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AI explanation

^{Show image generation prompt} Model: dalle-3
Prompt: [ A photo-realistic image ] A field in Ohio stretches out beneath a clear blue sky, and towering stalks of genetically modified corn create a striking pattern against the horizon. The vibrant green leaves of the corn shimmer in the sunlight as they reach for the sky. Among the rows of corn, small groups of people are gathered, their faces illuminated by the screens of their smartphones. The glow reflects in their eyes as they are engrossed in playing "Call of Duty." The sound of gunfire from the game fills the air, blending with the gentle rustling of the leaves. This unusual scene depicts the connection between advanced agriculture and modern technology, where the fascination with shooting games intertwines with the remarkable crop. 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: GMO use in corn grown in Ohio and Google searches for 'call of duty'. 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: Concept art A photo-realistic image Cute cartoon Realistic Scalar art
ChatGPT: A photo-realistic image

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 GMO use in corn grown in Ohio and Google searches for 'call of duty', in the following situation: The GMO corn was so good at shooting up to grow tall that it sparked a fervent interest in shooting games like Call of Duty. As the corn became more genetically modified, it also modified the gaming preferences of the people around it. The kernels of truth in this correlation show that the real game changer was the corn itself, planting the seeds for a-maize-ing virtual battles. Whether it's the stalky competition or the ear-resistible urge to play, one thing's for sure - this correlation is definitely a-MAIZE-ing.. Keep the description simple.
ChatGPT: In a field in Ohio, towering stalks of genetically modified corn reach for the sky under the bright sun. Among the rows of corn, groups of people are gathered around their smartphones, engrossed in playing "Call of Duty." The sound of gunfire from the game fills the air, blending with the rustling leaves of the remarkable corn. The connection between the advanced crop and the fascination with shooting games is evident in this unusual yet captivating scene.

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 photo-realistic image ] and depicting this scene: In a field in Ohio, towering stalks of genetically modified corn reach for the sky under the bright sun. Among the rows of corn, groups of people are gathered around their smartphones, engrossed in playing "Call of Duty." The sound of gunfire from the game fills the air, blending with the rustling leaves of the remarkable corn. The connection between the advanced crop and the fascination with shooting games is evident in this unusual yet captivating scene. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ A photo-realistic image ] A field in Ohio stretches out beneath a clear blue sky, and towering stalks of genetically modified corn create a striking pattern against the horizon. The vibrant green leaves of the corn shimmer in the sunlight as they reach for the sky. Among the rows of corn, small groups of people are gathered, their faces illuminated by the screens of their smartphones. The glow reflects in their eyes as they are engrossed in playing "Call of Duty." The sound of gunfire from the game fills the air, blending with the gentle rustling of the leaves. This unusual scene depicts the connection between advanced agriculture and modern technology, where the fascination with shooting games intertwines with the remarkable crop.

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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

Stalk and Shoot: The Kernel of Connection Between GMO Corn and Gaming Searches The Journal of Genetically Modified Organisms and Digital Engagement Jan 2024 Reminder: This paper is AI-generated. Not real!

Problem variable:	Which variable has a problem? GMO use in corn grown in Ohio Google searches for 'call of duty' 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([8,9,8,9,12,15,13,24,13,6,3,3,2,2,2,2,3,5,1,2,])
array_2 = np.array([25.3333,22.5,20.3333,25.1667,38.5,40.4167,44.1667,40.5,29.5833,28.3333,24.4167,20.4167,16.5833,15.25,10.8333,14.75,22.1667,14.1667,14.25,12,])
array_1_name = "GMO use in corn grown in Ohio"
array_2_name = "Google searches for 'call of duty'"

# 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 GMO use in corn grown in Ohio
• Line chart for only Google searches for 'call of duty'
• AI-generated correlation image
• The spurious research paper: Stalk and Shoot: The Kernel of Connection Between GMO Corn and Gaming Searches