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{"id":1530,"date":"2020-10-30T00:07:22","date_gmt":"2020-10-30T00:07:22","guid":{"rendered":"https:\/\/dailyonoff.com\/?p=1530"},"modified":"2022-09-28T11:26:17","modified_gmt":"2022-09-28T11:26:17","slug":"how-to-calculate-the-z-score-in-excel","status":"publish","type":"post","link":"https:\/\/dailyonoff.com\/how-to-calculate-the-z-score-in-excel\/","title":{"rendered":"How to Calculate the Z score in Excel?"},"content":{"rendered":"

How to Calculate the Z score in Excel<\/h2>\n

The Z-score is a statistical value that shows how many standard deviations a specific value happens to be from the mean of a given set of data. You can opt to use STDEV.S or STDEV.P and AVERAGE formulas to calculate the standard deviation and the mean values of the assigned group of data and use the values to calculate the Z-score of every value.<\/p>\n

In simple terms, the Z-score is a way of comparing values from two different sets of data. It is simply defined as the number of standard deviations away from mean a data point lies. When calculating the Z-score, the first statistical value you require is the mean. Excel’s AVERAGE function calculates the value. To get it, you simply need to add up the values in the cell range and then divide that summed number of cells containing numerical values.<\/p>\n

Standard deviation is another statistical value you will need, and Excel offers two ways to calculate it.<\/p>\n

The previous Excel versions only had the STDEV function, which will calculate the standard deviation while treating data as a sample of the population.<\/p>\n

STDEV.S function is identical to the previous STDEV function. This function calculates your standard deviation while treating the data as a sample population.<\/p>\n

STEDV.P function usually calculates your standard deviation while treating the data as an entire population.<\/p>\n

Whichever method you use, the difference is usually small, but the results of the STDEV.P function is always smaller than the results of the STDEV.S function of the same set of data. It is a more conventional approach to assume there is more variability in the data.<\/p>\n