Spurious correlation #5,594 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Deepest snow depth in Charlotte and the second variable is Google searches for 'is this a wart'. The chart goes from 2004 to 2022, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Deepest snow depth in Charlotte and the second variable is Google searches for 'is this a wart'. The chart goes from 2004 to 2022, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As the deepest snow in Charlotte melted away, it took with it all the misplaced hopes and dreams of finding unconventional cures for skin conditions buried in the frost. Just like snowflakes, people's skin woes are unique - no need to ice the competition!

^{Show image generation prompt} Model: dalle-3
Prompt: [ Cute cartoon ] Create an image of a vibrant wintery scene in Charlotte, with the sunlight casting a warm glow on the melting snow, revealing patches of green grass. Depict a person using their phone to search "is this a wart" while the snow around them slowly transforms into water, carrying away the hopes of finding unconventional cures for skin conditions. The unique melting patterns mirror the individual nature of skin issues, and the scene highlights the futility of seeking unconventional treatments buried beneath the snow. 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: Deepest snow depth in Charlotte and Google searches for 'is this a wart'. 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: Data cyber art Cute cartoon Cutsy graphics Vaporwave Charcoal
ChatGPT: Cute cartoon

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 Deepest snow depth in Charlotte and Google searches for 'is this a wart', in the following situation: As the deepest snow in Charlotte melted away, it took with it all the misplaced hopes and dreams of finding unconventional cures for skin conditions buried in the frost. Just like snowflakes, people's skin woes are unique - no need to ice the competition!. Keep the description simple.
ChatGPT: The scene shows the sunlight glistening off the melting snow in Charlotte, revealing patches of green grass peeking through. Nearby, a person is seen using their phone to search "is this a wart" while the snow around them slowly transforms into water, carrying away the hopes of finding unconventional cures for skin conditions. The unique melting patterns mirror the individual nature of skin issues, and the scene highlights the futility of seeking unconventional treatments buried beneath the snow.

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: [ Cute cartoon ] and depicting this scene: The scene shows the sunlight glistening off the melting snow in Charlotte, revealing patches of green grass peeking through. Nearby, a person is seen using their phone to search "is this a wart" while the snow around them slowly transforms into water, carrying away the hopes of finding unconventional cures for skin conditions. The unique melting patterns mirror the individual nature of skin issues, and the scene highlights the futility of seeking unconventional treatments buried beneath the snow. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ Cute cartoon ] Create an image of a vibrant wintery scene in Charlotte, with the sunlight casting a warm glow on the melting snow, revealing patches of green grass. Depict a person using their phone to search "is this a wart" while the snow around them slowly transforms into water, carrying away the hopes of finding unconventional cures for skin conditions. The unique melting patterns mirror the individual nature of skin issues, and the scene highlights the futility of seeking unconventional treatments buried beneath the snow.

*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 Deepest snow depth in Charlotte caused is this a wart (Google searches for 'is this a wart') to decrease. Add a funny one-liner at the end.

AI academic paper

(Because p < 0.01)

Winter Warts: A Correlative Study between Deepest Snow Depth in Charlotte and Google Searches for 'Is This a Wart'

The Journal of Seasonal Skin Studies

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 Deepest snow depth in Charlotte and Google searches for 'is this a wart'. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

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

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

Here is the title and abstract of the paper:
[[TITLE]]
"Winter Warts: A Correlative Study between Deepest Snow Depth in Charlotte and Google Searches for 'Is This a Wart'"

[[ABSTRACT]]
This study delves into the intriguing, frosty connection between the deepest snow depth in Charlotte and the frequency of Google searches for the perplexing query, "Is this a wart?" In this peculiar investigation, we aim to uncover whether a correlation exists between the depth of snow in Charlotte and the propensity of individuals to ponder the wart-like characteristics of certain skin blemishes.

Our research team sifted through the snow data provided by the NOAA National Climate Data Center, where we found an abundance of frosty facts and a blizzard of statistics. Meanwhile, on the digital front, we combed through the icy depths of Google Trends, where we encountered a flurry of frostbite-fueled queries.

After performing a thorough analysis, we discovered a correlation coefficient of 0.7995460 and p < 0.01 for the time period spanning from 2004 to 2022. The results suggest a remarkably strong association between the depth of snowfall and the sudden surge in curiosity about warts. It appears that as the snow deepens, so does the perplexity about those enigmatic skin protrusions, leaving individuals to ask, "Is this a wart or just a snowflake masquerading as a skin blemish?"

As the frosty findings thaw, we cannot help but wonder if our research has uncovered the chilling truth about the chilling effect of snow on our perceived skin anomalies. Indeed, it seems that some skin issues may be "snow" laughing matter!

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
As the winter months cast their icy spell over the city of Charlotte, North Carolina, an interesting phenomenon emerges, akin to a snow-covered enigma wrapped in a blanket of mystery. The unassuming search term "Is this a wart" experiences a surge in search activity, prompting us to unearth the cold, hard truth behind this curious correlation. It seems that Mother Nature's wintry embrace may have more riddles in store than just snowflakes and frostbite.
It is often said that in the coldest of times, we must remain vigilant for unexpected growths—what some may even call "chilling" developments! (Cue the cringe-worthy dad joke drumroll, please.) This study seeks to unravel the enigmatic connection between the depth of snow in Charlotte and the sudden surge in cyberspace's contemplation of skin irregularities. While it may seem like a frosty fusion of topics, the bond between snowy conditions and perplexing skin queries may hold more warmth than meets the eye.
One might question if this correlation is merely a frosty fluke, but our research aims to demonstrate otherwise. In the realm of academic inquiry, we must not shy away from piercing through the icy layers of convention to uncover the snow-covered truths that lie beneath. It's time to break the ice and shine a light on this peculiar pairing of weather patterns and dermatological ponderings. After all, perhaps the snow holds more than just frozen water—it might just hold the key to unlocking the skin-deep mysteries that bewilder us all.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

In a study conducted by Smith et al. (2015), the authors find a significant positive correlation between the deepest snow depth in Charlotte and the frequency of Google searches for "Is this a wart." The findings suggest that as the snowfall increases, so does the inclination of individuals to seek clarification on potential skin blemishes resembling warts. This research serves as a pivotal starting point for our investigation into this frigidly fascinating correlation.

Doe and Johnson (2018) further explore the relationship between weather patterns and dermatological inquiries, shedding light on the psychological impact of snowy conditions on skin-related concerns. Their work highlights the potential influence of environmental factors on individuals' perceptions of skin anomalies, incorporating snow depth as a prominent variable in their analysis.

Turning to the wider literature on climate and human behavior, the book "The Weather Factor: How Nature Has Changed History" by Jones (2011) provides insight into the diverse ways in which weather phenomena can shape human thought and action. While not directly focused on dermatological queries, the book offers a comprehensive overview of the intricate interplay between environmental conditions and human psychology, opening the door to potential connections between weather patterns and individuals' contemplation of skin irregularities.

On the fictional front, the novel "Snow Falling on Cedars" by David Guterson (1994) presents a gripping tale set amidst the backdrop of wintry landscapes. While the narrative centers on a different set of circumstances, the evocative description of snow's impact on the characters' lives offers a glimpse into the profound influence of winter elements on human thought and emotion—a connection that resonates with our exploration of snow depth and skin-related queries.

In a more lighthearted departure from traditional literature, the animated series "Frozen: The Animated Adventures" introduces viewers to a whimsical world where snow holds transformative power. While this cartoon may seem like a departure from scholarly pursuits, its imaginative portrayal of snow's influence on character dynamics serves as a playful reminder of the multifaceted nature of our winter-themed inquiry.

As we journey through the pages of scholarly works and literary imaginings, we are reminded that beneath the frosty veneer of our study lies a tale of intrigue, where the snow-covered landscapes of Charlotte intersect with the curious contemplation of skin irregularities. It appears that in the realm of academic exploration, even frosty inquiries can thaw into captivating discoveries.

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 methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between Deepest snow depth in Charlotte and Google searches for 'is this a wart'. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

Your research team collected data from all across the internet, but mostly just used information from NOAA National Climate Data Center and Google Trends . You used data from 2004 to 2022

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
"Winter Warts: A Correlative Study between Deepest Snow Depth in Charlotte and Google Searches for 'Is This a Wart'"

[[ABSTRACT]]
This study delves into the intriguing, frosty connection between the deepest snow depth in Charlotte and the frequency of Google searches for the perplexing query, "Is this a wart?" In this peculiar investigation, we aim to uncover whether a correlation exists between the depth of snow in Charlotte and the propensity of individuals to ponder the wart-like characteristics of certain skin blemishes.

Our research team sifted through the snow data provided by the NOAA National Climate Data Center, where we found an abundance of frosty facts and a blizzard of statistics. Meanwhile, on the digital front, we combed through the icy depths of Google Trends, where we encountered a flurry of frostbite-fueled queries.

After performing a thorough analysis, we discovered a correlation coefficient of 0.7995460 and p < 0.01 for the time period spanning from 2004 to 2022. The results suggest a remarkably strong association between the depth of snowfall and the sudden surge in curiosity about warts. It appears that as the snow deepens, so does the perplexity about those enigmatic skin protrusions, leaving individuals to ask, "Is this a wart or just a snowflake masquerading as a skin blemish?"

As the frosty findings thaw, we cannot help but wonder if our research has uncovered the chilling truth about the chilling effect of snow on our perceived skin anomalies. Indeed, it seems that some skin issues may be "snow" laughing matter!

[[INTRODUCTION]]
As the winter months cast their icy spell over the city of Charlotte, North Carolina, an interesting phenomenon emerges, akin to a snow-covered enigma wrapped in a blanket of mystery. The unassuming search term "Is this a wart" experiences a surge in search activity, prompting us to unearth the cold, hard truth behind this curious correlation. It seems that Mother Nature's wintry embrace may have more riddles in store than just snowflakes and frostbite.
It is often said that in the coldest of times, we must remain vigilant for unexpected growths—what some may even call "chilling" developments! (Cue the cringe-worthy dad joke drumroll, please.) This study seeks to unravel the enigmatic connection between the depth of snow in Charlotte and the sudden surge in cyberspace's contemplation of skin irregularities. While it may seem like a frosty fusion of topics, the bond between snowy conditions and perplexing skin queries may hold more warmth than meets the eye.
One might question if this correlation is merely a frosty fluke, but our research aims to demonstrate otherwise. In the realm of academic inquiry, we must not shy away from piercing through the icy layers of convention to uncover the snow-covered truths that lie beneath. It's time to break the ice and shine a light on this peculiar pairing of weather patterns and dermatological ponderings. After all, perhaps the snow holds more than just frozen water—it might just hold the key to unlocking the skin-deep mysteries that bewilder us all.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

To investigate the frosty correlation between the deepest snow depth in Charlotte and the surge in Google searches for "Is this a wart," our research team embarked on a frigid journey into the realms of climatology and digital ponderings. We utilized data spanning from 2004 to 2022, a period marked by both snowfall and skin-related inquiries in the digital sphere. Our approach involved a blend of traditional climate data analysis and digital trend scrutiny, creating an eclectic mix of methodologies to encapsulate the chilly essence of our research.

Our team first delved into the realm of meteorological data by accessing the vast repository provided by the NOAA National Climate Data Center. Within this frozen treasure trove of climatic information, we unearthed the deepest snow depth records for Charlotte, meticulously documenting the frosty embrace of each winter season. A snowstorm of statistical analyses ensued, where we computed the mean, median, and standard deviation of the snow depths, endeavoring to capture the essence of Charlotte's wintry wonders in numerical form.

After braving the blizzard of climatic data, we ventured into the digital domain, wielding the formidable power of Google Trends. Here, we tracked the frequency of searches for the intriguing query "Is this a wart," observing the ebb and flow of curiosity about skin abnormalities amidst the icy landscape of Charlotte's weather patterns. Like intrepid explorers of the digital tundra, we sought to discern patterns in the search activity that mirrored the rise and fall of the city's snow depths.

In a fusion of statistical rigor and cyber sleuthing, we employed correlation analyses to unravel the intricate dance between snowfall and inquiries about potential skin maladies. Armed with the tools of parametric and non-parametric tests, we sought to unearth the frozen truth that lay beneath the surface of these seemingly disparate phenomena. With a touch of statistical ingenuity and a hint of frosty humor, we embarked on this peculiar journey, all in the name of uncovering the chilling connection between wintry whims and dermatological dilemmas.

It's safe to say we didn't "snow" what we were getting into when we started this chilly research! But as the data unfolded, we couldn't help but chuckle at the snowballing evidence of a correlation between snow depth and skin-related queries. It's all fun and games until someone loses a mitten—a sentiment that certainly applies to our foray into the depths of wintry data analysis.

Much like a snowball rolling downhill, our research gained momentum as we tumbled through the frosty landscape of statistical analysis and digital scrutiny. With a twinkle in our eyes and a dash of snow-induced whimsy, we trudged through the snowdrifts of data to uncover the frosty truths that lay hidden beneath the surface. And yes, we did make a few snow angels along the way—scientifically, of course!

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 Deepest snow depth in Charlotte and Google searches for 'is this a wart'. 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]]
"Winter Warts: A Correlative Study between Deepest Snow Depth in Charlotte and Google Searches for 'Is This a Wart'"

[[ABSTRACT]]
This study delves into the intriguing, frosty connection between the deepest snow depth in Charlotte and the frequency of Google searches for the perplexing query, "Is this a wart?" In this peculiar investigation, we aim to uncover whether a correlation exists between the depth of snow in Charlotte and the propensity of individuals to ponder the wart-like characteristics of certain skin blemishes.

Our research team sifted through the snow data provided by the NOAA National Climate Data Center, where we found an abundance of frosty facts and a blizzard of statistics. Meanwhile, on the digital front, we combed through the icy depths of Google Trends, where we encountered a flurry of frostbite-fueled queries.

After performing a thorough analysis, we discovered a correlation coefficient of 0.7995460 and p < 0.01 for the time period spanning from 2004 to 2022. The results suggest a remarkably strong association between the depth of snowfall and the sudden surge in curiosity about warts. It appears that as the snow deepens, so does the perplexity about those enigmatic skin protrusions, leaving individuals to ask, "Is this a wart or just a snowflake masquerading as a skin blemish?"

As the frosty findings thaw, we cannot help but wonder if our research has uncovered the chilling truth about the chilling effect of snow on our perceived skin anomalies. Indeed, it seems that some skin issues may be "snow" laughing matter!

[[LITERATURE REVIEW]]
In a study conducted by Smith et al. (2015), the authors find a significant positive correlation between the deepest snow depth in Charlotte and the frequency of Google searches for "Is this a wart." The findings suggest that as the snowfall increases, so does the inclination of individuals to seek clarification on potential skin blemishes resembling warts. This research serves as a pivotal starting point for our investigation into this frigidly fascinating correlation.
Doe and Johnson (2018) further explore the relationship between weather patterns and dermatological inquiries, shedding light on the psychological impact of snowy conditions on skin-related concerns. Their work highlights the potential influence of environmental factors on individuals' perceptions of skin anomalies, incorporating snow depth as a prominent variable in their analysis.
Turning to the wider literature on climate and human behavior, the book "The Weather Factor: How Nature Has Changed History" by Jones (2011) provides insight into the diverse ways in which weather phenomena can shape human thought and action. While not directly focused on dermatological queries, the book offers a comprehensive overview of the intricate interplay between environmental conditions and human psychology, opening the door to potential connections between weather patterns and individuals' contemplation of skin irregularities.
On the fictional front, the novel "Snow Falling on Cedars" by David Guterson (1994) presents a gripping tale set amidst the backdrop of wintry landscapes. While the narrative centers on a different set of circumstances, the evocative description of snow's impact on the characters' lives offers a glimpse into the profound influence of winter elements on human thought and emotion—a connection that resonates with our exploration of snow depth and skin-related queries.
In a more lighthearted departure from traditional literature, the animated series "Frozen: The Animated Adventures" introduces viewers to a whimsical world where snow holds transformative power. While this cartoon may seem like a departure from scholarly pursuits, its imaginative portrayal of snow's influence on character dynamics serves as a playful reminder of the multifaceted nature of our winter-themed inquiry.
As we journey through the pages of scholarly works and literary imaginings, we are reminded that beneath the frosty veneer of our study lies a tale of intrigue, where the snow-covered landscapes of Charlotte intersect with the curious contemplation of skin irregularities. It appears that in the realm of academic exploration, even frosty inquiries can thaw into captivating discoveries.

[[RESULTS]]
The statistical analysis conducted on the data collected from the NOAA National Climate Data Center and Google Trends revealed a robust correlation between the deepest snow depth in Charlotte and the frequency of Google searches for the perplexing query, "Is this a wart." The correlation coefficient of 0.7995460 and an r-squared value of 0.6392738 suggested a strong linear relationship between these seemingly disparate phenomena.
Figure 1 illustrates this fascinating connection with a scatterplot displaying the positive correlation between the deepest snow depth in Charlotte and the frequency of searches for "Is this a wart." It seems that as the snow depth increases, so does the curiosity surrounding potential skin abnormalities. It's almost as if the snowflakes are inspiring deeper contemplation about skin blemishes, prompting individuals to wonder if what they see is indeed a wart or just a cleverly disguised snowflake.
In the cold, hard world of statistical analysis, it's not every day that we come across such a compelling relationship between meteorological data and dermatological ponderings. One might even say our findings have "snow"balled into something quite intriguing!

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of our study support the prior research by Smith et al. (2015), indicating a strong positive correlation between the deepest snow depth in Charlotte and the frequency of Google searches for "Is this a wart." This robust association aligns with our initial hypothesis that the wintry conditions in Charlotte could engender heightened curiosity regarding potential skin irregularities, akin to how snowflakes pique our interest in their unique forms.

Moreover, the results are consistent with the work of Doe and Johnson (2018), who delved into the impact of weather patterns on dermatological queries. Our study builds upon their findings, demonstrating the substantial influence of snow depth on individuals' propensity to seek clarification on skin blemishes resembling warts. It seems that as the snow blankets the landscape, it also blankets our minds with contemplation about our skin's wintry mysteries.

In the book "The Weather Factor: How Nature Has Changed History" by Jones (2011), the intricate interplay between environmental conditions and human psychology is explored, offering an expansive perspective on the potential psychological impact of weather phenomena. Our research underscores this notion, highlighting the perceptible effect of temperature and snow accumulation on individuals' curiosity pertaining to dermatological matters.

Turning to a more lighthearted source, the animated series "Frozen: The Animated Adventures" playfully beckons us to consider the transformative power of snow. Though a departure from scholarly avenues, this whimsical portrayal serves as a reminder of the multifaceted nature of our inquiry, where the wintry elements of Charlotte seemingly influence the contemplation of skin ailments.

The substantial correlation found in our study not only supports prior research but also sheds light on the chilling impact of winter conditions on individuals' dermatological musings. It seems that amidst the snow-cloaked vistas of Charlotte, the question "Is this a wart?" emerges as an enigmatic snowflake, beckoning us to unravel its icy mysteries.

As we consider the implications of our findings, it becomes apparent that the correlation between snow depth and inquiries about skin irregularities is not merely skin-deep—rather, it reflects the intricate interplay between environmental stimuli and human curiosity. In the frosty embrace of our research, we uncover a chilly truth: when it snows, it warts!

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 Deepest snow depth in Charlotte and Google searches for 'is this a wart'. 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]]
"Winter Warts: A Correlative Study between Deepest Snow Depth in Charlotte and Google Searches for 'Is This a Wart'"

[[ABSTRACT]]
This study delves into the intriguing, frosty connection between the deepest snow depth in Charlotte and the frequency of Google searches for the perplexing query, "Is this a wart?" In this peculiar investigation, we aim to uncover whether a correlation exists between the depth of snow in Charlotte and the propensity of individuals to ponder the wart-like characteristics of certain skin blemishes.

Our research team sifted through the snow data provided by the NOAA National Climate Data Center, where we found an abundance of frosty facts and a blizzard of statistics. Meanwhile, on the digital front, we combed through the icy depths of Google Trends, where we encountered a flurry of frostbite-fueled queries.

After performing a thorough analysis, we discovered a correlation coefficient of 0.7995460 and p < 0.01 for the time period spanning from 2004 to 2022. The results suggest a remarkably strong association between the depth of snowfall and the sudden surge in curiosity about warts. It appears that as the snow deepens, so does the perplexity about those enigmatic skin protrusions, leaving individuals to ask, "Is this a wart or just a snowflake masquerading as a skin blemish?"

As the frosty findings thaw, we cannot help but wonder if our research has uncovered the chilling truth about the chilling effect of snow on our perceived skin anomalies. Indeed, it seems that some skin issues may be "snow" laughing matter!

[[INTRDUCTION]]
As the winter months cast their icy spell over the city of Charlotte, North Carolina, an interesting phenomenon emerges, akin to a snow-covered enigma wrapped in a blanket of mystery. The unassuming search term "Is this a wart" experiences a surge in search activity, prompting us to unearth the cold, hard truth behind this curious correlation. It seems that Mother Nature's wintry embrace may have more riddles in store than just snowflakes and frostbite.
It is often said that in the coldest of times, we must remain vigilant for unexpected growths—what some may even call "chilling" developments! (Cue the cringe-worthy dad joke drumroll, please.) This study seeks to unravel the enigmatic connection between the depth of snow in Charlotte and the sudden surge in cyberspace's contemplation of skin irregularities. While it may seem like a frosty fusion of topics, the bond between snowy conditions and perplexing skin queries may hold more warmth than meets the eye.
One might question if this correlation is merely a frosty fluke, but our research aims to demonstrate otherwise. In the realm of academic inquiry, we must not shy away from piercing through the icy layers of convention to uncover the snow-covered truths that lie beneath. It's time to break the ice and shine a light on this peculiar pairing of weather patterns and dermatological ponderings. After all, perhaps the snow holds more than just frozen water—it might just hold the key to unlocking the skin-deep mysteries that bewilder us all.

[[RESULTS]]
The statistical analysis conducted on the data collected from the NOAA National Climate Data Center and Google Trends revealed a robust correlation between the deepest snow depth in Charlotte and the frequency of Google searches for the perplexing query, "Is this a wart." The correlation coefficient of 0.7995460 and an r-squared value of 0.6392738 suggested a strong linear relationship between these seemingly disparate phenomena.
Figure 1 illustrates this fascinating connection with a scatterplot displaying the positive correlation between the deepest snow depth in Charlotte and the frequency of searches for "Is this a wart." It seems that as the snow depth increases, so does the curiosity surrounding potential skin abnormalities. It's almost as if the snowflakes are inspiring deeper contemplation about skin blemishes, prompting individuals to wonder if what they see is indeed a wart or just a cleverly disguised snowflake.
In the cold, hard world of statistical analysis, it's not every day that we come across such a compelling relationship between meteorological data and dermatological ponderings. One might even say our findings have "snow"balled into something quite intriguing!

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

As the snow settles (quite literally) on our findings, it becomes evident that there is indeed a chilling correlation between the deepest snow depth in Charlotte and the frequency of Google searches for "Is this a wart." It seems that as the snow piles up, so does the uncertainty about those pesky skin protrusions. Perhaps we could coin the term "snowgical" quandary for this frosty phenomenon. But hey, at least it gives everyone an excuse to wear gloves, right?

Our results leave us snow-stalgic for more wintry insights into the world of dermatological ponderings, but the evidence is as clear as the frosty air: snow depth and skin blemish musings go hand in hand. It's almost as if Jack Frost himself is leaving a trail of skin-related mysteries in his icy wake. (Yes, I'm reaching with that one, but it's worth a shot, isn't it?)

In light of these findings, we assert with a snow of certainty that no further research is needed in this area. In the realm of peculiar research inquiries, we may have stumbled upon the "snow" gem—a correlation as unique and unexpected as finding a snowman in the Sahara. It's time to bid adieu to this area of study, leaving it as a snow-covered curiosity in the annals of scientific exploration. But hey, at least we can say we've left no stone unturned—snow or otherwise.

And with that, dear readers, we bid you a frosty farewell and leave you with this final dad joke to ponder: Why don't snowmen ever get sick? Because they have "frost" immunity! Thank you, thank you—try the veal, and don't forget to tip your waiters!

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 Winter Warts: A Correlative Study between Deepest Snow Depth in Charlotte and Google Searches for 'Is This a Wart'. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Seasonal Skin Studies

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 Winter Warts: A Correlative Study between Deepest Snow Depth in Charlotte and Google Searches for 'Is This a Wart'. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Frivolous Winter Ailments 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 Winter Warts: A Correlative Study between Deepest Snow Depth in Charlotte and Google Searches for 'Is This a Wart' with an abstract of This study delves into the intriguing, frosty connection between the deepest snow depth in Charlotte and the frequency of Google searches for the perplexing query, "Is this a wart?" In this peculiar investigation, we aim to uncover whether a correlation exists between the depth of snow in Charlotte and the propensity of individuals to ponder the wart-like characteristics of certain skin blemishes.
Our research team sifted through the snow data provided by the NOAA National Climate Data Center, where we found an abundance of frosty facts and a blizzard of statistics. Meanwhile, on the digital front, we combed through the icy depths of Google Trends, where we encountered a flurry of frostbite-fueled queries.
After performing a thorough analysis, we discovered a correlation coefficient of 0.7995460 and p < 0.01 for the time period spanning from 2004 to 2022. The results suggest a remarkably strong association between the depth of snowfall and the sudden surge in curiosity about warts. It appears that as the snow deepens, so does the perplexity about those enigmatic skin protrusions, leaving individuals to ask, "Is this a wart or just a snowflake masquerading as a skin blemish?"
As the frosty findings thaw, we cannot help but wonder if our research has uncovered the chilling truth about the chilling effect of snow on our perceived skin anomalies. Indeed, it seems that some skin issues may be "snow" laughing matter!

ChatGPT:

deepest snow depth, Charlotte, Google searches, is this a wart, correlation study, snowfall and skin blemishes, snow depth and skin anomalies

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

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Data details

Deepest snow depth in Charlotte
Detailed data title: Highest daily snow depth in the year at CHARLOTTE DOUGLAS AIRPORT, NC US
Source: NOAA National Climate Data Center
See what else correlates with Deepest snow depth in Charlotte

Google searches for 'is this a wart'
Detailed data title: Relative volume of Google searches for 'is this a wart' (United States, without quotes)
Source: Google Trends
Additional Info: ^{See full details} Relative search volume (not absolute numbers)

See what else correlates with Google searches for 'is this a wart'

Correlation r = 0.7995460 (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 = 0.6392738 (Coefficient of determination)
This means 63.9% of the change in the one variable (i.e., Google searches for 'is this a wart') is predictable based on the change in the other (i.e., Deepest snow depth in Charlotte) over the 19 years from 2004 through 2022.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 4.0E-5.^Show 0.0000399357797090148300000000
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 0.8 in 0.004% of random cases. Said differently, if you correlated 25,040 random variables^Note You don't actually need 25 thousand variables to find a correlation like this one. 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.

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 18 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 18 because we have two variables measured over a period of 19 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.

[ 0.54, 0.92 ] 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.

	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Deepest snow depth in Charlotte (Deepest snow (inches))	13	1	0	0	0	4	3	3	0	3	6	2	2	1	3	0	0	0	2
Google searches for 'is this a wart' (Rel. search volume)	27.0833	5.75	7.83333	2.83333	9.16667	4.25	6.16667	8.75	5.16667	6.58333	7.83333	8.08333	10.1667	7.91667	9	8.08333	7.75	5.83333	9

Why this works

Data dredging: I have 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
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([13,1,0,0,0,4,3,3,0,3,6,2,2,1,3,0,0,0,2,])
array_2 = np.array([27.0833,5.75,7.83333,2.83333,9.16667,4.25,6.16667,8.75,5.16667,6.58333,7.83333,8.08333,10.1667,7.91667,9,8.08333,7.75,5.83333,9,])
array_1_name = "Deepest snow depth in Charlotte"
array_2_name = "Google searches for 'is this a wart'"

# 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 Deepest snow depth in Charlotte
Line chart for only Google searches for 'is this a wart'
AI-generated correlation image
The spurious research paper: Winter Warts: A Correlative Study between Deepest Snow Depth in Charlotte and Google Searches for 'Is This a Wart'

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Correlation ID: 5594 · Black Variable ID: 25226 · Red Variable ID: 1434


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