# The mean E(X) of negative binomial distribution [closed]

What I know about the mean of the negative binomial distribution is E(x)=r(1-p)/p. but there are some questions use E(x)=r/p as the mean. Very confusing and I don't understand at all.

For example:

Repeatedly roll a fair die until outcome 3 has occurred for the 4th time. Let X be the number of times needed in order to achieve this goal. Find E(X) and Var(X)? My answer: negative binomial with r=4, p=1/6. E(x)=r(1-p)/p=20 However, the right answer is: E(x)=r/q=24

and for this question: The probability that a basketball player makes a free-throw shot is 60%. The player was asked not to leave practice unless he makes 10 shots. Let Y be the number of free-throws missed prior to the 10th shots. Find the mean and the variance of Y. My answer is right. Negative Binomial with r=10,p=0.6. E(y)=r(1-p)/p=6.67

I don't understand why there are 2 formulas and how to tell the difference, which one I should use?

## closed as off-topic by user44191, Yemon Choi, Chris Godsil, Gro-Tsen, Margaret FriedlandJul 11 at 21:58

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Unfortunately, different authors use different conventions, and there are two different families of distributions that are called "the negative binomial distribution". One is for the number of trials until the $$r$$'th success, the other is for the number of failures before the $$r$$'th success. These differ by exactly $$r$$, and their expected values also differ by $$r$$. So (if $$p$$ is the probability of success on each trial) the expected number of trials until the $$r$$'th success has mean $$r/p$$, while the expected number of failures before the $$r$$'th success has mean $$r(1-p)/p$$.
As another source of confusion, the roles of "success" and "failure" are sometimes interchanged. For some authors you are waiting for the $$r$$'th success, and for others you are waiting for the $$r$$'th failure. Of course it's entirely arbitrary which outcome you consider as "success" and which "failure", but traditionally $$p$$ is the probability of "success" and $$1-p$$, sometimes denoted as $$q$$, the probability of "failure".