What Is Z Score?

If you work in analytics or data science, you may have heard of the term “z score” before. But what is it? In this article, we’ll be exploring the answer to this question, with a discussion of what it is and why it’s so important.

What Is It?

A z score is a statistical measure that tells you how many standard deviations away a data point is from the mean of a sample population. The result of this calculation is traditionally expressed as a decimal number.

In a z score calculation, the data point being measured is the “x” (for example, the age of a given individual) and the sample population mean is the “μ”. The mean is found by adding all of the values of the given population together and then dividing by the number of data points, which is usually denoted by “n.” The sample population standard deviation is the “σ”.

Why Is It Important?

Different datasets can contain a range of values, from small to large. Since the goal of many data-driven projects is to compare between data sets and spot patterns, the ability to measure data points along a uniform scale becomes paramount.

That is to say, being able to compare apples to apples (or oranges to oranges) is often easier when dealing with a cross-sectional analysis. This is especially true when trying to compare data between different groups, such as smaller businesses to larger enterprises. The z score gives analysts the ability to make better cross-sectional comparisons by measuring data points in terms of standard deviations away from the population mean, rather than the absolute values.

What’s the Maximum Value of a Z Score?

The maximum value of a z score is calculated using a combination of factors, which include the values of the x, μ, and σ. Generally speaking, the z score of a given data point is limited by the sample population mean and standard deviation.

In other words, if the population mean is too large or too small, the result of a z score calculation may skew one way or the other. Additionally, if the standard deviation is too low, then it may limit the range of values that a z score can measure.

Conclusion

In conclusion, z score is a statistical measure that shows how many standard deviations away a given data point is from the population mean. This measurement allows analysts to make better cross-sectional comparisons between different datasets, as well as to spot patterns between them. The maximum limit of a z score is determined by the population mean and standard deviation of the given datasets.