Generate random numbers with the same distribution as sample data

How to generate random numbers (in between minimum and maximum limits) using "Monte-Carlo" method with a specified distribution using Matlab ?

Our steps to solve the problem were:

1- Specify the sample distribution ('generalized extreme value' distribution "using mark allfitist()")

2- Find the parameters of the sample data

3- Make object of the required distribution

4- Truncate the distribution by Data's minimum and maximum

5- Generate n sample

6- Draw curve

7- Compare mean, standard deviation and parameters

Here's the code:

function [] = GenRand_gev(Data,size)
    Minimum=min(Data);
    Maximum=max(Data);
    Data_Mean = mean(Data);
    Data_Standard_Diviation = std(Data);
    % get the parameters
    LN= mle(Data,'distribution','generalized extreme value');
    Data_K=LN(1);
    Data_sigma=LN(2);
    Data_mu=LN(3);
    % make object of the required distribution
    pd_gev = makedist('generalized extreme value','K',Data_K,'sigma',Data_sigma,'mu',Data_mu);
    % Truncate the distribution by minimum and maximum
    t= truncate(pd_gev,Minimum,Maximum);
    % generate n sample
    r = random(t,size,1);
    % draw distribution curve
    histfit(r,[],'generalized extreme value');
    legend('Generated sample','distribution curve');
    Gen_Mean = mean(r);
    Gen_Standard_Diviation = std(r);
    LN1 = gevfit(r);
    Gen_k=LN1(1);
    Gen_sigma=LN1(2);
    Gen_mu=LN1(3);
end

Is this correct??

When comparing the Mu, Sigma and the parameters of the sample data and the generated numbers (Data_Mean & Gen_Mean), (Data_Standard_Diviation & Gen_Standard_Diviation), (Data_K & Gen_k), (Data_sigma & Gen_sigma), (Data_mu & Gen_mu) for different sizes they found to be very different.

Thanks in advance