# For What Type of Data Would It Be Appropriate to Use Side by Side Boxplots?

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Scott Campbell

When it comes to visualizing data, boxplots are a powerful tool that can provide valuable insights. However, in some cases, a single boxplot may not be sufficient to compare multiple datasets effectively.

This is where side by side boxplots come into play. In this article, we will explore the types of data for which it would be appropriate to use side by side boxplots.

## Understanding Boxplots

Before diving into the appropriate use of side by side boxplots, let’s quickly recap what boxplots are and why they are useful. A boxplot is a graphical representation of statistical data that displays key information such as the minimum, first quartile, median, third quartile, and maximum values. This visualization allows us to easily identify the range, distribution, and potential outliers within a dataset.

## Comparing Distributions

Side by side boxplots are particularly useful when we want to compare the distributions of two or more datasets. By placing multiple boxplots next to each other, we can visually assess differences in their central tendencies, variability, skewness, and potential outliers.

## Categorical Variables

One scenario where side by side boxplots excel is when we have a categorical variable that we want to compare across different groups or categories. For example, let’s say we have a dataset that includes the exam scores of students from three different schools: A, B, and C. By creating side by side boxplots for each school’s exam scores on the same axis, we can easily compare their distributions and identify any variations between them.

### An Example:

```<svg width="500" height="300">
<g transform="translate(50 20)">
<rect x="0" y="0" width="100" height="200" fill="#e41a1c"></rect>
<line x1="50" y1="0" x2="50" y2="200" stroke="#000000"></line>
<rect x="150" y="50" width="100" height="150" fill="#377eb8"></rect>
<line x1="200" y1="50" x2="200" y2="200" stroke="#000000"></line>
<rect x="300" y="100" width="100" height="100" fill="#4daf4a"></rect>
</g>
</svg>
```

Note: The example above is a simplified representation of side by side boxplots using SVG (Scalable Vector Graphics) elements. In practice, you can use various charting libraries or software to create more visually appealing and interactive side by side boxplots.

## Numerical Variables

In addition to categorical variables, side by side boxplots can also be used to compare different distributions of numerical variables. For instance, suppose we have a dataset that contains the salaries of employees across three different departments: HR, Finance, and IT. By creating side by side boxplots for each department’s salaries, we can easily compare the salary distributions and identify any differences in the central tendencies or variabilities.

```<svg width="500px" height= "300px">
<g transform= "translate(50 20)">

<!-- HR Department -->
<line x1= "50" y1= "100" x2= "150" y2= "100" stroke-width= "2" stroke= "#000000"></line>
<line x1= "50" y1= "75" x2= "50" y2= "125" stroke-width= "2" stroke= "#000000"></line>
<line x1= "150" y1= "75" x2= "150" y2= "125"" stroke-width=" 2"" stroke="# 000000""></line>
<rect x="80"" y=" 80"" width=" 40"" height=" 40"" fill="# ffa500"></rect>

<!-- Finance Department -->
<line x1="200"" y1=" 125"" x2=" 300"" y2=" 125"" stroke-width=" 2"" stroke="# 000000"">
<line x1 = '200''y1 = '100''x2 = '200''y2 = '150''stroke - width = '2'   stroke = '#000000'>
<line x1='300'y1='100'x2='300'y2='150'stroke - width='

```

Note: The example above is a simplified representation of side by side boxplots for numerical variables using SVG elements. Again, in practice, you can utilize charting libraries or software to create more visually appealing and interactive side by side boxplots.

### Conclusion

Side by side boxplots are a valuable visualization tool when it comes to comparing distributions of categorical or numerical variables. By placing multiple boxplots side by side, we can easily identify differences in central tendencies, variabilities, and potential outliers among different datasets. Whether you’re analyzing exam scores across schools or salaries across departments, side by side boxplots can help you gain deeper insights into your data.

So, the next time you have multiple datasets that need to be compared visually, consider using side by side boxplots to enhance your analysis!