In a normal distribution, X ∼ N(μX, σX) indicates that X is distributed with which two parameters?

In a normal distribution, X ∼ N(μX, σX) indicates that X is distributed with which two parameters?

• A. Median and range
• B. Variance and skewness
• C. Mean and standard deviation
• D. Range and interquartile range

Answer: (C)

Think of a normal distribution as defined by where it sits and how wide it is. The notation X ∼ N(μX, σX) uses μX as the average value around which observations cluster (the mean) and σX as the amount of dispersion around that center (the standard deviation). So, the two parameters are the mean and the standard deviation. Remember that the variance is the square of the standard deviation, and here the second parameter is the standard deviation itself. The other options reference measures like median, range, or skewness, which are not the defining parameters of a normal distribution.

Think of a normal distribution as defined by where it sits and how wide it is. The notation X ∼ N(μX, σX) uses μX as the average value around which observations cluster (the mean) and σX as the amount of dispersion around that center (the standard deviation). So, the two parameters are the mean and the standard deviation. Remember that the variance is the square of the standard deviation, and here the second parameter is the standard deviation itself. The other options reference measures like median, range, or skewness, which are not the defining parameters of a normal distribution.