Random is often misinterpreted and misunderstood by a lot of people, which is
why I think it is important to discuss it here. You might that randomness is
synonymous with unpredictability; well this notion is completely false. All
random events are, for the most part, predictable with the help of probability.
When you toss a coin, you will have a 50% probability of the coin landing on
heads. Therefore, if you toss a coin 200 times, it is very likely that 100 of
those flips will land on heads.
The same exact concept applies to Random
Number Generators (or RNG) in computers. If you ask a computer to generate a
random number from 1 to 100, you have a 1/100 chance of the computer outputting
the number 42. However, RNGs beg a much more important question: how do
computers simulate randomness? (資料來源 )
There are also two types of random or probability in the field of statistics:
dependent and independent. It is important to distinguish the two of them
because they can highly impact decision-making in games and even game design or
balance. Independent events mean that one event does not affect the probability
of another event. For instance, you will get a 50% chance of getting tails in
the first coin toss and the same 50% chance in the second coin toss.
Dependent events are the opposite; separate events influence each
other’s probabilities. If you draw from a normal deck of cards, you have a
4/52 chance of drawing in King. If you don’t draw a king on the first
draw, you will have a 4/51 chance of drawing a King on the second draw. (資料來源 )
