When it comes to statistics, there are a lot of misconceptions floating around. Even people who have scientific backgrounds subscribe to some of these common misconceptions. One misconception that affects measurement in virtually every field is the perceived need for a large sample size before you can get useful information from a measurement.The article describes two approaches - the rule of five (taking a random sample of 5 to draw conclusions) or the urn of mystery (that a single case from a population can tell you more about the makeup of that population). The rule of five seems best when trying to get a continuous value (such as, in the example from the post, the average commute time of workers in a company), while the urn of mystery seems best when trying to determine if a population is predominantly one of two types (in the post, the example is whether an urn of marbles contains predominantly marbles of a certain color).
[I]f you can learn something useful using the limited data you have, you’re one step closer to measuring anything you need to measure — and thus making better decisions. In fact, it is in those very situations where you have a lot of uncertainty, that a few samples can reduce uncertainty the most. In other words, if you know almost nothing, almost anything will tell you something.
Obviously, there are times when you need more data. But if you're far better off making decisions with data (even very little) than with none at all.