Answer:
0.4332 = 43.32% probability that the sample mean is between 21 and 22.
Step-by-step explanation:
To solve this question, we need to understand the normal probability distribution and the central limit theorem.
Normal Probability Distribution:
Problems of normal distributions can be solved using the z-score formula.
In a set with mean 
and standard deviation 
, the z-score of a measure X is given by:
The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the p-value, we get the probability that the value of the measure is greater than X.
Central Limit Theorem
The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean and standard deviation , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean and standard deviation 
.
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
According to a report from a business intelligence company, smartphone owners are using an average of 22 apps per month.
This means that 
Standard deviation is 4:
This means that 
Sample of 36:
This means that 
What is the probability that the sample mean is between 21 and 22?
This is the p-value of Z when X = 22 subtracted by the p-value of Z when X = 21.
X = 22
By the Central Limit Theorem
has a p-value of 0.5.
X = 21
has a p-value of 0.0668.
0.5 - 0.0668 = 0.4332
0.4332 = 43.32% probability that the sample mean is between 21 and 22.
