python one sample z test
from statsmodels.stats.weightstats import ztest import numpy as np from scipy.stats import norm alpha = 0.05 Example 1 More Manual
shoe_distance = [ 370, 395, 400, 405, 390, 385, 410, 395, 400, 380, 390, 400, 410, 415, 395, 405, 390, 400, 420, 375, 400, 385, 390, 395, 410, 405, 400, 395, 380, 400 ] population_mean = 400 population_std = 15 sample_mean = np.mean(shoe_distance) print(sample_mean) 
sample_size = len(shoe_distance) print(sample_size) 
z_score = (sample_mean - population_mean) / (population_std / np.sqrt(sample_size)) print(z_score) 
if p_value < alpha: print("Reject the null hypothesis") else: print("Fail to reject the null hypothesis") 
#example 2 Faster with statsmodels #This code is performing a z-test on a dataset of marathon completion times to determine #if the sample mean is significantly different from the population mean #Contains 50 individual marathon completion times (assumed in minutes). #These represent the sample drawn from the population Null hypothesis (H₀): The sample mean is equal to the population mean (270) Alternative hypothesis (Hₐ): The sample mean is different from the population mean (270) marathon_data = [ 250, 260, 245, 255, 240, 265, 270, 260, 275, 255, 245, 250, 265, 255, 260, 245, 250, 275, 260, 255, 245, 250, 265, 255, 260, 275, 250, 260, 255, 245, 260, 265, 255, 250, 275, 260, 255, 245, 250, 265, 260, 270, 255, 245, 260, 265, 250, 260, 255, 245 ] population_mean = 270 # average population_std = 30 # Assumed standard deviation of marathon runners z_score, p_value = ztest(marathon_data, value=population_mean, alternative='two-sided') print('Z-test Score:', z_score,'P-value:', p_value) 
if p_value < alpha: print("Reject the null hypothesis: The sample mean is significantly different from the population mean.") else: print("Fail to reject the null hypothesis: No significant difference between the sample mean and population mean.") 
