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Bootstrap Confidence Intervals Repeated sampling from a population may be impractical, expensive or not possible (sample items destroyed during sampling) If we cannot resample from the population, (the true sampling distribution is unavailable) then we resample from the best approximation of the population we have  which is the sample itself (producing a bootstrap distribution) Video Chris Wilds overview of sample variation etc, (view from 7:20 for bootstrapping) Use the original sample to represent the population. The resampling produces a distribution of means (or medians) which form a distribution The bootstrap confidence interval provides an estimate for the population mean Review maui/hectors dolphin data sets Assuming our sample was representative of the population then the bootstrapped confidence interval can be used as an estimate of the true population mean (or median) 
Class notes, Class notes Teaching bootstrapping (link to census at school Class notes) Bootstrapping animations page (Link to stat.auckland) Stats Learning Workbook Ex D p95, 96 with explanation on pg 93  read it and weep tears of joy.

iNZight simulation popn > sample distribution iNZight simulation popn > sample > bootstrap distribution What do you notice 
Download and run iNZight ( iNZight free download and videos on how to get started ) 

iNZite Bootstrap confidence intervals 

Import data 

Add a Variable 

Analyse 

Mean or median 

Record your choices  
Resample and animation 

From this sample select a single item, record it, with replacement, repeat selection process 'n' times then calculate the median (or mean) Top box plot is of the sample Bottom box plot is of the resample and the median of the resample (Bootstrap) 

Include Bootstrap Distribution 

Repeat 100 times and... 

Show the CI to see the Bootstrap confidence interval. This is the interval within which we can be reasonably sure the population median lies. 

Now discuss these resultsWhat does the bootstrap confidence interval mean?
