Data & Analytics

Stopping at Significance Isn't a 5% Test

Checking a test once gives roughly the 5% false-positive rate you asked for. Checking ten times along the way and stopping at the first crossing gives about 20%, and twenty times gives about 24% - simulated here on two identical arms, where there is nothing to find and every winner is a false one. The threshold was never wrong; it was applied ten times to the same experiment. The page also sizes the test before it runs (5% to 6% at 5% significance and 80% power needs 8,159 per arm) and, for a test already finished, works out the smallest effect that sample could reliably have seen - so 2,000 an arm on a 5% baseline is told plainly that it could only detect about 2 points, and a null result at half a point means the test was never going to find it.

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Data & Analytics

Product and growth teams running conversion tests, and anyone who has been asked to call a winner on day three because the dashboard went green.

How it works

  1. Enter the baseline, the effect worth detecting, and your traffic to size the test.
  2. Set how many times you would check along the way and run the simulation on two identical arms.
  3. Read the calibration row: a single look must come back at the nominal rate, or the figure above it is not trustworthy either.
  4. Enter a finished test's numbers to see the smallest effect it could have detected.

What you gain

  • Simulates what happens when you peek at a test's result before it's finished, and shows how often that habit produces a 'significant' result that is actually false — on data you know has no real effect.

Screenshot

Technical details

Standard6.99 USD · 1500 requests · 31-day license · one-time payment · 31-day access
Pro13.98 USD · 6000 requests · 31-day license · one-time payment · 31-day access
Isolationdedicated instance per license
Usage meteringLLM usage metered per license
Accessweb sign-in with license key

How to set up & use

  1. Buy the license — your key (lic_...) appears on the order page and in your email.
  2. Sign in at app.synoriaai.com with your license key.
  3. No installation — the product runs in your browser, on your own isolated instance.
  4. 1. Enter your baseline conversion rate, the minimum effect you care about, the significance level, and the desired power, then click 'Save plan'.
  5. 2. The page shows the required sample size per arm and the critical z-value for your plan.
  6. 3. Set the number of times you plan to peek at the data, the number of simulation trials, and a seed for reproducibility.
  7. 4. Run the simulation to see the actual false-positive rate when peeking at two identical arms.
  8. 5. Review the results: the page will show how much the significance rate inflates due to peeking, and it will also display the smallest effect your actual sample could detect.

Frequently asked questions

Is my data safe when using this tool?

Yes. All calculations happen entirely in your browser. No data you enter is sent to any server, and nothing is stored or tracked.

Does this tool provide a corrected p-value or a sequential testing method?

No. This page only demonstrates the inflation of false positives from peeking. It does not implement a sequential design or offer any correction. For valid inference with multiple looks, you would need a proper sequential method.

What does the 'smallest effect your actual sample could detect' mean?

It calculates the minimum effect size that your current sample size (based on your inputs) would be able to detect with the specified power. This helps you interpret a null result: if the effect you care about is smaller than this, your study may be underpowered.

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