Respuesta :
Answer:
Find the difference between the two scores for a number of sample distributions. Make a plot of the differences and check for outliers.
Step-by-step explanation:
Checking for Normality means basically checking if one's data distribution approximates a normal distribution.
A normal distribution is represented by a bell-shaped curve, peaking around the mean, indicating that all of the data spreads out from the mean.
Th original aim of the teacher is to check the effects of the particular added unit on the performance of students in the subject.
The teacher goes about this by testing the students before and after learning the unit.
The best way to compare of course, is to take a difference of the test scores for different samples. This first gives the idea of whether the newly introduced unit affects performance.
This set of differences is then checked for normality.
So, the best manner to make a plot of these differences. Like we mentioned earlier, a normal distribution is bell shaped. So, the plot of these differences would be a bell shaped curve if the distribution was normal and we wouldn't get a bell shaped curve if the distribution wasn't normal.
Checking for outliers help to eliminate part of data that can totally scatter the regular behaviour of the data distribution.
So, the best way for the teacher to check for normality is to find the difference between the two scores for a number of sample distributions. Make a plot of the differences and check for outliers.
Hope this Helps!!!
Since the data include the pretest and post test scores of each student or subject, then, it is approached as a paired t test. Hence, checking for normality would require obtaining the difference between the two scores for each subject, then make a plot of the differences and check for outliers.
- Working on a paired sample involves taking the difference between the scores. Which is called the paired difference.
- The plot of the paired difference is made, and outliers are checked in the plot made. As outlier values are more associated with skewed distributions.
Hence, the most appropriate check for normality in this scenario is to check for outliers in the plot made from the score difference.
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