Type II Error Calculator — सूत्र
## Understanding Type II Error
### Concept
**Beta = P(fail to reject H0 | H0 is false)**
**Power = 1 - Beta**
Type II error occurs when you fail to detect a real effect. The probability depends on the true effect size, sample size, significance level, and population variability. A negative z_beta value indicates high power (likely to detect the effect).
### Concept
**Beta = P(fail to reject H0 | H0 is false)**
**Power = 1 - Beta**
Type II error occurs when you fail to detect a real effect. The probability depends on the true effect size, sample size, significance level, and population variability. A negative z_beta value indicates high power (likely to detect the effect).
हल किया गया उदाहरण
H0: mu = 100. True mu = 105. SD = 15, n = 25, z_crit = 1.96.
- SE = 15 / sqrt(25) = 15 / 5 = 3
- Non-centrality = (105 - 100) / 3 = 1.667
- z_beta = 1.96 - 1.667 = 0.293
- A z_beta of 0.293 corresponds to roughly beta = 0.615
- Power ≈ 1 - 0.615 = 0.385 (about 39%)
- This sample size gives low power to detect this effect