Spurious correlation #4,958 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air quality in Worcester, Massachusetts and the second variable is The number of pharmacy technicians in Massachusetts. The chart goes from 2003 to 2022, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Air quality in Worcester, Massachusetts and the second variable is The number of pharmacy technicians in Massachusetts. The chart goes from 2003 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

The cleaner air in Worcester made people breathe easier, leading to a surge in overall wellness. This sudden burst of good health prompted an increased demand for medications, causing pharmacies to frantically hire more technicians to keep up with the sudden spike in prescriptions. It's a case of the air getting cleaner and the pharmacies getting busier! Who knew that fresher air could lead to a breath of fresh employment opportunities in the pharmaceutical world?

^{Show image generation prompt} Model: dalle-3
Prompt: [ An image that looks like it is a scene from a Pixar movie ] A bustling city scene with a picturesque clear blue sky over Worcester, Massachusetts. People are seen leisurely strolling through the streets, enjoying the clean, fresh air and smiling. In the backdrop, bustling pharmacies are depicted with staff hurrying to attend to the increased demand, portraying a vibrant but organized chaos. 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: Air quality in Worcester, Massachusetts and The number of pharmacy technicians in Massachusetts. 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: Woodblock painting Art nouveau An image that looks like it is a scene from a Pixar movie A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s Cute cartoon
ChatGPT: An image that looks like it is a scene from a Pixar movie

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 Air quality in Worcester, Massachusetts and The number of pharmacy technicians in Massachusetts, in the following situation: The cleaner air in Worcester made people breathe easier, leading to a surge in overall wellness. This sudden burst of good health prompted an increased demand for medications, causing pharmacies to frantically hire more technicians to keep up with the sudden spike in prescriptions. It's a case of the air getting cleaner and the pharmacies getting busier! Who knew that fresher air could lead to a breath of fresh employment opportunities in the pharmaceutical world?. Keep the description simple.
ChatGPT: A clear blue sky hangs over Worcester, Massachusetts, with people seen strolling through clean, fresh air, breathing deeply and smiling. At the same time, bustling pharmacies in the city are hiring numerous pharmacy technicians to keep up with the sudden surge in prescriptions, with busy staff seen frantically attending to 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: [ An image that looks like it is a scene from a Pixar movie ] and depicting this scene: A clear blue sky hangs over Worcester, Massachusetts, with people seen strolling through clean, fresh air, breathing deeply and smiling. At the same time, bustling pharmacies in the city are hiring numerous pharmacy technicians to keep up with the sudden surge in prescriptions, with busy staff seen frantically attending to the increased demand. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ An image that looks like it is a scene from a Pixar movie ] A bustling city scene with a picturesque clear blue sky over Worcester, Massachusetts. People are seen leisurely strolling through the streets, enjoying the clean, fresh air and smiling. In the backdrop, bustling pharmacies are depicted with staff hurrying to attend to the increased demand, portraying a vibrant but organized chaos.

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

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

AI academic paper

(Because p < 0.01)

A Breath of Fresh Data: Examining the Relationship Between Air Quality in Worcester, Massachusetts, and the Dispensing of Pharmacy Technicians in Massachusetts

The Journal of Ecological Epidemiology

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Air quality in Worcester, Massachusetts and The number of pharmacy technicians in Massachusetts. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
A Breath of Fresh Data: Examining the Relationship Between Air Quality in Worcester, Massachusetts, and the Dispensing of Pharmacy Technicians in Massachusetts

[[ABSTRACT]]
This study delves into the often overlooked intersection of air quality and pharmacy technician employment in Massachusetts. Utilizing data from the Environmental Protection Agency and the Bureau of Labor Statistics, we conducted a thorough analysis covering the years 2003 to 2022. Despite the serious implications of air pollution, our findings reveal a lighthearted connection between the air quality in Worcester, Massachusetts, and the number of pharmacy technicians in the state. The correlation coefficient of 0.8486308 and a statistically significant p-value of less than 0.01 suggest a strong association between these seemingly disparate factors. In this paper, we unpack the complexities of this unlikely relationship, exploring the potential influence of air quality on the demand for pharmacy technicians. Our work also ponders the intriguing possibility of pharmacists dispensing a prescription of fresh air to combat the effects of pollution.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The realm of air quality and its impact on human health has long been a subject of earnest investigation, with a plethora of inquiries seeking to uncover the profound ramifications of atmospheric contamination. Concurrently, the labor market for pharmacy technicians in Massachusetts has undergone substantial fluctuations, prompting scholarly curiosity regarding the drivers of such workforce dynamics. However, the confluence of these two domains has received minimal attention, with the quaint union of air quality in Worcester, Massachusetts, and the dispensing of pharmacy technicians in the state being largely overlooked.
In this context, our research embarks on a playful yet rigorous exploration of the association between these apparently incongruent variables. While the earnest pursuit of scientific inquiry certainly permeates our endeavor, we cannot help but find amusement in the notion of air pollution influencing the labor market for pharmacy technicians. As we venture forth into this uncharted territory, we acknowledge the inherent duality of our findings, where the weighty implications of air pollution seamlessly coalesce with the whimsical notion of pharmacy technicians becoming entangled in its effects.
With a statistical correlation coefficient of 0.8486308 and a p-value signaling statistical significance, our analysis offers a compelling testament to the tangible relationship between the air quality in Worcester and the number of pharmacy technicians across Massachusetts. As we dissect the intricacies of this unexpected rapport, we are compelled to contemplate the possibility of pharmacists prescribing not only medications but also a remedy of fresh air to mitigate the deleterious effects of pollution. This curious juxtaposition of science and humor forms the crux of our investigation, as we endeavor to shed light on the hitherto unexplored connection between air quality and the dispensing of pharmacy technicians in the state.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The relationship between air quality and labor market outcomes has been the subject of earnest inquiry in several scholarly studies. Smith et al. (2015) elucidate the impact of air pollution on public health, emphasizing the deleterious effects of particulate matter on respiratory function. Similarly, Doe and colleagues (2018) delve into the economic repercussions of poor air quality, highlighting its association with reduced worker productivity and increased healthcare expenditures. Moreover, Jones et al. (2020) provide a comprehensive analysis of air pollution's ramifications on urban areas, underscoring its influence on residents' well-being. As these studies underscore the gravity of air pollution, they set the stage for our investigation into its unexpected connection with the number of pharmacy technicians in Massachusetts.

In "Air Quality and Its Effects on Public Health," the authors find compelling evidence of the adverse health outcomes associated with atmospheric contamination, prompting a closer examination of its societal implications. In a similar vein, "Economic Consequences of Air Pollution" offers valuable insights into the economic burdens imposed by poor air quality, setting the context for our unorthodox exploration of its link with pharmacy technician employment in Massachusetts.

Turning to non-fiction literature, "The Air We Breathe: Understanding Atmospheric Contamination" and "The Economics of Clean Air: Balancing Environmental Concerns and Economic Stability" provide pertinent perspectives on the myriad facets of air quality and its multifaceted impact on society.

On a more whimsical note, the scholarly discourse surrounding air quality finds unexpected companionship in fictional works. "The Airbender's Odyssey" and "A Tale of Two Smogs: A Dickensian Interpretation of Atmospheric Pollution" offer imaginative renditions of the interplay between air quality and human livelihoods. While these literary works may not adhere to the stringent standards of scholarly research, they serve as a lighthearted backdrop to our investigation into the unanticipated correlation between air quality in Worcester, Massachusetts, and the dispensing of pharmacy technicians in the state.

In addition to these scholarly and fictional explorations, childhood nostalgia channels a peculiar manifestation of relevance to our inquiry. Cartoons such as "Captain Planet and the Planeteers" and "The Magic School Bus" intricately weave environmental themes into their narratives, offering an unconventional yet strangely pertinent lens through which to view the connection between air quality and labor market dynamics. Although their didactic intentions may have been geared toward a juvenile audience, these animated series inadvertently impart a whimsical dimension to our rigorous investigation.

As we navigate the scholarly undercurrents and whimsical reverberations of our investigative landscape, it is imperative to contemplate the broader implications of our findings. While rooted in empirical evidence and statistical rigor, the unexpected synergy between air quality in Worcester, Massachusetts, and the number of pharmacy technicians in the state beckons a contemplation of the interplay between scientific inquiry and the serendipitous absurdities of life.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between Air quality in Worcester, Massachusetts and The number of pharmacy technicians in Massachusetts. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. 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 Environmental Protection Agency and Bureau of Larbor Statistics . You used data from 2003 to 2022

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]]
A Breath of Fresh Data: Examining the Relationship Between Air Quality in Worcester, Massachusetts, and the Dispensing of Pharmacy Technicians in Massachusetts

[[ABSTRACT]]
This study delves into the often overlooked intersection of air quality and pharmacy technician employment in Massachusetts. Utilizing data from the Environmental Protection Agency and the Bureau of Labor Statistics, we conducted a thorough analysis covering the years 2003 to 2022. Despite the serious implications of air pollution, our findings reveal a lighthearted connection between the air quality in Worcester, Massachusetts, and the number of pharmacy technicians in the state. The correlation coefficient of 0.8486308 and a statistically significant p-value of less than 0.01 suggest a strong association between these seemingly disparate factors. In this paper, we unpack the complexities of this unlikely relationship, exploring the potential influence of air quality on the demand for pharmacy technicians. Our work also ponders the intriguing possibility of pharmacists dispensing a prescription of fresh air to combat the effects of pollution.

[[INTRODUCTION]]
The realm of air quality and its impact on human health has long been a subject of earnest investigation, with a plethora of inquiries seeking to uncover the profound ramifications of atmospheric contamination. Concurrently, the labor market for pharmacy technicians in Massachusetts has undergone substantial fluctuations, prompting scholarly curiosity regarding the drivers of such workforce dynamics. However, the confluence of these two domains has received minimal attention, with the quaint union of air quality in Worcester, Massachusetts, and the dispensing of pharmacy technicians in the state being largely overlooked.
In this context, our research embarks on a playful yet rigorous exploration of the association between these apparently incongruent variables. While the earnest pursuit of scientific inquiry certainly permeates our endeavor, we cannot help but find amusement in the notion of air pollution influencing the labor market for pharmacy technicians. As we venture forth into this uncharted territory, we acknowledge the inherent duality of our findings, where the weighty implications of air pollution seamlessly coalesce with the whimsical notion of pharmacy technicians becoming entangled in its effects.
With a statistical correlation coefficient of 0.8486308 and a p-value signaling statistical significance, our analysis offers a compelling testament to the tangible relationship between the air quality in Worcester and the number of pharmacy technicians across Massachusetts. As we dissect the intricacies of this unexpected rapport, we are compelled to contemplate the possibility of pharmacists prescribing not only medications but also a remedy of fresh air to mitigate the deleterious effects of pollution. This curious juxtaposition of science and humor forms the crux of our investigation, as we endeavor to shed light on the hitherto unexplored connection between air quality and the dispensing of pharmacy technicians in the state.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this study resonates with the tradition of applying quantitative analysis to investigate the relationship between environmental factors and labor market dynamics. Data on air quality in Worcester, Massachusetts, was obtained from the Environmental Protection Agency, encompassing a comprehensive timeframe from 2003 to 2022. This dataset included measures of various air pollutants, such as particulate matter, ozone, carbon monoxide, and sulfur dioxide, which were aggregated to provide an overall Air Quality Index (AQI). Concurrently, information regarding the number of employed pharmacy technicians in Massachusetts was acquired from the Bureau of Labor Statistics, allowing for a robust examination of labor market dynamics in the state.

The analytic process embarked upon a convoluted journey through the realm of statistical analysis, unleashing the formidable power of regression models to disentangle the intricate web of relationships. The primary technique utilized was ordinary least squares (OLS) regression, serving as the trusty steed that galloped through the multifaceted terrain of data exploration. This approach facilitated the quantification of the association between air quality and the number of pharmacy technicians, offering a numerical cornerstone upon which to construct our findings.

Moreover, in an effort to traverse the tumultuous landscape of potential confounding variables, a series of robustness checks and sensitivity analyses were conducted. These examinations sought to ascertain the stability of our results in the face of alternative specifications and adjustments, ensuring that our conclusions stood resilient against the capricious winds of methodological uncertainty. Furthermore, the deployment of time series analysis techniques allowed for a nuanced exploration of the temporal dimensions encapsulated within the datasets, capturing the ebb and flow of air quality and pharmacy technician employment over the years.

Additionally, to infuse a touch of whimsy into the otherwise staid atmosphere of statistical analysis, a brief foray into qualitative methods was also undertaken. Intriguing anecdotes and personal reflections from pharmacy technicians were collected to offer a qualitative layer to the predominantly quantitative fabric of our investigation. These anecdotes were woven into the narrative of our findings, adding a human touch to the otherwise impersonal dance of numbers and equations.

Overall, the methodology embraced in this study reflects a balanced blend of rigorous statistical analysis, methodological diligence, and a dash of whimsical diversions, underscoring the multifaceted nature of our endeavor.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between Air quality in Worcester, Massachusetts and The number of pharmacy technicians in Massachusetts. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. 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]]
A Breath of Fresh Data: Examining the Relationship Between Air Quality in Worcester, Massachusetts, and the Dispensing of Pharmacy Technicians in Massachusetts

[[ABSTRACT]]
This study delves into the often overlooked intersection of air quality and pharmacy technician employment in Massachusetts. Utilizing data from the Environmental Protection Agency and the Bureau of Labor Statistics, we conducted a thorough analysis covering the years 2003 to 2022. Despite the serious implications of air pollution, our findings reveal a lighthearted connection between the air quality in Worcester, Massachusetts, and the number of pharmacy technicians in the state. The correlation coefficient of 0.8486308 and a statistically significant p-value of less than 0.01 suggest a strong association between these seemingly disparate factors. In this paper, we unpack the complexities of this unlikely relationship, exploring the potential influence of air quality on the demand for pharmacy technicians. Our work also ponders the intriguing possibility of pharmacists dispensing a prescription of fresh air to combat the effects of pollution.

[[LITERATURE REVIEW]]
The relationship between air quality and labor market outcomes has been the subject of earnest inquiry in several scholarly studies. Smith et al. (2015) elucidate the impact of air pollution on public health, emphasizing the deleterious effects of particulate matter on respiratory function. Similarly, Doe and colleagues (2018) delve into the economic repercussions of poor air quality, highlighting its association with reduced worker productivity and increased healthcare expenditures. Moreover, Jones et al. (2020) provide a comprehensive analysis of air pollution's ramifications on urban areas, underscoring its influence on residents' well-being. As these studies underscore the gravity of air pollution, they set the stage for our investigation into its unexpected connection with the number of pharmacy technicians in Massachusetts.
In "Air Quality and Its Effects on Public Health," the authors find compelling evidence of the adverse health outcomes associated with atmospheric contamination, prompting a closer examination of its societal implications. In a similar vein, "Economic Consequences of Air Pollution" offers valuable insights into the economic burdens imposed by poor air quality, setting the context for our unorthodox exploration of its link with pharmacy technician employment in Massachusetts.
Turning to non-fiction literature, "The Air We Breathe: Understanding Atmospheric Contamination" and "The Economics of Clean Air: Balancing Environmental Concerns and Economic Stability" provide pertinent perspectives on the myriad facets of air quality and its multifaceted impact on society.
On a more whimsical note, the scholarly discourse surrounding air quality finds unexpected companionship in fictional works. "The Airbender's Odyssey" and "A Tale of Two Smogs: A Dickensian Interpretation of Atmospheric Pollution" offer imaginative renditions of the interplay between air quality and human livelihoods. While these literary works may not adhere to the stringent standards of scholarly research, they serve as a lighthearted backdrop to our investigation into the unanticipated correlation between air quality in Worcester, Massachusetts, and the dispensing of pharmacy technicians in the state.
In addition to these scholarly and fictional explorations, childhood nostalgia channels a peculiar manifestation of relevance to our inquiry. Cartoons such as "Captain Planet and the Planeteers" and "The Magic School Bus" intricately weave environmental themes into their narratives, offering an unconventional yet strangely pertinent lens through which to view the connection between air quality and labor market dynamics. Although their didactic intentions may have been geared toward a juvenile audience, these animated series inadvertently impart a whimsical dimension to our rigorous investigation.
As we navigate the scholarly undercurrents and whimsical reverberations of our investigative landscape, it is imperative to contemplate the broader implications of our findings. While rooted in empirical evidence and statistical rigor, the unexpected synergy between air quality in Worcester, Massachusetts, and the number of pharmacy technicians in the state beckons a contemplation of the interplay between scientific inquiry and the serendipitous absurdities of life.

[[RESULTS]]
The analysis of the data spanning from 2003 to 2022 revealed a striking correlation between air quality in Worcester, Massachusetts, and the number of pharmacy technicians in the state. The correlation coefficient of 0.8486308 indicates a strong positive relationship between these seemingly unrelated variables. Additionally, the r-squared value of 0.7201742 underscores the robustness of this correlation, explaining approximately 72.02% of the variability in pharmacy technician employment attributed to air quality.
Furthermore, the p-value of less than 0.01 attests to the statistical significance of this correlation, providing compelling evidence for the influence of air quality in Worcester on the demand for pharmacy technicians across Massachusetts. In simpler terms, the likelihood of this relationship occurring by chance is about as rare as finding a statistically significant p-value in a dataset cooked up by a mischievous student.
Upon visualizing the data through a scatterplot (Fig. 1), the strong positive correlation is unmistakable, resembling the unyielding bond between a pharmacy technician and their trusty pill counter. The upward trend depicted in the scatterplot mirrors the upward trajectory of pharmacy technician employment as air quality in Worcester improves, aligning with the adage, "a breath of fresh air is just what the pharmacist ordered."
Overall, the findings of this study not only highlight the substantial association between air quality in Worcester, Massachusetts, and the number of pharmacy technicians in the state but also invite contemplation on the potential role of environmental factors in shaping labor market dynamics. After all, who would have thought that air quality could influence the career prospects of pharmacy technicians? This unexpected revelation underscores the intricate and often whimsical interplay of scientific inquiry and real-world phenomena, leaving us with the delightful image of pharmacists prescribing lungfuls of pristine air alongside their medications.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of the present study align with a burgeoning body of research that underscores the impact of air quality on various facets of human well-being and labor market dynamics. The significant positive correlation between air quality in Worcester, Massachusetts, and the number of pharmacy technicians in the state supports the earnest literature on the far-reaching effects of atmospheric pollution. The robust correlation coefficient and statistically significant p-value offer a firm statistical grounding for the unexpected bond between these seemingly disparate variables – a connection as surprising as stumbling upon a statistically significant p-value in a pile of randomized data.

Our findings not only validate the serious scholarly investigations into the adverse health outcomes and economic consequences of poor air quality but also lend empirical support to the seemingly whimsical connections we encountered in the literature review. The unexpected companionship between air quality research and fictional works such as "The Airbender's Odyssey" and "A Tale of Two Smogs" takes on a somewhat more serious undertone in light of our findings, perhaps indicating that truth in both science and literature might be stranger than fiction.

The metaphorical implications of our results are particularly striking. The upward trend in pharmacy technician employment as air quality in Worcester improves evokes the image of a pharmacist diligently counting pills, aligning with the metaphorical adage that "a breath of fresh air is just what the pharmacist ordered." This unorthodox correlation prompts a reconsideration of the interconnectedness of environmental factors and labor market phenomena, challenging us to contemplate the whimsical ways in which scientific inquiry can intersect with real-world anomalies.

In conclusion, our study sheds light on the unexpected intertwining of air quality in Worcester, Massachusetts, and the number of pharmacy technicians in the state, affirming the intricate interplay of atmospheric conditions and labor market dynamics. This unanticipated relationship not only underscores the potential influence of environmental factors on employment patterns but also serves as a lighthearted reminder of the delightful surprises that can emerge from rigorous scientific inquiry. After all, who said statistics couldn't have a sense of humor?

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Air quality in Worcester, Massachusetts and The number of pharmacy technicians in Massachusetts. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. 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]]
A Breath of Fresh Data: Examining the Relationship Between Air Quality in Worcester, Massachusetts, and the Dispensing of Pharmacy Technicians in Massachusetts

[[ABSTRACT]]
This study delves into the often overlooked intersection of air quality and pharmacy technician employment in Massachusetts. Utilizing data from the Environmental Protection Agency and the Bureau of Labor Statistics, we conducted a thorough analysis covering the years 2003 to 2022. Despite the serious implications of air pollution, our findings reveal a lighthearted connection between the air quality in Worcester, Massachusetts, and the number of pharmacy technicians in the state. The correlation coefficient of 0.8486308 and a statistically significant p-value of less than 0.01 suggest a strong association between these seemingly disparate factors. In this paper, we unpack the complexities of this unlikely relationship, exploring the potential influence of air quality on the demand for pharmacy technicians. Our work also ponders the intriguing possibility of pharmacists dispensing a prescription of fresh air to combat the effects of pollution.

[[INTRDUCTION]]
The realm of air quality and its impact on human health has long been a subject of earnest investigation, with a plethora of inquiries seeking to uncover the profound ramifications of atmospheric contamination. Concurrently, the labor market for pharmacy technicians in Massachusetts has undergone substantial fluctuations, prompting scholarly curiosity regarding the drivers of such workforce dynamics. However, the confluence of these two domains has received minimal attention, with the quaint union of air quality in Worcester, Massachusetts, and the dispensing of pharmacy technicians in the state being largely overlooked.
In this context, our research embarks on a playful yet rigorous exploration of the association between these apparently incongruent variables. While the earnest pursuit of scientific inquiry certainly permeates our endeavor, we cannot help but find amusement in the notion of air pollution influencing the labor market for pharmacy technicians. As we venture forth into this uncharted territory, we acknowledge the inherent duality of our findings, where the weighty implications of air pollution seamlessly coalesce with the whimsical notion of pharmacy technicians becoming entangled in its effects.
With a statistical correlation coefficient of 0.8486308 and a p-value signaling statistical significance, our analysis offers a compelling testament to the tangible relationship between the air quality in Worcester and the number of pharmacy technicians across Massachusetts. As we dissect the intricacies of this unexpected rapport, we are compelled to contemplate the possibility of pharmacists prescribing not only medications but also a remedy of fresh air to mitigate the deleterious effects of pollution. This curious juxtaposition of science and humor forms the crux of our investigation, as we endeavor to shed light on the hitherto unexplored connection between air quality and the dispensing of pharmacy technicians in the state.

[[RESULTS]]
The analysis of the data spanning from 2003 to 2022 revealed a striking correlation between air quality in Worcester, Massachusetts, and the number of pharmacy technicians in the state. The correlation coefficient of 0.8486308 indicates a strong positive relationship between these seemingly unrelated variables. Additionally, the r-squared value of 0.7201742 underscores the robustness of this correlation, explaining approximately 72.02% of the variability in pharmacy technician employment attributed to air quality.
Furthermore, the p-value of less than 0.01 attests to the statistical significance of this correlation, providing compelling evidence for the influence of air quality in Worcester on the demand for pharmacy technicians across Massachusetts. In simpler terms, the likelihood of this relationship occurring by chance is about as rare as finding a statistically significant p-value in a dataset cooked up by a mischievous student.
Upon visualizing the data through a scatterplot (Fig. 1), the strong positive correlation is unmistakable, resembling the unyielding bond between a pharmacy technician and their trusty pill counter. The upward trend depicted in the scatterplot mirrors the upward trajectory of pharmacy technician employment as air quality in Worcester improves, aligning with the adage, "a breath of fresh air is just what the pharmacist ordered."
Overall, the findings of this study not only highlight the substantial association between air quality in Worcester, Massachusetts, and the number of pharmacy technicians in the state but also invite contemplation on the potential role of environmental factors in shaping labor market dynamics. After all, who would have thought that air quality could influence the career prospects of pharmacy technicians? This unexpected revelation underscores the intricate and often whimsical interplay of scientific inquiry and real-world phenomena, leaving us with the delightful image of pharmacists prescribing lungfuls of pristine air alongside their medications.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our research has uncovered an unexpectedly robust connection between air quality in Worcester, Massachusetts, and the employment of pharmacy technicians across the state. Despite the ostensibly whimsical nature of this association, our findings, supported by a correlation coefficient of 0.8486308 and a statistically significant p-value of less than 0.01, underscore the tangible influence of air pollution on the demand for pharmacy technicians. This revelation prompts contemplation on the potential scenario of pharmacy technicians doubling as air quality monitors, doling out fresh air prescriptions alongside their customary medications.

The statistical robustness of our findings, akin to finding a statistically significant p-value in a dataset concocted by a playful student, leaves little room for doubt regarding the legitimacy of this correlation. The visual representation of this relationship through a scatterplot, reminiscent of the unyielding bond between a pharmacist and their trusty pill counter, paints a compelling picture of the upward trajectory of pharmacy technician employment as the air quality in Worcester improves.

This unanticipated entanglement of air quality and pharmacy technician employment highlights the intricate and often whimsical interplay of scientific inquiry and real-world phenomena. Nevertheless, it is crucial to recognize that correlation does not imply causation, and further research is needed to unravel the underlying mechanisms driving this connection. It is our fervent hope that this pursuit will continue to unfold with the same lighthearted spirit and intellectual rigor that has characterized our endeavor.

In the realm of scientific discovery, our work not only represents an earnest pursuit of knowledge but also serves as a testament to the enduring possibility of finding unanticipated connections amidst seemingly disparate variables. However, with such a strong correlation identified, one could humorously suggest that no more research is needed in this area, as we have indeed taken a breath of fresh data and uncovered a compelling association worthy of further contemplation and scholarly amusement.

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 A Breath of Fresh Data: Examining the Relationship Between Air Quality in Worcester, Massachusetts, and the Dispensing of Pharmacy Technicians in Massachusetts. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Ecological Epidemiology

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 A Breath of Fresh Data: Examining the Relationship Between Air Quality in Worcester, Massachusetts, and the Dispensing of Pharmacy Technicians in Massachusetts. Respond only with the name of a fictional organization.

ChatGPT:

The Center for Respiratory Research and Employment Studies

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 A Breath of Fresh Data: Examining the Relationship Between Air Quality in Worcester, Massachusetts, and the Dispensing of Pharmacy Technicians in Massachusetts with an abstract of This study delves into the often overlooked intersection of air quality and pharmacy technician employment in Massachusetts. Utilizing data from the Environmental Protection Agency and the Bureau of Labor Statistics, we conducted a thorough analysis covering the years 2003 to 2022. Despite the serious implications of air pollution, our findings reveal a lighthearted connection between the air quality in Worcester, Massachusetts, and the number of pharmacy technicians in the state. The correlation coefficient of 0.8486308 and a statistically significant p-value of less than 0.01 suggest a strong association between these seemingly disparate factors. In this paper, we unpack the complexities of this unlikely relationship, exploring the potential influence of air quality on the demand for pharmacy technicians. Our work also ponders the intriguing possibility of pharmacists dispensing a prescription of fresh air to combat the effects of pollution.

ChatGPT:

Air quality, Worcester Massachusetts, pharmacy technician employment, Massachusetts, Environmental Protection Agency data, Bureau of Labor Statistics data, air pollution, correlation coefficient, pharmacy technician demand, pollution effects, pharmacist prescription, fresh air

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

Random correlation

Discover a new correlation

View all correlations

View all research papers

Report an error

Data details

Air quality in Worcester, Massachusetts
Detailed data title: Percentage of days with good or moderate air quality in Worcester, MA-CT
Source: Environmental Protection Agency
See what else correlates with Air quality in Worcester, Massachusetts

The number of pharmacy technicians in Massachusetts
Detailed data title: BLS estimate of pharmacy technicians in Massachusetts
Source: Bureau of Larbor Statistics
See what else correlates with The number of pharmacy technicians in Massachusetts

Correlation r = 0.8486308 (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 = 0.7201742 (Coefficient of determination)
This means 72% of the change in the one variable (i.e., The number of pharmacy technicians in Massachusetts) is predictable based on the change in the other (i.e., Air quality in Worcester, Massachusetts) over the 20 years from 2003 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.3E-6.^Show 0.0000022565685930959250000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

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

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.85 in 0.00023% of random cases. Said differently, if you correlated 443,151 random variables^Note You don't actually need 443 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 19 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 19 because we have two variables measured over a period of 20 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.

[ 0.65, 0.94 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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

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

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

	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Air quality in Worcester, Massachusetts (Good air quality)	94.5205	95.9016	93.9726	95.3425	91.7808	96.9945	97.8082	98.0822	98.3562	97.2678	98.9041	99.4521	99.1781	98.6339	98.6301	98.0822	99.726	100	99.1781	100
The number of pharmacy technicians in Massachusetts (Pharmacy Technicians)	4180	5660	5780	5700	5810	6370	6920	7010	7150	7540	7820	8380	8540	8700	8610	8910	9150	8740	8880	8730

Why this works

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([94.5205,95.9016,93.9726,95.3425,91.7808,96.9945,97.8082,98.0822,98.3562,97.2678,98.9041,99.4521,99.1781,98.6339,98.6301,98.0822,99.726,100,99.1781,100,])
array_2 = np.array([4180,5660,5780,5700,5810,6370,6920,7010,7150,7540,7820,8380,8540,8700,8610,8910,9150,8740,8880,8730,])
array_1_name = "Air quality in Worcester, Massachusetts"
array_2_name = "The number of pharmacy technicians in Massachusetts"

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

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

Reuseable content

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

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

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

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

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

Download images for these variables:

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

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

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only Air quality in Worcester, Massachusetts
Line chart for only The number of pharmacy technicians in Massachusetts
AI-generated correlation image
The spurious research paper: A Breath of Fresh Data: Examining the Relationship Between Air Quality in Worcester, Massachusetts, and the Dispensing of Pharmacy Technicians in Massachusetts

View another random correlation

How fun was this correlation?

Your dedication to rating warms my heart!

Correlation ID: 4958 · Black Variable ID: 21478 · Red Variable ID: 9437


Problem variable:
Issue:
Additional details: Optional
Confirm you are a human: