[Math] Median of discrete and continuous random variables.

medianprobabilityprobability distributionsrandom variablesstatistics

If case of continuous random variables, we define median 'm' as a value such that the $P(X\ge m)=0.5$ and $P(X\le m)=0.5$.
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For discrete random variables, $P(X\ge m)\ge0.5$ and $P(X\le m)\ge0.5$

Why is it greater than or equal to in case of discrete random variables and only equals to in case of continuous random variable?

Best Answer

For strictly continuous random variables the value $m$ is unique and always in the support (the set of possible values) of $X$, thus there is no problem to define it as $P(X\ge m) = 0.5$. In discrete random variable whether with finite or infinite countable support, it is possible that no possible value of $X$ will satisfy $P(X\ge m )=0.5$. Let us consider a simple example. Let $X \sim \mathcal{B}in (3, 1/2)$, hence we have to find $m$ such that $ P(X \le m ) =0.5 $. Let us check possible value of $m$ $$ P(X\le 1) = \frac{1}{2^3} + \frac{3}{2^3} = 1/2. $$ Bingo! $1$ is the median. Now, take $p=1/4$ instead of $1/2$, $$ P(X\le 1) = \frac{3^3}{4^3} + \frac{3}{2^3} = 0.74, $$ that is way too high probability, however $$ P(X\le 0) = \frac{3^3}{4^3} = 0.42. $$ That is too low. I.e., there is no value that $X$ can get and it will satisfy $ P(X\le m) = 0.5, $ hence you have to chose $0$ or $1$ as the median. Any value in $(0,1)$ will not change a thing as $X$ cannot receive these values.

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