If we would like to visualize the interval (1991.35, 1999.53), we can once again pipe the bootstrap_distribution data frame into the visualize() function and add a shade_confidence_interval() layer to our plot. We set the endpoints argument to be standard_error_ci. The resulting standard-error method based on a 95% confidence interval for \(\mu\) can be seen in Figure 8.25.