The error of failing to reject a false null hypothesis, essentially missing a real effect that actually exists. Also called a 'false negative,' it occurs when researchers conclude there's no effect when there really is one.
Also introduced by Neyman and Pearson in 1928 as the second type of statistical error. The probability of Type II error is denoted by β (beta), and statistical power is defined as 1 - β, representing the ability to avoid this error.
Type II error is like missing a real fire because your smoke detector isn't sensitive enough! It's often caused by small sample sizes, weak effects, or too much noise in your data—the effect is there, but you can't detect it.
Complete word intelligence in one call. Free tier — 50 lookups/day.