Step 1 :Step 1: Calculate the standard error of the difference in means: \( SE = \sqrt{\frac{S_{1}^{2}}{n_{1}} + \frac{S_{2}^{2}}{n_{2}}} \) where \( S_{1} = 14 \), \( n_{1} = 12 \), \( S_{2} = 8.7 \), and \( n_{2} = 10 \).
Step 2 :Step 2: Calculate the test statistic using the formula: \( t = \frac{(\bar{x}_{1} - \bar{x}_{2}) - (\mu_{1} - \mu_{2})}{SE} \) where \( \bar{x}_{1} = 88.6 \), \( \bar{x}_{2} = 0 \), \( \mu_{1} - \mu_{2} = 0 \), and SE from Step 1.
Step 3 :Step 3: Since we do not know the true population variances, use the conservative degrees of freedom: \( df = min(n_{1} - 1, n_{2} - 1) \) where \( n_{1} = 12 \) and \( n_{2} = 10 \).
Step 4 :Step 4: Determine the corresponding p-value using the calculated t-statistic and degrees of freedom calculated in steps 2 and 3, respectively. To do this, find the corresponding area under the t-distribution curve to the right of the calculated t-statistic.