The probability is 3/8 = 0.375
Coins do not have numbers, there is only the probability of heads or tails.
The probability that the sum is seven all three times is 1/216.
The answer depends on how many times the coin is tossed. The probability is zero if the coin is tossed only once! Making some assumptions and rewording your question as "If I toss a fair coin twice, what is the probability it comes up heads both times" then the probability of it being heads on any given toss is 0.5, and the probability of it being heads on both tosses is 0.5 x 0.5 = 0.25. If you toss it three times and want to know what the probability of it being heads exactly twice is, then the calculation is more complicated, but it comes out to 0.375.
The probability that the die tossed will land on a number that is smaller than 5 is 4/6 or 2/3. Smaller than 5 is 1 - 4 and 6 is the sample space.
3/8 * * * * * That is the probability of getting EXACTLY 1H. The prob of getting one (or more) head is 7/8
The probability is 1/16.
A single fair die has the numbers 1 to 6, so when a single fair die is tossed the probability of obtaining a number different than 11 is: P(x diff than11) = 1.
Since a coin has two sides and it was tossed 5 times, there are 32 possible combinations of results. The probability of getting heads three times in 5 tries is 10/32. This is 5/16.
First, it is important to note that it is very unlikely that the experimental and theoretical probabilities will agree exactly. As an extreme example, if you toss a coin an odd number of times, the resulting experimental probability cannot possibly be exactly 1/2. It should be easy to see that this remains true even if the coin is tossed googleplex+1 number of times.A negative difference could be because the number of trials was too small and, with an increased number of trials, the experimental probability would gradually increase towards the theoretical probability.It is also possible that the theoretical model is wrong. You may have assumed that the coin that was being tossed was fair when it was not. Or there were some factors that you failed to take full account of in your theoretical model.Or, of course, it could be a mixture of both.First, it is important to note that it is very unlikely that the experimental and theoretical probabilities will agree exactly. As an extreme example, if you toss a coin an odd number of times, the resulting experimental probability cannot possibly be exactly 1/2. It should be easy to see that this remains true even if the coin is tossed googleplex+1 number of times.A negative difference could be because the number of trials was too small and, with an increased number of trials, the experimental probability would gradually increase towards the theoretical probability.It is also possible that the theoretical model is wrong. You may have assumed that the coin that was being tossed was fair when it was not. Or there were some factors that you failed to take full account of in your theoretical model.Or, of course, it could be a mixture of both.First, it is important to note that it is very unlikely that the experimental and theoretical probabilities will agree exactly. As an extreme example, if you toss a coin an odd number of times, the resulting experimental probability cannot possibly be exactly 1/2. It should be easy to see that this remains true even if the coin is tossed googleplex+1 number of times.A negative difference could be because the number of trials was too small and, with an increased number of trials, the experimental probability would gradually increase towards the theoretical probability.It is also possible that the theoretical model is wrong. You may have assumed that the coin that was being tossed was fair when it was not. Or there were some factors that you failed to take full account of in your theoretical model.Or, of course, it could be a mixture of both.First, it is important to note that it is very unlikely that the experimental and theoretical probabilities will agree exactly. As an extreme example, if you toss a coin an odd number of times, the resulting experimental probability cannot possibly be exactly 1/2. It should be easy to see that this remains true even if the coin is tossed googleplex+1 number of times.A negative difference could be because the number of trials was too small and, with an increased number of trials, the experimental probability would gradually increase towards the theoretical probability.It is also possible that the theoretical model is wrong. You may have assumed that the coin that was being tossed was fair when it was not. Or there were some factors that you failed to take full account of in your theoretical model.Or, of course, it could be a mixture of both.
1/4 if they are tossed only once.
The probability is 0.5The probability is 0.5The probability is 0.5The probability is 0.5
The probability is 50-50.
First, you find the probability of picking 1 in 5 disregarding order (combination). 5 C 1 = 5/1 = 5 Then you divide that by the possible number of outcomes which is 25. 5/ 2^5= .0195
The number of times a coin is tossed does not alter the probability of getting heads, which is 50% in every case, as long as the coin has not been rigged (i.e., a double-headed coin, a weighted coin) to alter the result.
The answer depends on what a winner is: 1 H?, a run of 3 H?If the winner is one H, the probability of getting exactly one winner - no more no fewer - is 5/32.
Usually, it takes 2-5 flips until a head appears.
The favourable outcomes are 1, 3, 5 or 6 so the probability is 4/6 = 2/3
There is a 50% chance that it will land on heads each toss. You need to clarify the question: do you mean what is the probability that it will land on heads at least once, exactly once, all five times?
it is a fair chance so 1/2 :P
The probability of rolling a number greater than 1 is 5/6.
If the coin is tossed and the die rolled sufficiently many times then the probability is 1: the event is a certainty.For just one toss and roll, the probability is 0.25