## Measures of Relative Standing

Measures of relative standing can be used to compare values from different data sets, or to compare values within the same data set.

### Learning Objectives

Outline how percentiles and quartiles measure relative standing within a data set.

### Key Takeaways

#### Key Points

- The common measures of relative standing or location are quartiles and percentiles.
- A percentile is a measure used in statistics indicating the value below which a given percentage of observations in a group of observations fall.
- The 25th percentile is also known as the first quartile (Q1), the 50th percentile as the median or second quartile (Q2), and the 75th percentile as the third quartile (Q3).
- To calculate quartiles and percentiles, the data must be ordered from smallest to largest.
- For very large populations following a normal distribution, percentiles may often be represented by reference to a normal curve plot.
- Percentiles represent the area under the normal curve, increasing from left to right.

#### Key Terms

**quartile**: any of the three points that divide an ordered distribution into four parts, each containing a quarter of the population**percentile**: any of the ninety-nine points that divide an ordered distribution into one hundred parts, each containing one per cent of the population

Measures of relative standing, in the statistical sense, can be defined as measures that can be used to compare values from different data sets, or to compare values within the same data set.

### Quartiles and Percentiles

The common measures of relative standing or location are quartiles and percentiles. A *percentile* is a measure used in statistics indicating the value below which a given percentage of observations in a group of observations fall. For example, the 20th percentile is the value (or score) below which 20 percent of the observations may be found. The term percentile and the related term, percentile rank, are often used in the reporting of scores from norm-referenced tests. For example, if a score is in the 86th percentile, it is higher than 86% of the other scores. The 25th percentile is also known as the first quartile (Q1), the 50th percentile as the median or second quartile (Q2), and the 75th percentile as the third quartile (Q3).

To calculate quartiles and percentiles, the data must be ordered from smallest to largest. Recall that quartiles divide ordered data into quarters. Percentiles divide ordered data into hundredths. To score in the 90th percentile of an exam does not mean, necessarily, that you received 90% on a test. It means that 90% of test scores are the same or less than your score and 10% of the test scores are the same or greater than your test score.

Percentiles are useful for comparing values. For this reason, universities and colleges use percentiles extensively. Percentiles are mostly used with very large populations. Therefore, if you were to say that 90% of the test scores are less (and not the same or less) than your score, it would be acceptable because removing one particular data value is not significant.

For very large populations following a normal distribution, percentiles may often be represented by reference to a normal curve plot. The normal distribution is plotted along an axis scaled to standard deviations, or sigma units. Percentiles represent the area under the normal curve, increasing from left to right. Each standard deviation represents a fixed percentile. Thus, rounding to two decimal places, [latex]-3[/latex] is the 0.13th percentile, [latex]-2[/latex] the 2.28th percentile, [latex]-1[/latex] the 15.87th percentile, 0 the 50th percentile (both the mean and median of the distribution), [latex]+1[/latex] the 84.13th percentile, [latex]+2[/latex] the 97.72nd percentile, and [latex]+3[/latex] the 99.87th percentile. This is known as the 68–95–99.7 rule or the three-sigma rule.

Note that in theory the 0^{th} percentile falls at negative infinity and the 100^{th} percentile at positive infinity; although, in many practical applications, such as test results, natural lower and/or upper limits are enforced.

### Interpreting Percentiles, Quartiles, and Median

A percentile indicates the relative standing of a data value when data are sorted into numerical order, from smallest to largest. [latex]\text{p}[/latex]% of data values are less than or equal to the [latex]\text{p}[/latex]^{th} percentile. For example, 15% of data values are less than or equal to the 15^{th} percentile. Low percentiles always correspond to lower data values. High percentiles always correspond to higher data values.

A percentile may or may not correspond to a value judgment about whether it is “good” or “bad”. The interpretation of whether a certain percentile is good or bad depends on the context of the situation to which the data applies. In some situations, a low percentile would be considered “good’; in other contexts a high percentile might be considered “good”. In many situations, there is no value judgment that applies.

Understanding how to properly interpret percentiles is important not only when describing data, but is also important when calculating probabilities.

### Guideline:

When writing the interpretation of a percentile in the context of the given data, the sentence should contain the following information:

- information about the context of the situation being considered,
- the data value (value of the variable) that represents the percentile,
- the percent of individuals or items with data values below the percentile.
- Additionally, you may also choose to state the percent of individuals or items with data values above the percentile.

## Median

The median is the middle value in distribution when the values are arranged in ascending or descending order.

### Learning Objectives

Identify the median in a data set and distinguish it’s properties from other measures of central tendency.

### Key Takeaways

#### Key Points

- The median divides the distribution in half (there are 50% of observations on either side of the median value). In a distribution with an odd number of observations, the median value is the middle value.
- When the distribution has an even number of observations, the median value is the mean of the two middle values.
- The median is less affected by outliers and skewed data than the mean, and is usually the preferred measure of central tendency when the distribution is not symmetrical.
- he median cannot be identified for categorical nominal data, as it cannot be logically ordered.

#### Key Terms

**outlier**: a value in a statistical sample which does not fit a pattern that describes most other data points; specifically, a value that lies 1.5 IQR beyond the upper or lower quartile**median**: the numerical value separating the higher half of a data sample, a population, or a probability distribution, from the lower half

A measure of central tendency (also referred to as measures of center or central location) is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or center of its distribution. There are three main measures of central tendency: the mode, the median and the mean. Each of these measures describes a different indication of the typical or central value in the distribution.

The median is the middle value in distribution when the values are arranged in ascending or descending order. The median divides the distribution in half (there are 50% of observations on either side of the median value). In a distribution with an odd number of observations, the median value is the middle value.

Looking at the retirement age distribution (which has 11 observations), the median is the middle value, which is 57 years:

54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60

When the distribution has an even number of observations, the median value is the mean of the two middle values. In the following distribution, the two middle values are 56 and 57, therefore the median equals 56.5 years:

52, 54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60

The median is less affected by outliers and skewed data than the mean, and is usually the preferred measure of central tendency when the distribution is not symmetrical. The median cannot be identified for categorical nominal data, as it cannot be logically ordered.

## Mode

The mode is the most commonly occurring value in a distribution.

### Learning Objectives

Define the mode and explain its limitations.

### Key Takeaways

#### Key Points

- There are some limitations to using the mode. In some distributions, the mode may not reflect the center of the distribution very well.
- It is possible for there to be more than one mode for the same distribution of data, (eg bi-modal). The presence of more than one mode can limit the ability of the mode in describing the center or typical value of the distribution because a single value to describe the center cannot be identified.
- In some cases, particularly where the data are continuous, the distribution may have no mode at all (i.e. if all values are different). In cases such as these, it may be better to consider using the median or mean, or group the data in to appropriate intervals, and find the modal class.

#### Key Terms

**skewness**: A measure of the asymmetry of the probability distribution of a real-valued random variable; is the third standardized moment, defined as where is the third moment about the mean and is the standard deviation.

A measure of central tendency (also referred to as measures of center or central location) is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or center of its distribution. There are three main measures of central tendency: the mode, the median and the mean. Each of these measures describes a different indication of the typical skewness in the distribution.

The mode is the most commonly occurring value in a distribution. Consider this dataset showing the retirement age of 11 people, in whole years:

54, 54, 54, 55, 56, 57, 57, 58, 58, 60, 60

The most commonly occurring value is 54, therefore the mode of this distribution is 54 years. The mode has an advantage over the median and the mean as it can be found for both numerical and categorical (non-numerical) data.

There are some limitations to using the mode. In some distributions, the mode may not reflect the center of the distribution very well. When the distribution of retirement age is ordered from lowest to highest value, it is easy to see that the center of the distribution is 57 years, but the mode is lower, at 54 years. It is also possible for there to be more than one mode for the same distribution of data, (bi-modal, or multi-modal). The presence of more than one mode can limit the ability of the mode in describing the center or typical value of the distribution because a single value to describe the center cannot be identified. In some cases, particularly where the data are continuous, the distribution may have no mode at all (i.e. if all values are different). In cases such as these, it may be better to consider using the median or mean, or group the data in to appropriate intervals, and find the modal class.