Introduction to Conditional Probability

Conditional probability is a concept in probability theory that assesses the likelihood of an event occurring, taking into account the occurrence of another event. This is particularly relevant when the probability of the second event is influenced by the outcome of the first event. The calculation involves multiplying the probability of the initial event by the updated probability of the second event, given the occurrence of the first.

Calculation of Conditional Probability

To calculate conditional probability (P(A|B)), follow these steps:

  1. Determine the total probability of the final event, B: P(B)=P(AB)+P(A′∩B)=P(AP(BA)+P(A′)×P(BA′)
  2. Compute the probability of the event A and B occurring together: P(AB)=P(AP(BA)
  3. Divide the two numbers to get the conditional probability: P(AB)=P(B)P(AB)​

Real-Life Example: Disease Test Scenario

Let’s consider a practical scenario to illustrate conditional probability. In a population, 5% are affected by a disease (D). A test with 91% sensitivity and 95% specificity is available. If a random person receives a positive test result, the task is to find the conditional probability that the person truly has the disease (P(D|⊕)).

Mathematical Formulas:

�(�)=0.05P(D)=0.05 �(�′)=1−0.05=0.95P(D′)=1−0.05=0.95 �(□∣�)=0.09P(□∣D)=0.09 �(⊕∣�)=1−0.09=0.91P(⊕∣D)=1−0.09=0.91 �(⊕∣�′)=0.05P(⊕∣D′)=0.05 �(□∣�′)=1−0.05=0.95P(□∣D′)=1−0.05=0.95

Calculation Steps:

  1. Compute the total probability of a positive result: P(⊕)=P(D∩⊕)+P(D′∩⊕)=P(DP(⊕∣D)+P(D′)×P(⊕∣D′)
  2. Calculate the probability of being infected and getting a positive result: P(D∩⊕)=P(DP(⊕∣D)
  3. Determine the conditional probability: P(D∣⊕)=P(⊕)P(D∩⊕)​

Conclusion and Application

The resulting conditional probability provides valuable information, helping us understand the likelihood of a person truly having the disease given a positive test result. This illustrates the importance of considering both sensitivity and specificity when interpreting test outcomes.


Which situations involve conditional probability?

Conditional probability is applicable in various real-life situations, such as predicting the probability of winning a second game round given a victory in the first round or estimating the chance of rain when it is cloudy.

What is the conditional probability rule?

The conditional probability rule expresses the likelihood of an event B occurring, given that another event A has occurred. It is denoted as P(B|A), where the probability of B depends on the occurrence of A.

Can the conditional probability be zero?

Yes, if the probability of a final event is zero, the conditional probability of a preceding event given that final event is also zero.