Violin plot

Some drawings look like a violin.

They show us many facts. These plots help us compare groups.

They show where most things are. They also show the middle value.

One person made box plots first. Then, two men made violin plots. They wanted to show more info.

A violin plot can have layers. Some layers show most of the data.

These plots can show many peaks. They help us see data clearly.

Imagine you want to compare two groups of things.

You might want to see how many items fall into different spots. A violin plot is a special way to show this data. It looks like a musical violin.

This tool helps you see the full shape of your data.

Long ago, a man named John Tukey made box plots. These plots show a middle value and a range. In 1997, Jerry L. Hintze and Ray D. Nelson made violin plots. They wanted to show even more information. A violin plot includes everything in a box plot. It also shows where the data is most likely to be. This is called probability density.

Violin plots can have many layers. One layer shows all possible results. Other layers show values that happen most of the time. These plots can also show many peaks. A peak is a spot where many data points gather. This helps you see different groups within one set. Some people find them hard to read. They might prefer other charts instead. But violin plots can tell a very big story.

Imagine you are looking at a group of different things.

You might want to see how many items fall into certain spots. A violin plot is a special way to show this data. It is a type of statistical graphic. This tool helps you compare different groups at once. It shows the shape of your data very clearly.

Scientists use these to see how values are spread out.

A violin plot works by showing many layers of information. It includes everything you would find in a box plot. It shows a marker for the median value. It also shows the interquartile range. This is a specific middle range of the data. Sometimes, it even shows every single sample point. The outer shape shows all the possible results. Other layers might show values that happen 95% of the time. Another layer could show values that happen 50% of the time.

People have been making charts like this for a long time. John Tukey created the box plot in 1977.

Later, two men wanted to add even more detail. Jerry L. Hintze and Ray D. Nelson proposed the violin plot in 1997. They wanted to show more than just a summary. The name comes from how the shape looks like a violin. Some people also call them bean plots. This is because of their rounded shape.

These plots are great for seeing peaks in data. A peak is a spot where many data points gather. This is called probability density. If there is more than one peak, the data is called multimodal. The plot shows where these peaks are located. It also shows how tall each peak is. You can use them to compare different categories. For example, you could compare temperatures during the day and night. You could also compare car prices from different makers.

Even though they are useful, they are not the most popular. Many people still prefer to use simple box plots. This is because violin plots can be hard to understand. They might be tricky for people who are not used to them. A simpler way to see the data is using histograms. You could also use stacked kernel density plots instead. Still, the violin plot remains a very powerful tool. It tells a much fuller story about the data than a simple box plot does.

A violin plot is a specialized statistical graphic used for comparing probability distributions. It is a tool that helps researchers visualize how data is spread out across different groups. While it shares many features with the box plot, it provides much more detailed information. This extra detail comes from adding a rotated kernel density plot to each side of the graphic.

By looking at the shape of the plot, a reader can see the full distribution of the data. This makes it much more descriptive than a simple summary of numbers.

To understand how a violin plot works, you must look at its various internal layers. The most basic version includes all the data found in a standard box plot. This includes a marker for the median, which is the middle value of the dataset. It also includes a box or a marker to show the interquartile range. In cases where the number of samples is low, the plot might even show every individual sample point.

Beyond these elements, the outer shape represents the probability density of the data. This density is usually smoothed out by a mathematical tool called a kernel density estimator.

These plots can be organized into several distinct layers of information. The outermost layer of the shape represents all possible results within the dataset. A second layer inside might represent the specific values that occur 95% of the time. If the plot includes a third layer, it might show the values that occur 50% of the time. This layering allows a scientist to see both the broad possibilities and the most common outcomes at once. This structure helps differentiate the violin plot from a basic kernel density plot.

One of the most significant advantages of this tool is its ability to show multimodal data. Multimodal data is information that contains more than one peak. In a standard box plot, these separate peaks might be hidden within a single summary. However, a violin plot clearly displays the presence of different peaks. It shows exactly where these peaks are located along the axis. It also shows their relative amplitude, or how tall each peak is compared to the others.

The history of this graphic is tied to the evolution of statistical visualization. In 1977, a mathematician named John Tukey created the box plot. For twenty years, the box plot was a standard way to show data summaries. Then, in 1997, Jerry L. Hintze and Ray D. Nelson proposed the violin plot. They wanted a way to display even more information than the box plot allowed. The name "violin plot" comes from the way the rounded density shapes resemble the body of a violin.

Some people also refer to these graphics as bean plots.

Researchers use violin plots to compare variables across different categories. For example, a scientist might use one to compare temperature distributions between day and night. Another researcher might use them to compare car prices across different car makers. These comparisons help identify how different groups behave. While they are powerful, violin plots are currently less popular than box plots. This is often because they can be harder for uninitiated readers to interpret correctly.

If a reader finds them too complex, they might use stacked histograms or kernel density plots instead.

It is important to note that the definition of a violin plot has shifted slightly over time. Originally, the term meant a combination of a box plot and a two-sided kernel density plot. Today, some people use the term to describe only the two-sided kernel density plots. They may leave out the box plot elements entirely. Despite these changes in usage, the core purpose remains the same. The goal is to provide a visual map of how likely different values are to occur within a population.