Which of the following is a common method to analyze the impact of changes in data?

A/B testing is a common method used to analyze the impact of changes in data, particularly in scenarios involving user experience, marketing strategies, or product features. This technique involves comparing two versions of a variable to determine which one performs better in achieving specific goals, such as conversion rates or user engagement. By randomly dividing a sample group into two segments and exposing each to a different version, analysts can observe how changes affect behavior, allowing for data-driven decisions based on statistical evidence.

In the context of data analysis, A/B testing is pivotal because it enables businesses to make informed changes rather than relying on intuition. This method provides quantifiable insights and helps determine the effectiveness of changes by clearly measuring outcomes against a control group.

Data normalization, although essential for organizing data for analysis, does not directly assess the impact of changes. Data warehousing is focused on the storage, retrieval, and management of data rather than testing changes, and data governance involves policies and standards that ensure data quality and compliance but does not analyze impact directly. This distinction highlights why A/B testing stands out as the suitable choice for understanding the direct effects of modifications in data.