我习惯用R软件（完全免费），简单易用。
> Avalue<-scan()
1: 0.2566
2: 0.2889
3: 0.4501
4: 0.4211
5: 0.2148
6: 0.4662
7:
Read 6 items
> Bvalue<-scan()
1: 0.2477
2: 0.2786
3: 0.4334
4: 0.4025
5: 0.2061
6: 0.4433
7:
Read 6 items
> cbind(Avalue,Bvalue)
    Avalue Bvalue
[1,] 0.2566 0.2477
[2,] 0.2889 0.2786
[3,] 0.4501 0.4334
[4,] 0.4211 0.4025
[5,] 0.2148 0.2061
[6,] 0.4662 0.4433
> t.test(Avalue,Bvalue,paired=T) #R中成对比较只需设置该参数值即可。

        Paired t-test

data:  Avalue and Bvalue
t = 5.9463, df = 5, p-value = 0.001922
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.008146513 0.020553487
sample estimates:
mean of the differences
                0.01435

> t.test(Avalue-Bvalue)#这是按我上面说的方法进行的检验

        One Sample t-test

data:  Avalue - Bvalue
t = 5.9463, df = 5, p-value = 0.001922
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
0.008146513 0.020553487
sample estimates:
mean of x
  0.01435
检验结果是拒绝原假设，所以认为两种方法的结果有显著差异