The proposition in probability theory known as the law of total expectation, the law of iterated expectations (LIE), Adam's law, the tower rule, and the smoothing property of conditional expectation, among other names, states that if X {\displaystyle X} is a random variable whose expected value E [ X ] {\displaystyle \operatorname {E} [X]} is defined, and Y {\displaystyle Y} is any random variable on the same probability space, then
E [ X ] = E [ E [ X ∣ Y ] ] , {\displaystyle \operatorname {E} [X]=\operatorname {E} [\operatorname {E} [X\mid Y]],}
i.e., the expected value of the conditional expected value of X {\displaystyle X} given Y {\displaystyle Y} is the same as the expected value of X {\displaystyle X} . The conditional expected value E [ X ∣ Y ] {\displaystyle \operatorname {E} [X\mid Y]} , with Y {\displaystyle Y} a random variable, is not a simple number; it is a random variable whose value depends on the value of Y {\displaystyle Y} . That is, the conditional expected value of X {\displaystyle X} given the event Y = y {\displaystyle Y=y} is a number and it is a function of y {\displaystyle y} . If we write g ( y ) {\displaystyle g(y)} for the value of E [ X ∣ Y = y ] {\displaystyle \operatorname {E} [X\mid Y=y]} then the random variable E [ X ∣ Y ] {\displaystyle \operatorname {E} [X\mid Y]} is g ( Y ) {\displaystyle g(Y)} . One special case states that if { A i } {\displaystyle {\left\{A_{i}\right\}}} is a finite or countable partition of the sample space, then
E [ X ] = ∑ i E [ X ∣ A i ] Pr ( A i ) . {\displaystyle \operatorname {E} [X]=\sum _{i}{\operatorname {E} [X\mid A_{i}]\Pr(A_{i})}.}
Example Suppose that only two factories supply light bulbs to the market. Factory X's bulbs work for an average of 5000 hours, whereas factory Y's bulbs work for an average of 4000 hours. It is known that factory X supplies 60% of the total bulbs available. What is the expected length of time, L, that a purchased bulb will work for? Applying the law of total expectation, we have:
E [ L ] = E [ L ∣ X ] Pr ( X ) + E [ L ∣ Y ] Pr ( Y ) = 5000 ( 0.6 ) + 4000 ( 0.4 ) = 4600 {\displaystyle {\begin{aligned}\operatorname {E} [L]&=\operatorname {E} [L\mid X]\Pr(X)+\operatorname {E} [L\mid Y]\Pr(Y)\\[3pt]&=5000(0.6)+4000(0.4)\\[2pt]&=4600\end{aligned}}}
where
E [ L ] {\displaystyle \operatorname {E} [L]} is the expected life of the bulb;
Pr ( X ) = 6 10 {\displaystyle \Pr(X)={\frac {6}{10}}} is the probability that the purchased bulb was manufactured by factory X {\displaystyle X} ;
Pr ( Y ) = 4 10 {\displaystyle \Pr(Y)={\frac {4}{10}}} is the probability that the purchased bulb was manufactured by factory Y {\displaystyle Y} ;
E [ L ∣ X ] = 5000 {\displaystyle \operatorname {E} [L\mid X]=5000} is the expected lifetime of a bulb manufactured by X {\displaystyle X} ;
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