Boxplots (also known as box and whister plots)

Boxplots (also known as box and whister plots)

By Saul McLeod, published 2019

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Boxplots are a type of chart often used in explanatory data analysis to visually show the distribution of numerical data and skewness through displaying the data quartiles (or percentiles) and averages.

Definitions

Minimum Score

The lowest score, excluding outliers (shown at the end of the left whisker).

Lower Quartile

Twenty-five percent of scores fall below the lower quartile value (also known as the first quartile).

Median

The median marks the mid-point of the data and is shown by the line that divides the box into two parts (sometimes known as the second quartile). Half the scores are greater than or equal to this value and half are less.

Upper Quartile

Seventy-five percent of the scores fall below the upper quartile value (also known as the third quartile). Thus, 25% of data are above this value.

Maximum Score

The highest score, excluding outliers (shown at the end of the right whisker).

Whiskers

The upper and lower whiskers represent scores outside the middle 50% (i.e. the lower 25% of scores and the upper 25% of scores).

The Interquartile Range (or IQR)

This is the boxplot showing the middle 50% of scores.

Boxplots divide the data into sections that each contain approximately 25% of the data in that set.

Boxplots are useful as they provide a visual summary of the data enabling researchers to quickly identify mean values, the dispersion of the data set, and signs of skewness.

Note the image above represents data which is a perfect normal distribution and most boxplots will not conform to this symmetry (where each quartile is the same length).

Box plots are a useful way to visualize differences among different samples or groups. They manage to provide a lot of statistical information, including — medians, ranges, and outliers.

Step 1: Compare the medians of boxplots

Compare the respective medians of each boxplot. If the median line of a boxplot lies outside of the box of a comparison boxplot, then there is likely to be a difference between the two groups.

Source: https://blog.bioturing.com/2018/05/22/how-to-compare-box-plots/

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Step 2: Compare the interquartile ranges and whiskers of boxplots

Compare the interquartile ranges (that is, the box lengths), to examine how the data is dispersed between each sample. The longer the box the more dispersed the data. The smaller the less dispersed the data.

Next, look at the overall spread as shown by the extreme values at the end of two whiskers. This shows the range of scores (another type of dispersion). Larger ranges indicate wider distribution, that is, more scattered data.

Step 3: Look for potential outliers (see above image)

When reviewing a boxplot, an outlier is defined as a data point that is located outside the whiskers of the boxplot.

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Step 4: Look for signs of skewness

If the data do not appear to be symmetric, does each sample show the same kind of asymmetry?

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