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🌱 來自：medical_statistics

statistical_power

import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import norm
 
# Set up variables for the power calculation
effect_size = 0.5  # Medium effect size
alpha = 0.05       # Significance level
sample_sizes = np.arange(10, 300, 10)  # Sample sizes from 10 to 300
power_values = []
 
# Calculate power for each sample size
for n in sample_sizes:
    # Calculate the critical z value at the desired alpha
    z_alpha = norm.ppf(1 - alpha)
    # Standard error of the mean for the given sample size
    se = 1 / np.sqrt(n)
    # Z score for the observed effect size
    z_effect = effect_size / se
    # Power is the probability that the Z score is beyond the critical value
    power = 1 - norm.cdf(z_alpha - z_effect)
    power_values.append(power)
 
# Plotting the power curve
plt.figure(figsize=(10, 6))
plt.plot(sample_sizes, power_values, label='Power Curve', color='blue', marker='o')
plt.title('Power Analysis')
plt.xlabel('Sample Size')
plt.ylabel('Power')
plt.grid(True)
plt.axhline(y=0.8, color='r', linestyle='--', label='80% Power Level')
plt.legend()
plt.show()