Percentile: Difference between revisions

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imported>Boris Tsirelson
(mistake in formula corrected: k, not p)
imported>Peter Schmitt
(→‎Special cases: correction : or - not and)
Line 28: Line 28:
it may happen that the separating value has positive probability:
it may happen that the separating value has positive probability:
:    <math> P(\omega = x) > 0 \Rightarrow
:    <math> P(\omega = x) > 0 \Rightarrow
             P(\omega\le x) > {k\over100}    \textrm{\ \ and \ \ }
             P(\omega\le x) > {k\over100}    \textrm{\ \ or \ \ }
             P(\omega\ge x) > 1-{k\over100}  </math>
             P(\omega\ge x) > 1-{k\over100}  </math>
or that there are two distinct values for which equality holds
or that there are two distinct values for which equality holds

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Percentiles are statistical parameters which describe the distribution of a (real) value in a population (or a sample). Roughly speaking, the k-th percentile separates the smallest k percent of values from the largest (100-k) percent.

Special percentiles are the median (50th percentile), the quartiles (25th and 75th percentile), and the deciles (the k-th decile is the (10k)-th percentile). Percentiles are special cases of quantiles: The k-th percentile is the same as the (k/100)-quantile.

Definition

The value x is k-th percentile if

Special cases

For a continuous distribution (like the normal distribution) the k-th percentile x is uniquely determined by

In the general case (e.g., for discrete distributions, or for finite samples) it may happen that the separating value has positive probability:

or that there are two distinct values for which equality holds such that

Then every value in the (closed) intervall between the smallest and the largest such value

is a k-th percentiles.

Examples

The following examples illustrate this:

  • Take a sample of 101 values, ordered according to their size:
.
Then the unique k-th percentile is .
  • If there are only 100 values
.
Then any value between and is a k-th percentile.

Example from the praxis:
Educational institutions (i.e. universities, schools...) frequently report admission test scores in terms of percentiles. For instance, assume that a candidate obtained 85 on her verbal test. The question is: How did this student compared to all other students? If she is told that her score correspond to the 80th percentile, we know that approximately 80% of the other candidates scored lower than she and that approximately 20% of the students had a higher score than she had.