Last updated on September 6, 2025
Probability is the possibility that an event will occur. A discrete probability distribution describes the likelihood of outcomes for individually countable variables. In this topic, we will learn more about discrete probability distribution, its types, application, and many more.
The discrete probability distribution is a simple way to show the different chances of different outcomes when you can count them one by one. It gives the different values of a random variable along with its different probabilities. A discrete probability distribution contrasts with a continuous distribution, where outcomes can take any value along a continuum, as the outcome falls anywhere on a continuum. The types of discrete probability are Binomial, Poisson, and Bernoulli distribution. If a probability distribution is said to be a discrete probability distribution, it should follow these two conditions :
Now let’s discuss the types of discrete probability. The types are;
Here, we will discuss how to calculate a discrete probability distribution. To find a discrete probability distribution, the probability of mass function is required. Follow these steps to find the probability;
Step 1: Identify the sample, set of all possible outcomes of an experiment.
Step 2: Then you have to find the discrete random variable (x). It is a function assigning a numerical value to each outcome.
Step 3: Identifying the possible value of X.
Step 4: Finding the probability of each outcome by using the formula;
P (X = x) = Number of favorable outcomes/Total number of possible outcomes.
Step 5: Use a table to organize the results
Now let’s learn the difference between the discrete and continuous distribution.
Discrete Distribution |
Continuous Distribution |
The probability distribution for countable values |
The probability distribution for measurable values |
Here, the type of variable is discrete |
Here, the type of variable is continuous |
The graph of discrete distribution is a bar |
The graph of continuous distribution is a curve |
In real-life discrete probability distribution are used to find the probability for countable values, now lets few real-world applications of it;
Now let’s learn a few common mistakes and ways to avoid them to master discrete probability distribution.
A factory produces light bulbs, and 5% of them are defective. If a random sample of 8 bulbs is taken, what is the probability that exactly 2 bulbs are defective?
The probability of getting 2 bulbs are 0.0515 or 5.15%
Here, to find the probability we use the equation, P(X = k) = \(\binom{n}{k}\) pk(1 - p)n-k
According to the problem,
n = 8
k = 2
p = 0.05
Substituting the values,
P(X = 2) = \(\binom{8}{2}\) (0.05)2(1 - 0.05)8-2
P(X = 2) = \(\binom{8}{2}\) (0.05)2( 0.95)6
The value of C \(\binom{8}{2}\)= 28, (0.05)2 = 0.0025, and (0.95)6 = 0.7358
So, the value of P(X = 2) = 28 × 0.0025 × 0.7358 = 0.0515.
A basketball player has a 40% probability of making a free throw. What is the probability that their first successful shot happens on the third attempt?
The probability here is 0.144 or 14.4%
Here the probability is of geometric distribution
So, P(X = k) = (1 - p)(k-1) × p.
Here, p = 0.4 and k = 3
Therefore, P(X = 3) = (0.6)2 × 0.4
= 0.36 × 0.4 = 0.144
A deck has 52 cards, including 4 Aces. If 5 cards are drawn randomly, what is the probability of getting exactly 2 Aces?
Probability ≈ 0.0399 or 3.99%.
Here, the probability is calculated using \(P (X = k) = \dfrac{\binom{K}{k} \binom{N-K}{n-k}}{\binom{N}{n}}.\)
Here,
N = 52
K = 4
k = 2
n = 5
So P(X = 2) =\( \dfrac{\binom{4}{2} \binom{52 - 4}{5 - 2}}{\binom{52}{5}}.\)
P(X = 2) = 0.0399 or 3.99%.
A multiple-choice quiz has 10 questions, each with 4 answer choices. A student guesses randomly on each question. What is the probability of getting exactly 3 correct answers?
The probability is 0.250 or 25%
Here, to find the probability we use the equation, P(X = k) = C \(\binom{n}{k}\) pk (1 - p)(n - k)
According to the problem,
n = 10
k = 3
p = ¼ = 0.25
Substituting the values,
P(X = 3) = \(\binom{10}{3}\)(0.25)2(1 - 0.25)10 - 3
P(X = 3) = \(\binom{10}{3}\) (0.25)2(0.75)7
Here, C\(\binom{10}{3}\) = 120
0.252 = 0.015625
0.757 = 0.1335
P(X = 3) ≈ 120 × 0.015625 × 0.1335 ≈ 0.250.
A factory produces screws with a 2% defect rate. What is the probability that the first defective screw appears on the 5th inspection?
The probability is 0.01845 or 1.845%
P(X = 5) = (1 - p)(k-1) × p.
Here, p = 0.02
K = 5
P(X=5) = (1 − p)(k-1)p = (0.98)4 × 0.02
As 0.984 = 0.9224
P(X = 5) = 0.9224 × 0.02 = 0.01845
Jaipreet Kour Wazir is a data wizard with over 5 years of expertise in simplifying complex data concepts. From crunching numbers to crafting insightful visualizations, she turns raw data into compelling stories. Her journey from analytics to education ref
: She compares datasets to puzzle games—the more you play with them, the clearer the picture becomes!