Is this good for educators?

Yes — this version of "A/B Test Suite for a click-through rate" is framed for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs.

Do I need to install anything?

No. It runs in any modern browser, free, with no account required.

For Educators · A/B Test Suite

A/B Test Suite for a click-through rate

Built for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs. Simulate a click-through rate live below — adjust the inputs and watch it respond, right in your browser.

A/B Test

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A/B Test SignificanceLive

two-proportion z-test

Controls

Presets

A visitors

A conversions

B visitors

B conversions

An A/B test compares two conversion rates to see if a difference is real or just noise. The two-proportion z-test pools the data to estimate the standard error, then a p-value below 0.05 signals a statistically significant difference. Bigger samples separate the two curves and make small lifts detectable.

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Data Inspector

Rate A10.00%

Rate B12.50%

Lift+25.0%

p-value0.0769

Resultnot significant

Governing equation

z = \frac{p ^ _{B} - p ^ _{A}}{p ^ ( 1 - p ^ ) ( \frac{1}{n _{A}} + \frac{1}{n _{B}} )} = \frac{0.025}{0.113 \cdot 0.887 ( \frac{1}{1000} + \frac{1}{1000} )} = 1.77

Reading this result: B looks 25.0% different, but with only 2,000 visitors p=0.077 — the sample is too small to call it real yet.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Central Limit

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Central Limit TheoremLive

means go normal

Controls

Speed1×

Presets

Sample size n

The central limit theorem is why the normal distribution is everywhere: no matter how skewed or lumpy the source distribution, the distribution of sample means becomes bell-shaped as the sample size grows. Try a heavily skewed exponential at n=1, then raise n and watch it turn normal.

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Data Inspector

Samples drawn0

Sample size10

Sourceexponential

Governing equation

\overset{x}{ˉ} \sim N (μ, \frac{σ ^{2}}{10})

Reading this result: At n=10 the sample means are already piling into a bell even though the exponential source is not normal — the central limit theorem kicking in.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Confidence Interval

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Confidence IntervalsLive

what 95% really means

Controls

Presets

Sample size n

A confidence interval is often misread. It does not mean the true value has a 95% chance of being in one interval — it means that if you repeated the experiment many times, about 95% of the intervals would contain the true mean. The red intervals here are the unlucky ones that miss it.

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Data Inspector

Confidence level95%

Observed coverage0%

Intervals shown50

Governing equation

\overset{x}{ˉ} \pm z \frac{σ}{n} = 50 \pm 1.96 \cdot \frac{10}{30}

Reading this result: About 0% of these 50 intervals captured the true mean — close to the 95% you asked for. Raising n from 30 tightens each interval (SE = σ/√n) but does not change that hit rate: the confidence level, not the sample size, sets how often you are right.

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Frequently asked questions

Is this good for educators?: Yes — this version of "A/B Test Suite for a click-through rate" is framed for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.