Question

Quantitative noninvasive techniques are needed for routinely assessing symptoms of peripheral neuropathies, such as carpal tunnel syndrome (CTS). An article reported on a test that involved sensing a tiny gap in an otherwise smooth surface by probing with a finger; this functionally resembles many work-related tactile activities, such as detecting scratches or surface defects. When finger probing was not allowed, the sample average gap detection threshold for m = 7 normal subjects was 1.83 mm, and the sample standard deviation was 0.54; for n = 10 CTS subjects, the sample mean and sample standard deviation were 2.35 and 0.88, respectively. Does this data suggest that the true average gap detection threshold for CTS subjects exceeds that for normal subjects? State and test the relevant hypotheses using a significance level of 0.01. (Use μ1 for normal subjects and μ2 for CTS subjects.)

Answer 1

`t=\frac{2.35-1.83}{\sqrt{(0.88^2)/(10)+(0.54^2)/(7)}}}=1.507`

`p_v =P(t_((15))>1.507)=0.076`

If we compare the p value and the significance level given
`\alpha=0.01` we see that
`p_v>\alpha` so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and the group CTS, NOT have a significant higher mean compared to the Normal group at 1% of significance.

Explanation:

1) Data given and notation

`\bar X_(CTS)=2.35` represent the mean for the sample CTS

`\bar X_(N)=1.83` represent the mean for the sample Normal

`s_(CTS)=0.88` represent the sample standard deviation for the sample of CTS

`s_(N)=0.54` represent the sample standard deviation for the sample of Normal

`n_(CTS)=10` sample size selected for the CTS

`n_(N)=7` sample size selected for the Normal

`\alpha=0.01` represent the significance level for the hypothesis test.

t would represent the statistic (variable of interest)

`p_v` represent the p value for the test (variable of interest)

State the null and alternative hypotheses.

We need to conduct a hypothesis in order to check if the mean for the group CTS is higher than the mean for the Normal, the system of hypothesis would be:

Null hypothesis:
`\mu_(CTS) \leq \mu_(N)`

Alternative hypothesis:
`\mu_(CTS) > \mu_(N)`

If we analyze the size for the samples both are less than 30 so for this case is better apply a t test to compare means, and the statistic is given by:

`t=\frac{\bar X_(CTS)-\bar X_(N)}{\sqrt{(s^2_(CTS))/(n_(CTS))+(s^2_(N))/(n_(N))}}` (1)

t-test: "Is used to compare group means. Is one of the most common tests and is used to determine whether the means of two groups are equal to each other".

Calculate the statistic

We can replace in formula (1) the info given like this:

`t=\frac{2.35-1.83}{\sqrt{(0.88^2)/(10)+(0.54^2)/(7)}}}=1.507`

P-value

The first step is calculate the degrees of freedom, on this case:

`df=n_(CTS)+n_(N)-2=10+7-2=15`

Since is a one side right tailed test the p value would be:

`p_v =P(t_((15))>1.507)=0.076`

We can use the following excel code to calculate the p value in Excel:"=1-T.DIST(1.507,15,TRUE)"

Conclusion

If we compare the p value and the significance level given
`\alpha=0.01` we see that
`p_v>\alpha` so we can conclude that we have enough evidence to FAIL to reject the null hypothesis, and the group CTS, NOT have a significant higher mean compared to the Normal group at 1% of significance.