Spurious correlation #4,219 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Milk consumption and the second variable is Violent crime rates. The chart goes from 1990 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Milk consumption and the second variable is Violent crime rates. The chart goes from 1990 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

It turns out that cows were actually the masterminds behind a series of elaborate heists and gang-related activities. Without their daily quota of milk, the cows were too udderly exhausted to engage in moosbehaving and were unable to orchestrate any more dairy daring escapades. As a result, the streets became pasture-sed and the overall beef between criminal factions just curdled away. So, in a strange twist, it seems the real culprits of the crime spree were none udder than the cows themselves!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a peaceful farmyard in the style of brutalism. Show a group of contented cows standing around a large, imposing barn filled with milk crates. The cows are sipping from buckets of milk while displaying an air of contentment. In the background, depict the nearby town where crime rates plummet as rival gangs of humans peacefully resolve their differences, with their weapons abandoned in favor of glasses of milk. 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: Milk consumption and Violent crime rates. 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 Cute cartoon Glitch art Brutalism Minimalism
ChatGPT: Brutalism

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 Milk consumption and Violent crime rates, in the following situation: It turns out that cows were actually the masterminds behind a series of elaborate heists and gang-related activities. Without their daily quota of milk, the cows were too udderly exhausted to engage in moosbehaving and were unable to orchestrate any more dairy daring escapades. As a result, the streets became pasture-sed and the overall beef between criminal factions just curdled away. So, in a strange twist, it seems the real culprits of the crime spree were none udder than the cows themselves!. Keep the description simple.
ChatGPT: In a peaceful farmyard, a group of contented cows stand around a large barn filled with milk crates, sipping from buckets of milk while plotting their nefarious schemes. Meanwhile, in the nearby town, crime rates plummet as rival gangs of humans peacefully resolve their differences, their weapons abandoned in favor of glasses of milk.

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: [ Brutalism ] and depicting this scene: In a peaceful farmyard, a group of contented cows stand around a large barn filled with milk crates, sipping from buckets of milk while plotting their nefarious schemes. Meanwhile, in the nearby town, crime rates plummet as rival gangs of humans peacefully resolve their differences, their weapons abandoned in favor of glasses of milk. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a peaceful farmyard in the style of brutalism. Show a group of contented cows standing around a large, imposing barn filled with milk crates. The cows are sipping from buckets of milk while displaying an air of contentment. In the background, depict the nearby town where crime rates plummet as rival gangs of humans peacefully resolve their differences, with their weapons abandoned in favor of glasses of milk.

*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 a decrease in Milk consumption caused Violent crime rates to decrease.

AI academic paper

(Because p < 0.01)

Got Milk? A Statistical Moovement: Exploring the Correlation Between Milk Consumption and Violent Crime Rates

The International Journal of Dairy Delinquency

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between Milk consumption and Violent crime rates. Make lots of jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Perhaps you also got inspiration from some board games that are vaugely related.

Here is the title and abstract of the paper:
[[TITLE]]
Got Milk? A Statistical Moovement: Exploring the Correlation Between Milk Consumption and Violent Crime Rates

[[ABSTRACT]]
In this udderly fascinating study, we set out to explore the seemingly outlandish idea of a potential link between milk consumption and violent crime rates. Utilizing data from the USDA and FBI Criminal Justice Information Services, we embarked on a statistical quest to separate the dairy myths from the facts. Our findings revealed a striking correlation coefficient of 0.8774659 and p < 0.01, covering the period from 1990 to 2021. So, does the phrase "got milk?" take on a whole new meaning when considering the impact on societal behavior? Let's not cry over spilled milk just yet while we dig into the curd-ious results of this bovine inquiry.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
Milk has long been regarded as a staple of the human diet, boasting an array of essential nutrients and a creamy, refreshing taste. From its role as a cozy companion to cookies to its frothy presence in the iconic milk mustache, this dairy delight has earned its place in the hearts and refrigerators of many. However, amidst this seemingly wholesome image, a provocative question arises: could there be a darker underbelly to the innocuous act of sipping on a glass of milk?
The idea of a potential link between milk consumption and violent crime rates may initially appear as far-fetched as a cow attempting a moonwalk. Nonetheless, it is this very skepticism that propelled our curiosity to take a closer look at the data. As we delved into the literature, we found hints of an unexplored correlation, leaving us both intrigued and udderly bewildered.
Our investigation transcended the realm of bovine conspiracy theories to embrace the serious task of statistical analysis. Armed with data from the USDA and the FBI Criminal Justice Information Services, we embarked on a quest to decipher whether there existed a tangible connection between these seemingly disparate domains. As we waded through datasets and statistical analyses, we found ourselves continually moo-ved by the potential implications of our findings.
In this paper, we present the findings of our study, which covers a substantial time span from 1990 to 2021. Our aim is not only to present the statistical evidence we have uncovered but also to stimulate further dialogue and research in this moovelous and unconventional intersection of nutritional habits and societal behaviors. As we approach this topic with the utmost seriousness, we invite readers to join us on this journey, embracing the unexpected twists and turns that lie ahead. So, without further ado, let us embark on this moooo-ving exploration of the correlation between milk consumption and violent crime rates.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

As we mosey through the scholarly landscape, we encounter a pasture of studies examining the potential correlation between dietary habits and behavioral outcomes. Smith et al. (2015) sought to untangle the complex web of factors influencing aggressive tendencies, shedding light on the role of nutrition in shaping social interactions. Meanwhile, Doe and Jones (2017) delved into the psychological underpinnings of dietary preferences, revealing intriguing connections between certain food choices and emotional regulation.

Venturing further down this dairy-infused rabbit hole, we stumble upon "Got Milk? The Surprising Impact of Dairy on Human Behavior" by M. Mooington (2019). Mooington eloquently navigates the unexplored terrain of milk's influence on the human psyche, peppering the narrative with anecdotes of lactose-induced mayhem and moo-inducing revelations.

In a parallel universe where cows roam freely and cheese reigns supreme, "The Milk Conspiracy: Udderly Shocking Revelations" by E. Agatha (2020) captivates readers with its tales of clandestine dairy agendas and milk-fueled escalations. While Agatha's work may border on the whimsical, it offers a whimsical lens through which to ponder the milk-crime nexus.

Bringing a fictional flair to the table, we encounter "The Curious Case of the Criminal Cows" by S. Boardgame (2018), where bovine shenanigans intertwine with detective work in a bovinely perplexing tale. While not a traditional source of academic inquiry, Boardgame's narrative imparts a playful perspective on the intersection of bovine behavior and criminal inclinations.

From the realms of non-fiction to the whimsy of fiction, our literature review traverses the varied pastures of human imagination and scientific inquiry, seeking to capture the essence of the dairy-crime dichotomy. As we milk every relevant source for its scholarly essence, it becomes udderly clear that this intersection is worthy of intellectual exploration and, dare we say, a few chuckles along the whey.

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

Please make up the results section of an academic research paper presenting the findings of the connection between Milk consumption and Violent crime rates. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

Your research team collected data from all across the internet, but mostly just used information from USDA and FBI Criminal Justice Information Services .

For the time period 1990 to 2021, you found a correlation 0.8774659, r-squared of 0.7699464, and p < 0.01.

One figure will be included. The figure (Fig. 1) is a scatterplot showing the strong correlation between the two variables. You don't need to specify where; I will add the figure.

Here is the title and abstract of the paper:
[[TITLE]]
Got Milk? A Statistical Moovement: Exploring the Correlation Between Milk Consumption and Violent Crime Rates

[[ABSTRACT]]
In this udderly fascinating study, we set out to explore the seemingly outlandish idea of a potential link between milk consumption and violent crime rates. Utilizing data from the USDA and FBI Criminal Justice Information Services, we embarked on a statistical quest to separate the dairy myths from the facts. Our findings revealed a striking correlation coefficient of 0.8774659 and p < 0.01, covering the period from 1990 to 2021. So, does the phrase "got milk?" take on a whole new meaning when considering the impact on societal behavior? Let's not cry over spilled milk just yet while we dig into the curd-ious results of this bovine inquiry.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
Milk has long been regarded as a staple of the human diet, boasting an array of essential nutrients and a creamy, refreshing taste. From its role as a cozy companion to cookies to its frothy presence in the iconic milk mustache, this dairy delight has earned its place in the hearts and refrigerators of many. However, amidst this seemingly wholesome image, a provocative question arises: could there be a darker underbelly to the innocuous act of sipping on a glass of milk?
The idea of a potential link between milk consumption and violent crime rates may initially appear as far-fetched as a cow attempting a moonwalk. Nonetheless, it is this very skepticism that propelled our curiosity to take a closer look at the data. As we delved into the literature, we found hints of an unexplored correlation, leaving us both intrigued and udderly bewildered.
Our investigation transcended the realm of bovine conspiracy theories to embrace the serious task of statistical analysis. Armed with data from the USDA and the FBI Criminal Justice Information Services, we embarked on a quest to decipher whether there existed a tangible connection between these seemingly disparate domains. As we waded through datasets and statistical analyses, we found ourselves continually moo-ved by the potential implications of our findings.
In this paper, we present the findings of our study, which covers a substantial time span from 1990 to 2021. Our aim is not only to present the statistical evidence we have uncovered but also to stimulate further dialogue and research in this moovelous and unconventional intersection of nutritional habits and societal behaviors. As we approach this topic with the utmost seriousness, we invite readers to join us on this journey, embracing the unexpected twists and turns that lie ahead. So, without further ado, let us embark on this moooo-ving exploration of the correlation between milk consumption and violent crime rates.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The statistical analysis of our data unveiled a surprisingly robust correlation between milk consumption and violent crime rates. Our findings revealed a correlation coefficient of 0.8774659, indicative of a strong positive relationship between these variables. This correlation was further supported by an r-squared value of 0.7699464, indicating that approximately 77% of the variability in violent crime rates could be explained by changes in milk consumption. The significance level, with p < 0.01, provided compelling evidence to reject the null hypothesis and accept the alternative hypothesis – that there is indeed a significant association between milk consumption and violent crime rates.

To visually encapsulate the striking correlation, we present Figure 1, a scatterplot that depicts the relationship between milk consumption and violent crime rates. This graphical representation serves as a testament to the compelling statistical connection we have unraveled, demonstrating a trend that is as clear as black and white (or should we say, as creamy as dairy and as alarming as crime rates?).

While it may be tempting to dismiss these findings as mere coincidence, we must not be too quick to cry over un-moo-ved milk. Instead, we encourage further exploration into the potential mechanisms underlying this association and the extent of its real-world implications. It seems that the age-old question "got milk?" has transcended its nutritional implications to beckon a deeper contemplation of its societal reverberations.

In the grand scheme of things, this study adds an unexpected layer to the multifaceted tapestry of human behavior and nutrition. As we ponder the implications of these findings, we find ourselves in a proverbial pasture of possibilities, with ample room for speculation and further inquiry. So, let us not curdle at the thought of unconventional correlations, but rather muster our intellectual fortitude to pursue the moovelous mysteries that lie beyond the confines of conventional wisdom.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between Milk consumption and Violent crime rates. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
Got Milk? A Statistical Moovement: Exploring the Correlation Between Milk Consumption and Violent Crime Rates

[[ABSTRACT]]
In this udderly fascinating study, we set out to explore the seemingly outlandish idea of a potential link between milk consumption and violent crime rates. Utilizing data from the USDA and FBI Criminal Justice Information Services, we embarked on a statistical quest to separate the dairy myths from the facts. Our findings revealed a striking correlation coefficient of 0.8774659 and p < 0.01, covering the period from 1990 to 2021. So, does the phrase "got milk?" take on a whole new meaning when considering the impact on societal behavior? Let's not cry over spilled milk just yet while we dig into the curd-ious results of this bovine inquiry.

[[LITERATURE REVIEW]]
As we mosey through the scholarly landscape, we encounter a pasture of studies examining the potential correlation between dietary habits and behavioral outcomes. Smith et al. (2015) sought to untangle the complex web of factors influencing aggressive tendencies, shedding light on the role of nutrition in shaping social interactions. Meanwhile, Doe and Jones (2017) delved into the psychological underpinnings of dietary preferences, revealing intriguing connections between certain food choices and emotional regulation.
Venturing further down this dairy-infused rabbit hole, we stumble upon "Got Milk? The Surprising Impact of Dairy on Human Behavior" by M. Mooington (2019). Mooington eloquently navigates the unexplored terrain of milk's influence on the human psyche, peppering the narrative with anecdotes of lactose-induced mayhem and moo-inducing revelations.
In a parallel universe where cows roam freely and cheese reigns supreme, "The Milk Conspiracy: Udderly Shocking Revelations" by E. Agatha (2020) captivates readers with its tales of clandestine dairy agendas and milk-fueled escalations. While Agatha's work may border on the whimsical, it offers a whimsical lens through which to ponder the milk-crime nexus.
Bringing a fictional flair to the table, we encounter "The Curious Case of the Criminal Cows" by S. Boardgame (2018), where bovine shenanigans intertwine with detective work in a bovinely perplexing tale. While not a traditional source of academic inquiry, Boardgame's narrative imparts a playful perspective on the intersection of bovine behavior and criminal inclinations.
From the realms of non-fiction to the whimsy of fiction, our literature review traverses the varied pastures of human imagination and scientific inquiry, seeking to capture the essence of the dairy-crime dichotomy. As we milk every relevant source for its scholarly essence, it becomes udderly clear that this intersection is worthy of intellectual exploration and, dare we say, a few chuckles along the whey.

[[RESULTS]]
The statistical analysis of our data unveiled a surprisingly robust correlation between milk consumption and violent crime rates. Our findings revealed a correlation coefficient of 0.8774659, indicative of a strong positive relationship between these variables. This correlation was further supported by an r-squared value of 0.7699464, indicating that approximately 77% of the variability in violent crime rates could be explained by changes in milk consumption. The significance level, with p < 0.01, provided compelling evidence to reject the null hypothesis and accept the alternative hypothesis – that there is indeed a significant association between milk consumption and violent crime rates.
To visually encapsulate the striking correlation, we present Figure 1, a scatterplot that depicts the relationship between milk consumption and violent crime rates. This graphical representation serves as a testament to the compelling statistical connection we have unraveled, demonstrating a trend that is as clear as black and white (or should we say, as creamy as dairy and as alarming as crime rates?).
While it may be tempting to dismiss these findings as mere coincidence, we must not be too quick to cry over un-moo-ved milk. Instead, we encourage further exploration into the potential mechanisms underlying this association and the extent of its real-world implications. It seems that the age-old question "got milk?" has transcended its nutritional implications to beckon a deeper contemplation of its societal reverberations.
In the grand scheme of things, this study adds an unexpected layer to the multifaceted tapestry of human behavior and nutrition. As we ponder the implications of these findings, we find ourselves in a proverbial pasture of possibilities, with ample room for speculation and further inquiry. So, let us not curdle at the thought of unconventional correlations, but rather muster our intellectual fortitude to pursue the moovelous mysteries that lie beyond the confines of conventional wisdom.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The results of our study have unveiled a compelling relationship between milk consumption and violent crime rates, shedding light on a dairy-licious correlation that captures the imagination and challenges conventional wisdom. Our findings, which revealed a robust correlation coefficient of 0.8774659 and a significance level of p < 0.01, offer a statistically firm foundation for plowing into the uncharted pastures of the milk-crime nexus.

Harnessing the scholarly insights from our literary foray, our findings dovetail with the whimsical tales of lactose-induced mayhem and milk-fueled escalations put forth by our esteemed predecessors. While M. Mooington's exploration of the surprising impact of dairy on human behavior initially appeared to dwell in the realm of whimsy, our study has added empirical weight to the bovine inquiry, affirming that there may indeed be method to the moo-inducing revelations.

Moreover, our results align with E. Agatha's audacious revelations about the clandestine dairy agendas and milk-fueled escalations, suggesting that beneath the veneer of whimsy lies a kernel of truth deserving of serious contemplation. It seems that the fictional flair of S. Boardgame's "The Curious Case of the Criminal Cows" was not so far-fetched after all, as our study has unearthed a tangible link between bovine behavior and societal dynamics. In essence, our findings breathe empirical life into the udderly playful pastures of academic inquiry and lend credence to the scholarly essence we have milked from our literary sources.

As we contemplate the implications of this dairy-licious correlation, it becomes evident that the phrase "got milk?" may indeed transcend its dietary implications to beckon a deeper contemplation of its societal reverberations. Our study paves the whey for a moovelous exploration of unconventional correlations, impelling us to muster our intellectual fortitude and delve into the multifaceted tapestry of human behavior and nutrition.

In conclusion, our findings challenge the conventional distinctions between dietary habits and behavioral outcomes, urging further exploration into the potential mechanisms underlying this association and its real-world implications. It seems that the dairy aisle harbors more than just calcium-rich choices—it harbors moovelous mysteries awaiting our scholarly investigation. So, let us not curdle at the thought of unconventional correlations, but rather muster our intellectual fortitude to embark on an udderly stimulating journey into the pasture of possibilities. The tale of milk and crime awaits its scholarly denouement, promising to be both gripping and utterly dairy-lightful.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between Milk consumption and Violent crime rates. Make lots of jokes, goofy observations, and puns. Stay serious and dry most of the time, but then occasionally let your wit shine through.

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]]
Got Milk? A Statistical Moovement: Exploring the Correlation Between Milk Consumption and Violent Crime Rates

[[ABSTRACT]]
In this udderly fascinating study, we set out to explore the seemingly outlandish idea of a potential link between milk consumption and violent crime rates. Utilizing data from the USDA and FBI Criminal Justice Information Services, we embarked on a statistical quest to separate the dairy myths from the facts. Our findings revealed a striking correlation coefficient of 0.8774659 and p < 0.01, covering the period from 1990 to 2021. So, does the phrase "got milk?" take on a whole new meaning when considering the impact on societal behavior? Let's not cry over spilled milk just yet while we dig into the curd-ious results of this bovine inquiry.

[[INTRDUCTION]]
Milk has long been regarded as a staple of the human diet, boasting an array of essential nutrients and a creamy, refreshing taste. From its role as a cozy companion to cookies to its frothy presence in the iconic milk mustache, this dairy delight has earned its place in the hearts and refrigerators of many. However, amidst this seemingly wholesome image, a provocative question arises: could there be a darker underbelly to the innocuous act of sipping on a glass of milk?
The idea of a potential link between milk consumption and violent crime rates may initially appear as far-fetched as a cow attempting a moonwalk. Nonetheless, it is this very skepticism that propelled our curiosity to take a closer look at the data. As we delved into the literature, we found hints of an unexplored correlation, leaving us both intrigued and udderly bewildered.
Our investigation transcended the realm of bovine conspiracy theories to embrace the serious task of statistical analysis. Armed with data from the USDA and the FBI Criminal Justice Information Services, we embarked on a quest to decipher whether there existed a tangible connection between these seemingly disparate domains. As we waded through datasets and statistical analyses, we found ourselves continually moo-ved by the potential implications of our findings.
In this paper, we present the findings of our study, which covers a substantial time span from 1990 to 2021. Our aim is not only to present the statistical evidence we have uncovered but also to stimulate further dialogue and research in this moovelous and unconventional intersection of nutritional habits and societal behaviors. As we approach this topic with the utmost seriousness, we invite readers to join us on this journey, embracing the unexpected twists and turns that lie ahead. So, without further ado, let us embark on this moooo-ving exploration of the correlation between milk consumption and violent crime rates.

[[RESULTS]]
The statistical analysis of our data unveiled a surprisingly robust correlation between milk consumption and violent crime rates. Our findings revealed a correlation coefficient of 0.8774659, indicative of a strong positive relationship between these variables. This correlation was further supported by an r-squared value of 0.7699464, indicating that approximately 77% of the variability in violent crime rates could be explained by changes in milk consumption. The significance level, with p < 0.01, provided compelling evidence to reject the null hypothesis and accept the alternative hypothesis – that there is indeed a significant association between milk consumption and violent crime rates.
To visually encapsulate the striking correlation, we present Figure 1, a scatterplot that depicts the relationship between milk consumption and violent crime rates. This graphical representation serves as a testament to the compelling statistical connection we have unraveled, demonstrating a trend that is as clear as black and white (or should we say, as creamy as dairy and as alarming as crime rates?).
While it may be tempting to dismiss these findings as mere coincidence, we must not be too quick to cry over un-moo-ved milk. Instead, we encourage further exploration into the potential mechanisms underlying this association and the extent of its real-world implications. It seems that the age-old question "got milk?" has transcended its nutritional implications to beckon a deeper contemplation of its societal reverberations.
In the grand scheme of things, this study adds an unexpected layer to the multifaceted tapestry of human behavior and nutrition. As we ponder the implications of these findings, we find ourselves in a proverbial pasture of possibilities, with ample room for speculation and further inquiry. So, let us not curdle at the thought of unconventional correlations, but rather muster our intellectual fortitude to pursue the moovelous mysteries that lie beyond the confines of conventional wisdom.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has unveiled a startling correlation between milk consumption and violent crime rates, shedding light on a connection that is as unexpected as finding a cow at a crime scene. The robust correlation coefficient of 0.8774659 moo-ves us to consider the potential ramifications of this unusual relationship. It seems that the phrase "got milk?" may now elicit an entirely different kind of response, perhaps prompting individuals to consider the societal implications of their dairy intake before reaching for the milk carton.

While we may be tempted to milk this connection for all it's worth, it is crucial to acknowledge the need for further research to delve into the underlying mechanisms behind this correlation. Perhaps a deeper exploration will reveal that milk truly does a body bad in ways we never imagined, or perhaps there are confounding variables at play, like being in a bad moo-d, that have not yet been considered.

In any case, we believe that our findings ignite a spark of curiosity in the academic community. The bovine inquiry into the relationship between milk consumption and violent crime rates beckons further investigation, though we will need to steer clear of any steer puns as we move forward with our research. In the grand scheme of things, our study adds a creamy, unexpected layer to the understanding of human behavior and nutrition, leaving us all to ponder the moovelous mysteries that lie beneath the surface of our dietary habits and societal tendencies.

In the spirit of academic inquiry, it is our firm belief that no further research is needed on this topic. After all, once you've discovered that milk consumption and violent crime rates are correlated, what more could you possibly want to know? It's time to put this pasture of possibilities out to pasture, and let these moovelous findings stand alone in all of their udderly fascinating glory.

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 Got Milk? A Statistical Moovement: Exploring the Correlation Between Milk Consumption and Violent Crime Rates. Respond only with the name of a fictional academic journal.

ChatGPT:

The International Journal of Dairy Delinquency

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 Got Milk? A Statistical Moovement: Exploring the Correlation Between Milk Consumption and Violent Crime Rates. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Dairy-Driven Social Science Research

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Got Milk? A Statistical Moovement: Exploring the Correlation Between Milk Consumption and Violent Crime Rates with an abstract of In this udderly fascinating study, we set out to explore the seemingly outlandish idea of a potential link between milk consumption and violent crime rates. Utilizing data from the USDA and FBI Criminal Justice Information Services, we embarked on a statistical quest to separate the dairy myths from the facts. Our findings revealed a striking correlation coefficient of 0.8774659 and p < 0.01, covering the period from 1990 to 2021. So, does the phrase "got milk?" take on a whole new meaning when considering the impact on societal behavior? Let's not cry over spilled milk just yet while we dig into the curd-ious results of this bovine inquiry.

ChatGPT:

milk consumption, violent crime rates, correlation, statistical analysis, USDA data, FBI Criminal Justice Information Services, dairy myths, correlation coefficient, societal behavior

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

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Data details

Milk consumption
Detailed data title: Per capita consumption of Fluid beverage milk in the US
Source: USDA
See what else correlates with Milk consumption

Violent crime rates
Detailed data title: The violent crime rate per 100,000 residents in United States
Source: FBI Criminal Justice Information Services
See what else correlates with Violent crime rates

Correlation r = 0.8774659 (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 = 0.7699464 (Coefficient of determination)
This means 77% of the change in the one variable (i.e., Violent crime rates) is predictable based on the change in the other (i.e., Milk consumption) over the 32 years from 1990 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 4.4E-11.^Show 0.0000000000436511160070805860
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 0.88 in 4.4E-9% of random cases. Said differently, if you correlated 22,908,921,729 random variables^Note You don't actually need 22 billion 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 31 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 31 because we have two variables measured over a period of 32 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.

[ 0.76, 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.

	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Milk consumption (Pounds per person)	220	218	215	210	208	205	205	201	198	197	196	192	191	188	185	185	185	183	181	181	177	173	169	164	158	155	153	149	145	141	141	134
Violent crime rates (Violent crime)	729.6	758.2	757.7	747.1	713.6	684.5	636.6	611	567.6	523	506.5	504.5	494.4	475.8	463.2	469	479.3	471.8	458.6	431.9	404.5	387.1	387.8	369.1	361.6	373.7	397.5	394.9	383.4	380.8	398.5	387

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([220,218,215,210,208,205,205,201,198,197,196,192,191,188,185,185,185,183,181,181,177,173,169,164,158,155,153,149,145,141,141,134,])
array_2 = np.array([729.6,758.2,757.7,747.1,713.6,684.5,636.6,611,567.6,523,506.5,504.5,494.4,475.8,463.2,469,479.3,471.8,458.6,431.9,404.5,387.1,387.8,369.1,361.6,373.7,397.5,394.9,383.4,380.8,398.5,387,])
array_1_name = "Milk consumption"
array_2_name = "Violent crime rates"

# 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 Milk consumption
Line chart for only Violent crime rates
AI-generated correlation image
The spurious research paper: Got Milk? A Statistical Moovement: Exploring the Correlation Between Milk Consumption and Violent Crime Rates

View another random correlation

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Correlation ID: 4219 · Black Variable ID: 554 · Red Variable ID: 20220


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