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 )
