You are comparing response-time distributions for four successive firmware versions deployed across 8,000 IoT gateways. The measurements are right-skewed and clearly bimodal because some devices cache results while others do not. Management wants a single side-by-side visualization that (1) reveals the multimodal shape of each version's distribution, (2) highlights differences in medians and interquartile ranges, and (3) makes the thickness of the long upper tails easy to inspect. Which type of chart will satisfy all three requirements with the least additional annotation?
A stacked bar chart showing the count of observations in predefined latency buckets.
A traditional box-and-whisker plot for each version without additional overlays.
A violin plot for each firmware version, sharing a common vertical response-time axis.
A faceted line plot of the cumulative distribution function (CDF) for each version.
A violin plot combines a box-and-whisker summary (median and IQR) with a mirrored kernel-density estimate whose width at each y-value is proportional to the data's probability density. This exposes multiple peaks as bulges in the "violin", shows tail thickness, and keeps quartile markers visible-meeting the three stated needs. Standard box plots do not display density or multimodality, line plots emphasize temporal trends rather than distributions, and stacked bar charts aggregate counts into bins that hide shape details.
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What is a violin plot and how does it work?
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What does 'right-skewed' and 'bimodal' mean in data distributions?
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Why are other visualizations like box plots or CDFs not suitable here?