Type II Error

A Type II error is a false negative: failing to reject the null when there really is an effect — missing something that is actually there. Its rate is written β (beta), and it usually grows when your sample is small or the true effect is faint.

This is the smoke detector that stays silent during a real fire. There is a built-in tension: tightening α (fewer false alarms) generally raises β (more misses), unless you compensate by collecting more data. Underpowered studies make Type II errors quietly common, so a non-significant result often means “we couldn’t tell,” not “there’s nothing.”