Final answer:
To evaluate the impact of sleep deprivation on problem-solving ability, a hypothesis test is conducted using a sample of college students' scores before and after a period of no sleep. The test compares the average difference in scores with the critical value from the t-distribution to determine significance. Furthermore, Cohen's d is calculated to measure the size of the effect.
Step-by-step explanation:
Given the sample size n=25, the average difference in scores MD=4.7, and the variance s2=64, we can conduct a hypothesis test to ascertain the significance of the change in problem-solving ability due to sleep deprivation. First, we calculate the standard deviation s by taking the square root of the variance, which gives us 8. Then, we use the formula for the t-statistic, which is t = MD / (s / sqrt(n)), resulting in a t-value. Comparing this value against the critical t-value from the t-distribution table for a two-tailed test with 24 degrees of freedom (n-1) at α=.05 helps us determine whether the change is significant.
To measure the effect size, we calculate Cohen's d using the formula d = MD / s, which provides insight into the practical significance of the difference in scores. A larger Cohen's d indicates a more substantial effect of sleep loss on problem-solving abilities.