This entry is part of the Chart Index, the reference library for the Chart Design Field Guide.

The strip plot is the most honest chart for showing a distribution: every observation is a dot. No bins, no smoothing, no summary. The eye reads the cloud and forms its own impression of centre, spread, and outliers. For small samples — fewer than 100 observations per group — it is almost always the right form.

The jitter is the trick that makes it work. When multiple observations share a value (or near-value), their dots overlap and the count is lost. A small random horizontal nudge — jitter — separates them, and density becomes visible as a horizontal smear rather than a single dot stuck on the value line.

What it is

A strip plot maps a categorical variable to category position along one axis and a quantitative variable to position along the other. Each observation is a single point. When points would overlap, they are jittered — given a small random offset perpendicular to the value axis — to make their count visible. The form is sometimes called a one-dimensional scatter plot or univariate scatter.

Conversion rate by experiment variant2026-W21 · 5 variants · A/B sample · individual cohorts shown

Five variants, each with 30–60 observations (one per cohort). The jittered cloud shows every observation; the spread within each variant is visible at a glance. A box plot of the same data would show medians; the strip plot shows the data.

When to use it

Strip plots are the right choice when:

  • You have small to medium samples (5–200 per group) and want every observation visible.
  • The reader's question is "what does the actual data look like?" rather than "summarise it".
  • You are comparing many groups and box plots feel too abstract.
  • You have few duplicate values — heavy overlap requires jitter or alpha blending.
  • You want to see outliers in context with the rest of the distribution.

When not to use it

  • Large samples. With 1,000+ observations per group, the cloud becomes a solid bar of dots. Use alpha blending, a bee swarm, or switch to a density / violin.
  • Many ties at exact values. Without jitter, dots stack and the cloud is unreadable. Without sufficient jitter, ties become smudges. Pick the form carefully.
  • Precise quartile reading. Strip plots show the data, not summaries. A box plot reads quartiles faster.
  • One-dimensional with no category. A single column of jittered dots is a frequency table in disguise. Use a histogram or density.

Design principles

Choose jitter width deliberately

Too little jitter and dots stack on the value line; too much and the cloud spreads across nearby categories and confuses comparison. A jitter width of around 30–50% of the category gap is the sweet spot.

Use alpha for medium samples

For samples in the 100–500 range, fully opaque dots produce a solid clump. Reduce opacity to 0.3–0.5 so overlapping dots produce visible density without losing individual points.

Pair with summary statistics

A strip plot alone shows the data but no summary. Overlay a median line, the quartile box, or both. Most reading tasks need both what does the data look like? and where is the centre?

Strip plot vs. box plot — different views of the same data
STRIP — EVERY POINTABCBOX — SUMMARY ONLYABC
The strip plot shows every observation; the box plot summarises. Combining them — as in a raincloud — gives both views without trade-off.

Annotate the sample size

Each strip's apparent density depends on how many observations were drawn. A small note — n = 60 — under each strip stops the eye reading wide clouds as more important than narrow ones.

Use the chart for the right N

Strip plots scale poorly. Below 30 observations, every dot is meaningful. Between 30 and 200, the form is informative. Above 500, dot overlap destroys the count even with jitter. Pick the form for your N.

Highlight specific points

If certain observations matter — outliers, named points, the value of interest — give them a different colour or label them. Few's principle of minimising visual lookup applies to point clouds as much as to bars.

Avoid horizontal bands of guides

Heavy horizontal gridlines compete with the dot cloud. Use a single subtle baseline at the category axis and at most one or two value reference lines (mean, target). Anything more drowns the data.

Anatomy

The Composition of a Strip / Jitter Plot
5.0%7.0%9.0%11.0%Variant An = 42Variant Bn = 38Variant Cn = 45Variant Dn = 40Variant En = 36JITTER WIDTHMEAN LINE
An anatomical guide

A strip / jitter plot is structurally minimal: an axis, a category position per group, and a cloud of jittered dots. Optional summary marks (mean line, median, quartile band) overlay the cloud without replacing it.

  • Box plot — summary instead of raw points. Faster reading; less honest about shape.
  • Violin plot — smoothed density. Better for medium-to-large samples.
  • Bee swarm — strip plot with collision avoidance. Every dot visible, no random jitter.
  • Raincloud plot — half-violin + box + jittered strip. The hybrid for ambitious distribution displays.
  • Dot plot — one dot per category for a value. Different question entirely (summary, not distribution).

Reading list

  • Tukey, J. (1977). Exploratory Data Analysis. The case for showing the data.
  • Wilkinson, L. (1999). Dot Plots. On univariate point displays.
  • Allen, M. et al. (2019). Raincloud Plots. The strip-violin-box hybrid.
  • Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Practical strip-plot recipes.