Law of Total Probability
Split an event over a partition.
Key idea
Split an event over a partition.
In depth
If B₁, ..., Bₙ partition the sample space, then P(A) = Σ P(A | Bᵢ)P(Bᵢ). Split a problem into cases, find the chance in each, and weigh by how likely each case is.
Worked example
A factory has machine X (60% of output, 2% defects) and Y (40%, 5%): P(defect) = 0.6×0.02 + 0.4×0.05 = 0.032.
Formula
Real-world uses
- Finding the overall defect rate when several factories supply the same parts
Common mistake
Forgetting that the cases must cover all possibilities without overlap.
How to learn this
- Read the key idea and say it in your own words.
- Work through the worked example, then try a similar problem.
- Use the interactive lesson’s visual and mini-quiz to check the pattern.
Frequently asked questions
What is Law of Total Probability?
Split an event over a partition. It is a topic at the expert level in Probability & Statistics.
What is the formula for Law of Total Probability?
The key formula is P(A) = ΣP(A|Bᵢ)P(Bᵢ).
Where is Law of Total Probability used in real life?
Real-world examples: Finding the overall defect rate when several factories supply the same parts.
What should I learn before Law of Total Probability?
Make sure you are comfortable with Multiplication Rule, the previous topic in Probability & Statistics.
What is a common mistake with Law of Total Probability?
Forgetting that the cases must cover all possibilities without overlap.
How can I practise Law of Total Probability?
Open the interactive lesson, play with the visual, then answer the three-question mini-quiz. Getting two right marks the topic complete.
What comes after Law of Total Probability?
Move on to Bayes’ Theorem next.
Related expert topics in Probability & Statistics
- Skewness and Kurtosis – Measures of asymmetry and tail heaviness.
- Residuals and R² – Error of a fit and proportion of variance explained.
- Simpson’s Paradox – A trend reverses when groups are combined.
- Bayes’ Theorem – Update beliefs with evidence.
- Medical Tests and Base Rates – Why positive results can still be probably false.
- Linearity of Expectation – Expectation of sums is sum of expectations.