[jpeloquin]

> Also, with 1-indexing I can multiply numbers by arrays and get reasonable offsets. 3 x 1 is three, so I would get the third element of the list. But with 0-indexing, I have 0 x 3 which gives me the same element, clearly inconsistent.

This is interesting. Suppose the task is to use this approach (index * stride) to pick every third item from a list of 9 items: [1, 2, 3, 4, 5, 6, 7, 8, 9].

With 1-indexing: Multiply the sequence of valid indices (1, 2, 3, ...) by the stride (3) and use the result to 1-index into the given list. Returns [3, 6, 9].

With 0-indexing: Multiply the sequence of valid indices (0, 1, 2, ...) by the stride (3) and use the result to 0-index into the given list. Returns [1, 4, 7].

0-indexing has the start point of the return values fixed to the origin. 1-indexing has its start point float around depending on the stride. Both work, but have different emergent properties in the given example.

[mruts]

It’s true that they both work, but I think the floating nature of the 1-indexing has some very desirable properties. If we wanted to sample from a distribution, the stride would relate to 1/n x 100 percent sampling. With 0-indexing we are always going to sample the first element. Granted this isn’t the best way to sample in the first place, so maybe it’s a contrived.