tidy desks of well-organized clerks – a testament to the strength of this correlation and the unanticipated influence of political preferences on clerical employment.

The insurance clerks in Florida must have been quite busy during these years, processing claims faster than a politician making promises during a campaign. It seems that the fate of Florida's senators may have a more direct impact on the clerks than initially presumed.

In conclusion, our findings not only highlight the surprising association between political votes and clerical employment but also emphasize the need for further inquiry into the underlying mechanisms driving this unexpected correlation. As researchers, we must remain vigilant for such unforeseen connections, as they may hold valuable insights into the intricate dance of factors shaping our economic and labor landscape.


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

Please make up the discussion section of an academic research paper presenting the findings of the connection between Democrat votes for Senators in Florida and The number of insurance claims and policy processing clerks in Florida. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

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

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

[[TITLE]]
Fate of Florida's Senators: Democrat Votes and the Clerks' Insurance Claims Rates

[[ABSTRACT]]
This study delves into the intriguing relationship between Democrat votes for Senators in Florida and the employment levels of insurance claims and policy processing clerks. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics, we evaluated this peculiar association. The findings revealed a remarkably high correlation coefficient of 0.9669320 and a p-value of less than 0.01 for the years 2003 to 2018.

Our analysis suggests that the fate of Florida's senators may indeed be related to the workload of insurance clerks, as surprising as it sounds. The correlation between these variables is striking and may prompt additional investigations into the underlying mechanisms at play. It appears that political preferences might have an unexpected impact on administrative roles within the insurance industry, stirring the curiosity of researchers and policymakers alike.

A Democrat-loving hurricane walks into the Senate, and the clerks can't stop making waves with their claims - a true tale of political winds shaping the workday grind!

[[LITERATURE REVIEW]]
The connection between political elections and occupational dynamics has been a subject of interest for academics and policymakers. Smith et al. (2016) examined the influence of political shifts on labor market outcomes, focusing on traditional economic measures such as unemployment and wage growth. However, little attention has been given to the peculiar relationship between political votes and the staffing levels of insurance claims and policy processing clerks.
In "Insurance Industry Trends and Analysis," Doe and Johnson (2018) discussed the impact of demographic and economic fluctuations on insurance labor demand, providing valuable insights into the industry's workforce trends. Meanwhile, Jones and Smith (2017) explored the role of political ideologies in shaping bureaucratic functions, offering a theoretical framework for understanding the potential interplay between political leanings and administrative occupations.
Nevertheless, the literature on the specific nexus of Democrat votes for Senators in Florida and the employment patterns of insurance clerks remains conspicuously sparse, akin to a desert island devoid of statistical insights. Amidst the serious scholarly inquiries, it seems that this enigmatic connection has eluded the academic limelight – until now.
In the realm of non-fiction, "Insurance and Politics: Unraveling the Underwriting of Democracy" by Anderson (2019) offers a thought-provoking exploration of the intersections between the insurance industry and political landscapes, although its focus lies outside the purview of our investigation. On the fictional front, "The Clerk Chronicles: Tales of Triumph and Tribulation in Bureaucratic Realms" by Roberts (2015) presents allegorical stories set in bureaucratic settings, evoking themes of resilience and humor amidst administrative challenges.
As we expand our horizons and seek unconventional inspiration, it is worth noting the educational value of children's cartoons and shows. While seemingly unrelated to our research, the antics of "SpongeBob SquarePants" and the organizational feats of "Paw Patrol" may offer unexpected parallels to the clerical intricacies and political undercurrents within the insurance sector. After all, a good laugh and a dash of creativity can invigorate scholarly pursuits and help navigate the quirks of statistical analysis.
A Democrat walks into an insurance office, and the clerks exclaim, "Looks like we've got a policy change coming!" It's evident that the whimsy of statistics permeates even the most unlikely scenarios, prompting us to embrace the unexpected with open arms and a healthy dose of statistical rigor.

[[RESULTS]]
The results of our analysis unveiled a remarkably strong correlation between Democrat votes for Senators in Florida and the employment levels of insurance claims and policy processing clerks. The correlation coefficient of 0.9669320 indicates a robust positive relationship between these seemingly unrelated variables. This finding suggests that as Democrat votes increased, so did the number of insurance clerks processing claims.
It seems that Democratic support in Florida created quite the stir, not just in the political arena, but also on the desks of insurance clerks. It's as if a hurricane of Democrat votes swept through the state, leaving a whirlwind of paperwork for the insurance clerks to handle.
The r-squared value of 0.9349574 further indicates that approximately 93.5% of the variation in the employment levels of insurance clerks can be explained by the variation in Democrat votes for Senators in Florida. The remaining 6.5% can perhaps be attributed to unforeseen factors – maybe a stray alligator in the data pond!
The p-value less than 0.01 lends credence to the significance of this correlation, indicating that the observed relationship is very unlikely to be a result of random chance. It's as unlikely as finding a statistical analyst who doesn't enjoy a good pun about data!
In Fig. 1, the scatterplot illustrates the striking relationship between Democrat votes and the number of insurance claims and policy processing clerks in Florida. The data points align themselves in a neat, upward-sloping pattern, akin to the tidy desks of well-organized clerks – a testament to the strength of this correlation and the unanticipated influence of political preferences on clerical employment.
The insurance clerks in Florida must have been quite busy during these years, processing claims faster than a politician making promises during a campaign. It seems that the fate of Florida's senators may have a more direct impact on the clerks than initially presumed.
In conclusion, our findings not only highlight the surprising association between political votes and clerical employment but also emphasize the need for further inquiry into the underlying mechanisms driving this unexpected correlation. As researchers, we must remain vigilant for such unforeseen connections, as they may hold valuable insights into the intricate dance of factors shaping our economic and labor landscape.

-------

Your response should start like this:

[[DISCUSSION]]



ChatGPT:

[[DISCUSSION]]

The results of our investigation present a compelling tale of intertwined destinies, where the political winds of Democrat votes for Senators in Florida appear to have left an indelible mark on the administrative realms of insurance claims and policy processing clerks. Our findings not only align with prior research concerning the influence of political shifts on labor market outcomes but also underscore the need to recognize the role of political preferences in shaping bureaucratic environments. It seems that these seemingly distinct worlds of politics and clerical work are more entwined than a pair of earbuds pulled out of a pocket!

Smith et al. (2016) may have paved the way with their exploration of political shifts' impact on labor market outcomes, but they merely skimmed the surface compared to the depth of our findings. Similarly, while Doe and Johnson (2018) shed light on demographic and economic fluctuations in insurance labor demand, they overlooked the captivating correlation between political votes and clerical employment. Our study not only supported the existing literature but also supplemented it with a twist that even the most ardent statistician couldn't have predicted!

The substantial correlation coefficient of 0.9669320 serves as the poignant punchline to our statistical saga, emphasizing the remarkably strong relationship between Democrat votes and the employment levels of insurance clerks. It's as if the political tides washed ashore, carrying with them a surge of paperwork and claims for the diligent clerks to navigate through, akin to an unexpected, yet impeccably timed punchline in a stand-up comedy show.

The results corroborate the assertions of Jones and Smith (2017) regarding the role of political ideologies in shaping bureaucratic functions, offering empirical evidence that the clerical landscapes in Florida indeed respond to the political undercurrents. The p-value less than 0.01 is a statistical gem that adds weight to this unexpected correlation, highlighting its robustness amidst the roiling sea of data, much like an undiscovered treasure chest within the statistical waters!

The scatterplot presents a visual narrative of the alignment between Democrat votes and the number of insurance clerks, akin to a meticulously choreographed dance routine that captivates the audience with its precise movements and unexpected elegance, a truly captivating performance of statistical significance.

As we embark on future research endeavors, it is imperative to consider the implications of our findings. The unexpected nexus between political votes and clerical employment not only invites further scholarly curiosity but also carries practical significance for policymakers and industry stakeholders. It underscores the need to account for political leanings in workforce planning and administrative resource allocation, emphasizing that the winds of political change can have tangible effects on the everyday workings of bureaucratic institutions.

In the grand orchestration of statistical research, our study has contributed a unique melody to the symphony of knowledge, showcasing how unanticipated connections can lead to revelatory insights. As we delve deeper into the interplay between political dynamics and occupational landscapes, let's remember to stay open to the unexpected, for in the realm of statistics, there is always room for one more laughably significant correlation!


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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Democrat votes for Senators in Florida and The number of insurance claims and policy processing clerks in Florida. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
Fate of Florida's Senators: Democrat Votes and the Clerks' Insurance Claims Rates

[[ABSTRACT]]
This study delves into the intriguing relationship between Democrat votes for Senators in Florida and the employment levels of insurance claims and policy processing clerks. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics, we evaluated this peculiar association. The findings revealed a remarkably high correlation coefficient of 0.9669320 and a p-value of less than 0.01 for the years 2003 to 2018.

Our analysis suggests that the fate of Florida's senators may indeed be related to the workload of insurance clerks, as surprising as it sounds. The correlation between these variables is striking and may prompt additional investigations into the underlying mechanisms at play. It appears that political preferences might have an unexpected impact on administrative roles within the insurance industry, stirring the curiosity of researchers and policymakers alike.

A Democrat-loving hurricane walks into the Senate, and the clerks can't stop making waves with their claims - a true tale of political winds shaping the workday grind!

[[INTRDUCTION]]
The intersection of politics and labor market dynamics has long been a subject of fascination for researchers and pundits alike. While most studies have scrutinized the relationship between political preferences and broader economic indicators, such as unemployment rates and income levels, few have ventured into the peculiar realm of insurance clerks' workloads. This study aims to unravel the mysterious connection between Democrat votes for Senators in Florida and the number of insurance claims and policy processing clerks, shedding light on an unexplored aspect of political influence.
It's not every day that politicians and insurance clerks find themselves in the same sentence, let alone the same statistical analysis. Much like the unexpected plot twists in a mystery novel, the correlation between these seemingly disparate variables piques our intellectual curiosity and prompts us to investigate further.
Now, imagine a scenario where a Democrat votes surge and the insurance clerks' desks become a whirlwind of activity. It's not just a political whirlwind; it's a statistical storm brewing with intriguing implications for labor market dynamics.
The empirical analysis presented in this study draws on rich datasets from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics, allowing us to embark on a journey through the labyrinth of statistics and research methods to uncover the link between political preferences and the insurance industry. As we navigate this territory, we are reminded of the sage advice that correlation does not imply causation, but it sure does make for a fascinating hypothesis.
Imagine the insurance clerks sifting through a pile of claims, much like researchers sifting through mountains of data, seeking patterns and connections that illuminate the complex web of political winds and occupational demands.
We embark on this scientific exploration with a twinkle in our eyes and a healthy dose of humor, for as every seasoned researcher knows, a good pun about correlation is always a significant finding.

[[RESULTS]]
The results of our analysis unveiled a remarkably strong correlation between Democrat votes for Senators in Florida and the employment levels of insurance claims and policy processing clerks. The correlation coefficient of 0.9669320 indicates a robust positive relationship between these seemingly unrelated variables. This finding suggests that as Democrat votes increased, so did the number of insurance clerks processing claims.
It seems that Democratic support in Florida created quite the stir, not just in the political arena, but also on the desks of insurance clerks. It's as if a hurricane of Democrat votes swept through the state, leaving a whirlwind of paperwork for the insurance clerks to handle.
The r-squared value of 0.9349574 further indicates that approximately 93.5% of the variation in the employment levels of insurance clerks can be explained by the variation in Democrat votes for Senators in Florida. The remaining 6.5% can perhaps be attributed to unforeseen factors – maybe a stray alligator in the data pond!
The p-value less than 0.01 lends credence to the significance of this correlation, indicating that the observed relationship is very unlikely to be a result of random chance. It's as unlikely as finding a statistical analyst who doesn't enjoy a good pun about data!
In Fig. 1, the scatterplot illustrates the striking relationship between Democrat votes and the number of insurance claims and policy processing clerks in Florida. The data points align themselves in a neat, upward-sloping pattern, akin to the tidy desks of well-organized clerks – a testament to the strength of this correlation and the unanticipated influence of political preferences on clerical employment.
The insurance clerks in Florida must have been quite busy during these years, processing claims faster than a politician making promises during a campaign. It seems that the fate of Florida's senators may have a more direct impact on the clerks than initially presumed.
In conclusion, our findings not only highlight the surprising association between political votes and clerical employment but also emphasize the need for further inquiry into the underlying mechanisms driving this unexpected correlation. As researchers, we must remain vigilant for such unforeseen connections, as they may hold valuable insights into the intricate dance of factors shaping our economic and labor landscape.

-------

Your response should start like this:

[[CONCLUSION]]



ChatGPT:

[[CONCLUSION]]

In conclusion, our study has illuminated an unexpected and compelling relationship between Democrat votes for Senators in Florida and the employment levels of insurance claims and policy processing clerks. The remarkably high correlation coefficient of 0.9669320 and a p-value of less than 0.01 for the years 2003 to 2018 point to a strong and significant connection, leaving us more surprised than a statistician finding joy in data analysis.

It's as if a Democrat-loving hurricane swept through the Sunshine State, leaving a trail of paperwork in its wake, much like the aftermath of an amateur scientist's experiment gone awry – unexpected, yet undeniably intriguing! The r-squared value of 0.9349574 suggests that approximately 93.5% of the variation in clerical employment can be explained by Democrat votes, leaving only 6.5% unexplained, which is about as rare as finding a statistician who doesn't enjoy a good probability pun.

Our findings add an unforeseen twist to the tale of political winds and clerical duties, akin to a plot twist in a labor market soap opera. The results not only highlight the surprising association between political preferences and clerical employment, but also accentuate the need for further investigation into this unusual correlation – the kind of mystery that would make even Sherlock Holmes envious.

In the end, as much as we’d love to embark on another statistical journey, it seems that no more research is needed in this area. The connection between Democrat votes and insurance clerks' workloads has been thoroughly unraveled, leaving us with a deeper understanding of the intricate interplay between political forces and labor market dynamics. This statistical saga of unexpected connections and research revelations has come to a satisfying conclusion.


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 Fate of Florida's Senators: Democrat Votes and the Clerks' Insurance Claims Rates. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Political Prognostication and Actuarial Analysis


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 Fate of Florida's Senators: Democrat Votes and the Clerks' Insurance Claims Rates. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Sunshine State Statistics and Social Science Research


Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Fate of Florida's Senators: Democrat Votes and the Clerks' Insurance Claims Rates with an abstract of This study delves into the intriguing relationship between Democrat votes for Senators in Florida and the employment levels of insurance claims and policy processing clerks. Leveraging data from the MIT Election Data and Science Lab, Harvard Dataverse, and the Bureau of Labor Statistics, we evaluated this peculiar association. The findings revealed a remarkably high correlation coefficient of 0.9669320 and a p-value of less than 0.01 for the years 2003 to 2018.
Our analysis suggests that the fate of Florida's senators may indeed be related to the workload of insurance clerks, as surprising as it sounds. The correlation between these variables is striking and may prompt additional investigations into the underlying mechanisms at play. It appears that political preferences might have an unexpected impact on administrative roles within the insurance industry, stirring the curiosity of researchers and policymakers alike.
A Democrat-loving hurricane walks into the Senate, and the clerks can't stop making waves with their claims - a true tale of political winds shaping the workday grind!

ChatGPT:

Florida Senators, Democrat votes, insurance clerks, insurance claims rates, Florida employment, MIT Election Data and Science Lab, Harvard Dataverse, Bureau of Labor Statistics, political preferences, administrative roles, insurance industry, correlation coefficient, p-value, 2003-2018 analysis

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



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Data details

Votes for Democratic Senators in Florida
Detailed data title: Total number of votes cast for Federal Democrat Senate candidates in Florida
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for Democratic Senators in Florida

The number of insurance claims and policy processing clerks in Florida
Detailed data title: BLS estimate of insurance claims and policy processing clerks in Florida
Source: Bureau of Larbor Statistics
See what else correlates with The number of insurance claims and policy processing clerks in Florida

Correlation r = 0.9669320 (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 = 0.9349574 (Coefficient of determination)
This means 93.5% of the change in the one variable (i.e., The number of insurance claims and policy processing clerks in Florida) is predictable based on the change in the other (i.e., Votes for Democratic Senators in Florida) over the 6 years from 2003 through 2018.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0016. 0.0016221619194915060000000000
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 0.97 in 0.16% of random cases. Said differently, if you correlated 616 random variables Which I absolutely did.
with the same 5 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 5 because we have two variables measured over a period of 6 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.

[ 0.72, 1 ] 95% correlation confidence interval (using the Fisher z-transformation)
The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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


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

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.
200420062010201220162018
Votes for Democratic Senators in Florida (Total votes)359020028905501092940452345041220904089470
The number of insurance claims and policy processing clerks in Florida (Laborers)166001674014660180601807017590




Why this works

  1. Data dredging: I have 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 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. Very low n: There are not many data points included in this analysis. Even if the p-value is high, we should be suspicious of using so few datapoints in a correlation.
  5. Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
    Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.




Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. 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([3590200,2890550,1092940,4523450,4122090,4089470,])
array_2 = np.array([16600,16740,14660,18060,18070,17590,])
array_1_name = "Votes for Democratic Senators in Florida"
array_2_name = "The number of insurance claims and policy processing clerks in Florida"

# 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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Correlation ID: 5393 · Black Variable ID: 26219 · Red Variable ID: 6595
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