Final answer:
A join/merge will combine two rows when both rows have similar values in a specific column used as a key for the operation. Neither table is inherently more correct, and data can be grouped in multiple ways for different analytical insights. Switching between tables is often done to ensure data reliability and consistency.
Step-by-step explanation:
Under the circumstances when both rows have similar values in a specified column, a join/merge operation will put together two rows. This is the standard behavior in SQL databases and in data manipulation libraries in programming languages such as Python's pandas. When performing a join or merge, you must specify a key or a set of keys that the operation will use to find related rows between two datasets. These keys generally are columns that have matching values.
1. Neither table in a join or merge operation is inherently more correct than the other; it depends on the specific merits of the data and what you are trying to achieve with the join or merge operation. The data must be examined in context to determine accuracy and relevance.
2. Data can be grouped differently based on the criteria used for grouping, such as different column keys or applying various filters. Grouping data in different ways can yield different insights and is advantageous depending on the analysis goals.
3. We might switch between tables when answering questions to compare results, check consistency, or validate data. The pith of the matter is finding the most reliable data that answers the question at hand.