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AI explanation

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of a meme style scene where a group of people in Seneca, South Carolina are gathered in a park, taking in the fresh, clear air. Their eyes light up with creativity as they start brainstorming clever, pun-filled titles for Deep Look YouTube videos. The atmosphere is filled with excitement and laughter as they come up with the most 'hip and with it' video titles, and are inspired by the revitalizing air quality. The meme style should capture the energetic and fun-filled brainstorming session as the group feels invigorated by the fresh air. 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: Air quality in Seneca, South Carolina and How 'hip and with it' Deep Look YouTube video titles are. 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: Pop art a funny image macro inspired by bauhaus Hyper-realistic a meme
ChatGPT: a meme

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 Air quality in Seneca, South Carolina and How 'hip and with it' Deep Look YouTube video titles are, in the following situation: The influx of oxygen in Seneca has sparked a creative renaissance, leading to a surge in pun productivity. As the locals breathe in the fresher air, their brains are receiving a steady stream of 'punny' oxygen, inspiring them to come up with the most 'hip and with it' video titles for Deep Look. It's like their creativity is on an all-time high, quite literally reaching new 'air-y' heights!. Keep the description simple.
ChatGPT: A group of people in Seneca, South Carolina are gathered in a park, breathing in the fresh, clear air. As they breathe in, their eyes light up with creativity, and they start brainstorming clever, pun-filled titles for Deep Look YouTube videos. The air is filled with excitement and laughter as they come up with the most 'hip and with it' video titles, inspired by the revitalizing air quality.

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 meme ] and depicting this scene: A group of people in Seneca, South Carolina are gathered in a park, breathing in the fresh, clear air. As they breathe in, their eyes light up with creativity, and they start brainstorming clever, pun-filled titles for Deep Look YouTube videos. The air is filled with excitement and laughter as they come up with the most 'hip and with it' video titles, inspired by the revitalizing air quality. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of a meme style scene where a group of people in Seneca, South Carolina are gathered in a park, taking in the fresh, clear air. Their eyes light up with creativity as they start brainstorming clever, pun-filled titles for Deep Look YouTube videos. The atmosphere is filled with excitement and laughter as they come up with the most 'hip and with it' video titles, and are inspired by the revitalizing air quality. The meme style should capture the energetic and fun-filled brainstorming session as the group feels invigorated by the fresh air.

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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

Clear Skies and Clever Titles: Exploring the Relationship Between Air Quality in Seneca, South Carolina and the Catchiness of Deep Look YouTube Video Titles The Journal of Atmospheric Wit and Media Analysis Jan 2024 Reminder: This paper is AI-generated. Not real!

Problem variable:	Which variable has a problem? Air quality in Seneca, South Carolina How 'hip and with it' Deep Look YouTube video titles are 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 25,237 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 636,906,169 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([1,1,0.994269,1,1,1,])
array_2 = np.array([6.8,6.85,6.3913,6.8571,6.85,6.8,])
array_1_name = "Air quality in Seneca, South Carolina"
array_2_name = "How 'hip and with it' Deep Look YouTube video titles are"

# 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 quality in Seneca, South Carolina
• Line chart for only How 'hip and with it' Deep Look YouTube video titles are
• AI-generated correlation image
• The spurious research paper: Clear Skies and Clever Titles: Exploring the Relationship Between Air Quality in Seneca, South Carolina and the Catchiness of Deep Look YouTube Video Titles