The Skeptic's Guide to Statistics

How Data Can Be Misused & What to Look For

Common Statistical Traps

Misinformation isn't always about fake data. Often, it's about presenting real data in a misleading way. Here are the most common traps.

Cherry-Picking Data

Presenting only the data that supports a specific argument while ignoring data that contradicts it. Also known as selective reporting.

Correlation vs. Causation Fallacy

Claiming that because two things happen together, one must be causing the other. Always ask: could a third factor be influencing both?

Biased Samples & Overgeneralization

Drawing broad conclusions from a group that isn't representative of the larger population (e.g., studying only college students and applying it to everyone).

Tiny Sample Sizes

Results from very small studies are less reliable and can be due to random chance. Be skeptical of big claims from small groups.

The Power of Framing & Language

The way a finding is worded can dramatically change its perception, even if the numbers are the same.

Relative vs. Absolute Risk

A headline might scream 'Doubles Your Risk!' (a relative risk). But if the initial risk was tiny (1 in a million), the new risk (2 in a million) is still tiny. The absolute risk increase is what matters for real-world impact.

Misleading Percentages

Watch out for percentages without context. A '200% improvement' sounds amazing, but if it's an improvement from 1 to 3 on a 100-point scale, it's not very meaningful.

Headline Deconstructor

See how headlines can spin the truth. Enter a headline and the real stat to see the analysis.

Find the Flaw

Read the scenario and spot the statistical pitfall. Our AI helper will explain.

"A study wants to know the average income in a city. They survey people at a luxury car dealership. The results show a very high average income."