Lets first start by defining some terms:
Probability (P) in statistics is defined as the chance of an event occurring.
Probability experiment is a chance process that leads to results called outcomes.
An outcome is the result of a single trial of a probability experiment.
A sample set is the set of all possible outcomes of a probability experiment.
An event consists of a set of outcomes of a probability experiment. An event can be one outcome or more than one outcome. The event can be anything from flipping a coin, to rolling a die, to picking a card.
The probability of any event (E) is:
(# of outcomes in E) / (total # of outcomes in sample space)
For example: Find the probability a die is rolled and you get a 4?
We know that there are 6 possibilities when rolling a die. We can either rolled a 1, or a 2, or a 3, or a 4, or a 5, or a 6.
Using the equation above:
P(rolling a 4)= 1/6
The event in this case is rolling a 4.
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If p refers to the probability of an event, then the answer is "certainty".If p refers to the probability of an event, then the answer is "certainty".If p refers to the probability of an event, then the answer is "certainty".If p refers to the probability of an event, then the answer is "certainty".
the probability a certain event will occur :-)
To find the experimental probability of an event you carry out an experiment or trial a very large number of times. The experimental probability is the proportion of these in which the event occurs.
The probability of the event is 25/36.
Odds of A to B in favour of an event states that for every A times an event occurs, the event does not occur B times. So, out of (A+B) trials, A are favourable to the event. that is, the probability of A is A/(A+B).