健全性检查：内部效度

Python 中的 A/B 测试

Moe Lotfy, PhD

Principal Data Science Manager

样本比例不匹配（SRM）

  • 样本比例不匹配（SRM）
    • 各变体的分配偏离设计
  • 卡方适配度检验

卡方公式

样本比例不匹配分配示例

Python 中的 A/B 测试

SRM Python 示例

# Calculate the unique IDs per variant
AdSmart.groupby('experiment')['auction_id'].nunique()

experiment
control    4071
exposed    4006

# Assign the unqiue counts to each variant
control_users=AdSmart[AdSmart['experiment']=='control']['auction_id'].nunique()
exposed_users=AdSmart[AdSmart['experiment']=='exposed']['auction_id'].nunique()
total_users=control_users+exposed_users
# Calculate allocation ratios per variant
control_perc = control_users / total_users
exposed_perc = exposed_users / total_users
print("Percentage of users in the Control group:",100*round(control_perc,5),"%")
print("Percentage of users in the Exposed group:",100*round(exposed_perc,5),"%")

Percentage of users in the Control group: 50.402 %
Percentage of users in the Exposed group: 49.598 %
1 Adsmart Kaggle dataset: https://www.kaggle.com/datasets/osuolaleemmanuel/ad-ab-testing
Python 中的 A/B 测试

SRM Python 示例

# Creat lists of observed and expected counts per variant
observed = [ control_users, exposed_users ]
expected = [ total_users/2, total_users/2 ]
# Import chisquare from scipy library
from scipy.stats import chisquare
# Run chisquare test on observed and expected lists
chi = chisquare(observed, f_exp=expected)
# Print test results and interpretation
print(chi)
if chi[1] < 0.01:
    print("SRM may be present")
else:
    print("SRM likely not present")

Power_divergenceResult(statistic=0.5230902562832735, pvalue=0.4695264353014863)
SRM likely not present
1 Adsmart Kaggle dataset: https://www.kaggle.com/datasets/osuolaleemmanuel/ad-ab-testing
Python 中的 A/B 测试

SRM 溯源

SRM 常见原因：$^1$

  • 分配：分桶错误或随机化函数有缺陷
  • 执行：变体启动时间或升流速率延迟
  • 数据记录：记录延迟或机器人过滤
  • 干扰：实验者暂停某变体
1 Diagnosing Sample Ratio Mismatch in Online Controlled Experiments: A Taxonomy and Rules of Thumb for Practitioners
Python 中的 A/B 测试

A/A 测试

  • A/A 测试
    • 向两组用户提供完全相同的体验
    • 暴露实验设置中的缺陷
    • 各指标间无统计显著差异
    • 在设定的 $\alpha$ 下仍可能出现假阳性（5% 概率）
    • 揭示组间分布不均（如浏览器、设备等）
Python 中的 A/B 测试

分布平衡的 Python 示例

  • 浏览器分布均衡
  • 测试有效
checkout.groupby('checkout_page')['browser'].value_counts(normalize=True)

checkout_page  browser
A              chrome     0.341333
               safari     0.332000
               firefox    0.326667
B              safari     0.352000
               firefox    0.325000
               chrome     0.323000
C              safari     0.346000
               chrome     0.330000
               firefox    0.324000
  • 浏览器分布不均
  • 测试无效
 AdSmart.groupby('experiment')['browser'].value_counts(normalize=True)

experiment  browser                   
control     Chrome Mobile                 0.591992
            Facebook                      0.137804
            Samsung Internet              0.120855
            Chrome Mobile WebView         0.071727
            Mobile Safari                 0.060427
            Chrome Mobile iOS             0.008352
            Mobile Safari UI/WKWebView    0.007369
exposed     Chrome Mobile                 0.535197
            Chrome Mobile WebView         0.298802
            Samsung Internet              0.082876
            Facebook                      0.050674
            Mobile Safari                 0.022716
            Chrome Mobile iOS             0.004244
1 Adsmart Kaggle dataset: https://www.kaggle.com/datasets/osuolaleemmanuel/ad-ab-testing
Python 中的 A/B 测试

Passons à la pratique !

Python 中的 A/B 测试

Preparing Video For Download...