Control Group Baseline Benchmark — Free Control vs Treatment Tool

This tool benchmarks a treatment group against its control-group baseline: control mean, treatment mean, difference, percent change versus control, and Cohen’s d using the pooled standard deviation. The control group is what would have happened without the treatment, so every number here is a comparison, not an absolute: a treatment mean of 19 means little alone, but 19 against a control mean of 13 — a difference of 6, a 46% rise, a Cohen’s d near 3.8 — means a large effect relative to the groups’ own variability. You paste both groups and the tool computes the benchmark.

How to use it

Paste control values, comma-separated.
Paste treatment values, comma-separated.
Click Compare groups.
Read the difference and percent change first.
Read Cohen’s d for the standardized size of the effect.

Worked example

Control values 12, 14, 13, 15, 11 average 13.0; treatment values 18, 20, 19, 21, 17 average 19.0. The difference is 6.0, the percent change is +46.2%, and Cohen’s d is about 3.79 — an enormous standardized effect, because the groups differ by nearly four pooled standard deviations with almost no overlap. In a real study, an effect this large with five per group would invite scrutiny (was randomization sound? was measurement blinded?), but the arithmetic is the same for modest effects: d near 0.2 is small, near 0.5 medium, near 0.8 large, by the conventional benchmarks — always interpreted against the field’s own standards.

Honest limits

This tool reports descriptive comparisons and one effect size, not inference: it gives no p-value, no confidence interval, and no claim that the difference is real rather than chance — that requires a statistical test with its assumptions checked. Cohen’s d also assumes roughly similar variances and roughly normal data; with skewed data or unequal variances, the pooled-SD version misleads, and a robust or non-parametric effect measure fits better. Percent change misleads when the control mean is near zero. For publication, pair this benchmark with a test, an interval, and a plot.

Who it is for

This tool is for students learning effect sizes, researchers summarizing a two-group comparison before formal testing, and anyone who wants the practical size of a difference, not just whether it is “significant”. It is a benchmark summary, not a significance test.

Going further

Report all three numbers — difference, percent change, and d — because each hides something the others show: the raw difference needs units to mean anything, percent change needs a sensible baseline, and d needs the variability context. A difference of 6 points is trivial on a 0–1000 scale and huge on a 0–10 scale; d tells you which. And never let “significant” replace “large”: with enough participants, a trivial difference becomes statistically significant, and with few participants, a large difference may not reach significance. The benchmark question is always practical — how big is it, in units that matter? — and this tool answers exactly that, leaving the inference question to the test that follows. Always show the control and treatment distributions, not just their means, alongside this benchmark. Two groups can share identical means and effect sizes while differing completely in shape — one tight, one scattered — and the shape changes what the result means for the next person who tries the treatment. A box plot or dot plot next to the table costs one figure and prevents the most common misreading of effect sizes: that a large d guarantees every treated individual improved. It does not; it guarantees the average moved relative to variability, and individual responses may still overlap heavily. The benchmark summarizes the groups; the plot shows the people, and both belong in the report.

Embed this tool on your website — free

Copy the code below to add this working tool to your own page. A small credit link stays with it — that’s all we ask.