Calculadora de Error Tipo II Gratis
Calcula la probabilidad de un error tipo II (falso negativo) y la potencia del test estadístico.
Z for Power Calculation
0.2933
Z for Power Calculation vs Null Hypothesis Mean
Fórmula
## 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).
Ejemplo Resuelto
H0: mu = 100. True mu = 105. SD = 15, n = 25, z_crit = 1.96.
- 01SE = 15 / sqrt(25) = 15 / 5 = 3
- 02Non-centrality = (105 - 100) / 3 = 1.667
- 03z_beta = 1.96 - 1.667 = 0.293
- 04A z_beta of 0.293 corresponds to roughly beta = 0.615
- 05Power ≈ 1 - 0.615 = 0.385 (about 39%)
- 06This sample size gives low power to detect this effect
Preguntas Frecuentes
How do I reduce Type II error?
Increase sample size, increase the significance level (accept higher Type I error), reduce measurement variability, or study a larger effect. Sample size is the most practical lever.
What is the relationship between alpha and beta?
For a fixed sample size and effect, decreasing alpha (stricter threshold) increases beta (more Type II errors). There is a tradeoff: reducing one type of error increases the other unless you also increase n.
What is an acceptable beta level?
Conventionally, beta = 0.20 (power = 0.80) is the minimum acceptable level. Clinical trials often aim for beta = 0.10 (power = 0.90) for more reliable detection.
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