This study investigates whether the bootstrap via minimum norm quadratic estimation procedure offers improved accuracy in the estimation of the parameters and their standard errors for a two-level hierarchical linear model when the observations follow a distribution. Through Monte Carlo simulations, the importance of this assumption for the accuracy of multilevel parameter estimates and their standard errors is assessed using the accuracy index of absolute relative bias and by observing the coverage percentages of 95\% confidence intervals constructed for both estimation procedures. Study results show that while both the restricted maximum likelihood and the bootstrap via MINQUE estimates of the fixed effects were accurate, the efficiencies of the estimates were affected by the distribution of errors with both procedures producing less efficient estimators under the distribution, particularly for the variance-covariance component estimates.