Interpret mixed results across segments from an experiment

Question

An experiment shows a 3% lift with p=0.08. What conclusions can you draw? What are the caveats?

Codemia · Accepted Answer

The analytical question we need to address is whether the observed 3% lift in the experiment is statistically significant and actionable for Cloudflare. To assess this, we need to gather data on the following: 
1. **Sample Size**: The number of users in both the control and experimental groups to gauge the power of the experiment. 
2. **Conversion Rate**: The baseline conversion rates for both groups. 
3. **Segment Information**: Data on which user segments showed the most significant lift (e.g., geographical regions, customer types, etc.). 
4. **Long-term Metrics**: Information on long-term effects of the change, as a short-term lift may not translate to sustained performance.

Given the p-value of 0.08, we can use hypothesis testing to assess the results. We set our null hypothesis (H0) as 'there is no lift in conversion rates' and the alternative hypothesis (H1) as 'there is a 3% lift in conversion rates'. Using a significance level of 0.05, we note that the p-value exceeds this threshold, indicating that we do not have enough evidence to reject the null hypothesis. However, we should also consider the practical significance of the 3% lift and evaluate the confidence interval around this estimate to understand its reliability.

Let's say the control group had a conversion rate of 10%, and the experimental group had a conversion rate of 10.3%. Assuming each group had 1,000 users: 
- Control Group Conversions: 1,000 * 10% = 100 
- Experimental Group Conversions: 1,000 * 10.3% = 103 
The lift is calculated as (103 - 100) / 100 = 3%. For the p-value of 0.08, we can calculate the confidence interval for the conversion rate increase using a simple formula: 
CI = Lift ± Z * SE, where SE (Standard Error) is calculated from the conversion rates. If the CI includes zero, we cannot claim statistical significance.

The 3% lift observed in the experiment is promising, but with a p-value of 0.08, we do not have strong statistical evidence to definitively conclude that the lift is real and not due to random chance. This implies that while there might be an upward trend, we should be cautious in interpreting this result as a definitive success. Furthermore, we need to analyze the segment data to identify if certain user cohorts are driving this lift, which might indicate a targeted approach for future experiments.

Common mistakes that could arise in this analysis include: 
1. **Overlooking P-value Significance**: Misinterpreting the p-value could lead to false confidence in the results. A p-value of 0.08 suggests caution in decision-making. 
2. **Ignoring Long-term Impact**: Focusing solely on short-term gains without considering potential long-term effects can misguide strategy. 
3. **Not Evaluating Segments**: Failing to dissect results by segment might obscure insights that could inform targeted optimizations. 
4. **Neglecting Sample Size**: A small sample size may yield unreliable results, leading to erroneous conclusions. Always ensure adequate power in experiments.

Interpret mixed results across segments from an experiment

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