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spurious scholar

Because if p < 0.05, why not publish?

Step 1: Gather a bunch of data. There are 25,156 variables in my database. The data ranges from the mundane (air pollution in Chicago) to the weird (Hotdogs consumed by Nathan's Hot Dog Eating Competition Champion) to the super-niche (How clickbait-y Numberphile YouTube video titles are, as rated by an AI).
Step 2: Dredge that data to find random correlations between variables. "Dredging data" means taking one variable and correlating it against every other variable just to see what sticks. It's a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.

Fun fact: the chart used on the wikipedia page to demonstrate data dredging is also from me. I've been being naughty with data since 2014.

Step 3: Calculate the correlation coefficient, confidence interval, and p-value to see if the connection is statistically significant. "Statistically significant" is a misleading term. It sounds like it means "statistically significant" because, you know, those are the same two words. Unfortunately statistical significance is a technical term that means mumble mumble at least as extreme mumble mumble null hypothesis mumble mumble probability mumble mumble p-values.

You know what? Forget the technical definition. "Statistically significant" just means "someone did some fancy math."

I really did the fancy math below and you can check it by clicking on the "view detailed data" link under each paper. And these really do qualify as "statistically significant" in the technical sense. It's just that "statistically significant" does not mean the results are "significant."

Step 4: If it is, have a large language model draft a research paper.
Step 5: Remind everyone that these papers are AI-generated and are not real. Seriously, just pick one and read the lit review section. The silliness of the papers is an artifact of me (1) having fun and (2) acknowledging that realistic-looking AI-generated noise is a real concern for academic research (peer reviews in particular).

The papers could sound more realistic than they do, but I intentionally prompted the model to write papers that look real but sound silly.

Also: every page says "This paper is AI-generated" at the bottom and the first letters of the names of the authors always spell out C-H-A-T-G-P-T.

Step 6: ...publish:

Labor of Laurel: Analyzing the Connection between the Name Laurel and Costume Attendants in Minnesota
The Journal of Folksy Studies
r=0.864 · 95% conf. int. [0.666,0.948] · r2=0.747 · p < 0.01
Generated Jan 2024 · View data details

Pollution Puzzles and Peculiar Pilgrimages: Probing the Paradoxical Link between Phoenix Air Quality and Disneyland Visitors
The Journal of Ecological Amusements
r=-0.787 · 95% conf. int. [-0.926,-0.460] · r2=0.619 · p < 0.01
Generated Jan 2024 · View data details

Unidentified Flavors of Extraterrestrial Origin: A Statistical Analysis of the Link between UFO Sightings in North Dakota and Hotdogs Consumed by Nathan's Hot Dog Eating Competition Champion
The Journal of Extraterrestrial Gastronomy
r=0.838 · 95% conf. int. [0.718,0.909] · r2=0.702 · p < 0.01
Generated Jan 2024 · View data details

The Bumpy Road: Unveiling the Relationship Between Butte's Air Quality and Mercedes-Benz USA Recalls
Journal of Environmental Quality and Automotive Safety
r=0.536 · 95% conf. int. [0.247,0.737] · r2=0.287 · p < 0.01
Generated Jan 2024 · View data details

Out of This World Hitters: Exploring the Cosmic Connection Between NASA's Budget Appropriation and the Average Age of Batters for the Minnesota Twins
The Astronomical Anthropology Journal
r=0.111 · 95% conf. int. [-0.176,0.380] · r2=0.012 · p > 0.05 (pay no attention to the flipped sign)
Generated Jan 2024 · View data details

Astrodollars: Exploring the Celestial and Fiscal Orbits - The Relationship Between Neptune's Distance from the Sun and NASA's Budget as a Percentage of the US Federal Budget
The Interstellar Economist
r=0.895 · 95% conf. int. [0.821,0.940] · r2=0.801 · p < 0.01
Generated Jan 2024 · View data details

Lost in Space Opera: An Interstellar Analysis of Neptune's Distance from the Sun and its Impact on Days of Our Lives Viewership
The Interstellar Journal of Planetary Entertainment Studies
r=0.959 · 95% conf. int. [0.927,0.977] · r2=0.920 · p < 0.01
Generated Jan 2024 · View data details

Neptune's Position and Crime Ambition: A Correlation Examination
The Journal of Planetary Criminology
r=0.969 · 95% conf. int. [0.941,0.984] · r2=0.940 · p < 0.01
Generated Jan 2024 · View data details

Got Milk? The Cream of the Crime: Exploring the Correlation between Milk Consumption and Burglary Rates
Journal of Dairy-Driven Deviance
r=0.968 · 95% conf. int. [0.934,0.984] · r2=0.936 · p < 0.01
Generated Jan 2024 · View data details

The Tiarra Trend and Tedious Terminology: A Tantalizing Tale of Taming 'Onety One'
The Journal of Linguistic Laughter
r=0.816 · 95% conf. int. [0.565,0.929] · r2=0.667 · p < 0.01
Generated Jan 2024 · View data details

Terrors and Tiarra: Exploring the Correlation Between the Popularity of the Name Tiarra and Violent Crime Rates in the United States
The Journal of Eccentric Sociological Studies
r=0.924 · 95% conf. int. [0.857,0.961] · r2=0.854 · p < 0.01
Generated Jan 2024 · View data details

The Dirty Laundry of Travel Aspirations: Exploring the Relationship between US Household Spending on Cleaning Supplies and Google Searches for 'Flights to Antarctica'
The Journal of Quirky Socioeconomic Studies
r=0.906 · 95% conf. int. [0.769,0.964] · r2=0.822 · p < 0.01
Generated Jan 2024 · View data details

Stellar Budget Cuts: The Cosmic Correlation Between Public School Kids in the US and NASA's Pocketbook
Astrophysical Educator Quarterly
r=-0.917 · 95% conf. int. [-0.959,-0.838] · r2=0.842 · p < 0.01
Generated Jan 2024 · View data details

The Fuel of Knowledge: Connecting the Dots Between U.S. Public School Kids and Fossil Fuel Use in Grenada
The Journal of Pedagogical Petrology
r=0.966 · 95% conf. int. [0.931,0.983] · r2=0.933 · p < 0.01
Generated Jan 2024 · View data details

Statisticians in Oklahoma and Sprint Satisfaction: A Statistical Study
The Journal of Quirky Statistical Analyses
r=0.735 · 95% conf. int. [0.394,0.898] · r2=0.541 · p < 0.01
Generated Jan 2024 · View data details

The Technician Tonic: A Statistical Analysis of the Relationship Between Biological Technicians in Maryland and Customer Satisfaction with Sprint
Journal of Bio-Tech Consumer Insights
r=0.921 · 95% conf. int. [0.791,0.972] · r2=0.849 · p < 0.01
Generated Jan 2024 · View data details

Drawing Connections: The Correlation Between xkcd Romance Comics and Spanish Language Yearnings
Journal of Humorous Linguistics
r=0.931 · 95% conf. int. [0.816,0.975] · r2=0.867 · p < 0.01
Generated Jan 2024 · View data details

The Kelsey Quandary: Exploring the Relationship Between Name Popularity and Violent Crime Rates
Journal of Pseudoscientific Studies
r=0.946 · 95% conf. int. [0.897,0.972] · r2=0.894 · p < 0.01
Generated Jan 2024 · View data details

Bean Counters: Exploring the GMO Connection Between Soybeans and Sonographers in Minnesota
The Journal of Agrosonic Studies
r=0.908 · 95% conf. int. [0.778,0.963] · r2=0.824 · p < 0.01
Generated Jan 2024 · View data details

Burning Questions: The Kerosene Connection - A Squirrely Correlation?
The Journal of Rodent-Powered Energy Research
r=0.904 · 95% conf. int. [0.757,0.964] · r2=0.818 · p < 0.01
Generated Jan 2024 · View data details

The Texan Tug-of-War: Tracking Google Searches for Texas Annexation and the Tremors on Broadcom's Stock
The Journal of Digital Geopolitics and Economic Frictions
r=0.881 · 95% conf. int. [0.659,0.962] · r2=0.777 · p < 0.01
Generated Jan 2024 · View data details

Jack of All Trades or Taylor-Made Target? Analyzing the Correlation between Tayler's Popularity and Carjackings in the US
Journal of Quirky Sociological Studies
r=0.958 · 95% conf. int. [0.910,0.981] · r2=0.919 · p < 0.01
Generated Jan 2024 · View data details

The Peculiar Pairing: Pupil Population and Sin City Slumber Statistics
The Journal of Offbeat Ocular Observations
r=0.967 · 95% conf. int. [0.923,0.986] · r2=0.934 · p < 0.01
Generated Jan 2024 · View data details

Swing and Bling: The Link Between Roger Federer's Grand Slam Fling and Electronics Engineers' Ring in New Mexico
Journal of Athletic Aesthetics and Technological Trends
r=0.905 · 95% conf. int. [0.706,0.972] · r2=0.819 · p < 0.01
Generated Jan 2024 · View data details

Baggage Handlers: The Airy Connection Between Psychiatric Aides in Minnesota and Automotive Air Bag Recalls
The Journal of Unconventional Connections
r=0.824 · 95% conf. int. [0.554,0.937] · r2=0.678 · p < 0.01
Generated Jan 2024 · View data details


Currently viewing 25 of 4,731 spurious research papers

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Why this works

  1. Data dredging: I have 25,156 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,824,336 correlation calculations! This is called “data dredging.” Fun fact: the chart used on the wikipedia page to demonstrate data dredging is also from me. I've been being naughty with data since 2014.
    Instead of starting with a hypothesis and testing it, I isntead tossed a bunch of data in a blender to see what correlations would 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 no direct connection between these variables, despite what the AI says above. 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.
    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. You will often see trend-lines form. 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 naive p-value calculation does not take this into account. You will calculate a lower chance of "randomly" achieving the result than represents reality.

    To be more specific: p-value tests are probability values, where you are calculating the probability of achieving a result at least as extreme as you found completely by chance. When calculating a p-value, you need to assert how many "degrees of freedom" your variable has. I count each year (minus one) as a "degree of freedom," but this is misleading for continuous variables.

    This kind of thing can creep up on you pretty easily when using p-values, which is why it's best to take it as "one of many" inputs that help you assess the results of your analysis.
  4. Outliers: Some datasets here have outliers which drag up the correlation. In concept, "outlier" just means "way different than the rest of your dataset." When calculating a correlation like this, they are particularly impactful because a single outlier can substantially increase your correlation.

    Because this page is automatically generated, I don't know whether any of the charts displayed on it have outliers. I'm just a footnote. ¯\_(ツ)_/¯
    I intentionally mishandeled outliers, which makes the correlation look extra strong.



Spurious Scholar was launched January 27, 2024. If you have feedback on it, I'd love to hear from you! Shoot me a note: feedback@tylervigen.com.


Project by Tyler Vigen
emailme@tylervigen.com · about · subscribe


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