Spurious correlation #5,851 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is The number of movies Michael Caine appeared in and the second variable is Votes for the Democratic Presidential candidate in Maryland. The chart goes from 1976 to 2020, 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 The number of movies Michael Caine appeared in and the second variable is Votes for the Democratic Presidential candidate in Maryland. The chart goes from 1976 to 2020, and the two variables track closely in value over that time.

Download png, svg

AI explanation

Michael Caine's unmatchable on-screen charm and wit inadvertently wooed the residents of Maryland, prompting them to lean towards the charismatic and confident demeanor often exhibited by Democratic candidates. As Caine graced the silver screen, his subtle influence seeped into the subconscious of Marylanders, swaying their political preferences towards the party that exuded a similar aura of sophistication and poise. His prolific presence in films became the reel reason behind the unexpected surge in Democratic votes in the state, proving that in the game of politics, as in the art of acting, sometimes, it's simply a matter of mastering the Caine effect.

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image that combines a classic movie theater setting with the influence of Michael Caine's on-screen charisma on the audience's political preferences. Show an atmospheric theater filled with moviegoers engrossed in the timeless charm of Michael Caine's films playing on the big screen. The scene should subtly convey the residents' admiration and subconscious sway towards the Democratic candidate, captured through their expressions and body language. Be sure to emphasize the captivating ambiance and the visual impact of Caine's presence as the focal point of the moviegoers' attention. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: The number of movies Michael Caine appeared in and Votes for the Democrat Presidential candidate in Maryland. 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: Magic realism Magic realism A Discovery magazine cover image Brutalism Parallax art
ChatGPT: A Discovery magazine cover image

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both The number of movies Michael Caine appeared in and Votes for the Democrat Presidential candidate in Maryland, in the following situation: Michael Caine's unmatchable on-screen charm and wit inadvertently wooed the residents of Maryland, prompting them to lean towards the charismatic and confident demeanor often exhibited by Democratic candidates. As Caine graced the silver screen, his subtle influence seeped into the subconscious of Marylanders, swaying their political preferences towards the party that exuded a similar aura of sophistication and poise. His prolific presence in films became the reel reason behind the unexpected surge in Democratic votes in the state, proving that in the game of politics, as in the art of acting, sometimes, it's simply a matter of mastering the Caine effect.. Keep the description simple.
ChatGPT: In a packed movie theater in Maryland, Michael Caine's classic films are projected onto the big screen, captivating the audience with his timeless charm and wit. As the residents watch in admiration, they are subconsciously swayed towards the Democratic candidate, their votes reflecting the influence of Caine's on-screen charisma on their political preferences.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ A Discovery magazine cover image ] and depicting this scene: In a packed movie theater in Maryland, Michael Caine's classic films are projected onto the big screen, captivating the audience with his timeless charm and wit. As the residents watch in admiration, they are subconsciously swayed towards the Democratic candidate, their votes reflecting the influence of Caine's on-screen charisma on their political preferences. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image that combines a classic movie theater setting with the influence of Michael Caine's on-screen charisma on the audience's political preferences. Show an atmospheric theater filled with moviegoers engrossed in the timeless charm of Michael Caine's films playing on the big screen. The scene should subtly convey the residents' admiration and subconscious sway towards the Democratic candidate, captured through their expressions and body language. Be sure to emphasize the captivating ambiance and the visual impact of Caine's presence as the focal point of the moviegoers' attention.

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

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

AI academic paper

(Because p < 0.01)

In Caine Sight: The Astonishing Correlation Between Michael Caine's Film Appearances and Democratic Votes in Maryland

The Journal of Cinematic Correlations

Jan 2024

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

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

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

Please make up a literature review section of an academic research paper presenting the findings of the connection between The number of movies Michael Caine appeared in and Votes for the Democrat Presidential candidate in Maryland. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then name a few real TV shows that sound like they might be relevant to the topic that you watched as research.

Here is the title and abstract of the paper:
[[TITLE]]
In Caine Sight: The Astonishing Correlation Between Michael Caine's Film Appearances and Democratic Votes in Maryland

[[ABSTRACT]]
This study examines the peculiar link between the number of movies featuring the esteemed actor Michael Caine and the electoral support for the Democratic presidential candidate in the state of Maryland. Leveraging data from The Movie DB and the MIT Election Data and Science Lab, Harvard Dataverse covering the period from 1976 to 2020, the correlation coefficient calculated is an impressive 0.8517681 (p < 0.01), suggesting a remarkably strong association. Despite the initial skepticism surrounding this seemingly arcane relationship, our analysis reveals a surprising connection that cannot be dismissed. While the mechanisms underlying this correlation remain enigmatic, the findings underscore the need for further investigation into the multifaceted influences on voting behavior. This investigation sheds light on the potential consequential impact of cinema and culture on political preferences, reinforcing the old adage, "All politics is Caine."

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of entertainment and politics has long been a subject of fascination, often prompting inquiries into the ways in which popular culture may exert influence on electoral dynamics. Within this context, the present study delves into the hitherto unexplored relationship between the prolific cinematic endeavors of the inimitable actor Michael Caine and the electoral preferences for the Democratic presidential candidate in the state of Maryland. At first glance, one might be inclined to dismiss such an investigation as merely an amusing diversion, akin to the whimsical pursuits of a statistical sleuth in search of an enigmatic correlation. However, as the data analysis unfolds, the results reveal a striking correlation that demands serious consideration amid the lighthearted chuckles.
The dataset utilized in this analysis, curated from The Movie DB and the MIT Election Data and Science Lab, Harvard Dataverse, spans the period from 1976 to 2020. This comprehensive temporal scope serves to capture the full breadth of Michael Caine's cinematic oeuvre and the corresponding electoral vicissitudes. Through the employment of rigorous statistical methods, including the calculation of correlation coefficients and significance testing, we arrive at an eyebrow-raising correlation coefficient of 0.8517681 (p < 0.01), signifying a robust and noteworthy association.
While, on the surface, this association may appear to be no more than a whimsical curio, the exceptionally high correlation value commands attention. Indeed, it prompts a reevaluation of the potential interplay between the realm of filmic artistry and the realm of political sentiment. The adage, "Art imitates life," takes on new significance when confronted with such unexpected statistical revelations.
As the scientific community grapples with the implications of this correlation, it becomes evident that the nexus of cinema and politics may hold subtler and more intricate dynamics than previously envisioned. This investigation thus stands as a testament to the delightful unpredictability of statistical inquiry, reminding us that in the world of data analysis, as in the world of Michael Caine's filmography, there are always surprises lurking around the corner, ready to illogically, yet captivatingly, make their presence felt.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The astonishment and skepticism prompted by the correlation between the number of movies featuring Michael Caine and the votes for the Democratic presidential candidate in Maryland have, quite predictably, elicited an extensive review of the existing literature on seemingly unrelated variables influencing electoral preferences.

Several serious-minded studies have contributed valuable insights into the potential realms of influence on voting behavior. Smith (2010) argues for the significance of socio-cultural factors in shaping political attitudes, a notion that may bear relevance to the cinematic influence under investigation. In a similar vein, Doe and Jones (2015) propose a theoretical framework encompassing diverse contextual variables that may impinge upon voter choices. While their models do not explicitly consider the filmic contributions of Michael Caine, the underlying principles may offer a conceptual groundwork for interpreting the unexpected correlation observed in Maryland.

Moving beyond the immediate domain of political science, a survey of non-fiction literature yields connections that might be seen as tenuous at first, but are certainly intriguing. In "Hollywood and Politics: A Sourcebook," the authors trace the historical interplay between the film industry and the political landscape, providing a broader context within which the current findings may be situated. "Cinema and Society: Filmic Reflections of Political Realities" (Johnson, 2018) offers a sociological perspective that posits cinema as both a mirror and a shaper of societal values, hinting at the potential resonance of Michael Caine's filmography within the political milieu.

Expanding the purview to fictional works that, in their obscure titles, may harbor hidden relevance, "The Dark Knight Rises: Political Machinations in Gotham" (Garcia, 2019) presents a fictional exploration of the intersections between popular culture and political narrative, albeit within the realm of comic book adaptations. Another thought-provoking entry, "Alfie's Influence: The Unlikely Power of a Charismatic Rogue" (Smith, 2006), ostensibly delves into the impact of a fictional character portrayed by Michael Caine, yet may offer allegorical insights into the actor's broader influence on public sentiment.

As for television programming that might shed light on the nuanced dynamics at play, the authors have undertaken the arduous task of watching politically relevant shows for the purposes of this investigation. "The West Wing" and "House of Cards" are pertinent examples, with their thematic explorations of electoral intrigue, albeit devoid of any explicit references to Michael Caine. The veracity of such programming remains subject to individual interpretations, but their relevance cannot be altogether disregarded.

In conclusion, while the literature has yet to explicitly address the curious correlation between Michael Caine's film appearances and Democratic votes in Maryland, the gamut of interdisciplinary insights reviewed herein paves the way for a more nuanced consideration of the multifaceted influences upon electoral preferences. It is within this comprehensive framework that the current investigation assumes its place, bolstered by a whimsical sense of inquiry and a readiness to confront statistical surprises with an inquisitive, yet levity-infused, mindset.

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

Please make up the methodology section of an academic research paper presenting the findings of the connection between The number of movies Michael Caine appeared in and Votes for the Democrat Presidential candidate in Maryland. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Your research team collected data from all across the internet, but mostly just used information from The Movie DB and MIT Election Data and Science Lab, Harvard Dataverse . You used data from 1976 to 2020

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]]
In Caine Sight: The Astonishing Correlation Between Michael Caine's Film Appearances and Democratic Votes in Maryland

[[ABSTRACT]]
This study examines the peculiar link between the number of movies featuring the esteemed actor Michael Caine and the electoral support for the Democratic presidential candidate in the state of Maryland. Leveraging data from The Movie DB and the MIT Election Data and Science Lab, Harvard Dataverse covering the period from 1976 to 2020, the correlation coefficient calculated is an impressive 0.8517681 (p < 0.01), suggesting a remarkably strong association. Despite the initial skepticism surrounding this seemingly arcane relationship, our analysis reveals a surprising connection that cannot be dismissed. While the mechanisms underlying this correlation remain enigmatic, the findings underscore the need for further investigation into the multifaceted influences on voting behavior. This investigation sheds light on the potential consequential impact of cinema and culture on political preferences, reinforcing the old adage, "All politics is Caine."

[[INTRODUCTION]]
The intersection of entertainment and politics has long been a subject of fascination, often prompting inquiries into the ways in which popular culture may exert influence on electoral dynamics. Within this context, the present study delves into the hitherto unexplored relationship between the prolific cinematic endeavors of the inimitable actor Michael Caine and the electoral preferences for the Democratic presidential candidate in the state of Maryland. At first glance, one might be inclined to dismiss such an investigation as merely an amusing diversion, akin to the whimsical pursuits of a statistical sleuth in search of an enigmatic correlation. However, as the data analysis unfolds, the results reveal a striking correlation that demands serious consideration amid the lighthearted chuckles.
The dataset utilized in this analysis, curated from The Movie DB and the MIT Election Data and Science Lab, Harvard Dataverse, spans the period from 1976 to 2020. This comprehensive temporal scope serves to capture the full breadth of Michael Caine's cinematic oeuvre and the corresponding electoral vicissitudes. Through the employment of rigorous statistical methods, including the calculation of correlation coefficients and significance testing, we arrive at an eyebrow-raising correlation coefficient of 0.8517681 (p < 0.01), signifying a robust and noteworthy association.
While, on the surface, this association may appear to be no more than a whimsical curio, the exceptionally high correlation value commands attention. Indeed, it prompts a reevaluation of the potential interplay between the realm of filmic artistry and the realm of political sentiment. The adage, "Art imitates life," takes on new significance when confronted with such unexpected statistical revelations.
As the scientific community grapples with the implications of this correlation, it becomes evident that the nexus of cinema and politics may hold subtler and more intricate dynamics than previously envisioned. This investigation thus stands as a testament to the delightful unpredictability of statistical inquiry, reminding us that in the world of data analysis, as in the world of Michael Caine's filmography, there are always surprises lurking around the corner, ready to illogically, yet captivatingly, make their presence felt.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

The methodology employed in this study involved a comprehensive collection of data pertaining to the cinematic output of the esteemed actor Michael Caine and the electoral voting patterns for the Democratic presidential candidate in the state of Maryland. This entailed mining extensive repositories, primarily sourcing information from The Movie DB, which catalogs film appearances, and the MIT Election Data and Science Lab, Harvard Dataverse, which houses electoral data. The dataset spans a substantial temporal range, encapsulating the period from 1976 to 2020, allowing for a thorough exploration of the potential correlation between these seemingly disparate variables.

To quantify Michael Caine's cinematic endeavors, a systematic query process was executed to compile a comprehensive list of his film appearances from diverse sources within The Movie DB, accounting for potential inaccuracies or omissions. Concurrently, electoral data pertaining to the Democratic votes in Maryland was meticulously extracted from the MIT Election Data and Science Lab, Harvard Dataverse. Given the idiosyncrasies inherent in electoral data collection, rigorous validation and cross-referencing procedures were enacted to ensure the reliability and integrity of the dataset.

Subsequently, the extracted data underwent meticulous preprocessing, encompassing standardization and correction for any temporal inconsistencies, to harmonize the variables for subsequent statistical analysis. The correlation between the frequency of Michael Caine's film appearances and the Democratic votes in Maryland was quantitatively assessed utilizing robust statistical measures, including the calculation of correlation coefficients and significance testing through advanced analytical software.

The statistical analysis unveiled a remarkably high correlation coefficient of 0.8517681 (p < 0.01), indicative of a compelling association between the variables under scrutiny. The robustness and significance of this correlation were further substantiated through stringent sensitivity analyses and bootstrapping techniques, affirming the resilience of the observed relationship.

While the methodological approach employed in this study may appear unconventional in its juxtaposition of disparate realms - that of cinematic artistry and political preferences - the interplay of these domains yielded intriguing findings that warrant further investigation. As with any novel research endeavor, the methodology employed herein encapsulates the inherent balance of rigor and creativity, seeking to unravel the unforeseen connections that enliven the tapestry of statistical inquiry.

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

Please make up the discussion section of an academic research paper presenting the findings of the connection between The number of movies Michael Caine appeared in and Votes for the Democrat Presidential candidate in Maryland. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

Limit your response to 500 tokens.

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

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

[[TITLE]]
In Caine Sight: The Astonishing Correlation Between Michael Caine's Film Appearances and Democratic Votes in Maryland

[[ABSTRACT]]
This study examines the peculiar link between the number of movies featuring the esteemed actor Michael Caine and the electoral support for the Democratic presidential candidate in the state of Maryland. Leveraging data from The Movie DB and the MIT Election Data and Science Lab, Harvard Dataverse covering the period from 1976 to 2020, the correlation coefficient calculated is an impressive 0.8517681 (p < 0.01), suggesting a remarkably strong association. Despite the initial skepticism surrounding this seemingly arcane relationship, our analysis reveals a surprising connection that cannot be dismissed. While the mechanisms underlying this correlation remain enigmatic, the findings underscore the need for further investigation into the multifaceted influences on voting behavior. This investigation sheds light on the potential consequential impact of cinema and culture on political preferences, reinforcing the old adage, "All politics is Caine."

[[LITERATURE REVIEW]]
The astonishment and skepticism prompted by the correlation between the number of movies featuring Michael Caine and the votes for the Democratic presidential candidate in Maryland have, quite predictably, elicited an extensive review of the existing literature on seemingly unrelated variables influencing electoral preferences.
Several serious-minded studies have contributed valuable insights into the potential realms of influence on voting behavior. Smith (2010) argues for the significance of socio-cultural factors in shaping political attitudes, a notion that may bear relevance to the cinematic influence under investigation. In a similar vein, Doe and Jones (2015) propose a theoretical framework encompassing diverse contextual variables that may impinge upon voter choices. While their models do not explicitly consider the filmic contributions of Michael Caine, the underlying principles may offer a conceptual groundwork for interpreting the unexpected correlation observed in Maryland.
Moving beyond the immediate domain of political science, a survey of non-fiction literature yields connections that might be seen as tenuous at first, but are certainly intriguing. In "Hollywood and Politics: A Sourcebook," the authors trace the historical interplay between the film industry and the political landscape, providing a broader context within which the current findings may be situated. "Cinema and Society: Filmic Reflections of Political Realities" (Johnson, 2018) offers a sociological perspective that posits cinema as both a mirror and a shaper of societal values, hinting at the potential resonance of Michael Caine's filmography within the political milieu.
Expanding the purview to fictional works that, in their obscure titles, may harbor hidden relevance, "The Dark Knight Rises: Political Machinations in Gotham" (Garcia, 2019) presents a fictional exploration of the intersections between popular culture and political narrative, albeit within the realm of comic book adaptations. Another thought-provoking entry, "Alfie's Influence: The Unlikely Power of a Charismatic Rogue" (Smith, 2006), ostensibly delves into the impact of a fictional character portrayed by Michael Caine, yet may offer allegorical insights into the actor's broader influence on public sentiment.
As for television programming that might shed light on the nuanced dynamics at play, the authors have undertaken the arduous task of watching politically relevant shows for the purposes of this investigation. "The West Wing" and "House of Cards" are pertinent examples, with their thematic explorations of electoral intrigue, albeit devoid of any explicit references to Michael Caine. The veracity of such programming remains subject to individual interpretations, but their relevance cannot be altogether disregarded.
In conclusion, while the literature has yet to explicitly address the curious correlation between Michael Caine's film appearances and Democratic votes in Maryland, the gamut of interdisciplinary insights reviewed herein paves the way for a more nuanced consideration of the multifaceted influences upon electoral preferences. It is within this comprehensive framework that the current investigation assumes its place, bolstered by a whimsical sense of inquiry and a readiness to confront statistical surprises with an inquisitive, yet levity-infused, mindset.

[[RESULTS]]
The analysis of the relationship between the number of movies featuring Michael Caine and the Democratic votes for the presidential candidate in Maryland for the years 1976 to 2020 revealed a remarkably strong correlation coefficient of 0.8517681 (p < 0.01). This significant correlation indicates a powerful relationship between these seemingly disparate variables, prompting us to consider that perhaps "Caine" does have a hand in political preferences.
The R-squared value of 0.7255089 further underscores the robustness of this association, suggesting that approximately 72.55% of the variation in Democratic votes in Maryland can be explained by the number of Michael Caine's film appearances. One cannot help but marvel at the sheer predictive power of Michael Caine's cinematic endeavors over the electoral preferences of Maryland residents.
Fig. 1 illustrates this probing correlation. The scatterplot unveils the strong linear relationship between Michael Caine's cinematic presence and Democratic votes, with each data point serving as a testament to the unexpected connections that can be unveiled through rigorous statistical analysis. One might even say that the plot thickens as we ponder the implications of this titillating association.
These results, while initially surprising, align with the broader interdisciplinary discourse on the intersection of popular culture and political phenomena. The inexplicable bond between Michael Caine's films and political leanings in Maryland serves as a tantalizing enigma that beckons further scholarly investigations. After all, as Michael Caine himself once observed, "The empirical method is a very convenient way of settling arguments."
Certainly, this peculiar correlation may seem like a statistical sleight of hand, but it beckons the discerning observer to delve deeper into the potential cultural influences on voting behaviors. As we continue to unravel the mysteries of statistical relationships and their real-world implications, one cannot help but wonder what other surprising connections may lurk beneath the surface, waiting to be brought to light through the lens of academia.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of this study provide empirical support for the astonishing correlation between the number of movies featuring Michael Caine and the votes for the Democratic presidential candidate in Maryland. This unexpected relationship has not only yielded statistically robust results but has also piqued the scholarly community's curiosity about the intricate interplay between popular culture and political preferences.

The literature review, which humorously traversed the realms of serious political science theories and playful fictional explorations, set the stage for this investigation by highlighting the need to consider diverse contextual variables that may influence voter choices. In a slight departure from tradition, the literature review, albeit with a twinkle in its scholarly eye, somewhat playfully referenced Gotham's political machinations and Alfie's unlikely influence, offering an amusing nod to the potential impact of Michael Caine's cinematic oeuvre on political realities. It seems that, indeed, the literature review's subtle jests were not so far-fetched after all, as the results of this study affirm the unexpected intersection between Michael Caine's film appearances and electoral outcomes.

The robust correlation coefficient and the substantial R-squared value underscore the notable influence of Michael Caine's cinematic endeavors on Democratic votes in Maryland. Indeed, the findings solidify the contention that, in the words of Michael Caine himself, the empirical method does indeed provide a convenient way of settling arguments, even if those arguments revolve around the unlikely relationship between a venerable actor's filmography and political leanings.

In conclusion, the remarkable strength of this correlation, which was initially greeted with skepticism and perhaps a touch of incredulity, highlights the need for further scholarly inquiry into the complex interconnections between seemingly unrelated variables. As the enigmatic association between Michael Caine's cinematic presence and political voting behavior continues to beguile and bemuse, it prompts an earnest, albeit whimsically delivered, call for more interdisciplinary investigations into the multifaceted influences shaping electoral preferences.

The current findings may serve as a gentle reminder to researchers that, much like Michael Caine's on-screen charisma, statistical relationships can be delightfully unpredictable, occasionally defying conventional expectations to reveal unexpected patterns that beckon us to delve deeper into the intricate tapestry of human behavior and its nuanced interaction with popular culture.

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

Please make up a short conclusion of an academic research paper presenting the findings of the connection between The number of movies Michael Caine appeared in and Votes for the Democrat Presidential candidate in Maryland. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns. Sneak in some jokes and clever puns about science, research, statistics, and the variables.

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

Here are the title, abstract, introduction, and results sections.
[[TITLE]]
In Caine Sight: The Astonishing Correlation Between Michael Caine's Film Appearances and Democratic Votes in Maryland

[[ABSTRACT]]
This study examines the peculiar link between the number of movies featuring the esteemed actor Michael Caine and the electoral support for the Democratic presidential candidate in the state of Maryland. Leveraging data from The Movie DB and the MIT Election Data and Science Lab, Harvard Dataverse covering the period from 1976 to 2020, the correlation coefficient calculated is an impressive 0.8517681 (p < 0.01), suggesting a remarkably strong association. Despite the initial skepticism surrounding this seemingly arcane relationship, our analysis reveals a surprising connection that cannot be dismissed. While the mechanisms underlying this correlation remain enigmatic, the findings underscore the need for further investigation into the multifaceted influences on voting behavior. This investigation sheds light on the potential consequential impact of cinema and culture on political preferences, reinforcing the old adage, "All politics is Caine."

[[INTRDUCTION]]
The intersection of entertainment and politics has long been a subject of fascination, often prompting inquiries into the ways in which popular culture may exert influence on electoral dynamics. Within this context, the present study delves into the hitherto unexplored relationship between the prolific cinematic endeavors of the inimitable actor Michael Caine and the electoral preferences for the Democratic presidential candidate in the state of Maryland. At first glance, one might be inclined to dismiss such an investigation as merely an amusing diversion, akin to the whimsical pursuits of a statistical sleuth in search of an enigmatic correlation. However, as the data analysis unfolds, the results reveal a striking correlation that demands serious consideration amid the lighthearted chuckles.
The dataset utilized in this analysis, curated from The Movie DB and the MIT Election Data and Science Lab, Harvard Dataverse, spans the period from 1976 to 2020. This comprehensive temporal scope serves to capture the full breadth of Michael Caine's cinematic oeuvre and the corresponding electoral vicissitudes. Through the employment of rigorous statistical methods, including the calculation of correlation coefficients and significance testing, we arrive at an eyebrow-raising correlation coefficient of 0.8517681 (p < 0.01), signifying a robust and noteworthy association.
While, on the surface, this association may appear to be no more than a whimsical curio, the exceptionally high correlation value commands attention. Indeed, it prompts a reevaluation of the potential interplay between the realm of filmic artistry and the realm of political sentiment. The adage, "Art imitates life," takes on new significance when confronted with such unexpected statistical revelations.
As the scientific community grapples with the implications of this correlation, it becomes evident that the nexus of cinema and politics may hold subtler and more intricate dynamics than previously envisioned. This investigation thus stands as a testament to the delightful unpredictability of statistical inquiry, reminding us that in the world of data analysis, as in the world of Michael Caine's filmography, there are always surprises lurking around the corner, ready to illogically, yet captivatingly, make their presence felt.

[[RESULTS]]
The analysis of the relationship between the number of movies featuring Michael Caine and the Democratic votes for the presidential candidate in Maryland for the years 1976 to 2020 revealed a remarkably strong correlation coefficient of 0.8517681 (p < 0.01). This significant correlation indicates a powerful relationship between these seemingly disparate variables, prompting us to consider that perhaps "Caine" does have a hand in political preferences.
The R-squared value of 0.7255089 further underscores the robustness of this association, suggesting that approximately 72.55% of the variation in Democratic votes in Maryland can be explained by the number of Michael Caine's film appearances. One cannot help but marvel at the sheer predictive power of Michael Caine's cinematic endeavors over the electoral preferences of Maryland residents.
Fig. 1 illustrates this probing correlation. The scatterplot unveils the strong linear relationship between Michael Caine's cinematic presence and Democratic votes, with each data point serving as a testament to the unexpected connections that can be unveiled through rigorous statistical analysis. One might even say that the plot thickens as we ponder the implications of this titillating association.
These results, while initially surprising, align with the broader interdisciplinary discourse on the intersection of popular culture and political phenomena. The inexplicable bond between Michael Caine's films and political leanings in Maryland serves as a tantalizing enigma that beckons further scholarly investigations. After all, as Michael Caine himself once observed, "The empirical method is a very convenient way of settling arguments."
Certainly, this peculiar correlation may seem like a statistical sleight of hand, but it beckons the discerning observer to delve deeper into the potential cultural influences on voting behaviors. As we continue to unravel the mysteries of statistical relationships and their real-world implications, one cannot help but wonder what other surprising connections may lurk beneath the surface, waiting to be brought to light through the lens of academia.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

The findings of this research present an intriguing paradox, akin to the mystifying plot twists in a Michael Caine thriller. Despite the seemingly implausible nature of the relationship between the number of movies featuring Michael Caine and the Democratic votes for the presidential candidate in Maryland, the statistically robust correlation coefficient of 0.8517681 (p < 0.01) cannot be casually dismissed. Much like Caine's characters, this correlation defies conventional expectations and demands closer scrutiny.

The R-squared value of 0.7255089 underscores the remarkable explanatory power of Caine's cinematic presence on political preferences, leaving one to ponder if perhaps "Cainescience" should be added to the pantheon of predictive theories. The scatterplot, with its tantalizingly linear relationship, serves as a visual testament to the enigmatic dance between filmic artistry and electoral outcomes. It seems that in the realm of statistics, truth can indeed be stranger than fiction.

While it may be tempting to dismiss this correlation as a statistical sleight of hand, these findings raise thought-provoking questions about the subtle influences of popular culture on political inclinations. Nevertheless, given the peculiar nature of this correlation, it would be wise to exercise caution before drawing definitive causal inferences.

In conclusion, the interplay between Michael Caine's film appearances and political proclivities in Maryland is a mystery worthy of further investigation. However, in the wise words of Michael Caine himself, "You might as well enjoy it - it's not every day a correlation this intriguing comes along." Therefore, it is prudent to assert that no further research is needed in this area because, in the world of statistical inquiry, as in the world of Michael Caine's filmography, some enigmas are best appreciated with a touch of bemused wonder.

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 In Caine Sight: The Astonishing Correlation Between Michael Caine's Film Appearances and Democratic Votes in Maryland. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Cinematic Correlations

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 In Caine Sight: The Astonishing Correlation Between Michael Caine's Film Appearances and Democratic Votes in Maryland. Respond only with the name of a fictional organization.

ChatGPT:

The Institute for Cinematic Political Analysis

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 In Caine Sight: The Astonishing Correlation Between Michael Caine's Film Appearances and Democratic Votes in Maryland with an abstract of This study examines the peculiar link between the number of movies featuring the esteemed actor Michael Caine and the electoral support for the Democratic presidential candidate in the state of Maryland. Leveraging data from The Movie DB and the MIT Election Data and Science Lab, Harvard Dataverse covering the period from 1976 to 2020, the correlation coefficient calculated is an impressive 0.8517681 (p < 0.01), suggesting a remarkably strong association. Despite the initial skepticism surrounding this seemingly arcane relationship, our analysis reveals a surprising connection that cannot be dismissed. While the mechanisms underlying this correlation remain enigmatic, the findings underscore the need for further investigation into the multifaceted influences on voting behavior. This investigation sheds light on the potential consequential impact of cinema and culture on political preferences, reinforcing the old adage, "All politics is Caine.

ChatGPT:

Michael Caine, film appearances, Democratic votes, Maryland, correlation, electoral support, presidential candidate, The Movie DB, MIT Election Data and Science Lab, Harvard Dataverse, voting behavior, cinema influence, culture impact, political preferences

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

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Data details

The number of movies Michael Caine appeared in
Source: The Movie DB
Additional Info: ^{See full details} Sleuth (2007); The Fourth Protocol (1987); Dressed to Kill (1980); A Shock to the System (1990); The Eagle Has Landed (1976); Is Anybody There? (2009); Midnight in Saint Petersburg (1996); The Island (1980); Beyond the Poseidon Adventure (1979); The Quiet American (2002); The Whistle Blower (1986); Harry Brown (2009); The Jigsaw Man (1984); Without a Clue (1988); The Actors (2003); Secondhand Lions (2003); The Holcroft Covenant (1985); Shiner (2000); The Statement (2003); Blame It on Rio (1984); The Hand (1981); Last Orders (2001); Bullseye! (1990); Bullet to Beijing (1995); Silver Bears (1977); Educating Rita (1983); Blue Ice (1992); The Swarm (1978); Deathtrap (1982); Water (1985); The Foreign Eye (2006); Hero (1986); Ashanti (1979); Pinter's Progress (2009); Peeper (1975); Shadow Run (1998); Mr. Morgan's Last Love (2013); Jekyll & Hyde (1990); Youth (2015); The Golden Gong: The Story of Rank Films - British Cinema's Legendary Studio (1985); Dear Dictator (2018); The Honorary Consul (1983); The Debtors (1999); King of Thieves (2018); My Generation (2017); Best Sellers (2021); The Muppet Christmas Carol (1992); Michael Caine: Breaking the Mold (1994); MCAINE: An Anagram of Cinema (2023); The Great Escaper (2023); The Man Who Got Carter (2018); The Making Of 'The Italian Job' (2003); Knights of Classic Drama at the BBC (2015); The Man Who Would Be King (1975); Dirty Rotten Scoundrels (1988); Mr. Destiny (1990); Hannah and Her Sisters (1986); The Weather Man (2005); The Wilby Conspiracy (1975); Little Voice (1998); Noises Off... (1992); On Deadly Ground (1994); Flawless (2007); Half Moon Street (1986); The Romantic Englishwoman (1975); Escape to Victory (1981); Sweet Liberty (1986); Surrender (1987); Mandela and de Klerk (1997); The Dark Knight Rises (2012); Interstellar: Nolan's Odyssey (2014); Quicksand (2003); Going in Style (2017); Arte 1 Documenta (2006); Twist (2021); AGFA MYSTERY MIXTAPE #3: SEQUELITIS (2020); Ending the Knight (2012); The Many Faces of... Michael Caine (2011); The Making of Jaws The Revenge (1987); The Dark Knight (2008); The Prestige (2006); Miss Congeniality (2000); Mona Lisa (1986); A Bridge Too Far (1977); Around the Bend (2004); Gnomeo & Juliet (2011); Curtain Call (1998); Cars 2 (2011); Journey 2: The Mysterious Island (2012); Medieval (2022); Inside 'the Swarm' (1978); Sir David Frost: That Was the Life That Was (2013); Kingsman: The Secret Service Revealed (2015); 50 Films to See Before You Die (2006); Batman Begins (2005); Jaws: The Revenge (1987); Quills (2000); California Suite (1978); Bewitched (2005); Harry and Walter Go to New York (1976); Interstellar (2014); Four Kids and It (2020); Come Away (2020); Rickles (1975); Sean Connery: Private (1993); Elle s'appelait Françoise (1996); The Movie Life of George (1989); The Cider House Rules (1999); Blood and Wine (1996); Children of Men (2006); The First 100 Years: A Celebration of American Movies (1995); Stonehearst Asylum (2014); Preminger: Anatomy of a Filmmaker (1991); Austin Powers in Goldmember (2002); Get Carter (2000); The Last Witch Hunter (2015); Kingsman: The Secret Service (2014); Sherlock Gnomes (2018); Charlie Sheen's Stunts Spectacular (1994); Mad About the Boy: The Noël Coward Story (2023); Now You See Me (2013); Now You See Me 2 (2016); Tenet (2020); Valentino: The Last Emperor (2008); And the Winner Isn't (2017); Inception (2010); John Huston: The Man, the Movies, the Maverick (1989); The Director's Notebook: The Cinematic Sleight of Hand of Christopher Nolan (2007); An Accidental Studio (2019); Roman Polanski: Wanted and Desired (2008); Night of 100 Stars III (1990); Heath Ledger: A Tribute (2009); Quincy (2018); Discovering Hamlet (2011); Terror in the Aisles (1984); Monty Python's The Meaning of Life (1983); Inside 'Interstellar' (2015); The Fire Rises: The Creation and Impact of The Dark Knight Trilogy (2013); Night of 100 Stars II (1985); Dunkirk (2017); The Sound of 007 (2022)

See what else correlates with The number of movies Michael Caine appeared in

Votes for the Democratic Presidential candidate in Maryland
Detailed data title: Percentage of all votes cast for the Democrat Presidential candidate in Maryland
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for the Democratic Presidential candidate in Maryland

Correlation r = 0.8517681 (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 = 0.7255089 (Coefficient of determination)
This means 72.6% of the change in the one variable (i.e., Votes for the Democratic Presidential candidate in Maryland) is predictable based on the change in the other (i.e., The number of movies Michael Caine appeared in) over the 12 years from 1976 through 2020.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.00044.^Show 0.0004370530709526557700000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

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

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.85 in 0.044% of random cases. Said differently, if you correlated 2,288 random variables^Note Which I absolutely did.
with the same 11 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 11 because we have two variables measured over a period of 12 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, 0.96 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

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

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

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

	1976	1980	1984	1988	1992	1996	2000	2004	2008	2012	2016	2020
The number of movies Michael Caine appeared in (Movie appearances)	2	2	3	2	3	3	4	1	3	3	1	4
Votes for the Democratic Presidential candidate in Maryland (Percentage of votes)	53.0354	47.1381	47.0164	48.199	49.8126	54.2548	56.4883	27.9859	61.9193	61.9742	30.1643	65.3607

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([2,2,3,2,3,3,4,1,3,3,1,4,])
array_2 = np.array([53.0354,47.1381,47.0164,48.199,49.8126,54.2548,56.4883,27.9859,61.9193,61.9742,30.1643,65.3607,])
array_1_name = "The number of movies Michael Caine appeared in"
array_2_name = "Votes for the Democratic Presidential candidate in Maryland"

# 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
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Line chart for only The number of movies Michael Caine appeared in
Line chart for only Votes for the Democratic Presidential candidate in Maryland
AI-generated correlation image
The spurious research paper: In Caine Sight: The Astonishing Correlation Between Michael Caine's Film Appearances and Democratic Votes in Maryland

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Correlation ID: 5851 · Black Variable ID: 26715 · Red Variable ID: 26109


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