I did notice that when I sample from a truncated distribution the sample variance I get is not the one that I would expect from the distribution, but one “corrected” by the truncation process:

```
using Distributions, Statistics
μ = 0
σ = 1
l = -0.5
u = 0.5
d = Truncated(Normal(μ,σ),l,u)
sampledData = rand(d,100000)
minimum(sampledData) # ok
maximum(sampledData) # ok
mean(sampledData) # ok
var(sampledData)^(1/2) # 0.28 instead of 1. Same if I use TruncatedNormal
```

Is there a way to “correct” the initial sigma so that the sampled data have a given known variance ?