Top 8 common experimentation mistakes and how to fix them

In a discussion with Allon Korem, CEO of Bell Statistics, and Tyler VanHaren, Software Engineer at Statsig, key insights were shared on common mistakes in A/B testing and experimentation, along with strategies for improvement. They emphasized the importance of maintaining data integrity through consistent allocation points and regular checks for sample ratio mismatches, which often occur due to technical issues or user experience inconsistencies. Proper metric selection, relevant statistical methods, and managing data peeking are crucial for accurate test results. Underpowered tests should be avoided by using power analysis calculators, and outliers should be managed through Windsorization to maintain data integrity. Cultural challenges in fostering a hypothesis-driven testing environment were also highlighted as essential for effective experimentation. By addressing these issues, companies can enhance the accuracy and reliability of their A/B tests, leading to better-informed decisions and improved business outcomes.