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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:

Zipping Down the Hydropower Highway: Exploring the Ripple Effects of Hydropower Energy in El Salvador on Rio Tinto Group's Stock Price
The Journal of Eco-Finance and Hydropower Economics
r=0.765 · 95% conf. int. [0.487,0.902] · r2=0.584 · p < 0.01
Generated Jan 2024 · View data details

From Cotton to Carbon: Unveiling the GMO Effect on Air Pollution in Lumberton, North Carolina
The Journal of Biotech Chronicles
r=0.862 · 95% conf. int. [0.400,0.975] · r2=0.742 · p < 0.01
Generated Jan 2024 · View data details

From Miami to Portugal: The Hazy Connection Between Air Pollution and Kerosene Consumption
The Journal of Atmospheric Anecdotes
r=0.867 · 95% conf. int. [0.765,0.926] · r2=0.751 · p < 0.01
Generated Jan 2024 · View data details

The Dirty Truth: Air Pollution in Memphis and the Marital Mess in Tennessee
The Journal of Environmental Entanglements
r=0.858 · 95% conf. int. [0.690,0.938] · r2=0.736 · p < 0.01
Generated Jan 2024 · View data details

A Breath of Fresh Air: Unpacking the Link Between Air Pollution in Coeur d'Alene and Norwegian Immigration Aspirations
The Journal of Eclectic Environmental Studies
r=0.753 · 95% conf. int. [0.466,0.897] · r2=0.567 · p < 0.01
Generated Jan 2024 · View data details

Clearing the Air: Unearthing the Link Between Air Pollution and Lampard's League Goals
The Journal of Sports Ecology and Environmental Health
r=0.861 · 95% conf. int. [0.541,0.963] · r2=0.742 · p < 0.01
Generated Jan 2024 · View data details

Blowing Smoke: Exploring the Link Between Air Pollution in Columbus and Kerosene Combustion in Syria
The International Journal of Environmental Emissions Research
r=0.716 · 95% conf. int. [0.527,0.838] · r2=0.512 · p < 0.01
Generated Jan 2024 · View data details

Who's Dishin' Out Wins? A Statistical Analysis of the Relationship Between Dishwashers in Illinois and the Chicago Bears' Season Success
Journal of Sports Appliance Analysis
r=0.550 · 95% conf. int. [0.142,0.798] · r2=0.302 · p < 0.05
Generated Jan 2024 · View data details

Goal-getting or Googling for Greatness? A Study of Lionel Messi's Goal Count for Argentina and Google Searches for 'Best Place to Work'
The International Journal of Sports Analytics and Workforce Psychology
r=0.742 · 95% conf. int. [0.406,0.901] · r2=0.550 · p < 0.01
Generated Jan 2024 · View data details

Cotton GMO-nomics: A Fiber-Tastic Connection to Lacrosse Glory
The Journal of Genetic Threads
r=0.880 · 95% conf. int. [0.733,0.948] · r2=0.774 · p < 0.01
Generated Jan 2024 · View data details

GMO Corn and 'I Can't Even': A Kernel of Truth?
The Journal of Agricultural Absurdities
r=0.914 · 95% conf. int. [0.792,0.966] · r2=0.835 · p < 0.01
Generated Jan 2024 · View data details

The Kernel of Legal Ambiguity: Exploring the Correlation Between GMO Corn in Kansas and the Number of Lawyers in the United States
Journal of Transgenic Legal Studies
r=0.988 · 95% conf. int. [0.972,0.995] · r2=0.976 · p < 0.01
Generated Jan 2024 · View data details

Pitchers and Propane: Exploring the Link Between Liquefied Petroleum Gas in Central African Republic and Wins for the New York Mets
The International Journal of Sports Analytics and Unusual Correlations
r=0.887 · 95% conf. int. [0.673,0.964] · r2=0.787 · p < 0.01
Generated Jan 2024 · View data details

Football Flourish or Warty Woes: An Empirical Examination of Google Searches for 'Is This a Wart' and Season Wins for the Pittsburgh Steelers
The Journal of Sports Medicine and Information Technology
r=0.508 · 95% conf. int. [0.084,0.776] · r2=0.258 · p < 0.05
Generated Jan 2024 · View data details

Engineered for Success: The Correlation Between Industrial Engineers in Illinois and Runs Scored by the Winning Team in the World Series
Journal of Industrial Engineering and Sports Analytics
r=0.854 · 95% conf. int. [0.522,0.961] · r2=0.730 · p < 0.01
Generated Jan 2024 · View data details

The GMOxkcd Connection: A Cotton-Candy of Correlation
The Journal of Silly Science
r=0.875 · 95% conf. int. [0.670,0.956] · r2=0.766 · p < 0.01
Generated Jan 2024 · View data details

The Soy-Apple Connection: Genetically Modified Soybeans and Customer Satisfaction with Apple Products
Journal of Agricultural Biotechnology and Consumer Behavior
r=0.821 · 95% conf. int. [0.611,0.923] · r2=0.674 · p < 0.01
Generated Jan 2024 · View data details

Byron's Breathing Brouhaha: The Correlation between the Popularity of the Name Bryon and Air Pollution in Allentown
The Journal of Quirky Quantitative Queries
r=0.818 · 95% conf. int. [0.687,0.898] · r2=0.670 · p < 0.01
Generated Jan 2024 · View data details

A Doggone Good Connection: Linking Air Pollution in Dickinson, North Dakota to the Consumption of Nathan's Hot Dogs by Competitive Eaters
The International Journal of Hot Dog Science
r=0.594 · 95% conf. int. [0.339,0.768] · r2=0.353 · p < 0.01
Generated Jan 2024 · View data details

Aerial Anthropogenic Artifacts and Auto Appropriation: Exploring the Link between Air Pollution in Youngstown and Motor Vehicle Thefts in Ohio
Journal of Ecological Criminology
r=0.827 · 95% conf. int. [0.689,0.907] · r2=0.684 · p < 0.01
Generated Jan 2024 · View data details

The Burning Issues Linking Air Pollution in Springfield, Ohio and Kerosene Usage in Syria
Journal of Global Environmental Interconnections
r=0.662 · 95% conf. int. [0.449,0.804] · r2=0.439 · p < 0.01
Generated Jan 2024 · View data details

The Ozone Connection: Air Pollution in the Big Apple and Remaining Forest Cover in the Brazilian Amazon
Journal of Atmospheric Chemistry and Ecological Dynamics
r=0.888 · 95% conf. int. [0.790,0.942] · r2=0.789 · p < 0.01
Generated Jan 2024 · View data details

Breaking and Entering: An Unconventional Link Between Master's Degrees in Area, Ethnic, Cultural, Gender, and Group Studies and Burglaries in Alabama
Journal of Southern Studies and Social Deviance
r=0.988 · 95% conf. int. [0.948,0.997] · r2=0.976 · p < 0.01
Generated Jan 2024 · View data details

Coughing up the Link: A Burglarious Connection between Robberies in West Virginia and Asthma Prevalence in American Children
Journal of Epidemiological Oddities
r=0.903 · 95% conf. int. [0.746,0.965] · r2=0.816 · p < 0.01
Generated Jan 2024 · View data details

Justin Time: The Surprising Link Between the Popularity of a Name and Motor Vehicle Thefts in Maine
The Journal of Quirky Sociological Studies
r=0.986 · 95% conf. int. [0.973,0.993] · r2=0.972 · 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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