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AI explanation

^{Show image generation prompt} Model: dalle-3
Prompt: Generate an image of the picturesque Colorado mountains with a high-tech twist. Imagine a futuristic cityscape nestled among the snow-capped peaks, with sleek, metallic buildings juxtaposed against the natural landscape. In the foreground, depict a series of holographic graphs and data visualizations floating in the air, symbolizing the decreasing number of statistical assistants available for data analysis. Meanwhile, in the background, illustrate Australian homes with high-tech energy-efficient lighting systems replacing traditional kerosene lamps, highlighting the result of the kerosene shortage. The scene should evoke a sense of technological advancement and transition in response to the changing energy landscape. 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: The number of statistical assistants in Colorado and Kerosene used in Australia. 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: Cubism a Polaroid photo a meme High-tech Celtic art
ChatGPT: High-tech

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 The number of statistical assistants in Colorado and Kerosene used in Australia, in the following situation: Because as the number of statistical assistants in Colorado decreased, there were fewer people available to crunch the numbers on kerosene usage. This led to a decrease in the demand for kerosene, causing a shortage Down Under. So, the Aussies had to ration their kerosene, which is why there was less kerosene used in Australia. It's a real case of "stats" quo leading to "kero"-chaos!. Keep the description simple.
ChatGPT: Picture the beautiful mountains of Colorado, where the number of statistical assistants is decreasing. As a result, there are fewer people available to analyze the data on kerosene usage. Meanwhile, in Australia, the shortage of kerosene has led to people rationing their usage. You can see fewer kerosene lamps glowing in Australian homes, symbolizing the decrease in kerosene usage.

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: [ High-tech ] and depicting this scene: Picture the beautiful mountains of Colorado, where the number of statistical assistants is decreasing. As a result, there are fewer people available to analyze the data on kerosene usage. Meanwhile, in Australia, the shortage of kerosene has led to people rationing their usage. You can see fewer kerosene lamps glowing in Australian homes, symbolizing the decrease in kerosene usage. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generate an image of the picturesque Colorado mountains with a high-tech twist. Imagine a futuristic cityscape nestled among the snow-capped peaks, with sleek, metallic buildings juxtaposed against the natural landscape. In the foreground, depict a series of holographic graphs and data visualizations floating in the air, symbolizing the decreasing number of statistical assistants available for data analysis. Meanwhile, in the background, illustrate Australian homes with high-tech energy-efficient lighting systems replacing traditional kerosene lamps, highlighting the result of the kerosene shortage. The scene should evoke a sense of technological advancement and transition in response to the changing energy landscape.

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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

Counting on Correlation: The Statistical Assistants of Colorado and the Kerosene Conundrum in Australia The Journal of Quirky Quantitative Studies Jan 2024 Reminder: This paper is AI-generated. Not real!

Problem variable:	Which variable has a problem? The number of statistical assistants in Colorado Kerosene used in Australia 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([330,160,100,70,90,90,70,40,140,200,290,200,230,130,170,180,130,70,])
array_2 = np.array([1.5863,0.871585,0.306849,0.254795,0.460274,0.431694,0.627397,0.238356,0.50411,0.565574,0.706849,0.553425,0.665753,0.846995,0.805479,0.452055,0.153425,0.273224,])
array_1_name = "The number of statistical assistants in Colorado"
array_2_name = "Kerosene used in Australia"

# 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 The number of statistical assistants in Colorado
• Line chart for only Kerosene used in Australia
• AI-generated correlation image
• The spurious research paper: Counting on Correlation: The Statistical Assistants of Colorado and the Kerosene Conundrum in Australia