The correct answer is:

z-score; the percentage of scores between the mean and 2.5 standard deviations above or below the mean.

A standardized score, also known as the z-score, measures how many standard deviations a data point is from the mean of the dataset. A z-score of 2.5 indicates that the value is 2.5 standard deviations above the mean. In a normal distribution, the area between the mean and a z-score of 2.5 represents a certain percentage of the data.

Would you like further clarification or more details on how z-scores relate to normal distributions?

Here are five related questions for deeper understanding:

1. What is the formula for calculating a z-score?
2. How is the area under the normal curve between two z-scores found?
3. What percentage of data lies between the mean and a z-score of 1 in a standard normal distribution?
4. How would you interpret a negative z-score?
5. How do z-scores help in comparing data from different distributions?

Tip: A z-score allows you to compare values from different datasets by standardizing them to a common scale (the standard deviation).