Which technique uses different entries from the same data set to represent data?

The technique that utilizes different entries from the same data set to represent data is shuffling. This method randomly reorganizes the entries within the data set, ensuring that the representation of data varies with each iteration or operation performed. Shuffling is particularly beneficial in scenarios like machine learning, where it helps in reducing bias by presenting data in a non-sequential order, thereby enhancing the model's ability to generalize.

By mixing the data entries, shuffling creates a diverse array of combinations which can lead to more robust training outcomes and prevents any patterns that may bias the learning process. This technique exemplifies how manipulating data can aid in the analysis and interpretation of results while maintaining the integrity of the original dataset's informational content.