[Math] Why is the Expected Value different from the number needed for 50% chance of success

probability

An event with probability $p$ of being success is executed $\frac{1}{p}$ times. For example, if $p=5\%$, the event would then be executed $20$ times.

The Expected Value for the total number of trials needed to get one success is $\frac{1}{p}$. In this case, it's $20$.

What I'm confused is, as p approaches zero, the chance of having a success in the first $\frac{1}{p}$ trials always approaches to $1-\frac{1}{e}$, or about $63\%$. This means: $P$(at least $1$ success in all $\frac{1}{p}$ trials) is about $63\%$.

This $63\%$ is higher than $50\%$. It seems to suggest that, if I take all $\frac{1}{p}$ trials and consider them as one big event, and do this big event multiple times, I'd get more successes than failures. But on the other hand, since $\frac{1}{p}$ times is the EV mentioned earlier, shouldn't the big event have an equal chance of being a success or a failure?

Best Answer

Let's take your example of an event that has a p=5% chance of success, and repeat it until it succeeds. Let's call this one experiment, and let's call the number of times you had to repeat the event in one experiment the number of runs. And let's repeat the experiment many, many times.

What can we say about the average number of runs you have to do for one experiment? Well, that depends on what you mean by average:

  • Say we want the mean number of runs. This would be the weighted infinite sum $(.05*1) + (.95*.05*2) + (.95^2*.05*3) + ... = 20$. This is called the expected value, or the "expected number of runs."
  • Say we want the median number of runs. This would be the number of runs at which you have exactly 50% chance of succeeding at or before that point. In other words, the number of runs $x$ for which $(1-.95^x)=0.5$, which is $x = \log_{0.95}{0.5} \approx 13.5$.

Thus, if you were to repeat this experiment many times, you'd find that you'd have to do an average (mean) of 20 trials before getting a success. However, in any given instance of the experiment, you'd find that you'd most likely finish by the 14th trial. The rare cases that you do way, way more are what bump up the expected value (eg. approximately 0.6% of the time, you'll need to do more than 100 runs before succeeding!).

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