P-value Misconceptions Explorer

Overview

The P-value Misconceptions Explorer helps you understand what p-values actually mean and what they don’t mean. One of the most misunderstood concepts in statistics, p-values are often misinterpreted in ways that lead to flawed conclusions. This interactive tool demonstrates the true meaning of p-values and tests your understanding with common misconceptions.

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Tips

• Remember: a p-value is P(data or more extreme | H₀ is true), NOT P(H₀ is true | data)
• A small p-value doesn’t tell you the size of the effect - even tiny, meaningless effects can have small p-values with large samples
• A large p-value doesn’t prove the null hypothesis is true - absence of evidence is not evidence of absence
• The p-value threshold of 0.05 is arbitrary - there’s nothing magical about crossing this boundary
• Watch how sample size affects p-values: the same effect size can yield very different p-values depending on n
• Statistical significance (p < 0.05) does not automatically mean practical significance or importance
• A p-value of 0.04 is not fundamentally different from 0.06 - avoid bright-line thinking
• Try the quiz multiple times to reinforce your understanding of these subtle but crucial distinctions