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6 Οκτ 2023 · Learn how to calculate and interpret z-scores, which are statistical measures of the distance from the mean in standard deviation units. Find out how z-scores are used for probability estimation, hypothesis testing, comparing datasets, and identifying outliers.
A z-score measures exactly how many standard deviations above or below the mean a data point is. Here's the formula for calculating a z-score: z = data point − mean standard deviation
A z-score (also called a standard score) gives you an idea of how far from the mean a data point is. More technically, it’s a measure of how many standard deviations below or above the population mean a raw score is. A z-score can be placed on a normal distribution curve.
Learn how to calculate and interpret z-scores, which measure the distance between a data point and the mean using standard deviations. Z-scores help you compare observations across variables, identify outliers, and find probabilities and percentiles.
A z-score is an example of a standardized score. A z-score measures how many standard deviations a data point is from the mean in a distribution.
Learn how to use z-standardization to compare data points from different datasets and convert them into z-scores. Find out how to calculate z-scores, interpret them, and use the z-table to find probabilities.
Calculator to find out the z-score of a normal distribution, convert between z-score and probability, and find the probability between 2 z-scores.
