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AI explanation

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a bustling city street with a pop art style. People are walking about, and their name badges and shop signs show fewer occurrences of the name "Kelsey." As you observe, scenarios of individuals with the name Kelsey smoothly talking their way out of confrontations become less frequent. Show a specific scene where a person tries to charm their way out of a difficult situation, but their efforts are less effective. Visualize a reduction in smooth-talking individuals leading to a decrease in instances where charm helps people to evade consequences. The visual representation should convey the impact of the declining name Kelsey on the city, resulting in a decrease in both smooth-talking and violent crime rates. The image should be vibrant and colorful, capturing the essence of pop art. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

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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 situation described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here is a situation: The decrease in popularity of the first name Kelsey caused a decrease in violent crime rates because Kelseys are known for their irresistible charm and ability to talk their way out of any sticky situation. As the number of Kelseys decreased, so did the instances of smooth-talking perpetrators who could sweet talk their way out of trouble. With fewer Kelseys around, there were also fewer opportunities for criminals to "Kelsey" their way into committing violent acts. It's a case of the Kelsey effect – as the name faded into obscurity, so did the slick-talking, trouble-making individuals who might have otherwise caused chaos. It's like they say, when the Kelseys are away, the criminals will play... a lot less!. Given that situation, which of the following image descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand and the tone of the explanation. Please respond with only one of the following potential descriptions: Hyper-realistic Pop art Woodblock painting An image that looks like it is a scene from 'Star Wars' Cyberpunk
ChatGPT: Pop art

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated and the explanation described. Do not reference graphs or charts; describe physical objects.
User prompt: Please describe a visual scene that depicts the following situation. Keep the description simple. The decrease in popularity of the first name Kelsey caused a decrease in violent crime rates because Kelseys are known for their irresistible charm and ability to talk their way out of any sticky situation. As the number of Kelseys decreased, so did the instances of smooth-talking perpetrators who could sweet talk their way out of trouble. With fewer Kelseys around, there were also fewer opportunities for criminals to "Kelsey" their way into committing violent acts. It's a case of the Kelsey effect – as the name faded into obscurity, so did the slick-talking, trouble-making individuals who might have otherwise caused chaos. It's like they say, when the Kelseys are away, the criminals will play... a lot less!
ChatGPT: Imagine a bustling city street, where people are going about their day. As you look around, you see fewer and fewer individuals with the name Kelsey. This is evident in the name badges of the people walking by, or on the signs of the shops and cafes. You notice that as the number of Kelseys decreases, there are fewer incidents of smooth-talking individuals getting their way in tricky situations. You see a scenario where a person attempts to talk their way out of a confrontation, but their persuasive charm isn't as effective. With fewer Kelseys around, the atmosphere is less prone to slick-talking and fewer opportunities for individuals to escape consequences through charm. As fewer Kelseys are spotted in the crowd, the once frequent scenarios of effortlessly talking themselves out of trouble become rare. This visual representation illustrates how the decrease in the name Kelsey has impacted the city, leading to a decline in smooth-talking individuals and a decrease in violent crime rates.

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: [ Pop art ] and depicting this scene: Imagine a bustling city street, where people are going about their day. As you look around, you see fewer and fewer individuals with the name Kelsey. This is evident in the name badges of the people walking by, or on the signs of the shops and cafes. You notice that as the number of Kelseys decreases, there are fewer incidents of smooth-talking individuals getting their way in tricky situations. You see a scenario where a person attempts to talk their way out of a confrontation, but their persuasive charm isn't as effective. With fewer Kelseys around, the atmosphere is less prone to slick-talking and fewer opportunities for individuals to escape consequences through charm. As fewer Kelseys are spotted in the crowd, the once frequent scenarios of effortlessly talking themselves out of trouble become rare. This visual representation illustrates how the decrease in the name Kelsey has impacted the city, leading to a decline in smooth-talking individuals and a decrease in violent crime rates. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a bustling city street with a pop art style. People are walking about, and their name badges and shop signs show fewer occurrences of the name "Kelsey." As you observe, scenarios of individuals with the name Kelsey smoothly talking their way out of confrontations become less frequent. Show a specific scene where a person tries to charm their way out of a difficult situation, but their efforts are less effective. Visualize a reduction in smooth-talking individuals leading to a decrease in instances where charm helps people to evade consequences. The visual representation should convey the impact of the declining name Kelsey on the city, resulting in a decrease in both smooth-talking and violent crime rates. The image should be vibrant and colorful, capturing the essence of pop art.

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*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.

AI academic paper

The Kelsey Quandary: Exploring the Relationship Between Name Popularity and Violent Crime Rates Journal of Pseudoscientific Studies Jan 2024 Reminder: This paper is AI-generated. Not real!

Problem variable:	Which variable has a problem? Popularity of the first name Kelsey Violent crime rates Neither / both
Issue:	What is the issue? The data is incorrect The chart isn't displaying properly There is a typo The source link is incorrect A calculation is wrong Something else
Additional details: Optional
Confirm you are a human:

Data details

Why this works

1. 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.
2. 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.
3. 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.
4. Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. ^Note Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
   Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.
5. Outlandish outliers: There are "outliers" in this data.^Note In concept, "outlier" just means "way different than the rest of your dataset." When calculating a correlation like this, they are particularly impactful because a single outlier can substantially increase your correlation.
   
   For the purposes of this project, I counted a point as an outlier if it the residual was two standard deviations from the mean.
   
   (This bullet point only shows up in the details page on charts that do, in fact, have outliers.)
   They stand out on the scatterplot above: notice the dots that are far away from any other dots. I intentionally mishandeled outliers, which makes the correlation look extra strong.

Try it yourself

# 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([1820,2656,4735,5666,9506,9691,11619,11882,11539,9915,8340,7795,5672,5648,4491,3731,3337,2934,2611,2270,1998,1945,1737,1749,1545,1368,1291,1239,1061,1037,973,867,806,756,679,584,486,489,])
array_2 = np.array([558.1,620.1,612.5,640.6,666.9,729.6,758.2,757.7,747.1,713.6,684.5,636.6,611,567.6,523,506.5,504.5,494.4,475.8,463.2,469,479.3,471.8,458.6,431.9,404.5,387.1,387.8,369.1,361.6,373.7,397.5,394.9,383.4,380.8,398.5,387,380.7,])
array_1_name = "Popularity of the first name Kelsey"
array_2_name = "Violent crime rates"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

• High resolution line chart ^Note The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.
  
  If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.
  
  Alternatively you can use a tool like Inkscape.
  
• High resolution line chart, optimized for mobile
• Alternative high resolution line chart
• Scatterplot
• Portable line chart (png)
• Portable line chart (png), optimized for mobile
• Line chart for only Popularity of the first name Kelsey
• Line chart for only Violent crime rates
• The spurious research paper: The Kelsey Quandary: Exploring the Relationship Between Name Popularity and Violent Crime Rates