<p style="text-align:justify"><span style="font-size:14px">A percentile indicates the relative standing of a data value when data are sorted into numerical order from smallest to largest.</span></p>

<p style="text-align:justify">&nbsp;</p>

<p style="text-align:justify"><span style="font-size:14px">Percentages of data values are less than or equal to the <em>p</em>th percentile. For example, 15 percent of data values are less than or equal to the 15th percentile.</span></p>

<p style="text-align:justify">&nbsp;</p>

<p style="margin-left:48px; text-align:justify"><span style="font-size:14px">&bull; Low percentiles always correspond to lower data values.</span></p>

<p style="margin-left:48px; text-align:justify"><span style="font-size:14px">&bull; High percentiles always correspond to higher data values.</span></p>

<p style="text-align:justify">&nbsp;</p>

<p style="text-align:justify"><span style="font-size:14px">A percentile may or may not correspond to a value judgment about whether it is <em>good </em>or <em>bad</em>. The interpretation of whether a certain percentile is <em>good </em>or <em>bad </em>depends on the context of the situation to which the data apply. In some situations, a low percentile would be considered <em>good; </em>in other contexts, a high percentile might be considered <em>good</em>. In many situations, there is no value judgment that applies. A high percentile on a standardized test is considered good, while a lower percentile on body mass index might be considered good. A percentile associated with a person&#39;s height doesn&#39;t carry any value judgment.</span></p>

<p style="text-align:justify">&nbsp;</p>

<p style="text-align:justify"><span style="font-size:14px">Understanding how to interpret percentiles properly is important not only when describing data, but also when calculating probabilities in later chapters of this text.</span></p>

<p style="text-align:justify">&nbsp;</p>

<p style="text-align:justify">&nbsp;</p>