What is a Probability Distribution?
A probability distribution is a table or an equation that links each outcome
of a statistical experiment with its probability of occurrence.
Probability Distribution Prerequisites
To understand probability distributions, it is important to understand variables.
random variables, and some notation.
Generally, statisticians use a capital letter to represent a random variable and
a lower-case letter, to represent one of its values. For example,
-
P(X = x) refers to the probability that the random variable X is equal to a
particular value, denoted by x. As an example, P(X = 1) refers to the
probability that the random variable X is equal to 1.
Probability Distributions
An example will make clear the relationship between random variables and
probability distributions. Suppose you flip a coin two times. This simple
statistical experiment can have four possible outcomes: HH, HT, TH, and TT.
Now, let the variable X represent the number of Heads that result from this
experiment. The variable X can take on the values 0, 1, or 2. In this example,
X is a random variable; because its value is determined by the outcome of a
statistical experiment.
A probability distribution is a table or an equation that links
each outcome of a statistical experiment with its probability of occurrence.
Consider the coin flip experiment described above. The table below, which
associates each outcome with its probability, is an example of a probability
distribution.
| Number of heads
|
Probability |
| 0
|
0.25 |
| 1
|
0.50 |
| 2
|
0.25 |
The above table represents the probability distribution of the random variable
X.
Cumulative Probability Distributions
A cumulative probability refers to the probability that the
value of a random variable falls within a specified range.
Let us return to the coin flip experiment. If we flip a coin two times, we might
ask: What is the probability that the coin flips would result in one or fewer
heads? The answer would be a cumulative probability. It would be the
probability that the coin flip experiment results in zero heads plus the
probability that the experiment results in one head.
P(X < 1) = P(X = 0) + P(X = 1) = 0.25 + 0.50 = 0.75
Like a probability distribution, a cumulative probability distribution can be
represented by a table or an equation. In the table below, the cumulative
probability refers to the probability than the random variable X is less than
or equal to x.
Number of heads:
x
|
Probability:
P(X = x)
| Cumulative Probability:
P(X < x) |
| 0
|
0.25 |
0.25 |
| 1
|
0.50 |
0.75 |
| 2
|
0.25 |
1.00 |
Uniform Probability Distribution
The simplest probability distribution occurs when all of the values of a
random variable occur with equal probability. This probability
distribution is called the uniform distribution.
Uniform Distribution. Suppose the
random variable X can assume k different values. Suppose also that the P(X = x
k)
is constant. Then,
P(X = xk) =
1/k
Example 1
Suppose a die is tossed. What is the probability that the die will land on 5 ?
Solution: When a die is tossed, there are 6 possible outcomes represented
by: S = { 1, 2, 3, 4, 5, 6 }. Each possible outcome is a random variable (X),
and each outcome is equally likely to occur. Thus, we have a uniform
distribution. Therefore, the P(X = 5) = 1/6.
Example 2
Suppose we repeat the dice tossing experiment described in Example 1. This
time, we ask what is the probability that the die will land on a number that is
smaller than 5 ?
Solution: When a die is tossed, there are 6 possible outcomes represented
by: S = { 1, 2, 3, 4, 5, 6 }. Each possible outcome is equally likely to occur.
Thus, we have a uniform distribution.
This problem involves a cumulative probability. The probability that the die
will land on a number smaller than 5 is equal to:
P( X < 5 ) = P(X = 1) + P(X = 2) + P(X = 3) + P(X = 4) = 1/6 +
1/6 + 1/6 + 1/6 = 2/3