This example shows how to find the z-score for a data point. Remember that the z-score tells you how many standard deviations away from the mean a particula Specifically, given a one-dimensional numpy.array instance z_scores, one can obtain the p-values as. p_values = 1 - scipy.special.ndtr(z_scores) or alternatively. p_values = scipy.special.ndtr(-z_scores) Using a z-score table to calculate the proportion (%) of the SND to the left of the z-score. The corresponding area is 0.8621, which translates into 86.21% of the standard normal distribution being below (or to the left) of the z-score. To find the p-value, subtract this from 1 (which gives you 0.1379), then multiply by 2 (which gives you p = 0 The formula to compute a Z-score for a data point given that we know the value of the population mean μ \mu and standard deviation σ \sigma is: Z(x)= x − μ σ. Intuitively, you can think of a Z-score as telling you how far away from the mean any data point is, in units of standard deviation. Z-Score Formula. To convert any bell curve into a standard bell curve, we use the above formula. Let x be any number on our bell curve with mean, denoted by mu, and standard deviation denoted by sigma. The formula produces a z -score on the standard bell curve. Any bell curve can be transformed into the standard bell curve by using this formula. Z score is an important concept in statistics. Z score is also called standard score. This score helps to understand if a data value is greater or smaller than mean and how far away it is from the mean. More specifically, Z score tells how many standard deviations away a data point is from the mean. Z score = (x -mean) / std. deviation How to Calculate Z-Scores for Confidence Intervals To calculate the z-score of a confidence interval: Subtract the confidence level (as a decimal) from 1. Divide by 2. Subtract this result from 1. Look up this area in the z-table to obtain the z-score. For example, calculate the z-score required for a 92% confidence interval. Step 1. How to Calculate Z-Scores in R. In statistics, a z-score tells us how many standard deviations away a value is from the mean. We use the following formula to calculate a z-score: z = (X – μ) / σ. where: X is a single raw data value. μ is the population mean. σ is the population standard deviation. Select the cell where you want to display the Z Score. Enter the formula: = (X - Mean) / Standard Deviation. Replace X with the cell reference of the data point. Press Enter to get the Z Score. It is important to note that the Z Score is a measure of how many standard deviations a data point is from the mean. This can easily be done by using the AVERAGE formula in Excel. In a new cell, enter the following formula. =AVERAGE (number1) Replace number1 with the range of cells containing the data. The mean for my data set is 76.9. 2. Calculate the SD of the data set. The next step is to calculate the SD of the data set. tGoVZ.