Science versus pseudoscience

[Laird]

There Is No Pseudoscience—There Is Only Pseudo-Method
July 28, 2026
Arnaud Delorme, IONS Scientist
IONS blog

There is no single five-step recipe for all science. Field observation, laboratory experiment, mathematical modeling and historical reconstruction work differently. Good inquiry nevertheless has a recognizable backbone. Imagine testing whether Maya can predict the color of a card before a computer chooses it.

• Be specific. Maya predicts “red” or “blue” before each card is chosen.
  
• Make it fair. The computer chooses randomly, and nobody can see the card early.
  
• Decide the rules first. Before starting, choose 100 trials and define what counts as success.
  
• Don’t stop when Maya gets lucky. Complete all 100 trials—even if she begins with ten correct guesses.
  
• Report everything. Share every guess, not just Maya’s best streak.
  
• Try again. Let another laboratory repeat the same test.
  
• Accept the result. If the effect disappears under fair testing, change the claim—not the rules.
  

What does pseudo-method look like?

Imagine testing a new pill that is supposed to improve memory.

• Too many chances. Researchers test memory, mood, sleep, energy, and attention—then report only the one result that looks positive.
  
• Too few people. Three of five patients improve, so the researchers declare the drug effective.
  
• An unfair comparison. The drug group is younger or healthier than the control group.
  
• No blinding. Patients and doctors know who receives the drug, so their expectations may influence the results.
  
• Changing the rules. Researchers stop the study during a lucky streak or remove patients whose results do not fit, classifying them as outliers.
  
• Hiding failures. Positive studies are published, while negative studies disappear.
  
• Rejecting criticism. When other laboratories cannot repeat the result, the researchers make excuses instead of questioning the claim.
  

These problems can happen in any field.