P-Value

← Back to Glossary

Definition

A p-value is the probability of observing results at least as extreme as those measured, assuming the null hypothesis is true.

Correct Scientific Usage

P-values quantify how compatible the observed data are with a specific statistical model. They are used to assess whether results cross a pre-defined significance threshold. Researchers recognize that p-values are continuous measures of evidence strength rather than binary indicators of true vs false.

Common Misunderstandings

The p-value is one of the most widely misunderstood statistics in science. Most people incorrectly believe it represents the probability that the null hypothesis is true or that results occurred by chance.

Many people also assume that smaller p-values indicate larger or more important effects. In reality, with enough data, even small, meaningless effects can produce very small p-values.

Why It Matters

Because p-values are easy to report and easy to misinterpret, they are often used to oversimplify complex findings. Treating p-values as definitive answers obscures uncertainty and exaggerates evidence strength.

References

← Back to Glossary