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AI explanation

^{Show image generation prompt} Generate an image of a classic 1960s comic-book-style illustration with muted colors and a dotted halftone pattern, depicting a scene at Disneyland. In the illustration, the smog-filled sky hangs heavily over the iconic castle, barely visible through the haze. Dust and dirt coat the rides and attractions, creating a weary and oppressive atmosphere. A tired-looking Mickey Mouse struggles to catch his breath as he walks through the nearly empty park, with only a handful of scattered visitors. The typical bustling energy and excitement of Disneyland is noticeably absent in this surreal and muted representation. 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: Unhealthy air quality in Phoenix causes potential Disneyland visitors to feel "Dismalia" as they decide to "air" on the side of caution and stay away from the theme park. The "pollutant" air is leading to a "decrease" in attendance, leaving Mickey feeling "exhausted" as he wheezes through the empty park.. 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: Macabre Macabre A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s Low poly Steampunk
ChatGPT: A comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s 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. Unhealthy air quality in Phoenix causes potential Disneyland visitors to feel "Dismalia" as they decide to "air" on the side of caution and stay away from the theme park. The "pollutant" air is leading to a "decrease" in attendance, leaving Mickey feeling "exhausted" as he wheezes through the empty park.
ChatGPT: In the scene, the sky over Disneyland is hazy and filled with smog, making it difficult to see the iconic castle clearly. A thick layer of dust and dirt coats the rides and attractions, and the air feels heavy and difficult to breathe. Mickey Mouse, looking weary and tired, is seen struggling to catch his breath as he walks through a nearly empty park, with only a few visitors scattered around. The usual bustling energy and excitement of the park is noticeably absent. 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 comic-book-style illustration with muted colors and a dotted halftone pattern from the 1960s ] and depicting this scene: In the scene, the sky over Disneyland is hazy and filled with smog, making it difficult to see the iconic castle clearly. A thick layer of dust and dirt coats the rides and attractions, and the air feels heavy and difficult to breathe. Mickey Mouse, looking weary and tired, is seen struggling to catch his breath as he walks through a nearly empty park, with only a few visitors scattered around. The usual bustling energy and excitement of the park is noticeably absent. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a classic 1960s comic-book-style illustration with muted colors and a dotted halftone pattern, depicting a scene at Disneyland. In the illustration, the smog-filled sky hangs heavily over the iconic castle, barely visible through the haze. Dust and dirt coat the rides and attractions, creating a weary and oppressive atmosphere. A tired-looking Mickey Mouse struggles to catch his breath as he walks through the nearly empty park, with only a handful of scattered visitors. The typical bustling energy and excitement of Disneyland is noticeably absent in this surreal and muted representation.

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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

Pollution Puzzles and Peculiar Pilgrimages: Probing the Paradoxical Link between Phoenix Air Quality and Disneyland Visitors The Journal of Ecological Amusements Jan 2024 Reminder: This paper is AI-generated. Not real!

Problem variable:	Which variable has a problem? Air pollution in Phoenix Visitors to Disneyland 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. Inverted Y-axis: I inverted the Y-axis on the chart above so that the lines would move together. This is visually pleasing, but not at all intuitive. Below is a line graph that does not invert the Y-axis and starts at zero.

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([60.8219,54.918,21.3699,16.1644,36.4384,30.3279,28.7671,32.6027,19.1781,20.4918,26.0274,23.2877,14.7945,62.8415,65.2055,])
array_2 = np.array([14.87,14.721,15.9,15.98,16.14,15.963,16.202,16.769,18.278,17.943,18.3,18.666,18.666,3.674,8.573,])
array_1_name = "Air pollution in Phoenix"
array_2_name = "Visitors to Disneyland"

# 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 Air pollution in Phoenix
• Line chart for only Visitors to Disneyland
• The spurious research paper: Pollution Puzzles and Peculiar Pilgrimages: Probing the Paradoxical Link between Phoenix Air Quality and Disneyland Visitors