 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 Test for Two Population Means is an alternative to the twosample $t$test 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 Test for Two Population Proportions is an alternative to the twolargesample $z$test 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:  
Test for a Difference of Two:  
$H_0: \mu$$\mu_0$  
$H_a:\mu$ $\mu_0$  
Level of Significance: $\alpha=$  
Number of Bootstrap Samples: 
Sample Sizes:  $n_1=$  $n_2=$ 
Sample Means:  $\overline{x}_1=$  $\overline{x}_2=$ 
Sample Difference:  $\overline{x}_1\overline{x}_2=$  
Sample Proportions:  $\hat{p}_1=$  $\hat{p}_2=$ 
Sample Difference:  $\hat{p}_1\hat{p}_2=$  
Lower Critical Value:  $\delta_{L}^*=$  
Upper Critical Value:  $\delta_{U}^*=$  
$p\mbox{value}$: 
Frequency  
Sample 1 and Sample 2 Data  Bootstrap Differences $\delta^*$ 