Spurious correlation #5,096 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Votes for Republican Senators in Alaska and the second variable is Car crashes in the US. The chart goes from 1991 to 2014, 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 Votes for Republican Senators in Alaska and the second variable is Car crashes in the US. The chart goes from 1991 to 2014, and the two variables track closely in value over that time.

Download png, svg

AI explanation

As Republican votes for Senators in Alaska increased, their support for less regulation may have led to a bump in car crashes across the US. It seems their political sway really drove up the number of collisions, making for a lot of GOP (Get Out and Push) moments on the roads. It's like they say, with great political power comes great car responsibility! Remember, always vote responsibly and drive safely – let's keep the roads and the Senate race collision-free!

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a realistic scene with a map of Alaska on the left, depicting an increase in Republican votes for Senators through a graph overlay. Superimposed on the map is a network of roads crisscrossing the US, with cars colliding and bumping into each other, creating a chaotic scene. Amidst the chaos, signs with the acronym "GOP" (Get Out and Push) are displayed, indicating the collisions caused by the increase in Republican votes. The scene conveys the need for responsible voting and safe driving to maintain a collision-free political landscape and roads. 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: Republican votes for Senators in Alaska and Car crashes in the US. 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: Macabre Brutalism Voxel art Cute 2D animation Realistic
ChatGPT: Realistic

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 Republican votes for Senators in Alaska and Car crashes in the US, in the following situation: As Republican votes for Senators in Alaska increased, their support for less regulation may have led to a bump in car crashes across the US. It seems their political sway really drove up the number of collisions, making for a lot of GOP (Get Out and Push) moments on the roads. It's like they say, with great political power comes great car responsibility! Remember, always vote responsibly and drive safely – let's keep the roads and the Senate race collision-free!. Keep the description simple.
ChatGPT: In a visual scene, we see a map of Alaska with a graph showing an increase in Republican votes for Senators. Superimposed on this map, we see a network of roads crisscrossing across the US, with cars colliding and bumping into each other, creating a chaotic scene. In the midst of this, there are signs with the acronym "GOP" (Get Out and Push) written on them, indicating the collisions caused by the increase in Republican votes. The scene emphasizes the need for responsible voting and safe driving to keep both the political landscape and the roads collision-free.

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: [ Realistic ] and depicting this scene: In a visual scene, we see a map of Alaska with a graph showing an increase in Republican votes for Senators. Superimposed on this map, we see a network of roads crisscrossing across the US, with cars colliding and bumping into each other, creating a chaotic scene. In the midst of this, there are signs with the acronym "GOP" (Get Out and Push) written on them, indicating the collisions caused by the increase in Republican votes. The scene emphasizes the need for responsible voting and safe driving to keep both the political landscape and the roads collision-free. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a realistic scene with a map of Alaska on the left, depicting an increase in Republican votes for Senators through a graph overlay. Superimposed on the map is a network of roads crisscrossing the US, with cars colliding and bumping into each other, creating a chaotic scene. Amidst the chaos, signs with the acronym "GOP" (Get Out and Push) are displayed, indicating the collisions caused by the increase in Republican votes. The scene conveys the need for responsible voting and safe driving to maintain a collision-free political landscape and roads.

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

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how increases in Republican votes for Senators in Alaska positively influenced Car crashes in the US. Include one good pun.

AI academic paper

(Because p < 0.01)

Republican Votes in Alaska and Roadway Wrecks: A Revelatory Relationship

The Journal of Political Analysis and Unintended Consequences

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 Republican votes for Senators in Alaska and Car crashes in the US. You can't resist a good dad joke. Add a relevant dad joke related to the content every couple of sentences.

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

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

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then devolve ever further, and mention something completely ridiculous, like you conducted literature review by reading CVS receipts.

Here is the title and abstract of the paper:
[[TITLE]]
Republican Votes in Alaska and Roadway Wrecks: A Revelatory Relationship

[[ABSTRACT]]
The relationship between Republican votes for Senators in Alaska and car crashes in the United States has long been the subject of speculation and inquiry. In this study, we utilized data from the MIT Election Data and Science Lab, Harvard Dataverse, and Statista to elucidate this curious connection. Through rigorous statistical analysis, we discovered a remarkably strong correlation coefficient of 0.8863078 and p < 0.01 for the period spanning 1991 to 2014.

Our findings indicate a striking association between the two variables, shedding light on the interplay between political preferences and vehicular mishaps. We propose that the notable correlation may potentially be attributed to a multitude of factors, including but not limited to regional policy influences, driver behavior in response to election outcomes, and the elusive yet impactful "elephant in the room."

Admittedly, the findings may seem rather absurd at first glance, prompting us to question whether this is purely a case of "coincidental collisions." However, as the data suggest, this connection appears to be more than just a "wheely" good joke. Further investigation is warranted to comprehensively delineate the mechanisms underlying this unexpected relationship, providing valuable insights and paving the way for future research in the whimsical intersection of political voting patterns and traffic turmoil.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intuitive assumption that political decisions can have a significant impact on various aspects of societal functioning is not without merit. A correlation between Republican votes for Senators in the state of Alaska and car crashes across the United States is as unexpected as a car snowboarding down a mountain. The potential associations between these seemingly disparate phenomena have captured the curiosity of researchers and laypersons alike, fueling inquiries into the underlying mechanisms and, perhaps, providing a new lens through which to view political and traffic landscapes. This study aims to delve into this intriguing relationship, navigating the uncharted territory of political voting patterns and roadway ruckus with the precision of a GPS and the deduction of a traffic officer.
The revelatory potential of such an investigation lies not in its amusement value alone. As much as one might be tempted to dismiss these findings as mere "driving under the influence of political partisanship," the statistical rigor of our analysis signals a more substantive underpinning to this peculiar link. The data speak for themselves, beckoning us to unravel the intricacies of political ideologies driving down the road of statistical significance. As we merge onto this research expressway, we acknowledge the need for caution amidst the potential potholes of overinterpretation and unwarranted generalization.
Given the complex nature of human behavior and the interplay of myriad societal factors, the allure of drawing causal inferences from a correlation as strong as a moose's grip on a snow-covered highway is not lost on us. Yet, the details of our regression analysis and sensitivity tests yield intriguing insights that warrant further scrutiny, defying the adage that "correlation does not imply causation" with the evasiveness of a driver weaving through heavy traffic to secure a prized parking spot.
The potential implications of our findings extend beyond the realms of casual fascination, illuminating the need to consider the nuanced intertwining of political landscapes and the pixels of roadways. By doing so, we may not only expand our understanding of the far-reaching effects of political decisions, but also shift the gears of research inquiry toward unexpected arenas, revving up the engines of curiosity and opening new lanes of investigation in the body politic.

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The relationship between political voting patterns and their influence on societal phenomena is a topic that has garnered significant interest among researchers. Smith (2010) has found associations between voting behaviors and various societal outcomes, prompting further investigation into the potential impacts of political decisions on diverse aspects of societal functioning. Similarly, Doe and Jones (2015) have examined the interconnectedness of political ideology and its manifestations in societal dynamics, shedding light on the far-reaching implications of political preferences.

In "The Political Mind" by George Lakoff, the author explores the cognitive science behind political ideology and its influence on decision-making, providing valuable insights into the underlying mechanisms of political voting patterns. Additionally, "The Road" by Cormac McCarthy presents a dystopian world where the remnants of societal structure intertwine with individual moral dilemmas, offering a fictional yet thought-provoking perspective on the interplay of societal dynamics and roadways.

Furthermore, a comprehensive review of relevant literature has unearthed unexpected sources of insight, including the seemingly unrelated yet surprisingly informative content of grocery store receipts and the cryptic messages embedded within the prose of tabloid newspapers. (Smithsonian, 2020; The National Enquirer, 2015)

As the body of research on this eccentric association continues to expand, the need for rigorous inquiry and scholarly exploration becomes increasingly apparent. While the initial revelation of this peculiar relationship may instigate a bout of incredulous amusement, the empirical evidence beckons for further contemplation and investigation. The ensuing sections of this paper aim to provide a meticulous examination of the multifaceted relationship between Republican votes for Senators in Alaska and car crashes in the United States, navigating the uncharted terrain of political voting patterns and roadway ruckus with the dexterity of a driver evading a surprise pothole.

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 Republican votes for Senators in Alaska and Car crashes in the US. 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 MIT Election Data and Science Lab, Harvard Dataverse and Statista . You used data from 1991 to 2014

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]]
Republican Votes in Alaska and Roadway Wrecks: A Revelatory Relationship

[[ABSTRACT]]
The relationship between Republican votes for Senators in Alaska and car crashes in the United States has long been the subject of speculation and inquiry. In this study, we utilized data from the MIT Election Data and Science Lab, Harvard Dataverse, and Statista to elucidate this curious connection. Through rigorous statistical analysis, we discovered a remarkably strong correlation coefficient of 0.8863078 and p < 0.01 for the period spanning 1991 to 2014.

Our findings indicate a striking association between the two variables, shedding light on the interplay between political preferences and vehicular mishaps. We propose that the notable correlation may potentially be attributed to a multitude of factors, including but not limited to regional policy influences, driver behavior in response to election outcomes, and the elusive yet impactful "elephant in the room."

Admittedly, the findings may seem rather absurd at first glance, prompting us to question whether this is purely a case of "coincidental collisions." However, as the data suggest, this connection appears to be more than just a "wheely" good joke. Further investigation is warranted to comprehensively delineate the mechanisms underlying this unexpected relationship, providing valuable insights and paving the way for future research in the whimsical intersection of political voting patterns and traffic turmoil.

[[INTRODUCTION]]
The intuitive assumption that political decisions can have a significant impact on various aspects of societal functioning is not without merit. A correlation between Republican votes for Senators in the state of Alaska and car crashes across the United States is as unexpected as a car snowboarding down a mountain. The potential associations between these seemingly disparate phenomena have captured the curiosity of researchers and laypersons alike, fueling inquiries into the underlying mechanisms and, perhaps, providing a new lens through which to view political and traffic landscapes. This study aims to delve into this intriguing relationship, navigating the uncharted territory of political voting patterns and roadway ruckus with the precision of a GPS and the deduction of a traffic officer.
The revelatory potential of such an investigation lies not in its amusement value alone. As much as one might be tempted to dismiss these findings as mere "driving under the influence of political partisanship," the statistical rigor of our analysis signals a more substantive underpinning to this peculiar link. The data speak for themselves, beckoning us to unravel the intricacies of political ideologies driving down the road of statistical significance. As we merge onto this research expressway, we acknowledge the need for caution amidst the potential potholes of overinterpretation and unwarranted generalization.
Given the complex nature of human behavior and the interplay of myriad societal factors, the allure of drawing causal inferences from a correlation as strong as a moose's grip on a snow-covered highway is not lost on us. Yet, the details of our regression analysis and sensitivity tests yield intriguing insights that warrant further scrutiny, defying the adage that "correlation does not imply causation" with the evasiveness of a driver weaving through heavy traffic to secure a prized parking spot.
The potential implications of our findings extend beyond the realms of casual fascination, illuminating the need to consider the nuanced intertwining of political landscapes and the pixels of roadways. By doing so, we may not only expand our understanding of the far-reaching effects of political decisions, but also shift the gears of research inquiry toward unexpected arenas, revving up the engines of curiosity and opening new lanes of investigation in the body politic.

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data for this study were collected from a variety of sources, including the MIT Election Data and Science Lab, Harvard Dataverse, and Statista, with the majority of data spanning the period from 1991 to 2014. To begin, we downloaded the Republican votes for Senators in Alaska for every election year within the study period. These data, akin to a diligent snowplow, cleared the path for our exploration of the relationship between political preferences and roadway mishaps.

Following this, we ventured into the realm of car crash data, sourced from the National Highway Traffic Safety Administration (NHTSA) and the Federal Highway Administration (FHWA). The collision data navigated through the byzantine traffic of the internet, eventually merging onto the lanes of our analytical toolset, allowing us to ascertain the frequency and severity of roadway incidents across different states.

After reconciling these disparate datasets, we embarked on a journey through the winding roads of statistical analysis. Employing a variety of regression models and time series analysis, we diligently sought to untangle the knotty relationship between Republican votes in Alaska and car crash occurrences across the United States.

Utilizing a confluence of econometric techniques and data visualization tools, we traversed the data landscape with the precision of a seasoned driver and the adaptability of a snow tire in a surprise blizzard. Our efforts culminated in the delineation of a robust statistical model, steering us toward uncovering the potential drivers of the observed correlation.

To ensure the robustness of our findings, we also conducted a sensitivity analysis, akin to testing the traction of different tires on icy terrain. This involved varying the specification of our model, altering the inclusion of control variables and the handling of outliers, to ascertain the resilience of our results to alternative methodological pathways.

In a fashion analogous to a driver adjusting their route in response to unexpected roadblocks, we also conducted extensive sub-sample analyses and robustness checks. These rigorous examinations served as our proverbial road flares, illuminating the potential contours of the relationship and guarding against the peril of spurious correlations.

At each step of our methodological journey, we maintained a steadfast commitment to rigor and thoroughness, akin to a driver conducting meticulous maintenance on their vehicle to ensure a smooth ride. This allowed us to navigate the convoluted and occasionally treacherous terrains of statistical analysis and data interpretation, shedding light on the intriguing relationship between Republican votes in Alaska and roadway wrecks in the United States.

--------------
I thoroughly enjoyed employing a bit of whimsy in drafting this methodological section, blending the serious tone of academic research with a touch of humor. If you require further adjustments or expansions, please do not hesitate to signal your intentions.

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 results section of an academic research paper. You speak in the past tense (you found...).
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the results section of an academic research paper presenting the findings of the connection between Republican votes for Senators in Alaska and Car crashes in the US. 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 MIT Election Data and Science Lab, Harvard Dataverse and Statista .

For the time period 1991 to 2014, you found a correlation 0.8863078, r-squared of 0.7855415, and p < 0.01.

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

Here is the title and abstract of the paper:
[[TITLE]]
Republican Votes in Alaska and Roadway Wrecks: A Revelatory Relationship

[[ABSTRACT]]
The relationship between Republican votes for Senators in Alaska and car crashes in the United States has long been the subject of speculation and inquiry. In this study, we utilized data from the MIT Election Data and Science Lab, Harvard Dataverse, and Statista to elucidate this curious connection. Through rigorous statistical analysis, we discovered a remarkably strong correlation coefficient of 0.8863078 and p < 0.01 for the period spanning 1991 to 2014.

Our findings indicate a striking association between the two variables, shedding light on the interplay between political preferences and vehicular mishaps. We propose that the notable correlation may potentially be attributed to a multitude of factors, including but not limited to regional policy influences, driver behavior in response to election outcomes, and the elusive yet impactful "elephant in the room."

Admittedly, the findings may seem rather absurd at first glance, prompting us to question whether this is purely a case of "coincidental collisions." However, as the data suggest, this connection appears to be more than just a "wheely" good joke. Further investigation is warranted to comprehensively delineate the mechanisms underlying this unexpected relationship, providing valuable insights and paving the way for future research in the whimsical intersection of political voting patterns and traffic turmoil.

-------

And here is the methodology section that led to this result:
[[METHODOLOGY]]
The intuitive assumption that political decisions can have a significant impact on various aspects of societal functioning is not without merit. A correlation between Republican votes for Senators in the state of Alaska and car crashes across the United States is as unexpected as a car snowboarding down a mountain. The potential associations between these seemingly disparate phenomena have captured the curiosity of researchers and laypersons alike, fueling inquiries into the underlying mechanisms and, perhaps, providing a new lens through which to view political and traffic landscapes. This study aims to delve into this intriguing relationship, navigating the uncharted territory of political voting patterns and roadway ruckus with the precision of a GPS and the deduction of a traffic officer.
The revelatory potential of such an investigation lies not in its amusement value alone. As much as one might be tempted to dismiss these findings as mere "driving under the influence of political partisanship," the statistical rigor of our analysis signals a more substantive underpinning to this peculiar link. The data speak for themselves, beckoning us to unravel the intricacies of political ideologies driving down the road of statistical significance. As we merge onto this research expressway, we acknowledge the need for caution amidst the potential potholes of overinterpretation and unwarranted generalization.
Given the complex nature of human behavior and the interplay of myriad societal factors, the allure of drawing causal inferences from a correlation as strong as a moose's grip on a snow-covered highway is not lost on us. Yet, the details of our regression analysis and sensitivity tests yield intriguing insights that warrant further scrutiny, defying the adage that "correlation does not imply causation" with the evasiveness of a driver weaving through heavy traffic to secure a prized parking spot.
The potential implications of our findings extend beyond the realms of casual fascination, illuminating the need to consider the nuanced intertwining of political landscapes and the pixels of roadways. By doing so, we may not only expand our understanding of the far-reaching effects of political decisions, but also shift the gears of research inquiry toward unexpected arenas, revving up the engines of curiosity and opening new lanes of investigation in the body politic.

-------

Your response should start like this:

[[RESULTS]]

ChatGPT:

[[RESULTS]]

The statistical analysis revealed a strong correlation of 0.8863078 between Republican votes for Senators in Alaska and the rate of car crashes in the United States from 1991 to 2014. This finding suggests a remarkably robust relationship between these apparently unrelated variables, leaving us to ponder whether drivers across the nation have been swayed by Alaskan political preferences more than the allure of a scenic drive.

The r-squared value of 0.7855415 further underscores the substantive nature of this association, indicating that a notable proportion of the variance in car crash rates can be explained by the variation in Republican votes in Alaska. It appears that this connection is not merely a fleeting flirtation but a longstanding and influential partnership, reminiscent of a dependable carpool arrangement.

Furthermore, the p-value of less than 0.01 provides compelling evidence against the null hypothesis of no relationship between the two variables. This result indicates that the correlation observed is highly unlikely to be a result of random chance. It seems that the connection between this particular political preference and the likelihood of roadway mishaps is as real as a traffic jam on a Monday morning.

Fig. 1 displays a scatterplot illustrating the strong positive correlation between Republican votes for Senators in Alaska and car crash rates in the United States. The figure serves as a visual testament to the striking relationship uncovered in this study, reminding us that statistical evidence can be as clear as a freshly washed windshield.

In summary, our analysis points to a significant and robust connection between Republican votes in Alaska and car crashes in the United States. While the prospect of political decisions influencing highway havoc may initially elicit a chuckle, the data urge us to take this relationship seriously and embark on further exploration. This unexpected connection may ultimately serve as a cautionary tale for drivers to exercise prudence and "republicare" on the road, avoiding politically charged collisions at all costs.

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 Republican votes for Senators in Alaska and Car crashes in the US. 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]]
Republican Votes in Alaska and Roadway Wrecks: A Revelatory Relationship

[[ABSTRACT]]
The relationship between Republican votes for Senators in Alaska and car crashes in the United States has long been the subject of speculation and inquiry. In this study, we utilized data from the MIT Election Data and Science Lab, Harvard Dataverse, and Statista to elucidate this curious connection. Through rigorous statistical analysis, we discovered a remarkably strong correlation coefficient of 0.8863078 and p < 0.01 for the period spanning 1991 to 2014.

Our findings indicate a striking association between the two variables, shedding light on the interplay between political preferences and vehicular mishaps. We propose that the notable correlation may potentially be attributed to a multitude of factors, including but not limited to regional policy influences, driver behavior in response to election outcomes, and the elusive yet impactful "elephant in the room."

Admittedly, the findings may seem rather absurd at first glance, prompting us to question whether this is purely a case of "coincidental collisions." However, as the data suggest, this connection appears to be more than just a "wheely" good joke. Further investigation is warranted to comprehensively delineate the mechanisms underlying this unexpected relationship, providing valuable insights and paving the way for future research in the whimsical intersection of political voting patterns and traffic turmoil.

[[LITERATURE REVIEW]]
The relationship between political voting patterns and their influence on societal phenomena is a topic that has garnered significant interest among researchers. Smith (2010) has found associations between voting behaviors and various societal outcomes, prompting further investigation into the potential impacts of political decisions on diverse aspects of societal functioning. Similarly, Doe and Jones (2015) have examined the interconnectedness of political ideology and its manifestations in societal dynamics, shedding light on the far-reaching implications of political preferences.
In "The Political Mind" by George Lakoff, the author explores the cognitive science behind political ideology and its influence on decision-making, providing valuable insights into the underlying mechanisms of political voting patterns. Additionally, "The Road" by Cormac McCarthy presents a dystopian world where the remnants of societal structure intertwine with individual moral dilemmas, offering a fictional yet thought-provoking perspective on the interplay of societal dynamics and roadways.
Furthermore, a comprehensive review of relevant literature has unearthed unexpected sources of insight, including the seemingly unrelated yet surprisingly informative content of grocery store receipts and the cryptic messages embedded within the prose of tabloid newspapers. (Smithsonian, 2020; The National Enquirer, 2015)
As the body of research on this eccentric association continues to expand, the need for rigorous inquiry and scholarly exploration becomes increasingly apparent. While the initial revelation of this peculiar relationship may instigate a bout of incredulous amusement, the empirical evidence beckons for further contemplation and investigation. The ensuing sections of this paper aim to provide a meticulous examination of the multifaceted relationship between Republican votes for Senators in Alaska and car crashes in the United States, navigating the uncharted terrain of political voting patterns and roadway ruckus with the dexterity of a driver evading a surprise pothole.

[[RESULTS]]
The statistical analysis revealed a strong correlation of 0.8863078 between Republican votes for Senators in Alaska and the rate of car crashes in the United States from 1991 to 2014. This finding suggests a remarkably robust relationship between these apparently unrelated variables, leaving us to ponder whether drivers across the nation have been swayed by Alaskan political preferences more than the allure of a scenic drive.
The r-squared value of 0.7855415 further underscores the substantive nature of this association, indicating that a notable proportion of the variance in car crash rates can be explained by the variation in Republican votes in Alaska. It appears that this connection is not merely a fleeting flirtation but a longstanding and influential partnership, reminiscent of a dependable carpool arrangement.
Furthermore, the p-value of less than 0.01 provides compelling evidence against the null hypothesis of no relationship between the two variables. This result indicates that the correlation observed is highly unlikely to be a result of random chance. It seems that the connection between this particular political preference and the likelihood of roadway mishaps is as real as a traffic jam on a Monday morning.
Fig. 1 displays a scatterplot illustrating the strong positive correlation between Republican votes for Senators in Alaska and car crash rates in the United States. The figure serves as a visual testament to the striking relationship uncovered in this study, reminding us that statistical evidence can be as clear as a freshly washed windshield.
In summary, our analysis points to a significant and robust connection between Republican votes in Alaska and car crashes in the United States. While the prospect of political decisions influencing highway havoc may initially elicit a chuckle, the data urge us to take this relationship seriously and embark on further exploration. This unexpected connection may ultimately serve as a cautionary tale for drivers to exercise prudence and "republicare" on the road, avoiding politically charged collisions at all costs.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

The findings of the present study provide compelling support for the notion that there exists a substantial association between Republican votes for Senators in Alaska and car crashes in the United States, reinforcing and extending prior research in this whimsical domain. The robust correlation coefficient of 0.8863078 aligns with the initial assertions put forth by Smith (2010) and Doe and Jones (2015), and hints at a deeper interplay between political predispositions and their influence on societal dynamics.

The unexpectedly strong relationship between these seemingly disparate variables prompts us to reflect on the profound impact of political ideology on driving behavior and roadway safety. It appears that the influence of political preferences extends beyond the voting booth, potentially permeating into the realm of traffic adherence and vehicular prudence. The implications of this finding evoke a chuckle and a furrowed brow, akin to the bemusement of an individual encountering a "yield to elephants" sign on the freeway.

The present study builds upon the curious literature referring to grocery store receipts and tabloid newspapers, highlighting the pertinent and unexpected sources of insight that contribute to our understanding of this peculiar correlation. While the initial inclination may be to dismiss these connections in jest, the empirical evidence speaking to the strength of these associations warrants a more sober consideration, akin to the sober consideration one must exercise when approaching a political debate with a wise-cracking uncle.

Our results also underscore the need for continued exploration into the underlying mechanisms driving this relationship. Future research may delve into the nuanced pathways through which political preferences exert their influence on driver behavior, uncovering potential mediating factors and shedding light on the hitherto enigmatic "elephant in the room." Perhaps, as George Lakoff articulates, the cognitive underpinnings of political ideology may intertwine with decision-making processes behind the wheel, thereby shaping the observed correlation in an unexpected and fascinating manner.

In conclusion, the findings of this study warrant a thoughtful reevaluation of the intersection between political voting patterns and traffic tumult, moving beyond the initial mirth that arises from the seemingly preposterous connection. The unexpected correlation between Republican votes in Alaska and car crashes in the United States beckons for prudence on the part of drivers, urging them to navigate the political landscape with the same dexterity they exhibit when avoiding a subject at the Thanksgiving table. As we advance into uncharted territory, the jest and the earnestness of this research invite further scholarly inquiry and contemplation, blending the levity of a dad joke with the gravity of a critical 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 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 Republican votes for Senators in Alaska and Car crashes in the US. 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]]
Republican Votes in Alaska and Roadway Wrecks: A Revelatory Relationship

[[ABSTRACT]]
The relationship between Republican votes for Senators in Alaska and car crashes in the United States has long been the subject of speculation and inquiry. In this study, we utilized data from the MIT Election Data and Science Lab, Harvard Dataverse, and Statista to elucidate this curious connection. Through rigorous statistical analysis, we discovered a remarkably strong correlation coefficient of 0.8863078 and p < 0.01 for the period spanning 1991 to 2014.

Our findings indicate a striking association between the two variables, shedding light on the interplay between political preferences and vehicular mishaps. We propose that the notable correlation may potentially be attributed to a multitude of factors, including but not limited to regional policy influences, driver behavior in response to election outcomes, and the elusive yet impactful "elephant in the room."

Admittedly, the findings may seem rather absurd at first glance, prompting us to question whether this is purely a case of "coincidental collisions." However, as the data suggest, this connection appears to be more than just a "wheely" good joke. Further investigation is warranted to comprehensively delineate the mechanisms underlying this unexpected relationship, providing valuable insights and paving the way for future research in the whimsical intersection of political voting patterns and traffic turmoil.

[[INTRDUCTION]]
The intuitive assumption that political decisions can have a significant impact on various aspects of societal functioning is not without merit. A correlation between Republican votes for Senators in the state of Alaska and car crashes across the United States is as unexpected as a car snowboarding down a mountain. The potential associations between these seemingly disparate phenomena have captured the curiosity of researchers and laypersons alike, fueling inquiries into the underlying mechanisms and, perhaps, providing a new lens through which to view political and traffic landscapes. This study aims to delve into this intriguing relationship, navigating the uncharted territory of political voting patterns and roadway ruckus with the precision of a GPS and the deduction of a traffic officer.
The revelatory potential of such an investigation lies not in its amusement value alone. As much as one might be tempted to dismiss these findings as mere "driving under the influence of political partisanship," the statistical rigor of our analysis signals a more substantive underpinning to this peculiar link. The data speak for themselves, beckoning us to unravel the intricacies of political ideologies driving down the road of statistical significance. As we merge onto this research expressway, we acknowledge the need for caution amidst the potential potholes of overinterpretation and unwarranted generalization.
Given the complex nature of human behavior and the interplay of myriad societal factors, the allure of drawing causal inferences from a correlation as strong as a moose's grip on a snow-covered highway is not lost on us. Yet, the details of our regression analysis and sensitivity tests yield intriguing insights that warrant further scrutiny, defying the adage that "correlation does not imply causation" with the evasiveness of a driver weaving through heavy traffic to secure a prized parking spot.
The potential implications of our findings extend beyond the realms of casual fascination, illuminating the need to consider the nuanced intertwining of political landscapes and the pixels of roadways. By doing so, we may not only expand our understanding of the far-reaching effects of political decisions, but also shift the gears of research inquiry toward unexpected arenas, revving up the engines of curiosity and opening new lanes of investigation in the body politic.

[[RESULTS]]
The statistical analysis revealed a strong correlation of 0.8863078 between Republican votes for Senators in Alaska and the rate of car crashes in the United States from 1991 to 2014. This finding suggests a remarkably robust relationship between these apparently unrelated variables, leaving us to ponder whether drivers across the nation have been swayed by Alaskan political preferences more than the allure of a scenic drive.
The r-squared value of 0.7855415 further underscores the substantive nature of this association, indicating that a notable proportion of the variance in car crash rates can be explained by the variation in Republican votes in Alaska. It appears that this connection is not merely a fleeting flirtation but a longstanding and influential partnership, reminiscent of a dependable carpool arrangement.
Furthermore, the p-value of less than 0.01 provides compelling evidence against the null hypothesis of no relationship between the two variables. This result indicates that the correlation observed is highly unlikely to be a result of random chance. It seems that the connection between this particular political preference and the likelihood of roadway mishaps is as real as a traffic jam on a Monday morning.
Fig. 1 displays a scatterplot illustrating the strong positive correlation between Republican votes for Senators in Alaska and car crash rates in the United States. The figure serves as a visual testament to the striking relationship uncovered in this study, reminding us that statistical evidence can be as clear as a freshly washed windshield.
In summary, our analysis points to a significant and robust connection between Republican votes in Alaska and car crashes in the United States. While the prospect of political decisions influencing highway havoc may initially elicit a chuckle, the data urge us to take this relationship seriously and embark on further exploration. This unexpected connection may ultimately serve as a cautionary tale for drivers to exercise prudence and "republicare" on the road, avoiding politically charged collisions at all costs.

-------

Your response should start like this:

[[CONCLUSION]]

ChatGPT:

[[CONCLUSION]]

In conclusion, our study has brought to light a connection between Republican votes for Senators in Alaska and car crashes in the United States that is as clear as a windshield wiped clean on a sunny day. The remarkably strong correlation coefficient and p-value of less than 0.01 suggest a relationship that is more than just a passing "political pit stop."

This unexpected finding challenges conventional wisdom and highlights the need to consider the potential impact of regional political preferences on roadway safety. It seems that, in addition to checking blind spots, drivers may also need to keep an eye on the political climate to navigate the highways without getting caught in an "elephant" traffic jam.

Our results indicate a need for further investigation into the mechanisms underlying this relationship, leaving ample room for future research to steer into uncharted territory and explore the intersection of political leanings and vehicular mayhem.

No more research is needed in this area, as we have exhaustively examined the connection between Republican votes in Alaska and car crashes in the United States. The findings are wheelie good and conclusive, offering valuable insights into the whimsical convergence of political voting patterns and traffic turmoil.

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 Republican Votes in Alaska and Roadway Wrecks: A Revelatory Relationship. Respond only with the name of a fictional academic journal.

ChatGPT:

The Journal of Political Analysis and Unintended Consequences

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 Republican Votes in Alaska and Roadway Wrecks: A Revelatory Relationship. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Alaskan Transportation Studies and Political Dynamics

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 Republican Votes in Alaska and Roadway Wrecks: A Revelatory Relationship with an abstract of The relationship between Republican votes for Senators in Alaska and car crashes in the United States has long been the subject of speculation and inquiry. In this study, we utilized data from the MIT Election Data and Science Lab, Harvard Dataverse, and Statista to elucidate this curious connection. Through rigorous statistical analysis, we discovered a remarkably strong correlation coefficient of 0.8863078 and p < 0.01 for the period spanning 1991 to 2014.
Our findings indicate a striking association between the two variables, shedding light on the interplay between political preferences and vehicular mishaps. We propose that the notable correlation may potentially be attributed to a multitude of factors, including but not limited to regional policy influences, driver behavior in response to election outcomes, and the elusive yet impactful "elephant in the room."
Admittedly, the findings may seem rather absurd at first glance, prompting us to question whether this is purely a case of "coincidental collisions." However, as the data suggest, this connection appears to be more than just a "wheely" good joke. Further investigation is warranted to comprehensively delineate the mechanisms underlying this unexpected relationship, providing valuable insights and paving the way for future research in the whimsical intersection of political voting patterns and traffic turmoil.

ChatGPT:

Republican votes Alaska, car crashes United States, correlation coefficient political preferences vehicular mishaps, MIT Election Data Science Lab, Harvard Dataverse, Statista, regional policy influences driver behavior election outcomes, political voting patterns traffic turmoil

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

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Data details

Votes for Republican Senators in Alaska
Detailed data title: Total number of votes cast for Federal Republican Senate candidates in Alaska
Source: MIT Election Data and Science Lab, Harvard Dataverse
See what else correlates with Votes for Republican Senators in Alaska

Car crashes in the US
Detailed data title: Total annual vehicle crash count in the US
Source: Statista
See what else correlates with Car crashes in the US

Correlation r = 0.8863078 (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 = 0.7855415 (Coefficient of determination)
This means 78.6% of the change in the one variable (i.e., Car crashes in the US) is predictable based on the change in the other (i.e., Votes for Republican Senators in Alaska) over the 8 years from 1991 through 2014.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 0.0034.^Show 0.0033677921993799194000000000
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 0.89 in 0.34% of random cases. Said differently, if you correlated 297 random variables^Note Which I absolutely did.
with the same 7 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 7 because we have two variables measured over a period of 8 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.

[ 0.48, 0.98 ] 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.

	1992	1996	1998	2002	2004	2008	2010	2014
Votes for Republican Senators in Alaska (Total votes)	127163	177893	165227	179438	149773	147814	90839	135445
Car crashes in the US (Car crashes)	6000	6770	6335	6316	6181	5811	5419	6064

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.
Very low n: There are not many data points included in this analysis. Even if the p-value is high, we should be suspicious of using so few datapoints in a correlation.

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([127163,177893,165227,179438,149773,147814,90839,135445,])
array_2 = np.array([6000,6770,6335,6316,6181,5811,5419,6064,])
array_1_name = "Votes for Republican Senators in Alaska"
array_2_name = "Car crashes in the US"

# 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.

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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.
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Line chart for only Votes for Republican Senators in Alaska
Line chart for only Car crashes in the US
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The spurious research paper: Republican Votes in Alaska and Roadway Wrecks: A Revelatory Relationship

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Correlation ID: 5096 · Black Variable ID: 26200 · Red Variable ID: 229


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