As a data scientist evaluating the outcome of an experiment where 5% of one group clicks more, I would need to consider several factors before determining whether this result is good and whether to proceed with launching based on this outcome. Here's a step-by-step approach I would take:
1. Statistical Significance: The first thing to assess is whether the observed difference in click rates is statistically significant. This involves conducting hypothesis testing to determine if the difference is likely due to chance or if it's a genuine effect. If the p-value is low (typically below 0.05), it suggests that the difference is not likely due to random variation.
2. Practical Significance: While a difference might be statistically significant, it's important to consider the practical significance or effect size. A small increase in click rates might be statistically significant, but if it's not practically significant, it might not be worth pursuing.
3. Sample Size: The size of the groups being compared is crucial. If the sample size is small, even a small difference might be statistically significant. Larger sample sizes provide more reliable estimates of the true effect.
4. Context and Domain Knowledge: Understanding the industry, market, and user behavior is vital. A 5% increase might be considered substantial in some contexts but negligible in others. It's important to know the baseline click rates and how this increase might impact the overall business goals.
5. Costs and Benefits: Consider the potential costs and benefits of implementing changes based on this result. Will the increased click-through rate lead to more conversions, revenue, or user engagement? What are the potential costs of making the changes?
6. A/B Test Design: If the experiment was an A/B test, it's essential to ensure that the experimental design was rigorous and that there were no confounding variables that could have influenced the outcome.
7. Long-Term Impact: Consider the long-term effects of the changes. Will the increase in click rates be sustained over time, or is it just a short-term fluctuation?
8. Risk Tolerance: Every decision involves risk. Evaluate the risk associated with implementing the changes and weigh them against the potential benefits.
9. Feedback and Stakeholders: Gather feedback from stakeholders, product managers, and other relevant teams. They might provide valuable insights and perspectives that you might have missed.
10. Iterative Approach: If the result seems promising but not overwhelming, consider taking an iterative approach. Implement the change on a smaller scale or with a specific segment of users to gather more data and insights before a full-scale launch.
Ultimately, deciding whether to launch based on a 5% increase in click rates depends on the combination of statistical significance, effect size, domain knowledge, costs, benefits, and overall business strategy. It's a complex decision that requires careful consideration of multiple factors.