Addendum on Efficiency.

So I did it. I decided to explain Big O notation to my 77 Year old Grandmother (she had a birthday earlier this month!). It was I think somewhat successful, but here was what I said:

Alright, so we recently learned this thing in class called Big O notation, which we use to give a rough estimate of how good, or god awful a function we wrote is. So imagine, you are cooking dinner and you have two ways you could chop an onion. One is to chop it in 10 slices horizontally, 10 vertically, and 10 lengthwise. No matter what, this will take you 30 slices, which is constant no matter what the size of the onion, so we would say that the expected time that this algorithm takes is ALWAYS 30, or O(1), the 1 just stands for constant. Now contrast this to an algorithm that takes an onion, and cuts it into .5 cm pieces. Well, a 6 cm diameter onion would have the same expected time, but if it was 7 cm, all of a sudden we have 36 cuts to do, 8 cm would take us 42 etc, so now we have something that grows depending on the onion. If we carefully look at how this grows, we see that for every 1 cm of extra onion, you do 6 extra cuts, so we notate that as O(n) where the one stands for "grows with n".

Unfortunately though I couldn't think of a proper way to explain more complex algorithms in terms of onions.