Balance Checks
Before analyzing results in an A/B test, you need to ensure your control and treatment groups are comparable. This process is called a balance check.
Random assignment should create groups that are similar in all characteristics except for the experimental intervention. However, due to chance, imbalances can still occur—especially with smaller sample sizes.
If groups differ significantly on important variables before the experiment starts, any observed effect might be due to these pre-existing differences rather than the treatment itself.
Balance checks help you:
- Confirm that randomization worked as intended;
- Increase trust in your experiment's results;
- Ensure that differences in outcomes are likely due to the treatment, not group imbalances.
Typical balance checks include comparing distributions of:
- Age;
- Gender;
- Location;
- Device type;
- Other relevant user attributes.
You might look at means, counts, or proportions to identify any significant differences.
12345678910111213141516171819import pandas as pd # Example experiment data data = { "group": ["control", "treatment", "control", "treatment", "control", "treatment"], "age": [25, 26, 30, 29, 22, 24], "gender": ["F", "F", "M", "M", "F", "M"] } df = pd.DataFrame(data) # Compare mean age by group mean_age = df.groupby("group")["age"].mean() print("Mean age by group:") print(mean_age) # Compare gender counts by group gender_counts = pd.crosstab(df["group"], df["gender"]) print("\nGender counts by group:") print(gender_counts)
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Before analyzing results in an A/B test, you need to ensure your control and treatment groups are comparable. This process is called a balance check.
Random assignment should create groups that are similar in all characteristics except for the experimental intervention. However, due to chance, imbalances can still occur—especially with smaller sample sizes.
If groups differ significantly on important variables before the experiment starts, any observed effect might be due to these pre-existing differences rather than the treatment itself.
Balance checks help you:
- Confirm that randomization worked as intended;
- Increase trust in your experiment's results;
- Ensure that differences in outcomes are likely due to the treatment, not group imbalances.
Typical balance checks include comparing distributions of:
- Age;
- Gender;
- Location;
- Device type;
- Other relevant user attributes.
You might look at means, counts, or proportions to identify any significant differences.
12345678910111213141516171819import pandas as pd # Example experiment data data = { "group": ["control", "treatment", "control", "treatment", "control", "treatment"], "age": [25, 26, 30, 29, 22, 24], "gender": ["F", "F", "M", "M", "F", "M"] } df = pd.DataFrame(data) # Compare mean age by group mean_age = df.groupby("group")["age"].mean() print("Mean age by group:") print(mean_age) # Compare gender counts by group gender_counts = pd.crosstab(df["group"], df["gender"]) print("\nGender counts by group:") print(gender_counts)
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