What is the probability of flipping a coin and rolling a 6 sided dice at the same time and getting tails and a 3?

The possibility of rolling a #3# on a fair die and getting tails on a coil is #1/12#. Based on this probability, there are #12# possible outcomes. As only 1 out of 12 possiblities would get this outcome, it is not a likely outcome.

The probability of rolling a #3# on a fair die is #1/6# because there is only one #3# out of #6# numbers. The probability of getting tails on a coin is #1/2# because there are #2# possibilities and tails is one of them. To get the probability of rolling a #3# on a fair die and getting tails on a coil, we multiply their probabilities:
#1/6 * 1/2 = 1/12#
There are #12# possiblities because for each of the #6# possibilites for rolling a die, there are #2# possibilites for flipping a coin.

A branch of mathematics that deals with the happening of a random event is termed probability. It is used in Maths to predict how likely events are to happen.

The probability of any event can only be between 0 and 1 and it can also be written in the form of a percentage.

The probability of event A is generally written as P(A).

Here P represents the possibility and A represents the event. It states how likely an event is about to happen. The probability of an event can exist only between 0 and 1 where 0 indicates that event is not going to happen i.e. Impossibility and 1 indicates that it is going to happen for sure i.e. Certainty

If we are not sure about the outcome of an event, we take help of the probabilities of certain outcomes—how likely they occur. For a proper understanding of probability we take an example as tossing a coin:

There will be two possible outcomes—heads or tails.

The probability of getting heads is half. You might already know that the probability is half/half or 50% as the event is an equally likely event and is complementary so the possibility of getting heads or tails is 50%.

Formula of Probability

Probability of an event, P(A) = Favorable outcomes / Total number of outcomes

Some Terms of Probability Theory

• Experiment: An operation or trial done to produce an outcome is called an experiment.
• Sample Space: An experiment together constitutes a sample space for all the possible outcomes. For example, the sample space of tossing a coin is head and tail.
• Favorable Outcome: An event that has produced the required result is called a favorable outcome.  For example, If we roll two dice at the same time then the possible or favorable outcomes of getting the sum of numbers on the two dice as 4 are (1,3), (2,2), and (3,1).
• Trial: A trial means doing a random experiment.
• Random Experiment: A random experiment is an experiment that has a well-defined set of outcomes. For example, when we toss a coin, we would get ahead or tail but we are not sure about the outcome that which one will appear.
• Event: An event is the outcome of a random experiment.
• Equally Likely Events: Equally likely events are rare events that have the same chances or probability of occurring. Here The outcome of one event is independent of the other. For instance, when we toss a coin, there are equal chances of getting a head or a tail.
• Exhaustive Events: An exhaustive event is when the set of all outcomes of an experiment is equal to the sample space.
• Mutually Exclusive Events: Events that cannot happen simultaneously are called mutually exclusive events. For example, the climate can be either cold or hot. We cannot experience the same weather again and again.
• Complementary Events: The Possibility of only two outcomes which is an event will occur or not. Like a person will eat or not eat the food, buying a bike or not buying a bike, etc. are examples of complementary events.

Some Probability Formulas

Addition rule: Union of two events, say A and B, then

P(A or B) = P(A) + P(B) – P(A∩B)

P(A ∪ B) = P(A) + P(B) – P(A∩B)

Complementary rule: If there are two possible events of an experiment so the probability of one event will be the Complement of another event. For example – if A and B are two possible events, then

P(B) = 1 – P(A) or P(A’) = 1 – P(A)

P(A) + P(A′) = 1

Conditional rule: When the probability of an event is given and the second is required for which first is given, then

 P(B, given A) = P(A and B), P(A, given B). It can be vice versa

P(B∣A) = P(A∩B)/P(A)

Multiplication rule: Intersection of two other events i.e. events A and B need to occur simultaneously. Then 

P(A and B) = P(A)⋅P(B).

P(A∩B) = P(A)⋅P(B∣A)

What is the probability of flipping a coin 20 times and getting 20 heads?

Solution: 

20 times coin tosses. This means,

Total observations = 220 (According to binomial concept)

Required outcome → 20 Heads {H,H,H,H,H,H,H,H,H,H,H,H,H,H,H,H,H,H,H,H,}

This can occur only ONCE!

Thus, required outcome =1

Now put the probability formula  

Probability (20 Heads) = (1⁄2)20 = 1⁄1048576

Hence, the probability that it will always land on the HEAD side will be, (1⁄2)20 = 1⁄1048576

Similar Questions

Question 1: What are the chances of flipping 15 heads in a row?

Solution:

Probability of an event =  (number of favorable event) / (total number of event)

P(B) = (occurrence of Event B) / (total number of event)

Probability of getting one head = 1/2

here Tossing a coin is an independent event, its not dependent on how many times it has been tossed.

Probability of getting 2 heads in a row = probability of getting head first time × probability of getting head second time.

Probability of getting 2 head in a row  = (1/2) × (1/2)

Therefore, the probability of getting 15 heads in a row = (1/2)15

Question 2: What are the chances of flipping 20 heads in a row?

Solution:

Probability of an event = (number of favorable event) / (total number of event).

P(B) = (occurrence of Event B) / (total number of event)

Probability of getting one head = 1/2.

here Tossing a coin is an independent event, its not dependent on how many times it has been tossed.

Probability of getting 3 heads in a row = probability of getting head first time × probability of getting head second time x probability of getting head third time

Probability of getting 3 head in a row = (1/2) × (1/2) × (1/2)

Therefore, the probability of getting 20 heads in a row = (1/2)20

Question 3: What are the chances of flipping 10 tails in a row?

Solution:

Probability of an event =  (number of favorable event) / (total number of event).

P(B) = (occurrence of Event B) / (total number of event).

Probability of getting one tail = 1/2

here if Tossing a coin is an independent event, its not dependent on how many times it has been tossed.

Probability of getting 3 tails in a row = probability of getting tail first time × probability of getting tail second time x probability of getting tail third time

Probability of getting 3 tails in a row  = (1/2) × (1/2) × (1/2)

Therefore, the probability of getting 10 tails in a row = (1/2)10

Question 4: What is the probability of 10 heads in 10 coins tossed together.?

Solution:

If the coin is fair, then the probability of getting either a head or a tail is 1/2.

So the probability of getting 10 heads in a row is  1/210

What is the probability of rolling a 3 on a 6 sided dice?

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