If you want to include the first N rows of a dataset, which sampling method would you use?

The most effective way to include the first N rows of a dataset is to use the method that specifically targets those rows directly, which is to select the first N rows. This approach allows you to extract a predetermined number of rows starting from the beginning of the dataset, ensuring that the specific records you want are included without any ambiguity or additional calculations.

Using the first N rows ensures precision in data handling when your analysis requires an exact subset from the start of the data. This method is straightforward and aligns perfectly with the intention of including records from the beginning of the dataset, which is especially useful in scenarios like creating samples or examining trends in sorted data.

In contrast, the other methods—selecting 1 of every N rows, the first N%, or the last N rows—serve different purposes. The first method may introduce randomness and disrupt the specific sequence you want. The first N% could lead to an uncertain number of rows depending on the total dataset size, and the last N rows completely diverges from the goal of selecting the initial portion of the dataset. Thus, focusing on the first N rows guarantees clarity and correctness in your data manipulation tasks.