选择合适的统计检验
Python 实验设计
James Chapman
Curriculum Manager, DataCamp
选择合适的检验
- 数据集特征
- 数据类型
- 分布 → 许多检验假定正态！
- 变量数量
- 假设
结果：结论更准确、可靠！
- t 检验、ANOVA、卡方
数据集：运动表现
- 训练方案与饮食对运动表现的影响
athletic_perf.sample(n=5)
Athlete_ID Training_Program Diet_Type Initial_Fitness Performance_Inc
167 Endurance Plant-Based High 9.113040
289 Endurance Keto Low 11.039744
164 Endurance Plant-Based Medium 11.614835
30 Strength Keto Medium 7.384686
186 HIIT High-Protein Low 6.776078
独立样本 t 检验
- 比较_两组_均值
- 假设：正态分布，方差齐性
from scipy.stats import ttest_ind group1 = athletic_perf[athletic_perf['Training_Program'] == 'HIIT']['Performance_Inc'] group2 = athletic_perf[athletic_perf['Training_Program'] == 'Endurance']['Performance_Inc']t_stat, p_val = ttest_ind(group1, group2) print(f"T-statistic: {t_stat}, P-value: {p_val}")
T-statistic: 0.20671020082911742, P-value: 0.8364563849070663
p_val > $\alpha$ → 证据不足，无法认定均值有差异
单因素 ANOVA
- 比较多组（>2）均值
- 假设：组间方差齐性
from scipy.stats import f_oneway program_types = ['HIIT', 'Endurance', 'Strength'] groups = [athletic_perf_data[athletic_perf_data['Training_Program'] == program] ['Performance_Increase'] for program in program_types]f_stat, p_val = f_oneway(*groups) print(f"F-statistic: {f_stat}, P-value: {p_val}")
F-statistic: 1.5270022393256704, P-value: 0.2188859009050602
p_val > $\alpha$ → 证据不足，无法认定均值有差异
卡方关联检验
- 检验分类变量之间的关系
- 对分布无假设
from scipy.stats import chi2_contingency
import pandas as pd
contingency_table = pd.crosstab(athletic_perf['Training_Program'],
athletic_perf['Diet_Type'])
Diet_Type High-Protein Keto Plant-Based
Training_Program
Endurance 33 28 33
HIIT 27 32 40
Strength 38 29 40
卡方关联检验
chi2_stat, p_val, dof, expected = chi2_contingency(contingency_table)
print(f"Chi2-statistic: {chi2_stat}, P-value: {p_val}")
Chi2-statistic: 2.154450885821988, P-value: 0.7073764021451127
p_val > $\alpha$ → 证据不足，无法认定存在关联
Passons à la pratique !
Python 实验设计
