For each analysis, write a hypothesis with correct notation. Discuss the comparison distribution, that is, what is the assumptions about the data? Is it normal or non-parametric? Determine the t-critical score. Calculate the t-sample score from StatCrunch (show screenshots). Write a conclusion. The conclusion should discuss a summary of the analysis, the type of t-test used, the number of samples in the data set, the degree of freedom, the t-critical scores and the t-statistics score. The conclusion should state if the null hypothesis was rejected based on comparing the t-critical value to the t-statistics. It should also state if the analysis is statistically significant.
A researcher wishes to determine if a particular drug affects pilot reaction time to air traffic controller instructions. The researcher has 10 pilots. The pilots are observed in normal performance and their reaction times are recorded. Then the pilots are administered the drug, observed again, and their reaction times are recorded. The expectation is that the drug will reduce reaction time.
Pilot Trial 1 Time (sec) Trail 2 Time (sec)
A .83 .69
B .74 .71
C .82 .79
D .86 .87
E .66 .65
F .63 .68
G .81 .67
H .77 .72
I .73 .71
J .69 .65
A researcher wishes to determine if the college students with a “B” average have attain more flying hours by the end of their first semester of college than those that do not have a “B” average at the end of the first semester. The researcher surveys 20 college students that are also student pilots and records their flight hours as well as their GPA (noted as “below B” and “B or better”), and, fortunately, there is an equal number “below B” and “B or better.” The expectation is that those students with a B average or better will have more flying hours by the end of their first semester than those students that are below a “B” average. Here are the survey results:
Below B flying hours B or Better flying hours
43 52
41 32
31 44
36 58
39 51
37 30
36 44
41 55
45 62