In this master thesis we presented bootstrap method. We made simple example of parametric and nonparametric simulation based on which we observed histogram of means. We explained methods where using bootstrap can be useful method to estimate confidence intervals for statistic of interest. We described bootstrap-t method, percentile and \(\text{BC}_a\) method for estimation of confidence interval. All methods were supported by simulations for mean confidence intervals on sample from normal distribution. We saw that depending on method our intervals can be more or less wide and independent from parametric or nonparametric simulation real value of parameter does not always have to be contained in estimated confidence interval. \(\text{BC}_a\) method mostly gave us the narrowest interval. On the other hand, student or bootstrap-t method in most of the cases gave us the widest interval for parameter of interest. In the last chapter we saw simulations for mean, median and variance on samples that came from diverse distributions. In this thesis we did simulation for normal, exponential and gamma distribution. We concluded that accuracy of our estimation mostly depends on statistic, sample and distribution. The best results were obtained for symmetrical distributions.