Sampling distribution need not be bell shaped? Considering the following sample $X = $  [32.5,  31.7,  29.2,  28.8,  29. ,  28.9,  29.9,  30.4,  26.9,  26.5]
I planned to evaluate the hypothesis of this sample having been generated from a normal distribution with an hypothetic $\mu$ and $sigma^2$ trough the use of the Kolmogorov Smirnov test statistic.
My strategy was to solve this problem using bootstrapping, so for each simulation I sampled from $X$ and computed the functional $T(X^*)$ where $T$ is the maximum distance from the empirical distribution of $X^*$ and the theoretical distribution of the distribution I considered.
After all the simulations were through I had enough data to plot the empirical distribution of the t statistic, which I am right to call sampling distribution right?! 
But when plotting an histogram of this same sampling distribution of the kolmogorov smirnov test statistic I got the following graphic:

My sample size is very low and some discretization was made to the observation, fixed precision.
Is this distribution ok and perfectly normal? I am trying to understand if this can happen of (I speculate that it can), or if I messed up during the implementation of the ks-test. 
Here's my implementation of the ks-test in R:
$H_0: Sample ~ N(\mu, \sigma^2)$
kstest = function(Xsample, mu, var) {

  n = length(Xsample)
  X_sample = sort(Xsample)

  #List of differences
  diffs = numeric(2*n)

  for(j in 1:n){
    f = pnorm(X_sample[j],mean=mu,sd=sqrt(var))
    diffs[j*2-1] = j/n - f
    diffs[j*2] = f - (j - 1)/n
  }
  return(max(diffs))
}

 A: As @whuber suggested the bootstrap distribution, sampling distribution, can exhibit strong multimodality and points of discontinuity. For the sample mean there are only $C^{2N-1}_{N-1}$ possible values that $\bar{X}^*$ can take, this means that such a small sample as mine will have a very discrete behaviour like above. Fortunately as $N \rightarrow \infty$ the sampling distribution becomes more continuous.
I solved the problem by using the parametric bootstrap instead of the non parametric strategy I was using before. That is, I assume that X is normally distributed and estimate the distribution parameters through maximum likelihood. Then at each bootstrap I sample from the estimated distribution and apply the kstest to that sample. The sampling distribution becomes much more well behaved using this alternate strategy:

This really helped me understand concepts such as sampling distribution, parametric and non parametric bootstrap much better. For reference, in cases such that no prior distribution can be assumed and we are forced to use a non parametric bootstrap then we can also use a smoothed bootstrap to force a more or less well behaved sampling distribution.
