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Answer :
It can be analyzed through comparing data characteristics like mean, range, and interquartile range with their theoretical expectations, constructing a normal probability plot, and plotting a histogram. Both graphical tools (the probability plot and the histogram) should show specific patterns for data to be considered normally distributed.
To analyze the data characteristics, you look at parameters such as the mean, range, and interquartile range. If these figures conform to what you would theoretically expect from a normally distributed set, it may be an indication that the data is indeed normally distributed.
The normal probability plot is a graphical tool that is used to understand if a data set is normally distributed. If the dots (representing data points) in the graph form a straight, diagonal line then it means the data is normally distributed. Any significant deviations from that straight line may indicate that the data isn't normally distributed.
Similarly, a histogram can also help to understand the distribution of a data set. A normally distributed data set would result in a bell-shaped curve. If the histogram of the given data forms this bell curve, it's another indication of normally distributed data. However, if the data is skewed to the right or left, or has too many outliers, it may not be normally distributed.
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