Spurious correlation #2,282 · View random

AI explanation

As the number of social workers in Iowa increases, so does the amount of interpersonal support in the state. This leads to a higher overall sense of well-being among the population. Happier people in Iowa are more likely to answer phone surveys, leading to a mistaken perception of increased demand for oil and gas. This drives up Devon Energy's stock price as investors scramble to capitalize on the supposed surge in energy needs. It's a case of social work indirectly fueling the stock market!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image that resembles a scene from 'Star Wars', set in a bustling office in downtown Des Moines. In the foreground, a group of social workers are gathered together in deep discussion, their faces determined and compassionate. In the background, a massive digital billboard displays the stock price of Devon Energy, steadily rising and reflecting the company's positive performance in the market. The overall setting should have a futuristic and otherworldly vibe while maintaining the realistic elements of the office environment and the stock market display. 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: The number of social workers in Iowa and Devon Energy's stock price. 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: Psychedelic An image that looks like it is a scene from 'Star Wars' Cutsy art illustration Celtic art Data cyber art
ChatGPT: An image that looks like it is a scene from 'Star Wars'

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 The number of social workers in Iowa and Devon Energy's stock price together. Keep the description simple.
ChatGPT: In a bustling office in downtown Des Moines, a group of social workers gather together, discussing new strategies to support their community. Meanwhile, on a nearby digital billboard, the stock price of Devon Energy steadily rises, reflecting the company's positive performance in the market.

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 'Star Wars' ] and depicting this scene: In a bustling office in downtown Des Moines, a group of social workers gather together, discussing new strategies to support their community. Meanwhile, on a nearby digital billboard, the stock price of Devon Energy steadily rises, reflecting the company's positive performance in the market. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that resembles a scene from 'Star Wars', set in a bustling office in downtown Des Moines. In the foreground, a group of social workers are gathered together in deep discussion, their faces determined and compassionate. In the background, a massive digital billboard displays the stock price of Devon Energy, steadily rising and reflecting the company's positive performance in the market. The overall setting should have a futuristic and otherworldly vibe while maintaining the realistic elements of the office environment and the stock market display.

*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 increases in The number of social workers in Iowa positively influenced Devon Energy's stock price (DVN).

AI academic paper

(Because p < 0.01)

The Social Worker Stock Slide: A Statistically Significant Study of Social Worker Numbers in Iowa and Devon Energy's DVN Stock Price

Journal of Social Work and Economic Trends

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 The number of social workers in Iowa and Devon Energy's stock price (DVN). 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 devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading the backs of shampoo bottles.

Here is the title and abstract of the paper:
[[TITLE]]
The Social Worker Stock Slide: A Statistically Significant Study of Social Worker Numbers in Iowa and Devon Energy's DVN Stock Price

[[ABSTRACT]]
This study delves into the intriguing relationship between the number of social workers in the state of Iowa and the fluctuation of Devon Energy's stock price (DVN). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team sought to unearth the potential connection between these seemingly disparate entities.

With a twinkle in our academic eyes and a depth of statistical analysis, we discovered a noteworthy correlation coefficient of 0.8965722 and p < 0.01 over the span of 2003 to 2022. Our findings not only raised eyebrows but also prompted a wave of quizzical expressions among our colleagues. Who knew that the presence of social workers in Iowa could wield such influence over Devon Energy's stock price?

The implications of our study go beyond mere numbers and charts; they speak to the intricate dance between seemingly unrelated variables in the financial world. As the old saying goes, "When social workers are on the rise, DVN's price flies!" This study sheds light on the unexpected connections that underpin the dynamic tapestry of stock market movements and social workforce dynamics, adding a touch of whimsy to the oftentimes serious world of finance research.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intertwining of social welfare and financial markets has long been a subject of fascination and speculation. As the saying goes, "When social workers are on the rise, DVN's price flies!" However, it is not often that research endeavors to explore the potential link between the number of social workers in a particular state and the stock price of a specific company.
This study seeks to fill that gap by examining the relationship between the number of social workers in Iowa and the stock price of Devon Energy (DVN). It aims to provide empirical evidence and statistical analysis to support the often-overlooked influence of social worker numbers on stock market movements.
The topic at hand may appear unconventional, even eliciting a chuckle or two, but the implications of understanding such associations can have profound effects on our comprehension of market dynamics. After all, who wouldn't want to uncover the hidden influence of social workers on stock prices – it's like finding a hidden "stock-pot" at the end of a financial rainbow!

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

Smith and Doe (2015) conducted a comprehensive analysis of social worker employment trends in the Midwest, with a particular focus on the state of Iowa. The authors found a gradual increase in the number of social workers practicing in Iowa over the past decade, attributing this growth to factors such as evolving social welfare policies and an increased awareness of mental health issues. Meanwhile, Jones (2018) explored the fluctuation of Devon Energy's stock price (DVN) and identified key market trends that influenced its value over time. Both studies, while unrelated in their primary focus, contribute valuable insights to the broader understanding of the respective domains.

Speaking of social workers, did you hear about the one who refused to wear a nametag at work? It was because she didn't want her clients to know her name.

While non-fiction works such as "Social Work Practice and Evaluation: A Chainsaw Approach" and "The Economics of Energy and Natural Resources" provide foundational knowledge in their respective fields, the connection between social worker numbers and stock prices may not have received significant attention in traditional literature.

However, the realm of fiction offers intriguing narratives that touch upon elements of both social welfare and financial markets. In "The Grapes of Math: How Social Workers Count Their Way to Success," Steinbeck weaves a tale of social worker empowerment amidst the backdrop of economic struggles. Similarly, "The Wealthy Social Worker's Guide to Wall Street" by Fitzgerald presents a satirical commentary on the intersection of social work ideals and stock market ambitions.

As the authors delved further into the literature, they encountered anecdotal accounts and unconventional sources that offered unexpected insights. One particularly engaging revelation came from the back of a shampoo bottle, where the statement "Lather, rinse, repeat" provided a remarkable parallel to the cyclical nature of market fluctuations and the iterative process of social work interventions. While not a scholarly text per se, this serendipitous encounter attests to the unanticipated sources of inspiration that can emerge when exploring the nexus of seemingly distinct disciplines.

Well, I hope this section was illuminating and mildly entertaining!

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 The number of social workers in Iowa and Devon Energy's stock price (DVN). You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Limit your response to 500 tokens.

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

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

[[TITLE]]
The Social Worker Stock Slide: A Statistically Significant Study of Social Worker Numbers in Iowa and Devon Energy's DVN Stock Price

[[ABSTRACT]]
This study delves into the intriguing relationship between the number of social workers in the state of Iowa and the fluctuation of Devon Energy's stock price (DVN). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team sought to unearth the potential connection between these seemingly disparate entities.

With a twinkle in our academic eyes and a depth of statistical analysis, we discovered a noteworthy correlation coefficient of 0.8965722 and p < 0.01 over the span of 2003 to 2022. Our findings not only raised eyebrows but also prompted a wave of quizzical expressions among our colleagues. Who knew that the presence of social workers in Iowa could wield such influence over Devon Energy's stock price?

The implications of our study go beyond mere numbers and charts; they speak to the intricate dance between seemingly unrelated variables in the financial world. As the old saying goes, "When social workers are on the rise, DVN's price flies!" This study sheds light on the unexpected connections that underpin the dynamic tapestry of stock market movements and social workforce dynamics, adding a touch of whimsy to the oftentimes serious world of finance research.

[[LITERATURE REVIEW]]
Smith and Doe (2015) conducted a comprehensive analysis of social worker employment trends in the Midwest, with a particular focus on the state of Iowa. The authors found a gradual increase in the number of social workers practicing in Iowa over the past decade, attributing this growth to factors such as evolving social welfare policies and an increased awareness of mental health issues. Meanwhile, Jones (2018) explored the fluctuation of Devon Energy's stock price (DVN) and identified key market trends that influenced its value over time. Both studies, while unrelated in their primary focus, contribute valuable insights to the broader understanding of the respective domains.
Speaking of social workers, did you hear about the one who refused to wear a nametag at work? It was because she didn't want her clients to know her name.
While non-fiction works such as "Social Work Practice and Evaluation: A Chainsaw Approach" and "The Economics of Energy and Natural Resources" provide foundational knowledge in their respective fields, the connection between social worker numbers and stock prices may not have received significant attention in traditional literature.
However, the realm of fiction offers intriguing narratives that touch upon elements of both social welfare and financial markets. In "The Grapes of Math: How Social Workers Count Their Way to Success," Steinbeck weaves a tale of social worker empowerment amidst the backdrop of economic struggles. Similarly, "The Wealthy Social Worker's Guide to Wall Street" by Fitzgerald presents a satirical commentary on the intersection of social work ideals and stock market ambitions.
As the authors delved further into the literature, they encountered anecdotal accounts and unconventional sources that offered unexpected insights. One particularly engaging revelation came from the back of a shampoo bottle, where the statement "Lather, rinse, repeat" provided a remarkable parallel to the cyclical nature of market fluctuations and the iterative process of social work interventions. While not a scholarly text per se, this serendipitous encounter attests to the unanticipated sources of inspiration that can emerge when exploring the nexus of seemingly distinct disciplines.
Well, I hope this section was illuminating and mildly entertaining!

[[RESULTS]]
The data analysis revealed a striking correlation coefficient of 0.8965722 between the number of social workers in Iowa and Devon Energy's stock price (DVN) over the period from 2003 to 2022. This correlation was statistically significant, with a p-value of less than 0.01, indicating a strong relationship between these two seemingly unrelated variables. It seems that when it comes to social workers and stock prices, correlation does imply causation, to the surprise of many.
The r-squared value of 0.8038417 further strengthens the evidence of a robust relationship between these variables. One might say that the presence of social workers in Iowa exerts a considerable gravitational pull on DVN's stock price, much like the gravitational pull felt when a dad tells a classic stock market joke – it's "punny" how influential these social workers turned out to be!
Fig. 1 displays a scatterplot depicting the strong positive correlation between the number of social workers in Iowa and DVN's stock price. The scatterplot reinforces the statistical findings, visually capturing the upward trend in DVN's stock price as the number of social workers in Iowa increases. This symbiotic relationship between social workers and stock prices is indeed a sight to behold, much like witnessing a "stockbroker" walk into a social worker convention – an unexpected and intriguing sight!
The results of this study not only contribute to the budding field of unconventional finance analysis but also underscore the far-reaching impact of seemingly unrelated factors in the stock market. Further research could delve into the specific mechanisms through which social worker numbers influence DVN's stock price, perhaps through a series of "stock market and social workers" themed dad joke seminars to lighten the often-serious mood of financial research symposiums.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of this study provide compelling evidence for the unexpected and robust relationship between the number of social workers in Iowa and Devon Energy's stock price (DVN). This revelatory connection aligns with previous research that has highlighted the influence of social welfare dynamics on various economic indicators. The findings corroborate Smith and Doe's (2015) observations regarding the gradual surge in social worker numbers in Iowa, and they further solidify Jones's (2018) insights into the intricate patterns of DVN's stock price fluctuation.

It appears that the presence of social workers in Iowa yields substantial gravitational pull on DVN's stock price, akin to the gravitational pull felt when a dad tells a zinger of a stock market joke – it's "punny" how influential these social workers turned out to be! The scatterplot in Fig. 1 visually captures the upward trend in DVN's stock price as the number of social workers in Iowa increases, illustrating an unexpected symbiotic relationship that mirrors the astonishment of witnessing a "stockbroker" walk into a social worker convention – truly an intriguing and unexpected sight!

The strong correlation coefficient and r-squared value point to a significant interplay between these seemingly incongruous variables, challenging traditional conceptions of financial analysis. As the old adage goes, "When social workers are on the rise, DVN's price flies!" This study sheds light on the unanticipated connections that underpin the dynamic tapestry of stock market movements and social workforce dynamics, adding a touch of whimsy to the oftentimes serious world of finance research, much like a well-timed dad joke at a tense board meeting.

These findings beckon further exploration into the mechanisms through which social worker numbers exert their influence on DVN's stock price. Perhaps a series of "stock market and social workers" themed dad joke seminars could be the gateway to uncovering the underlying mechanisms and providing a lighthearted respite from the sometimes too serious business of financial research.

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 The number of social workers in Iowa and Devon Energy's stock price (DVN). You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
The Social Worker Stock Slide: A Statistically Significant Study of Social Worker Numbers in Iowa and Devon Energy's DVN Stock Price

[[ABSTRACT]]
This study delves into the intriguing relationship between the number of social workers in the state of Iowa and the fluctuation of Devon Energy's stock price (DVN). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team sought to unearth the potential connection between these seemingly disparate entities.

With a twinkle in our academic eyes and a depth of statistical analysis, we discovered a noteworthy correlation coefficient of 0.8965722 and p < 0.01 over the span of 2003 to 2022. Our findings not only raised eyebrows but also prompted a wave of quizzical expressions among our colleagues. Who knew that the presence of social workers in Iowa could wield such influence over Devon Energy's stock price?

The implications of our study go beyond mere numbers and charts; they speak to the intricate dance between seemingly unrelated variables in the financial world. As the old saying goes, "When social workers are on the rise, DVN's price flies!" This study sheds light on the unexpected connections that underpin the dynamic tapestry of stock market movements and social workforce dynamics, adding a touch of whimsy to the oftentimes serious world of finance research.

[[INTRDUCTION]]
The intertwining of social welfare and financial markets has long been a subject of fascination and speculation. As the saying goes, "When social workers are on the rise, DVN's price flies!" However, it is not often that research endeavors to explore the potential link between the number of social workers in a particular state and the stock price of a specific company.
This study seeks to fill that gap by examining the relationship between the number of social workers in Iowa and the stock price of Devon Energy (DVN). It aims to provide empirical evidence and statistical analysis to support the often-overlooked influence of social worker numbers on stock market movements.
The topic at hand may appear unconventional, even eliciting a chuckle or two, but the implications of understanding such associations can have profound effects on our comprehension of market dynamics. After all, who wouldn't want to uncover the hidden influence of social workers on stock prices – it's like finding a hidden "stock-pot" at the end of a financial rainbow!

[[RESULTS]]
The data analysis revealed a striking correlation coefficient of 0.8965722 between the number of social workers in Iowa and Devon Energy's stock price (DVN) over the period from 2003 to 2022. This correlation was statistically significant, with a p-value of less than 0.01, indicating a strong relationship between these two seemingly unrelated variables. It seems that when it comes to social workers and stock prices, correlation does imply causation, to the surprise of many.
The r-squared value of 0.8038417 further strengthens the evidence of a robust relationship between these variables. One might say that the presence of social workers in Iowa exerts a considerable gravitational pull on DVN's stock price, much like the gravitational pull felt when a dad tells a classic stock market joke – it's "punny" how influential these social workers turned out to be!
Fig. 1 displays a scatterplot depicting the strong positive correlation between the number of social workers in Iowa and DVN's stock price. The scatterplot reinforces the statistical findings, visually capturing the upward trend in DVN's stock price as the number of social workers in Iowa increases. This symbiotic relationship between social workers and stock prices is indeed a sight to behold, much like witnessing a "stockbroker" walk into a social worker convention – an unexpected and intriguing sight!
The results of this study not only contribute to the budding field of unconventional finance analysis but also underscore the far-reaching impact of seemingly unrelated factors in the stock market. Further research could delve into the specific mechanisms through which social worker numbers influence DVN's stock price, perhaps through a series of "stock market and social workers" themed dad joke seminars to lighten the often-serious mood of financial research symposiums.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has illuminated a rather unexpected and whimsical connection between the number of social workers in the state of Iowa and the fluctuation of Devon Energy's stock price. The noteworthy correlation coefficient and statistically significant p-value leave little doubt about the influence wielded by these social welfare professionals.

It appears that when it comes to the stock market, the presence of social workers in Iowa is no laughing matter – well, unless you enjoy a good "stock broker" joke! Our findings prompt a reevaluation of the factors that sway stock prices, demonstrating that even the most seemingly unrelated variables can have a tangible impact on financial markets.

As the saying goes, "When social workers are on the rise, DVN's price flies!" – a sentiment that now holds an empirical backing that few could have anticipated. The unexpected nature of this correlation serves as a reminder of the delightful surprises that can emerge from rigorous statistical analysis, adding a touch of levity to the often austere world of financial research.

With that said, it appears that no further research is needed in this area. The correlation is clear, the dad jokes are solid, and the study has certainly proved to be an intriguing addition to the literature. Further investigation would only serve to belabor the point – much like explaining a joke that everyone already gets!

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited The Social Worker Stock Slide: A Statistically Significant Study of Social Worker Numbers in Iowa and Devon Energy's DVN Stock Price. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Social Work and Economic Trends

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited The Social Worker Stock Slide: A Statistically Significant Study of Social Worker Numbers in Iowa and Devon Energy's DVN Stock Price. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Empirical Research in Social Services

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled The Social Worker Stock Slide: A Statistically Significant Study of Social Worker Numbers in Iowa and Devon Energy's DVN Stock Price with an abstract of This study delves into the intriguing relationship between the number of social workers in the state of Iowa and the fluctuation of Devon Energy's stock price (DVN). Utilizing data from the Bureau of Labor Statistics and LSEG Analytics (Refinitiv), our research team sought to unearth the potential connection between these seemingly disparate entities.
With a twinkle in our academic eyes and a depth of statistical analysis, we discovered a noteworthy correlation coefficient of 0.8965722 and p < 0.01 over the span of 2003 to 2022. Our findings not only raised eyebrows but also prompted a wave of quizzical expressions among our colleagues. Who knew that the presence of social workers in Iowa could wield such influence over Devon Energy's stock price?
The implications of our study go beyond mere numbers and charts; they speak to the intricate dance between seemingly unrelated variables in the financial world. As the old saying goes, "When social workers are on the rise, DVN's price flies!" This study sheds light on the unexpected connections that underpin the dynamic tapestry of stock market movements and social workforce dynamics, adding a touch of whimsy to the oftentimes serious world of finance research.

ChatGPT:

social workers, Iowa, Devon Energy, DVN stock price, correlation, Bureau of Labor Statistics, LSEG Analytics, Refinitiv, stock market, social workforce dynamics, finance research

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

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Data details

The number of social workers in Iowa
Detailed data title: BLS estimate of child, family, and school social workers in Iowa
Source: Bureau of Larbor Statistics
See what else correlates with The number of social workers in Iowa

Devon Energy's stock price (DVN)
Detailed data title: Opening price of Devon Energy (DVN) on the first trading day of the year
Source: LSEG Analytics (Refinitiv)
Additional Info: ^{See full details} Via Microsoft Excel Stockhistory function

See what else correlates with Devon Energy's stock price (DVN)

Correlation r = 0.8965722 (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 = 0.8038417 (Coefficient of determination)
This means 80.4% of the change in the one variable (i.e., Devon Energy's stock price (DVN)) is predictable based on the change in the other (i.e., The number of social workers in Iowa) over the 20 years from 2003 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 8.8E-8.^Show 0.0000000878999389935307000000
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 0.9 in 8.8E-6% of random cases. Said differently, if you correlated 11,376,572 random variables^Note You don't actually need 11 million variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.

In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 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.

[ 0.75, 0.96 ] 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
The number of social workers in Iowa (Laborers)	2960	3080	3240	3510	3810	4500	4460	4660	5010	4480	4370	3590	4070	3140	3500	2930	2620	2130	2020	2970
Devon Energy's stock price (DVN) (Stock price)	23.2	28.58	38.93	62.76	66.03	89.99	65.94	75.31	79.13	64.86	53.06	61.69	60.63	31.78	46.82	41.79	21.94	26.21	16	44.14

Why this works

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

Try it yourself

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

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

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

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

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

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

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

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

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

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

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

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

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

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([2960,3080,3240,3510,3810,4500,4460,4660,5010,4480,4370,3590,4070,3140,3500,2930,2620,2130,2020,2970,])
array_2 = np.array([23.2,28.58,38.93,62.76,66.03,89.99,65.94,75.31,79.13,64.86,53.06,61.69,60.63,31.78,46.82,41.79,21.94,26.21,16,44.14,])
array_1_name = "The number of social workers in Iowa"
array_2_name = "Devon Energy's stock price (DVN)"

# 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 The number of social workers in Iowa
Line chart for only Devon Energy's stock price (DVN)
AI-generated correlation image
The spurious research paper: The Social Worker Stock Slide: A Statistically Significant Study of Social Worker Numbers in Iowa and Devon Energy's DVN Stock Price

View another random correlation

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Correlation ID: 2282 · Black Variable ID: 7899 · Red Variable ID: 1824


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