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AI explanation

^{Show image generation prompt} Model: dalle-3
Prompt: Create a visually striking black-and-white newspaper illustration of a baseball stadium scene. Depict the bright floodlights casting dimmer-than-usual illumination onto the field. Show the players swinging their bats with determination, capturing the absence of the usual electrifying crackling sound indicating powerful hits. The crowd should be portrayed as subdued, with expressions of disappointment and surprise. The scoreboard, prominently displayed, should show a lower total of runs scored than expected. Finally, subtly illustrate the influence of the lack of polar power from Antarctica on the energy of the game. Capture the essence of the scene in a simple yet evocative design. 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: Because the decrease in electricity generation in Antarctica led to a shortage of polar power, the batters in the World Series couldn't catch a charge, resulting in fewer electrifying hits and lower total runs scored. It was a shocking turn of events as the players struggled to amp up their performance without the spark of Antarctic electricity to light up the game.. 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: Vaporwave Art nouveau A black-and-white newspaper illustration 3D Pixar Style Futuristic
ChatGPT: A black-and-white newspaper illustration

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. Because the decrease in electricity generation in Antarctica led to a shortage of polar power, the batters in the World Series couldn't catch a charge, resulting in fewer electrifying hits and lower total runs scored. It was a shocking turn of events as the players struggled to amp up their performance without the spark of Antarctic electricity to light up the game.
ChatGPT: In a baseball stadium, the bright floodlights normally shining down on the field are dimmer than usual. The players are swinging their bats but the crackling sound of electrifying hits is noticeably absent. The crowd seems subdued, and the scoreboard shows a lower total of runs scored than expected. The lack of polar power from Antarctica has clearly affected the energy of the game.

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 black-and-white newspaper illustration ] and depicting this scene: In a baseball stadium, the bright floodlights normally shining down on the field are dimmer than usual. The players are swinging their bats but the crackling sound of electrifying hits is noticeably absent. The crowd seems subdued, and the scoreboard shows a lower total of runs scored than expected. The lack of polar power from Antarctica has clearly affected the energy of the game. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create a visually striking black-and-white newspaper illustration of a baseball stadium scene. Depict the bright floodlights casting dimmer-than-usual illumination onto the field. Show the players swinging their bats with determination, capturing the absence of the usual electrifying crackling sound indicating powerful hits. The crowd should be portrayed as subdued, with expressions of disappointment and surprise. The scoreboard, prominently displayed, should show a lower total of runs scored than expected. Finally, subtly illustrate the influence of the lack of polar power from Antarctica on the energy of the game. Capture the essence of the scene in a simple yet evocative design.

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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

Shocking Statistics: Exploring the Electrifying Link Between Electricity Generation in Antarctica and Total Runs Scored in the World Series The Journal of Polar Power and Baseball Metrics Jan 2024 Reminder: This paper is AI-generated. Not real!

Problem variable:	Which variable has a problem? Electricity generation in Antarctica Total runs scored in the World Series 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. 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

# 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([0.002,0.002,0.002,0.002,0.004,0.004,0.004,0.0005,0.0005,])
array_2 = np.array([34,33,39,39,59,41,68,22,41,])
array_1_name = "Electricity generation in Antarctica"
array_2_name = "Total runs scored in the World Series"

# 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 Electricity generation in Antarctica
• Line chart for only Total runs scored in the World Series
• The spurious research paper: Shocking Statistics: Exploring the Electrifying Link Between Electricity Generation in Antarctica and Total Runs Scored in the World Series