## 1.1. Hypothesis testing and p-value

Here are few slides explaining the concept of hypothesis testing and p-value:

## 1.2. Parametric test for means (t-test)

When we work with real data, we need to estimate variance based on experimental observation. This adds additional randomeness => sampling distribution (uncertainty) of mean follows Student (t) distribution.

### Simple (unpaired) t-test

Let’s test, whether mean weight change differs for male & female mice. Animals in the groups are different, therefore we use unpaired t-test. Corresponding function is t.test()

## load data

xm = Mice$Weight.change[Mice$Sex=="m"]
xf = Mice$Weight.change[Mice$Sex=="f"]
t.test(xm,xf)
##
##  Welch Two Sample t-test
##
## data:  xm and xf
## t = 3.2067, df = 682.86, p-value = 0.001405
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  0.009873477 0.041059866
## sample estimates:
## mean of x mean of y
##  1.119401  1.093934
## you can also compare to a constant
## check whether weight change is over 1:
t.test(xm, mu = 1, alternative = c("greater"))
##
##  One Sample t-test
##
## data:  xm
## t = 27.334, df = 393, p-value < 2.2e-16
## alternative hypothesis: true mean is greater than 1
## 95 percent confidence interval:
##  1.112199      Inf
## sample estimates:
## mean of x
##  1.119401

### Paired t-test

Example. The systolic blood pressures of n=12 women between the ages of 20 and 35 were measured before and after usage of a newly developed oral contraceptive.

BP=read.table("http://edu.modas.lu/data/txt/bloodpressure.txt",header=T,sep="\t")
str(BP)
## 'data.frame':    12 obs. of  3 variables:
##  $Subject : int 1 2 3 4 5 6 7 8 9 10 ... ##$ BP.before: int  122 126 132 120 142 130 142 137 128 132 ...
##  $Gulf.Park: num 21.6 20.5 23.3 18.8 17.2 7.7 18.6 18.7 20.4 22.4 ... # see variances apply(Bus,2,var,na.rm=TRUE) ## Milbank Gulf.Park ## 48.02062 19.99996 # test whether they could be the same var.test(Bus[,1], Bus[,2]) ## ## F test to compare two variances ## ## data: Bus[, 1] and Bus[, 2] ## F = 2.401, num df = 25, denom df = 15, p-value = 0.08105 ## alternative hypothesis: true ratio of variances is not equal to 1 ## 95 percent confidence interval: ## 0.8927789 5.7887880 ## sample estimates: ## ratio of variances ## 2.401036 ## Exercise 1 1. Compare Ending.weight for male and female mice of 129S1/SvImJ strain t.test(), Mice$Ending.weight, Mice$Strain == "129S1/SvImJ" & Mice$Sex=="m"

1. Compare Bleeding.time between CAST/EiJ and NON/ShiLtJ (case sensitive!) using parametric and non-parametric tests.

t.test(), wilcoxon.test()

 By Petr Nazarov