Suppose we have a IID sample $X_1, X_2, \cdots, X_n$ with each $X_i$ distributed as $\mathcal{N}(\mu, \sigma^2)$. Now suppose we construct (a rather peculiar) estimator for the mean $\mu$: we only choose values from the sample that are greater than a pre-decided value, say $1$, and then take the sample average of only those values:
$$\hat{\mu}=\frac{1}{n_1}\sum\limits_{X_i > 1} X_i$$
Here $n_1$ is the number of values that are greater than $1$. Now I was expecting this estimator to be highly biased. However, we have:
$$\mathbb{E}(\hat{\mu}) = \frac{1}{n_1}\sum\limits_{X_i>1}\mathbb{E}(X_i)=\frac{1}{n_1}n_1\mu=\mu$$
This simply would mean that the estimator is unbiased! But obviously if I do this by generating many numbers and choosing only those which are greater than $1$, I will never get the estimator value less than $1$ and so the estimator has to be biased. What am I missing?