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6: Percentiles and Quartiles

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    The concept of percentile[1] applies to either a data set (sample, as represented by a histogram — a discrete distribution) or to a continuous distribution (which represents a population) as shown in Figure 6.1.

    fig43png-300x116.jpg
    Figure 6.1: The concept of percentile applies to either a data set or to a continuous distribution.

    The percentile position of the data point \(x_i\), denoted here by \(P(x_{i})\), is the percentage of the area under the curve up to the point \(x_{i}\). Notation warning : Do not confuse percentile and probability, we use \(P\) to denote both!! (They are related though.)

    To determine the percentile position for \(x_{i}\) from a normal distribution of values, convert \(x_{i}\) to \(z_{i}\) via the \(z\)-transformation, determine the area under the standard normal curve up to \(z_{i}\) and multiply by 100. We have, therefore, already seen how to compute \(P(x_{i})\) given \(x_{i}\) or how to compute \(x_{i}\) for a given percentile \(P\). See Case 5 in Section 5.3 and remember how to use the Standard Normal Distribution Table forward and backwards.


    1. This percentile stuff is all about cumulative frequency or (thinking about probabilities) cumulative relative frequencies. The corresponding probability functions are called Cumulative Distribution Functions or CDFs. You will encounter CDFs in SPSS; they are mentioned later in this chapter.

    This page titled 6: Percentiles and Quartiles is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Gordon E. Sarty via source content that was edited to the style and standards of the LibreTexts platform.