 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.
 For inference for a single population mean, singleparameter bootstrap tests are an alternative to the onesample $t$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).
 For inference for a single population proportion, singleparameter bootstrap tests are an alternative to the onesample $z$test when the guidelines for its use are not met, that is when the number of successes and failures are not large enough.
Data  Frequencies  
Sample data goes here (enter numbers in columns):  
Number of Successes  Sample Size  
Test for a:  
$H_0: \mu =$ $=\mu_0$  
$H_a:\mu$ $\mu_0$  
Level of Significance: $\alpha=$  
Number of Bootstrap Samples:  
Input data as Frequency Table: 
Population Mean
Sample Size:  $n=$ 
Sample Mean:  $\overline{x}=$ 
Lower Critical Value:  $\mu_{L}^*=$ 
Upper Critical Value:  $\mu_{U}^*=$ 
$p\mbox{value}$:  
Frequency  
Sample Data  Bootstrap Means $\mu^*$ 
Population Proportion
Sample Size:  $n=$ 
Sample Proportion:  $\hat{p}=$ 
Lower Critical Value:  $p_{L}^*=$ 
Upper Critical Value:  $p_{U}^*=$ 
$p\mbox{value}$:  
Frequency  
Failures and Successes  Bootstrap Proportions $p^*$ 
Population Median
Sample Size:  $n=$ 
Sample Median:  $M=$ 
Lower Critical Value:  $M_{L}^*=$ 
Upper Critical Value:  $M_{U}^*=$ 
$p\mbox{value}$:  
Frequency  
Sample Data  Bootstrap Medians $M^*$ 
Population Standard Deviation
Sample Size:  $n=$ 
Sample Standard Deviation:  $s=$ 
Lower Critical Value:  $\sigma_{L}^*=$ 
Upper Critical Value:  $\sigma_{U}^*=$ 
$p\mbox{value}$:  
Frequency  
Sample Data  Bootstrap Standard Deviations $\sigma^*$ 