![]() ![]() If the result is positive, the score is above the mean. Note that if the result is a negative number, the score is below the mean. Then we take that result and divide by the standard deviation. So how do we actually calculate a Z-score? It’s a simple formula:įor any given raw score (X) that we want to translate, we subtract off the mean. Then we can compare Jagdeep’s performance in statistics to their performance in English, and we can also compare their performance to Jasmine’s performance. With Z-scores, we can take their grades from those two different classes and figure out their performance relative to the mean in each class. These were entirely different tests, so some translation to standard scores is needed. It would not make sense to directly compare Jagdeep’s test score of 20 in statistics with Jasmine’s test score of 45 in English. In English, however, the mean grade is a raw score of 40, and the standard deviation is 10. From the distance between Z-scores, we can surmise that the standard deviation in statistics is 5. Notice that in statistics, the mean grade is a raw score of 30. In addition to its position above or below the mean, a Z-score also communicates that score’s distance from the mean in terms of how many standard deviations away it is.įor example, with a mean of 65 and standard deviation of 3, the raw score 59 can be converted into a Z-score. A positive Z-score means that raw score is above the mean. A negative Z-score means that raw score is below the mean. This allows for precise comparison of a particular score to the rest of the scores in a dataset, and even across different datasets.Ī Z-score is just a raw score expressed in terms of its position relative to the mean and in terms of standard deviations. This will allow us to transform scores in any numeric dataset, using any scale, into a standard metric. In this chapter, we will learn how to use the statistics of the mean and standard deviation to generate standard scores, or Z-scores. The same is true of trying to compare numbers from different datasets. In fact, if you are a native speaker of English, you might have heard before “that’s like comparing oranges to apples,” meaning it’s impossible to compare. One is more tart, the other quite mild in flavour, so it is difficult to compare oranges and apples. Why? When we think about it, it is tough to directly compare them in terms of the property of sweetness. How sweet are these fruits? Is an apple sweeter? Or is an orange sweeter? What do you think? Is it difficult to say? Usually when I survey people with this question, there is a pretty even split, with half saying an apple is sweeter, and the other half saying the orange is sweeter. ![]() “Oranges” by Dious is marked with CC PDM 1.0 Have a look at these images: “Apple” by Open Grid Scheduler / Grid Engine is marked with CC0 1.0 ![]() In this chapter, we will address the topic of Z-scores, one type of what are commonly called standard scores.īefore we begin, we will examine a real world example of why standardizing scores is useful and important. ![]()
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