Answer:Table 2.2: Differences in runstitching times (standard − ergonomic).
1.03 -.04 .26 .30 -.97 .04 -.57 1.75 .01 .42
.45 -.80 .39 .25 .18 .95 -.18 .71 .42 .43
-.48 -1.08 -.57 1.10 .27 -.45 .62 .21 -.21 .82
A paired t-test is the standard procedure for testing this null hypothesis.
We use a paired t-test because each worker was measured twice, once for Paired t-test for
each workplace, so the observations on the two workplaces are dependent. paired data
Fast workers are probably fast for both workplaces, and slow workers are
slow for both. Thus what we do is compute the difference (standard − er-
gonomic) for each worker, and test the null hypothesis that the average of
these differences is zero using a one sample t-test on the differences.
Table 2.2 gives the differences between standard and ergonomic times.
Recall the setup for a one sample t-test. Let d1, d2, . . ., dn be the n differ-
ences in the sample. We assume that these differences are independent sam-
ples from a normal distribution with mean µ and variance σ
2
, both unknown.
Our null hypothesis is that the mean µ equals prespecified value µ0 = 0
(H0 : µ = µ0 = 0), and our alternative is H1 : µ > 0 because we expect the
workers to be faster in the ergonomic workplace.
The formula for a one sample t-test is
t =
¯d − µ0
s/√
n
,
where ¯d is the mean of the data (here the differences d1, d2, . . ., dn), n is the The paired t-test
sample size, and s is the sample standard deviation (of the differences)
s =
vuut
1
n − 1
Xn
i=1
(di − ¯d )
2 .
If our null hypothesis is correct and our assumptions are true, then the t-
statistic follows a t-distribution with n − 1 degrees of freedom.
The p-value for a test is the probability, assuming that the null hypothesis
is true, of observing a test statistic as extreme or more extreme than the one The p-value
we did observe. “Extreme” means away from the the null hypothesis towards
the alternative hypothesis. Our alternative here is that the true average is
larger than the null hypothesis value, so larger values of the test statistic are
extreme. Thus the p-value is the area under the t-curve with n − 1 degrees of
freedom from the observed t-value to the right. (If the alternative had been
µ < µ0, then the p-value is the area under the curve to the left of our test
Explanation: The curve represents the sum total of the evaluation
