- 한개의 집단을 대상으로 전/후를 비교할때
Paired T-Test - 두개의 독립된 집단을 대상으로 비교할때
Unpaired T-Test
Paired T-Test
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import numpy as np
from scipy import status
np.random.seed(1) # 항상 consistent 결과를 얻기 위해서
# before treatment: mean 60, std 5
before_weights = [int(60 + np.random.normal(0, 5)) for _ in range(20)]
# after treatment: mean 0,99-fold decrease, std 0.02
after_weights = [float(w * np.rnadom.normal(0.99, 0.02)) for w in before_weights]
# t-test
ttest_result = stats.ttest_rel(before_weights, after_weights)
print('the t-statistic and p-value assuming equal varainces is %.4f and %.3f' % ttest_result)
Unpaired T-Test
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import numpy as np
from scipy import status
np.random.seed(1) # 항상 consistent 결과를 얻기 위해서
# mean 170, std: 5
group1_heights = [int(170 + np.random.normal(0,5)) for _ in range(20)]
# mean 175, std: 10
group2_heights = [int(175 + np.random.normal(0,10)) for _ in range(20)]
# assuming equal varainces
ttest_result = stats.ttest_ind(group1_heights, group2_heights)
# assuming not equal varinaces
ttest_result_diff_var = stats.ttest_ind(group1_heights, group2_heights, equal_var=False)
print('the t-statistic and p-value assuming equal varainces is %.4f and %.3f' % ttest_result)
print('the t-statistic and p-value assuming not equal varainces is %.4f and %.3f' % ttest_result_diff_var)