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AI explanation

^{Show image generation prompt} Model: dalle-3
Prompt: [ Scalar art ] Generate an image of a vibrant Florida post office with multiple clerks working diligently behind the counters, while long lines of customers eagerly await their turn. The atmosphere is bustling and filled with energy as the clerks sort through piles of mail, some of which are adorned with clever puns and funny wordplay. Nearby, the minutephysics team's workspace is visible, with a whiteboard filled with brainstormed YouTube video titles that match the cleverness of the puns on the envelopes. Capture the flow of creativity as the energy of the postal workers seems to inspire the team's creative process, leading them to craft more and more provocatively titled videos. 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: How provocative minutephysics YouTube video titles are and The number of postal service clerks in Florida. 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: 3D Pixar Style Futuristic Cutsy graphics Scalar art Glitch art
ChatGPT: Scalar art

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 How provocative minutephysics YouTube video titles are and The number of postal service clerks in Florida, in the following situation: As the number of postal service clerks in Florida increases, so does the amount of pun mail being sorted and delivered. This influx of witty wordplay and sassy semantics seeps into the creative subconscious of the minutephysics team, leading to a surge in provocatively titled videos. It's a chain reaction of linguistic levity - the postage professionals are quite literally delivering the 'package' of provocative phrasing to the YouTube algorithms.. Keep the description simple.
ChatGPT: A bustling Florida post office with an increasing number of clerks serving long lines of customers. As the clerks sort through piles of mail, they come across envelopes adorned with clever puns and funny wordplay. Meanwhile, nearby the minutephysics team's workspace, the whiteboard is filled with brainstormed YouTube video titles that match the cleverness of the puns on the envelopes. The energy of the postal workers seems to flow into the creative process, as the team crafts more and more provocatively titled videos.

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: [ Scalar art ] and depicting this scene: A bustling Florida post office with an increasing number of clerks serving long lines of customers. As the clerks sort through piles of mail, they come across envelopes adorned with clever puns and funny wordplay. Meanwhile, nearby the minutephysics team's workspace, the whiteboard is filled with brainstormed YouTube video titles that match the cleverness of the puns on the envelopes. The energy of the postal workers seems to flow into the creative process, as the team crafts more and more provocatively titled videos. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ Scalar art ] Generate an image of a vibrant Florida post office with multiple clerks working diligently behind the counters, while long lines of customers eagerly await their turn. The atmosphere is bustling and filled with energy as the clerks sort through piles of mail, some of which are adorned with clever puns and funny wordplay. Nearby, the minutephysics team's workspace is visible, with a whiteboard filled with brainstormed YouTube video titles that match the cleverness of the puns on the envelopes. Capture the flow of creativity as the energy of the postal workers seems to inspire the team's creative process, leading them to craft more and more provocatively titled videos.

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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

Spinning in Circles: Unraveling the Link between Provocative minutephysics YouTube Video Titles and the Postal Service Clerk Puzzlement in Florida The Journal of Quirky Interdisciplinary Studies Jan 2024 Reminder: This paper is AI-generated. Not real!

Problem variable:	Which variable has a problem? How provocative minutephysics YouTube video titles are The number of postal service clerks in Florida 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. Confounding variable: 2020 is particularly different from the other years on this graph. Confounding variables (like global pandemics) will cause two variables to look connected when in fact a "sneaky third" variable is influencing both of them behind the scenes.

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([3.4643,3.2963,3.2105,3.28,3.1765,3.625,4.2917,3,3.8333,4.5,3,4.1,])
array_2 = np.array([3650,3800,3700,3700,3630,3760,3860,3680,3840,4130,3760,3860,])
array_1_name = "How provocative minutephysics YouTube video titles are"
array_2_name = "The number of postal service clerks in Florida"

# 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 How provocative minutephysics YouTube video titles are
• Line chart for only The number of postal service clerks in Florida
• AI-generated correlation image
• The spurious research paper: Spinning in Circles: Unraveling the Link between Provocative minutephysics YouTube Video Titles and the Postal Service Clerk Puzzlement in Florida