whitney test

## use

- non-parm
- for non-normal dist
- test if 2 samples from same dist
- analogous to t-test
- calculated from ranks

## calc

- rank both data sets, in one array, keeping labels (groups)
- sum ranks for each sample
- higher sum is
*sample 1* - calc the U value
- U=n_1n_2 + (n_1(n_1+1)/2)-R_1
- R_1 is sum of sample 1 ranks

## significance

- use table “Critical Vals of Mann-Whit U”
- two-tailed testing
- signif level of 0.05
- < than table val indics significant diff between samples

## look at “pig weight T-test”

## Excel

- Sort
- Filter aggregates

## Stat Review

t-test - MW U test

## - Margin of error

significance 5% level (5% prob that could be chance)

t-test

- diff between means of 2 sampls
- assumes normal dist
- in excel, use 2-tailed and type-2 test

t-test signif

mann-whitney U

- non-parametric
- for not-normal

mw sig

- table
- 2-tailed
- smaller value indicates sig diff

if margins overlap, % vals not sig different

correlation - is one factor related to another? on pairs of data -1 perf neg correl 0 no corr 1 perf corr

types of study

- randomized
- prospective
- two samples, exposed and not (eg down and upwind from hanford)
- effect is correlated?

- retrospective
- group by presence of effect