10 Lessons Learned From Online Experiments

10 hard-earned lessons from controlled experiment, considered the gold standard approach for determining the true effect of a change.

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Shopify
•
Ian Whitestone
•13 min read•beginner•
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•View Original
Golang

Overview

The article discusses ten valuable lessons learned from conducting online experiments, emphasizing the importance of careful planning, data-driven decision-making, and understanding system dynamics. It provides actionable insights for software engineers to improve their experimentation processes and outcomes.

What You'll Learn

1

How to choose the appropriate randomization unit for online experiments

2

Why simulations can validate experimental designs before implementation

3

How to leverage data for both major and minor decision-making processes

4

When to conduct A/A tests to ensure system reliability before A/B testing

5

How to analyze user segments to avoid skewed results in experiments

Key Questions Answered

What are the key considerations when choosing a randomization unit in experiments?
Choosing the right randomization unit is crucial for ensuring independence in experimental design. If the units are not independent, the results may not accurately reflect the true effects of changes made, potentially leading to misleading conclusions.
How can simulations help in the context of online experiments?
Simulations can be used to model potential outcomes of experiments before they are conducted. By running simulations, teams can assess the likelihood of false positives and validate their experimental designs, ensuring that the chosen methodology is sound.
What is the importance of logging in online experiments?
Logging is essential for tracking user interactions and understanding system behavior during experiments. Generous logging allows teams to diagnose issues like sample ratio mismatch and provides valuable data for refining metrics and improving future experiments.
Why is it important to analyze results across different user segments?
Analyzing results across user segments helps identify specific demographics or conditions that may respond differently to changes. This ensures that overall positive results do not mask negative impacts on particular groups, leading to more informed product decisions.

Technologies & Tools

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Backend
Kafka
Used for logging user assignments and tracking interactions during experiments.

Key Actionable Insights

1
Carefully select your randomization unit to ensure independence in your experiments.
Choosing the right unit can prevent carryover effects that skew results, ensuring that your findings accurately reflect the impact of changes.
2
Utilize simulations to test your experimental design and validate assumptions before implementation.
Simulations can reveal potential issues such as false positives, allowing for adjustments to be made proactively rather than reactively.
3
Incorporate generous logging practices to capture detailed user interactions.
This data can be invaluable for diagnosing issues during experiments and refining your analysis methods for better accuracy.
4
Conduct A/A tests to validate system behavior before running A/B tests.
A/A tests help ensure that your experimental setup is functioning correctly, which is crucial for obtaining reliable results in subsequent A/B tests.
5
Regularly analyze user segments to detect skewed results in your experiments.
This practice helps in identifying outliers or biases that could misrepresent the overall effectiveness of changes made.

Common Pitfalls

1
Failing to recognize the impact of user skew can lead to misleading experimental results.
High-volume users or stores can disproportionately influence metrics, making it essential to analyze results across different segments to avoid such biases.
2
Neglecting to run A/A tests before A/B tests can result in undetected issues with the experimental setup.
Without validating that the system behaves as expected, teams risk drawing incorrect conclusions from A/B test results.