Percentile

Scoring in the 90th percentile means 90% of test scores are at or below your score, and 10% are at or above it. It does not mean you received 90% on the test itself. Percentiles describe your relative standing compared to all other test takers, not your actual percentage correct.

Percentiles divide an ordered dataset into 100 equal parts. Each percentile represents the percentage of data values that fall at or below a specific point. For example, the 50th percentile has approximately 50% of data points below it and 50% above it, making it the median of the distribution.

The kth percentile is found using the formula i = (k/100) × n, where k is the percentile and n is the total number of data points. If i is an integer, the percentile is the data value at position i. If i is not an integer, round up and down to the nearest integers and average the two corresponding data values.

Percentiles allow universities to compare individual student scores against an entire population. By converting each score to its percentile rank, institutions can determine a student's relative standing. This comparison is more meaningful than raw scores alone because it shows how a student performed relative to peers.

Percentiles require data to be arranged in ascending order from lowest to highest. This ordering establishes the position of each data value and enables calculation of its percentile rank. Low percentiles always correspond to lower data values, while high percentiles correspond to higher data values in the ordered set.

No. Whether a percentile is 'good' or 'bad' depends entirely on context. In some situations, a low percentile is desirable; in others, a high percentile is preferred. Many datasets have no inherent value judgment, so interpretation requires understanding the specific application and what the data represents.

Percentiles are a type of quantile or fractile that measure relative standing by showing where a data value falls within a distribution. Understanding percentiles is foundational for calculating probabilities and interpreting data position. They complement other measures like quartiles and what are outliers when analyzing dataset characteristics.