Expert Answer • 2 min read

How long should I run an A/B test to get reliable results?

I'm struggling to determine the optimal duration for A/B testing in my e-commerce store. Every time I run a test, I'm uncertain whether I've collected enough data to make statistically significant decisions. I want to understand how long I should let my tests run, what factors influence test duration, and how to ensure I'm not making premature or incorrect conclusions about my conversion rates, discount strategies, or website design changes. What are the best practices for determining the right test length?
Muhammed Tüfekyapan

Muhammed Tüfekyapan

Founder & CEO

2 min

TL;DR - Quick Answer

A/B test duration depends on your traffic volume, conversion rate, and desired statistical significance. Typically, aim for 2-4 weeks or until you have at least 1,000-2,000 conversions per variant, ensuring a 95% confidence level and minimum detectable effect of 10-15%.

Complete Expert Analysis

Comprehensive A/B Testing Duration Strategy

Understanding the precise timing for A/B tests is critical for making data-driven decisions that genuinely improve your e-commerce performance. This guide will break down the science behind test duration and help you achieve statistically robust results.

Key Factors Determining Test Length

FactorImpact on DurationRecommended Approach
Traffic VolumeHigher traffic = Faster resultsMinimum 1,000 unique visitors per variant
Conversion RateLower conversion rates require longer testsAim for 1,000-2,000 total conversions
Minimum Detectable EffectSmaller effects need more data10-15% minimum change recommended
Statistical SignificanceHigher confidence requires more data95% confidence level standard

Recommended Test Duration Guidelines

Low Traffic Stores (< 5,000 monthly visitors)

  • Test Duration: 4-6 weeks
  • Minimum Conversions: 500-1,000 total
  • Confidence Level: 90%

High Traffic Stores (> 50,000 monthly visitors)

  • Test Duration: 2-3 weeks
  • Minimum Conversions: 2,000-5,000 total
  • Confidence Level: 95%

Statistical Significance Calculation

Confidence Level Formula

p = 1 - (Type I Error Rate)

Typically set at 0.05 (5% chance of false positive)

Sample Size Calculator

n = (Z² * p * (1-p)) / e²

Where Z = Z-score, p = expected conversion rate, e = margin of error

Common A/B Testing Mistakes to Avoid

1. Stopping Tests Too Early

Wait until you have statistically significant results to prevent false conclusions.

2. Testing Multiple Variables

Change only one element per test to isolate the impact of that specific modification.

3. Ignoring Seasonal Variations

Consider running tests during consistent periods to minimize external influences.

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Muhammed Tüfekyapan

Muhammed Tüfekyapan

Founder & CEO of Growth Suite

With over a decade of experience in e-commerce optimization, Muhammed founded Growth Suite to help Shopify merchants maximize their conversion rates through intelligent behavior tracking and personalized offers. His expertise in growth strategies and conversion optimization has helped thousands of online stores increase their revenue.

E-commerce Expert Shopify Partner Growth Strategist

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