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AI explanation
Puzzled farmers mistook barley for a strange new breed of paper crop, leading to a nationwide shortage of newspaper stock. This confusion stemmed from a viral internet hoax claiming that barley paper was the latest eco-friendly innovation in the publishing industry. The barley mix-up left newspaper publishers shaken and stirred, as they scrambled to find alternative sources for paper production. As a result, the once-booming newspaper business took a grainy, unexpected turn, leaving us all to wonder, "What's news, barley there?"
Model: dalle-3
Prompt: Generating Prompt: [High-tech] In a vast barley field, under the radiant glow of the sun, a farmer tends to the ripe barley plants. Nearby, several barrels overflow with freshly harvested barley. On the opposite end, a stack of untouched daily newspapers rests near a newsstand, with a diminishing number and fewer beer enthusiasts in the area, hinting at a decline in patronage. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.
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: US Barley Production Volume and Count of daily newspapers in the United States. 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: Low poly Oragami Hyper-realistic Cybernetic art High-tech
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 US Barley Production Volume and Count of daily newspapers in the United States, in the following situation: Because with less barley available, there were fewer beer enthusiasts to spill their drinks on the newspapers after a night of heavy drinking, leading to a decrease in demand for daily newspapers.. Keep the description simple.
ChatGPT: In a sprawling barley field under a bright sun, a farmer is tending to the ripe barley plants, a few barrels filled with harvested barley at the edge of the field. On the other side, a stack of daily newspapers lies untouched, with a dwindling number under a nearby newsstand. Fewer beer enthusiasts can be seen, perhaps indicating a decrease in their patronage, and less spilled drinks on the newspapers.
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: In a sprawling barley field under a bright sun, a farmer is tending to the ripe barley plants, a few barrels filled with harvested barley at the edge of the field. On the other side, a stack of daily newspapers lies untouched, with a dwindling number under a nearby newsstand. Fewer beer enthusiasts can be seen, perhaps indicating a decrease in their patronage, and less spilled drinks on the newspapers. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Generating Prompt: [High-tech] In a vast barley field, under the radiant glow of the sun, a farmer tends to the ripe barley plants. Nearby, several barrels overflow with freshly harvested barley. On the opposite end, a stack of untouched daily newspapers rests near a newsstand, with a diminishing number and fewer beer enthusiasts in the area, hinting at a decline in patronage.
*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.
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how a decrease in US Barley Production Volume caused Count of daily newspapers in the United States to decrease. Make it feel plausible.
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Data details
US Barley Production VolumeSource: Statista
See what else correlates with US Barley Production Volume
Count of daily newspapers in the United States
Source: Statista
See what else correlates with Count of daily newspapers in the United States

Correlation is a measure of how much the variables move together. If it is 0.99, when one goes up the other goes up. If it is 0.02, the connection is very weak or non-existent. If it is -0.99, then when one goes up the other goes down. If it is 1.00, you probably messed up your correlation function.
r2 = 0.6548868 (Coefficient of determination)
This means 65.5% of the change in the one variable (i.e., Count of daily newspapers in the United States) is predictable based on the change in the other (i.e., US Barley Production Volume) over the 19 years from 2000 through 2018.
p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.7E-5. 0.0000271359957518138730000000
The p-value is a measure of how probable it is that we would randomly find a result this extreme. More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.
But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.
Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.81 in 0.0027% of random cases. Said differently, if you correlated 36,851 random variables You don't actually need 36 thousand variables to find a correlation like this one. You can also correlate variables that are not independent. I do this a lot.
p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.
Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 18 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 18 because we have two variables measured over a period of 19 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.
[ 0.56, 0.92 ] 95% correlation confidence interval (using the Fisher z-transformation)
The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.
This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!
All values for the years included above: If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.
Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.
2000 | 2001 | 2002 | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | |
US Barley Production Volume (Thousand Bushels) | 317804 | 248329 | 226906 | 278283 | 279743 | 211896 | 180165 | 210110 | 240193 | 227323 | 180.268 | 154788 | 218990 | 216745 | 181542 | 218187 | 199914 | 143258 | 153527 |
Count of daily newspapers in the United States (Newspapers) | 1480 | 1468 | 1457 | 1456 | 1457 | 1452 | 1437 | 1422 | 1408 | 1387 | 0 | 1452 | 1381 | 1425 | 1395 | 1331 | 1286 | 1277 | 1279 |
Why this works
- 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.
- Lack of causal connection: There is probably
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. - 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
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
You can calculate the values on this page on your own! Try running the Python code to see the calculation results. Step 1: Download and install Python on your computer.Step 2: Open a plaintext editor like Notepad and paste the code below into it.
Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"
Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.
Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.
Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.
Step 7: Run the Python script by typing "python calculate_correlation.py"
If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:
"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."
# 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([317804,248329,226906,278283,279743,211896,180165,210110,240193,227323,180.268,154788,218990,216745,181542,218187,199914,143258,153527,])
array_2 = np.array([1480,1468,1457,1456,1457,1452,1437,1422,1408,1387,0,1452,1381,1425,1395,1331,1286,1277,1279,])
array_1_name = "US Barley Production Volume"
array_2_name = "Count of daily newspapers in the United States"
# 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)
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Download images for these variables:
- High resolution line chart
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 US Barley Production Volume
- Line chart for only Count of daily newspapers in the United States
- AI-generated correlation image
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Correlation ID: 3576 · Black Variable ID: 441 · Red Variable ID: 390