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AI explanation

^{Show image generation prompt} Model: dalle-3
Prompt: [ Whimsical illustration ] Create an image of a cozy coffee shop interior, with warm lighting and a rustic decor. A group of friendly statisticians, dressed in tweed and cozy sweaters, are gathered around a table covered in data charts and graphs. They are engaged in a lively discussion, gesturing and analyzing the data with fascination. Meanwhile, outside the coffee shop, a cheerful group of parents can be seen strolling with their little ones, calling out the name "Aliza" with delight. The children, filled with joy, giggle and skip alongside their parents. The image captures the cozy atmosphere inside the coffee shop and the joyful scene outside, with a subtle touch of statistical whimsy. 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 variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: Popularity of the first name Aliza and The number of statisticians in Maine. 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 Pop art Concept art Whimsical illustration Cutsy art illustration
ChatGPT: Whimsical illustration

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 Popularity of the first name Aliza and The number of statisticians in Maine, in the following situation: Because statisticians in Maine feel a kinship with the name Aliza and believe that their statistical prowess is amplified by the growing number of Alizas in the world. It's a statistical phenomenon known as the "Aliza Effect," where the presence of Alizas is directly correlated with an increase in statistical expertise.. Keep the description simple.
ChatGPT: In a cozy coffee shop in Maine, a group of statisticians gather around a table covered with data charts and graphs. They are engaged in a lively discussion about the "Aliza Effect." Meanwhile, outside the coffee shop, a group of parents call out the name "Aliza" as they walk with their little ones, prompting a chorus of giggles and laughter. The statisticians inside share a knowing nod as they notice the growing popularity of the name Aliza and the increase in statistical expertise it seems to bring.

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: [ Whimsical illustration ] and depicting this scene: In a cozy coffee shop in Maine, a group of statisticians gather around a table covered with data charts and graphs. They are engaged in a lively discussion about the "Aliza Effect." Meanwhile, outside the coffee shop, a group of parents call out the name "Aliza" as they walk with their little ones, prompting a chorus of giggles and laughter. The statisticians inside share a knowing nod as they notice the growing popularity of the name Aliza and the increase in statistical expertise it seems to bring. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ Whimsical illustration ] Create an image of a cozy coffee shop interior, with warm lighting and a rustic decor. A group of friendly statisticians, dressed in tweed and cozy sweaters, are gathered around a table covered in data charts and graphs. They are engaged in a lively discussion, gesturing and analyzing the data with fascination. Meanwhile, outside the coffee shop, a cheerful group of parents can be seen strolling with their little ones, calling out the name "Aliza" with delight. The children, filled with joy, giggle and skip alongside their parents. The image captures the cozy atmosphere inside the coffee shop and the joyful scene outside, with a subtle touch of statistical whimsy.

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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 Aliza Effect: Exploring the Curious Correlation Between the Popularity of the Name Aliza and the Number of Statisticians in Maine The Journal of Quirky Statistics and Unlikely Correlations Jan 2024 Reminder: This paper is AI-generated. Not real!

Problem variable:	Which variable has a problem? Popularity of the first name Aliza The number of statisticians in Maine 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.

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([291,332,351,314,343,324,360,325,280,305,276,339,422,424,427,410,427,427,429,381,])
array_2 = np.array([40,80,60,50,40,50,60,60,40,40,40,70,70,100,60,90,70,90,80,80,])
array_1_name = "Popularity of the first name Aliza"
array_2_name = "The number of statisticians in Maine"

# 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 Aliza
• Line chart for only The number of statisticians in Maine
• AI-generated correlation image
• The spurious research paper: The Aliza Effect: Exploring the Curious Correlation Between the Popularity of the Name Aliza and the Number of Statisticians in Maine