This README provides an overview of the A/B test conducted to address duplication issues. The test aimed to evaluate the impact of a proposed solution on revenue metrics while analyzing the distribution of revenue in both treatment and control groups.
The presence of duplication poses a challenge, potentially affecting revenue metrics. Addressing this issue is crucial for maintaining data integrity and ensuring accurate performance analysis.
The primary objective of this A/B test is to assess the effectiveness of the implemented solution in mitigating duplication issues. The success metric for this test is revenue.
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Experimental Setup:
- Two groups were established: the treatment group (exposed to the solution) and the control group (not exposed).
- Participants were randomly assigned to either group to ensure unbiased results.
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Data Collection and Analysis:
- Common Statistical values was shown
- Aditional metrics such as revenue per user and order per suer added
- Distribution of revenue was plotted for both the treatment and control groups to visualize any differences.
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Statistical Analysis:
- A rigorous statistical analysis was conducted to determine the significance of any observed differences in revenue between the treatment and control groups.
- Hypothesis testing was performed to assess the impact of the solution on revenue metrics.
- Also, lift nad confident intervals considered for clearer conclusion.
The results of this A/B test will provide valuable insights into the efficacy of the solution in addressing duplication issues and its impact on revenue metrics. Any significant findings will inform future decisions and optimizations regarding our system's performance and data integrity. you can see the code here.