Violin plots + outlier dots + additional markers are more helpful. Sometimes the CDF is also more useful than the PDF, e.g. for latencies.
Violin plots + outlier dots + additional markers are more helpful. Sometimes the CDF is also more useful than the PDF, e.g. for latencies.
I like swarm plots for this kind of task.
https://seaborn.pydata.org/generated/seaborn.swarmplot.html
Edit: well, I suppose I should clarify the comment on violin plots is implementation dependent and biased by my personal preferences for visualization libraries
Showing the number of samples is good though. The combined version seems useful.
Consider inner="stick" from [1] instead. It probably comes closer to what you're looking for.
[1] https://seaborn.pydata.org/generated/seaborn.violinplot.html
https://wellcomeopenresearch.org/articles/4-63/v1
They can visualize common statistics like box-plots, the distribution shape like violin plots, as well as the raw data!
Like all data visualization techniques, which plot type is better depends on the context.
It's basically just my boss making suggestions and me implementing, so the results are probably less than optimal for this kind of thing.
[0] is just one of the first results from a search that makes this case.
[0] https://stanfordreview.org/calculus-is-overrated-why-we-shou...