Quartiles in Statistics
The first quartile is the middle point between the minimum number and the median of the dataset. It's assumed 25% of the data is below it.
The second quartile is the median of the dataset, thus 50% of the data is assumed to lie below it.
The third quartile is the middle point between the median and the maximum number of the dataset. 75% of the data lies below it.
The definitions above will help us to better answer the following questions:
11. In general, 50% of the values of a data set lie at or below the median
This had already been defined in the definition of the second quartile.
12. 75% of the values of a data set lie at or below the third quartile
This has also been defined above.
13. If a sample consists of 1700 test scores, 850 of them would lie at or below the second quartile.
The number comes from taking 50% of 1700 = 0.5*1700 = 850
14. If a sample consists of 1700 test scores, 1275 of them would be at or above the first quartile
We have bolded the words above and first to highlight the fact that the first quartile has 25% of the data below it, thus 100% - 25% = 75% of the data set if above it.
75% of 1700 is 0.75 * 1700 = 1275.