Question

Which assumption will work best for a large data set with a normal distribution?

Answer 1

Most of the data points are close to the mean value of the set.

Answer 2

Answer:

The correct answer is 3) Most of the data points are close to the mean value of the set.

Explanation:

This is an informal approach. However, to test for the normality of a distribution, you want to ensure that you compare a histogram of the sample data to a normal probability curve.

If the empirical distribution of the data is bell-shaped then it is normal. Of course, this alignment may be difficult to see if the sample data is too small.

It is important to look out for normality because, in statistics, normality tests are required to establish if a data set is properly modeled by a normal distribution and to calculate the probability for a random variable within the data set to be normally distributed.

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