Description: The Z-score distribution is a statistical tool that allows for the standardization of a dataset, transforming its values into scores that indicate how many standard deviations they are above or below the mean. This technique is fundamental in data analysis as it facilitates comparison between different datasets that may have different means and standard deviations. The Z-score is calculated by subtracting the mean of the dataset from a specific value and dividing the result by the standard deviation. This results in a standard normal distribution, where the mean is 0 and the standard deviation is 1. The Z-score distribution is particularly useful in statistical analysis and hypothesis testing, as it enables researchers to determine how extreme a particular observation is relative to the average performance. By using Z-scores, researchers can identify which values are significantly above or below the mean, helping to make more informed decisions in data interpretation and analysis.
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