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### Comparing two samples using t-test
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A.acc = c(0.853, 0.859, 0.863, 0.871, 0.832, 0.848, 0.863, 0.860, 0.850, 0.849)
mean(A.acc)
# [1] 0.8548

B.acc = c(0.851, 0.848, 0.862, 0.871, 0.835, 0.836, 0.860, 0.859, 0.841, 0.843) 
mean(B.acc)
# [1] 0.8506


### Overlapping confidence intervals

t.test(A.acc, mu = 0.8506)

	One Sample t-test

data:  A.acc
t = 1.2195, df = 9, p-value = 0.2537
alternative hypothesis: true mean is not equal to 0.8506
95 percent confidence interval:
 0.8470088 0.8625912
sample estimates:
mean of x 
   0.8548 



t.test(B.acc, mu = 0.8548)

	One Sample t-test

data:  B.acc
t = -1.0974, df = 9, p-value = 0.301
alternative hypothesis: true mean is not equal to 0.8548
95 percent confidence interval:
 0.8419418 0.8592582
sample estimates:
mean of x 
   0.8506 


### Paired t-test

t.test(A.acc, B.acc, paired=T)

	Paired t-test

data:  A.acc and B.acc
t = 2.6296, df = 9, p-value = 0.02738
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.0005868378 0.0078131622
sample estimates:
mean of the differences 
                 0.0042 


# Explanation -- the same thing using one-sample t-test
t.test(A.acc - B.acc)

	One Sample t-test

data:  A.acc - B.acc
t = 2.6296, df = 9, p-value = 0.02738
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 0.0005868378 0.0078131622
sample estimates:
mean of x 
   0.0042