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Chapter 5 of the textbook tells us that, “The purpose of z-scores, or standard scores, is to identify and describe the exact location of each score in a distribution” (Gravetter, Wallnau, Forzano & Witnauer, 2021, Pg. 151). By using the mean and standard deviation, an entire set of scores is transformed into z-scores, thus standardizing the distribution (Gravetter, Wallnau, Forzano & Witnauer, 2021). The z-score, or standard score, highlight where each score is within a distribution. What I gathered from an example in the reading is that a z-score is essentially giving context to a number. I believe it was referred to as a raw score before becoming a z-score and how I interpreted that was comparing it an incident involving a group of people, then becoming more of an investigation as each individual’s story is accounted for. Other useful information provided is that, “the z-scores from a standardized distribution that can be directly compared to other distributions that also have been transformed into z-scores” (Gravetter, Wallnau, Forzano & Witnauer, 2021, Pg. 152). By providing the sign (+/-), the precise location of a z-score is identified; (+) indicates that the score is above the mean, and (-) for below. The numerical value tells the distance. This is done by counting the number of standard deviations between X and the mean (Gravetter, Wallnau, Forzano & Witnauer, 2021, Pg. 152).