But in the case of multiple choice answers, card games or lotteries it is different because the independence condition is no longer verified: a number cannot be selected again if already occurred. This calculation is valid every time that the extractions are unpredictable and independent. E.g., if I ask for a random number between, the possible results are: 1 number with 4 digits, 900. If you are generating random numbers from a very large base, most of the numbers are likely to be close to that base. The probability of guessing correctly a number between 1 and 100 is 1/100 (1%) I occasionally get feedback on this page about how it’s not random enough. The probability of guessing correctly a number between 1 and 20 is 1/20 (5%)
The probability of guessing correctly a number between 1 and 10 is 1/10 (10%) If the seed value is kept on changing, the number generated will new every time the program is compiled else it will return the same value. A seed is a value that is used by rand function to generate the random value. The randomness comes from atmospheric noise, which for many purposes is better than the pseudo-random number algorithms typically used in computer programs. Let’s start simple with some probability examples. The rand () function in C could be used in order to generate the random number and the generated number is totally deleting seed. RANDOM.ORG offers true random numbers to anyone on the Internet.
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