(Note: the links in this post take you to explainers by the same author.)
Duplicated data
Maybe you measured 10,000 real human heights but you want 20,000 datapoints. One approach you take is to suppose your existing dataset already represents your population fairly well. (Assumptions are always dangerous, proceed with caution.) Then you could simply duplicate the dataset or duplicate some portion of it using ye olde copy-paste. Ta-da! More data! But is it good and useful data? That always depends on what you need it for. For most situations, the answer would be no. But hey, there are reasons you were born with a head, and those reasons are to chew and to apply your best judgment.
Resampled data
Speaking of duplicating only a portion of your data, there’s a way to inject a spot of randomness to assist you in figuring out which portion to pick. You can use a random number generator to assist you in picking which height to draw from your existing list of heights. You could do this “without replacement”, meaning that you make at most one copy of each existing height, but…
Bootstrapped data
You’ll more often see people doing this “with replacement”, meaning that every time you randomly pick a height to copy, you immediately forget you did this so that the same height could make its way into your dataset as a second, third, fourth, etc. copy. Perhaps if there’s enough interest in the comments, I’ll explain why this is a powerful and effective technique (yes, it sounds like witchcraft at first, I thought so…
