Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_twosampletests_mean.wasp
Title produced by softwarePaired and Unpaired Two Samples Tests about the Mean
Date of computationThu, 18 Dec 2014 18:37:53 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/18/t1418927923gniksztmy0sjhb9.htm/, Retrieved Thu, 31 Oct 2024 23:36:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271195, Retrieved Thu, 31 Oct 2024 23:36:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Percentiles] [Intrinsic Motivat...] [2010-10-12 12:10:58] [b98453cac15ba1066b407e146608df68]
- RMPD  [Kernel Density Estimation] [] [2011-10-18 22:42:23] [b98453cac15ba1066b407e146608df68]
- RMPD    [Percentiles] [] [2011-10-18 22:46:45] [b98453cac15ba1066b407e146608df68]
- RMPD      [Notched Boxplots] [] [2011-10-18 22:58:56] [b98453cac15ba1066b407e146608df68]
-    D        [Notched Boxplots] [] [2011-10-18 23:01:09] [b98453cac15ba1066b407e146608df68]
- RM            [Notched Boxplots] [] [2014-10-16 14:03:19] [4c4ebb0b36a379d1d949ba77427e658a]
- RMPD              [Paired and Unpaired Two Samples Tests about the Mean] [] [2014-12-18 18:37:53] [e866c71a5847370df800ce0257ab155d] [Current]
Feedback Forum

Post a new message
Dataseries X:
2.0	2.1
2.1	2.0
2.0	2.1
2.3	2.3
2.1	1.8
2.2	2.0
2.1	2.0
2.1	2.0
2.1	2.2
2.0	1.9
2.2	2.0
2.1	1.9
1.8	2.0
2.2	2.0
1.7	2.1
2.1	2.2
2.3	1.7
2.7	2.0
2.0	1.6
2.0	2.0
2.1	1.9
2.0	2.1
1.8	2.3
2.2	2.1
2.2	2.2
1.8	1.9
1.9	1.8
2.1	2.0
2.0	2.1
1.9	2.1
2.0	1.8
2.0	2.1
2.0	1.9
2.2	2.1
1.7	2.2
2.0	2.0
2.2	1.9
2.0	2.0
1.9	2.0
2.0	2.0
2.1	2.1
2.1	2.0
2.2	2.1
1.8	1.3
2.3	1.8
2.2	1.9
2.1	2.1
1.7	3
2.1	3
2.0	0.75
1.0	3
2.1	2.25
1.9	3
1.8	2.25
2.0	2.25
2.0	2.25
1.8	3
1.1	1.5
1.8	2.25
1.8	1.5
2.0	2.25
1.9	1.5
1.6	2.25
2.2	3
1.9	3
1.8	2.25
0.75	2.25
1.5	2.25
3	3
2.25	3
1.5	1.5
3	3
3	1.5
2.25	3
1.5	3
1.5	2.25
2.25	2.25
3	0.75
3	3
1.5	1.5
2.25	3
1.5	3
2.25	1.5
1.5	3
2.25	1.5
2.25	2.25
3	1.5
3	3
1.5	1.5
3	2.25
3	2.25
2.25	2.25
3	3
3	0.75
2.25	3
3	3
1.5	3
2.25	3
2.25	3
3	3
3	2.25
1.5	2.25
1.5	3
2.25	3
1.5	3
2.25	3
2.25	3
3	2.25
2.25	2.25
2.25	2.25
3	1.5
3	0.75
3	2.25
0.75	1.5
1.5	3
1.5	0.75
2.25	3
3	3
1.5	1.5
1.5	2.25
2.25	
1.5	
3	
0.75	
1.5	
1.5	
2.25	
2.25	
0.75	
0.75	
3	
1.5	
3	
1.5	
2.25	
0.75	
0.75	
3	
2.25	
3	
1.5	
0.75	
1.5	
3	
2.25	
2.25	
1.5	
2.25	
0.75	
2.25	
1.5	
1.5	
1.5	
1.5	
2.25	
1.5	
2.25	
1.5	
0.75	




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271195&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271195&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271195&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.wessa.net







Two Sample t-test (unpaired)
Mean of Sample 12.05534591194969
Mean of Sample 22.13710691823899
t-stat-1.31047359672441
df316
p-value0.190987348862886
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.204514139915864,0.0409921273372473]
F-test to compare two variances
F-stat0.764155492571657
df158
p-value0.0919238594405993
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.558799383615203,1.04497899237043]

\begin{tabular}{lllllllll}
\hline
Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 2.05534591194969 \tabularnewline
Mean of Sample 2 & 2.13710691823899 \tabularnewline
t-stat & -1.31047359672441 \tabularnewline
df & 316 \tabularnewline
p-value & 0.190987348862886 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-0.204514139915864,0.0409921273372473] \tabularnewline
F-test to compare two variances \tabularnewline
F-stat & 0.764155492571657 \tabularnewline
df & 158 \tabularnewline
p-value & 0.0919238594405993 \tabularnewline
H0 value & 1 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [0.558799383615203,1.04497899237043] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271195&T=1

[TABLE]
[ROW][C]Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]2.05534591194969[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]2.13710691823899[/C][/ROW]
[ROW][C]t-stat[/C][C]-1.31047359672441[/C][/ROW]
[ROW][C]df[/C][C]316[/C][/ROW]
[ROW][C]p-value[/C][C]0.190987348862886[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.95[/C][/ROW]
[ROW][C]CI[/C][C][-0.204514139915864,0.0409921273372473][/C][/ROW]
[ROW][C]F-test to compare two variances[/C][/ROW]
[ROW][C]F-stat[/C][C]0.764155492571657[/C][/ROW]
[ROW][C]df[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C]0.0919238594405993[/C][/ROW]
[ROW][C]H0 value[/C][C]1[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.95[/C][/ROW]
[ROW][C]CI[/C][C][0.558799383615203,1.04497899237043][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271195&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271195&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Two Sample t-test (unpaired)
Mean of Sample 12.05534591194969
Mean of Sample 22.13710691823899
t-stat-1.31047359672441
df316
p-value0.190987348862886
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.204514139915864,0.0409921273372473]
F-test to compare two variances
F-stat0.764155492571657
df158
p-value0.0919238594405993
H0 value1
Alternativetwo.sided
CI Level0.95
CI[0.558799383615203,1.04497899237043]







Welch Two Sample t-test (unpaired)
Mean of Sample 12.05534591194969
Mean of Sample 22.13710691823899
t-stat-1.31047359672441
df310.451550423121
p-value0.191004343098879
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.204522574707974,0.0410005621293575]

\begin{tabular}{lllllllll}
\hline
Welch Two Sample t-test (unpaired) \tabularnewline
Mean of Sample 1 & 2.05534591194969 \tabularnewline
Mean of Sample 2 & 2.13710691823899 \tabularnewline
t-stat & -1.31047359672441 \tabularnewline
df & 310.451550423121 \tabularnewline
p-value & 0.191004343098879 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
CI Level & 0.95 \tabularnewline
CI & [-0.204522574707974,0.0410005621293575] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271195&T=2

[TABLE]
[ROW][C]Welch Two Sample t-test (unpaired)[/C][/ROW]
[ROW][C]Mean of Sample 1[/C][C]2.05534591194969[/C][/ROW]
[ROW][C]Mean of Sample 2[/C][C]2.13710691823899[/C][/ROW]
[ROW][C]t-stat[/C][C]-1.31047359672441[/C][/ROW]
[ROW][C]df[/C][C]310.451550423121[/C][/ROW]
[ROW][C]p-value[/C][C]0.191004343098879[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]CI Level[/C][C]0.95[/C][/ROW]
[ROW][C]CI[/C][C][-0.204522574707974,0.0410005621293575][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271195&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271195&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Welch Two Sample t-test (unpaired)
Mean of Sample 12.05534591194969
Mean of Sample 22.13710691823899
t-stat-1.31047359672441
df310.451550423121
p-value0.191004343098879
H0 value0
Alternativetwo.sided
CI Level0.95
CI[-0.204522574707974,0.0410005621293575]







Wicoxon rank sum test with continuity correction (unpaired)
W11394
p-value0.125243005748129
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.10062893081761
p-value0.396561952928136
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.144654088050314
p-value0.0717948085162843

\begin{tabular}{lllllllll}
\hline
Wicoxon rank sum test with continuity correction (unpaired) \tabularnewline
W & 11394 \tabularnewline
p-value & 0.125243005748129 \tabularnewline
H0 value & 0 \tabularnewline
Alternative & two.sided \tabularnewline
Kolmogorov-Smirnov Test to compare Distributions of two Samples \tabularnewline
KS Statistic & 0.10062893081761 \tabularnewline
p-value & 0.396561952928136 \tabularnewline
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples \tabularnewline
KS Statistic & 0.144654088050314 \tabularnewline
p-value & 0.0717948085162843 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271195&T=3

[TABLE]
[ROW][C]Wicoxon rank sum test with continuity correction (unpaired)[/C][/ROW]
[ROW][C]W[/C][C]11394[/C][/ROW]
[ROW][C]p-value[/C][C]0.125243005748129[/C][/ROW]
[ROW][C]H0 value[/C][C]0[/C][/ROW]
[ROW][C]Alternative[/C][C]two.sided[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributions of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.10062893081761[/C][/ROW]
[ROW][C]p-value[/C][C]0.396561952928136[/C][/ROW]
[ROW][C]Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples[/C][/ROW]
[ROW][C]KS Statistic[/C][C]0.144654088050314[/C][/ROW]
[ROW][C]p-value[/C][C]0.0717948085162843[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271195&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271195&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Wicoxon rank sum test with continuity correction (unpaired)
W11394
p-value0.125243005748129
H0 value0
Alternativetwo.sided
Kolmogorov-Smirnov Test to compare Distributions of two Samples
KS Statistic0.10062893081761
p-value0.396561952928136
Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples
KS Statistic0.144654088050314
p-value0.0717948085162843



Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = unpaired ; par6 = 0.0 ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = unpaired ; par6 = 0.0 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1) #column number of first sample
par2 <- as.numeric(par2) #column number of second sample
par3 <- as.numeric(par3) #confidence (= 1 - alpha)
if (par5 == 'unpaired') paired <- FALSE else paired <- TRUE
par6 <- as.numeric(par6) #H0
z <- t(y)
if (par1 == par2) stop('Please, select two different column numbers')
if (par1 < 1) stop('Please, select a column number greater than zero for the first sample')
if (par2 < 1) stop('Please, select a column number greater than zero for the second sample')
if (par1 > length(z[1,])) stop('The column number for the first sample should be smaller')
if (par2 > length(z[1,])) stop('The column number for the second sample should be smaller')
if (par3 <= 0) stop('The confidence level should be larger than zero')
if (par3 >= 1) stop('The confidence level should be smaller than zero')
(r.t <- t.test(z[,par1],z[,par2],var.equal=TRUE,alternative=par4,paired=paired,mu=par6,conf.level=par3))
(v.t <- var.test(z[,par1],z[,par2],conf.level=par3))
(r.w <- t.test(z[,par1],z[,par2],var.equal=FALSE,alternative=par4,paired=paired,mu=par6,conf.level=par3))
(w.t <- wilcox.test(z[,par1],z[,par2],alternative=par4,paired=paired,mu=par6,conf.level=par3))
(ks.t <- ks.test(z[,par1],z[,par2],alternative=par4))
m1 <- mean(z[,par1],na.rm=T)
m2 <- mean(z[,par2],na.rm=T)
mdiff <- m1 - m2
newsam1 <- z[!is.na(z[,par1]),par1]
newsam2 <- z[,par2]+mdiff
newsam2 <- newsam2[!is.na(newsam2)]
(ks1.t <- ks.test(newsam1,newsam2,alternative=par4))
mydf <- data.frame(cbind(z[,par1],z[,par2]))
colnames(mydf) <- c('Variable 1','Variable 2')
bitmap(file='test1.png')
boxplot(mydf, notch=TRUE, ylab='value',main=main)
dev.off()
bitmap(file='test2.png')
qqnorm(z[,par1],main='Normal QQplot - Variable 1')
qqline(z[,par1])
dev.off()
bitmap(file='test3.png')
qqnorm(z[,par2],main='Normal QQplot - Variable 2')
qqline(z[,par2])
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Two Sample t-test (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
if(!paired){
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 1',header=TRUE)
a<-table.element(a,r.t$estimate[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 2',header=TRUE)
a<-table.element(a,r.t$estimate[[2]])
a<-table.row.end(a)
} else {
a<-table.row.start(a)
a<-table.element(a,'Difference: Mean1 - Mean2',header=TRUE)
a<-table.element(a,r.t$estimate)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'t-stat',header=TRUE)
a<-table.element(a,r.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,r.t$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,r.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,r.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,r.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(r.t$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',r.t$conf.int[1],',',r.t$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'F-test to compare two variances',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'F-stat',header=TRUE)
a<-table.element(a,v.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,v.t$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,v.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,v.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,v.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(v.t$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',v.t$conf.int[1],',',v.t$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Welch Two Sample t-test (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
if(!paired){
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 1',header=TRUE)
a<-table.element(a,r.w$estimate[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Mean of Sample 2',header=TRUE)
a<-table.element(a,r.w$estimate[[2]])
a<-table.row.end(a)
} else {
a<-table.row.start(a)
a<-table.element(a,'Difference: Mean1 - Mean2',header=TRUE)
a<-table.element(a,r.w$estimate)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a,'t-stat',header=TRUE)
a<-table.element(a,r.w$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'df',header=TRUE)
a<-table.element(a,r.w$parameter[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,r.w$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,r.w$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,r.w$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI Level',header=TRUE)
a<-table.element(a,attr(r.w$conf.int,'conf.level'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'CI',header=TRUE)
a<-table.element(a,paste('[',r.w$conf.int[1],',',r.w$conf.int[2],']',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Wicoxon rank sum test with continuity correction (',par5,')',sep=''),2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'W',header=TRUE)
a<-table.element(a,w.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,w.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'H0 value',header=TRUE)
a<-table.element(a,w.t$null.value[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Alternative',header=TRUE)
a<-table.element(a,w.t$alternative)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Kolmogorov-Smirnov Test to compare Distributions of two Samples',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'KS Statistic',header=TRUE)
a<-table.element(a,ks.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,ks.t$p.value)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Kolmogorov-Smirnov Test to compare Distributional Shape of two Samples',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'KS Statistic',header=TRUE)
a<-table.element(a,ks1.t$statistic[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,ks1.t$p.value)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')