Test Plan for Calculator

Calculator

Test type

Tail type

Effect direction

Confidence level (%)

Power (%)

Total groups

Baseline rate (%)

Minimum detectable effect (%)

Standard deviation

Target mean difference

Treatment to control ratio

Dropout reserve (%)

Daily traffic

Eligible traffic (%)

Plan owner

Test notes

Formula Used

Alpha: alpha = 1 - confidence level.

Bonferroni alpha: adjusted alpha = alpha / number of comparisons.

Two-sided z alpha: z = inverse normal value of 1 - adjusted alpha / 2.

One-sided z alpha: z = inverse normal value of 1 - adjusted alpha.

Proportion sample: n = [(z alpha × sqrt(2p pooled(1-p pooled)) + z beta × sqrt(p1(1-p1)+p2(1-p2)))²] / (p2-p1)².

Mean sample: n = [2 × standard deviation² × (z alpha + z beta)²] / target difference².

Dropout adjustment: final sample = base sample / (1 - dropout rate).

Duration: days = final sample / qualified daily traffic.

How to Use This Calculator

Choose the test type first. Use proportion for rates, conversions, or pass counts. Use mean difference for averages, scores, or measured values.

Enter confidence and power values. Higher values make the test safer, but they increase sample size.

Add the baseline metric and the smallest useful effect. This effect is the change worth detecting.

Enter groups, allocation ratio, dropout reserve, traffic, and eligibility. Press submit to see the plan above the form.

Use CSV for records. Use PDF for review notes and signoff documents.

Example Data Table

Scenario	Test Type	Confidence	Power	Baseline	Effect	Purpose
Conversion check	Proportion	95%	80%	12%	3%	Plan a basic A/B calculator test.
Average score check	Mean	95%	90%	N/A	5 points	Estimate sample for score changes.
Three group release	Proportion	99%	80%	20%	4%	Apply alpha control across variants.
Low traffic page	Proportion	95%	80%	8%	2%	Estimate duration with limited visits.

Why Test Plans Matter

Test planning protects a calculator before users rely on its answers. A statistical calculator needs more than happy path checks. It needs designed cases, sample rules, boundary values, precision rules, and review notes. This tool turns those planning needs into a practical sample target. It supports proportion tests and mean difference tests. It also adjusts alpha for several comparisons. That matters when one control is checked against many variants.

Assumptions and Sample Size

A good plan starts with a clear hypothesis. Define the metric first. Then select the smallest effect that should trigger action. Small effects need larger samples. Higher power also needs larger samples. A stricter confidence level increases the target again. The calculator shows these tradeoffs immediately.

Inputs should match real product behavior. Baseline rate should come from recent stable data. Standard deviation should come from a pilot report. Traffic should include only eligible users. Dropout should cover missing logs, invalid sessions, and removals. Allocation ratio helps teams test uneven splits when risk is high.

Use Results Carefully

The result is not a legal guarantee. It is a planning estimate. Use it with practical checks. Make sure each group has enough observations. For conversion metrics, each group should have enough successes and failures. For mean metrics, inspect outliers and normality. When data is skewed, add extra review.

The example table helps teams document repeatable scenarios. Add edge cases for zero traffic, tiny effects, high dropout, and many variants. Add regression cases for old bugs. Include accepted rounding behavior. Keep expected results beside each case.

Records and Reviews

Use the exported CSV for quality records. Use the PDF for test signoff. Share both with developers, analysts, and reviewers. A short, clear plan reduces confusion. It also prevents changing success rules after seeing results. That discipline makes calculator testing fair and easier to audit.

Before release, review the plan with someone who didn't build the page. Independent review catches hidden assumptions. Record browser names, device sizes, and calculation modes. Confirm downloads match screen results. Recheck formulas after every design change. When issues appear, fix the cause, not only the symptom. Then rerun the affected cases. This habit builds trust, and it keeps future calculator updates controlled.

FAQs

What does this calculator plan?

It plans statistical calculator tests. It estimates sample size, alpha, power, duration, and review notes for proportion or mean based tests.

When should I use a proportion test?

Use it for conversion rates, pass rates, click rates, failure rates, and other yes or no outcomes.

When should I use a mean difference test?

Use it for averages, scores, times, weights, costs, or other continuous values that compare two or more groups.

What is minimum detectable effect?

It is the smallest change worth detecting. Smaller effects need larger samples and usually take longer to test.

Why does power matter?

Power shows the chance of detecting the target effect when it is real. Higher power lowers missed effect risk.

Why adjust alpha for many groups?

Many comparisons increase false positive risk. Adjusted alpha keeps the full test plan more controlled and consistent.

Why add dropout reserve?

Dropout covers missing records, invalid sessions, bot traffic, failed logs, and removed observations before final analysis.

Can I export the result?

Yes. Use the CSV button for spreadsheet records. Use the PDF button after calculation for signoff notes.