Data completeness implies that:

Data completeness is fundamentally concerned with the presence and availability of all necessary information within a dataset. When we talk about data completeness, it specifically refers to the idea that no records are missing from the data collection process. This means that every instance or record that should be part of the dataset is present and accounted for.

For a dataset to be considered complete, it should contain all relevant observations, ensuring that decision makers have access to a full and comprehensive view of the data they require for analysis. If any records are missing, it can lead to biased results and poor decision-making based on inadequate information.

In contrast, the other choices focus on different aspects of data quality. Validating data for accuracy pertains more to the correctness of the data rather than its completeness. Considering all variables addresses the breadth of data points but does not ensure that all records are included. Collecting feedback from users relates to usability and user experience rather than the presence of data records. Thus, the concept of data completeness is best captured by the idea that no records are missing.