about · email me · subscribe
Spurious correlation #4,971 · View random

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Rain in Toronto and the second variable is The number of transportation security screeners in Arizona.  The chart goes from 2012 to 2022, and the two variables track closely in value over that time. Small Image
Download png
, svg

AI explanation

While it may seem unrelated, the logic is crystal clear - as rainfall in Toronto increased, so did the overall moisture in the atmosphere. This inadvertently led to an uptick in the production of cacti in Arizona, and you know what thrives in cacti? That's right, the rare and highly elusive screening officer bug. As their favorite plant flourished, so did their population, prompting a need for more transportation security screeners to manage the sudden surge of buggy employees buzzing around the Arizona airports. It's a case of mother nature sprinkling her influence across borders, one tiny, winged TSA officer at a time.

Model: dalle-3
Prompt: Create an image of a surreal scene where a heavy rainstorm is pouring down on a cactus-filled landscape in Arizona. The rain is specifically drawn to resemble the urban environment of Toronto, with city buildings and landmarks visible within the raindrops. Within the cactus plants, vivid and intricately detailed winged TSA officer bugs swarm and multiply, creating a surreal and fantastical atmosphere. Additionally, depict additional transportation security screeners being called in to manage the influx of bug employees at the Arizona airports. Ensure that the overall visual composition is striking and captivating, with attention to detail and a balance between the contrasting elements of the desert setting and the urban rainstorm. 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: Rain in Toronto and The number of transportation security screeners in Arizona. 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: Cutsy graphics Sci-fi fantacy Minimalism A scene from Skyrim An image that looks like it is a scene from 'Star Wars'
ChatGPT: Rain in Toronto

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 Rain in Toronto and The number of transportation security screeners in Arizona, in the following situation: While it may seem unrelated, the logic is crystal clear - as rainfall in Toronto increased, so did the overall moisture in the atmosphere. This inadvertently led to an uptick in the production of cacti in Arizona, and you know what thrives in cacti? That's right, the rare and highly elusive screening officer bug. As their favorite plant flourished, so did their population, prompting a need for more transportation security screeners to manage the sudden surge of buggy employees buzzing around the Arizona airports. It's a case of mother nature sprinkling her influence across borders, one tiny, winged TSA officer at a time.. Keep the description simple.
ChatGPT: In a cactus-filled landscape in Arizona, a heavy rainstorm pummels Toronto. As the rain pours down, the cacti in Arizona thrive and multiply. Among the cacti, a swarm of small, winged TSA officer bugs flutters around, their population exploding. In response to this surge, more transportation security screeners are brought in to manage the influx of bug employees at the Arizona airports.

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: [ Rain in Toronto ] and depicting this scene: In a cactus-filled landscape in Arizona, a heavy rainstorm pummels Toronto. As the rain pours down, the cacti in Arizona thrive and multiply. Among the cacti, a swarm of small, winged TSA officer bugs flutters around, their population exploding. In response to this surge, more transportation security screeners are brought in to manage the influx of bug employees at the Arizona airports. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: Create an image of a surreal scene where a heavy rainstorm is pouring down on a cactus-filled landscape in Arizona. The rain is specifically drawn to resemble the urban environment of Toronto, with city buildings and landmarks visible within the raindrops. Within the cactus plants, vivid and intricately detailed winged TSA officer bugs swarm and multiply, creating a surreal and fantastical atmosphere. Additionally, depict additional transportation security screeners being called in to manage the influx of bug employees at the Arizona airports. Ensure that the overall visual composition is striking and captivating, with attention to detail and a balance between the contrasting elements of the desert setting and the urban rainstorm.

*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.

Model: gpt-3.5-turbo-1106 (fine-tuned)
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 increases in Rain in Toronto positively influenced The number of transportation security screeners in Arizona.


Random correlation

Discover a new correlation

View all correlations

View all research papers

Report an error


Data details

Rain in Toronto
Detailed data title: Total Annual Precipitation at TORONTO CITY, ON CA
Source: NOAA National Climate Data Center
See what else correlates with Rain in Toronto

The number of transportation security screeners in Arizona
Detailed data title: BLS estimate of transportation security screeners in Arizona
Source: Bureau of Larbor Statistics
See what else correlates with The number of transportation security screeners in Arizona

Correlation r = 0.5060210 (Pearson correlation coefficient)
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 = 0.2560572 (Coefficient of determination)
This means 25.6% of the change in the one variable (i.e., The number of transportation security screeners in Arizona) is predictable based on the change in the other (i.e., Rain in Toronto) over the 11 years from 2012 through 2022.

p > 0.05 (pay no attention to the flipped sign)(Null hypothesis significance test)
The p-value is 0.11. 0.1122569962472653400000000000
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 0.51 in 11% of random cases. Said differently, if you correlated 9 random variables Which I absolutely did.
with the same 10 degrees of freedom, Degrees of freedom is a measure of how many free components we are testing. In this case it is 10 because we have two variables measured over a period of 11 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.

[ -0.13, 0.85 ] 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.
20122013201420152016201720182019202020212022
Rain in Toronto (Inches precipitation)32.3539.8528.0126.44030.07035.7332.0929.628.7
The number of transportation security screeners in Arizona (Laborers)12701350112010701100113011001150113011301100




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 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 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. Y-axis doesn't start at zero: I truncated the Y-axes of the graph above. I also used a line graph, which makes the visual connection stand out more than it deserves. Nothing against line graphs. They are great at telling a story when you have linear data! But visually it is deceptive because the only data is at the points on the graph, not the lines on the graph. In between each point, the data could have been doing anything. Like going for a random walk by itself!
    Mathematically what I showed is true, but it is intentionally misleading. Below is the same chart but with both Y-axes starting at zero.




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([32.35,39.85,28.01,26.44,0,30.07,0,35.73,32.09,29.6,28.7,])
array_2 = np.array([1270,1350,1120,1070,1100,1130,1100,1150,1130,1130,1100,])
array_1_name = "Rain in Toronto"
array_2_name = "The number of transportation security screeners in Arizona"

# 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

You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

Full license details.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:


View another random correlation

How fun was this correlation?

Your dedication to rating warms my heart!


Correlation ID: 4971 · Black Variable ID: 25434 · Red Variable ID: 18884
about · subscribe · emailme@tylervigen.com · twitter

CC BY 4.0