What is Violin Plot?
A violin plot visualises the distribution of a numeric variable across one or more groups by combining a box plot with a mirrored density curve. It shows where values cluster and how spread-out or multi-peaked a set of responses is.
A violin plot displays the distribution of numeric data for a group as a symmetrical shape (like a violin) formed from a smoothed estimate of the data density. The plot's width at each value reflects how many observations fall near that value. Many violin plots also include a box plot or markers for the median and quartiles inside the shape, and can overlay individual data points. Unlike a simple bar chart or mean/median summary, a violin plot reveals the full shape of the distribution — for example whether responses are tightly clustered, widely spread, or have multiple peaks.
Usage example
Compare survey satisfaction scores across languages using violin plots: each language gets a violin showing how ratings are distributed. A wide section near the top for one language means many respondents gave high scores, while a bimodal violin for another language indicates two common but different response clusters.
Practical application
Violin plots help you understand not just average responses but how responses are distributed across a community or language group. For Hearo users this is useful to spot patterns that signal translation or engagement issues (for example, one language showing a lot of neutral responses or a bimodal pattern), identify where follow-up questions are needed, and make fair comparisons between groups. They’re more informative than boxplots when you need to see multimodality or nuances in response spread, but should be used with enough data to give a stable density estimate.
FAQ
How do I read the width of a violin plot?
The width at any point represents how many responses fall near that value: wider means more responses there, narrower means fewer. Peaks show common response values; gaps or thin sections indicate rare values.
When should I use a violin plot instead of a box plot?
Use a violin plot when you want to see the full shape of the distribution (e.g. multimodal or skewed data). If you only need summaries like median and quartiles or you have very small samples, a box plot or a plot of individual points may be simpler and more reliable.
Are there limitations I should watch for?
Yes—violin plots rely on a smoothed density estimate, which can be misleading with small sample sizes or with extreme smoothing settings. Always check sample sizes, consider overlaying raw data points, and use consistent smoothing parameters when comparing groups.