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A/B-Test Signifikanz-Rechner — Kostenloses Online-Tool

Ist dein A/B-Test-Ergebnis statistisch signifikant?

Berechne statistische Signifikanz von A/B-Tests. P-Wert, Konfidenzintervalle, benötigte Stichprobengröße. Für Marketer und Produktteams.

🅰️ Control (A)

🅱️ Variant (B)

A conversion rate
0.00%
B conversion rate
0.00%
Uplift (relative)
0%
P-value
—
Enter numbers to see verdict
📚
Mehr erfahren

Free A/B test significance calculator — z-test for conversions

Calculate if your A/B test result is statistically significant. Two-proportion z-test with pooled variance. See p-value, confidence intervals, and clear verdict.

How to use this tool

  1. 1

    Enter visitors + conversions

    For both control (A) and variant (B): how many visitors and how many converted.

  2. 2

    Check significance

    See conversion rates, uplift %, p-value, confidence interval, statistical significance.

  3. 3

    Interpret result

    p < 0.05 = statistically significant. 95%+ confidence means the result isn't random noise.

Interpretation guide

  • p < 0.01: very strong evidence against null (99%+ confidence)
  • p < 0.05: standard "significant" threshold (95% confidence)
  • p < 0.10: weak evidence — don\'t call it significant
  • p ≥ 0.10: no significant difference detected

Frequently Asked Questions

What is statistical significance?
The probability that your observed difference is NOT random chance. p < 0.05 = less than 5% chance the difference is random — typically the threshold to consider a test "significant".
What sample size do I need?
Depends on your baseline conversion rate + minimum detectable effect. Rough rule: 1,000-10,000 per variant for typical web A/B tests. Low conversion rates (1%) need more samples; high rates (20%) need fewer.
What if my test is "not significant"?
Three options: (1) Run longer until sample size is adequate — but don't peek and stop early. (2) Accept the null hypothesis — the variants perform equally. (3) Bigger change next time — tiny differences need enormous samples to detect.
Z-test vs t-test vs chi-square?
For proportions (conversion rates), z-test with pooled variance is standard and what we use. For continuous metrics (revenue per visitor), t-test is more appropriate. This tool focuses on conversion-rate tests.
What's confidence interval?
The range where the true difference likely falls. "B is 15% better (CI: 8% to 22%)" means the real uplift is probably between 8-22% — we just don't know exactly where.

Wichtigste Punkte

  • A/B Test Significance Rechner is a free, browser-based calculator tool — is your a/b test result statistically significant?.
  • Nein signup, no downloads, no file uploads — your data stays on your device.
  • Works on desktop, tablet, and mobile. Install as a PWA for offline access.

How to Use A/B Test Significance Rechner

  1. Open the tool: Launch A/B Test Significance Rechner on Toololis — no account or download needed.
  2. Enter your data: Paste text, enter values, or select a file directly in your browser.
  3. Get instant results: Everything is processed locally — results appear immediately.
  4. Copy or download: Save your output or share it. Bookmark for quick access next time.

A/B Test Significance Rechner — Quick Facts

Preis
Kostenlos — keine Limits, kein Wasserzeichen, keine Paywall
Privatsphäre
100% browser-basiert — keine Daten verlassen dein Gerät
Plattform
Jeder moderne Browser — Desktop, Tablet, Mobil
Kategorie
Rechner Tools on Toololis
Offline
Works offline after first visit (Progressive Web App)
MerkmalDetails
ToolA/B Test Significance Rechner
KategorieRechner
Anmeldung nötigNein
Datei-UploadKeine — wird im Browser verarbeitet
Mobile-UnterstützungVoll responsive
KostenFür immer kostenlos

Why Use A/B Test Significance Rechner?

You should try A/B Test Significance Rechner for a quick, private way to is your a/b test result statistically significant?. All processing happens in your browser. Your files and data never leave your device. According to web.dev, client-side processing is the gold standard for privacy.

On the other hand, dedicated APIs or desktop tools suit batch processing better. They also handle server-side automation. For everyday tasks, browser tools offer the best speed, privacy, and convenience.

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