![]() This is an important characteristic of the Z score which is widely applicable for real-life problems. The Z-Score is also known as the standard score because it standardizes the distribution and allows the comparison of scores between different kinds of variables and different normal distribution. Why the Z-Score is also known as the ‘Standard Score‘? If we know the mean and standard deviation of a data set, the Z score can be calculated. Z score can be observed as a location in a distribution. Z score determines how many standard deviations away an element is from the mean value and whether the value is below or above the mean. While the Z score gives the position of each element with respect to the average. It gives the amount of deviation of an element from the average but does not say the element is below or above the average. It gives us a range in which the values are dispersed. Standard deviation indicates how the values are spread around the average. Understanding the difference between these two terms is essential. But the standard deviation and Z score of a specific element are not the same. There may arise a doubt regarding the difference between standard deviation and Z score since both of these parameters relate an element to the dataset mean value. Z-Score is negative Z Score Vs Standard Deviation A Z-Score equal to 0 means that the element is zero standard deviation away from the mean.
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