Calcolatore Analisi della Potenza
Calcola la potenza statistica di un test per determinare la probabilità di rilevare un effetto reale.
Sample Size per Group
31.40
Sample Size per Group vs Effect Size (Cohen's d)
Formula
How to Perform a Power Analysis
Formula
n per group = ((z_alpha/2 + z_beta) / d)^2
Power is the probability of correctly rejecting a false null hypothesis (detecting a real effect). This formula estimates the sample size needed per group for a two-sample test. Higher power, smaller effect sizes, and lower alpha all require larger samples.
Esempio Risolto
Detect a medium effect (d = 0.5) with 80% power at alpha = 0.05.
- 01n = ((1.96 + 0.842) / 0.5)^2
- 02= (2.802 / 0.5)^2
- 03= 5.604^2
- 04= 31.4
- 05Round up: 32 per group, 64 total
Domande Frequenti
What is an acceptable power level?
80% (0.80) is the conventional minimum. Many researchers aim for 90% power. Lower power means a higher risk of failing to detect a real effect (Type II error).
What is the relationship between alpha, power, and sample size?
They are interconnected: fixing any two determines the third (given the effect size). Decreasing alpha or increasing power both require larger sample sizes.
What if the effect size is unknown?
Use pilot study estimates, published benchmarks (small=0.2, medium=0.5, large=0.8), or determine the minimum clinically important difference and convert it to d by dividing by the expected SD.
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