 Bootstrap methods assume that our sample is an SRS and will give trustworthy conclusions only if this condition is met.

These methods make no assumptions about the distribution your data comes from.
 The Bootstrap Interval for the Difference of Two Population Means is an alternative to the twosample $t$interval when the guidelines for its use are not met (such as when the data is strongly skewed or has outliers with low sample size).
 The Bootstrap Interval for Two Population Proportions is an alternative to the twolargesample $z$interval when the guidelines for its use are not met (such as an insufficient number of successes and failures).
Sample 1  Sample 2  
Sample data goes here (enter numbers in columns):  
Number of Successes  Sample Size  
Sample 1:  
Sample 2:  
Calculate Interval for a Difference of Two:  
Confidence Level:  
Number of Bootstrap Samples: 
Sample Sizes:  $n_1=$  $n_2=$ 
Sample Means:  $\overline{x}_1=$  $\overline{x}_2=$ 
Sample Difference:  $\overline{x}_1\overline{x}_2=$  
Confidence Interval for the Difference of Means:  
Sample Proportions:  $\hat{p}_1=$  $\hat{p}_2=$ 
Sample Difference:  $\hat{p}_1\hat{p}_2=$  
Confidence Interval for the Difference of Proportions: 
Frequency  
Sample 1 and Sample 2 Data  Bootstrap Differences $\delta^*$  Shifted Bootstrap Differences $\delta^*+\overline{x}_1\overline{x}_2$ 