The text explores the importance of time series analysis in online experimentation, emphasizing its role in revealing insights that might be obscured in aggregated data, such as novelty effects and pre-experiment bias. It highlights how time series charts, particularly those showing metric impact by user tenure, can validate or challenge assumptions about experimental results. The text stresses the significance of understanding daily variations to identify anomalies or false positives, and it explains how cumulative time series can help determine the stability of results and the potential need for extended experimentation. Additionally, it cautions against over-reliance on slicing data, which can lead to p-hacking, while advocating for a balanced approach to data interpretation to inform decision-making. This comprehensive guide underscores the value of time series analysis in refining experiment strategy and ensuring robust, evidence-based conclusions.