A/B Testing

A/B testing, also called split testing, randomly assigns visitors to a control (A) and a variant (B), then compares a pre-chosen metric such as signups or purchases. Statistical analysis indicates whether any difference is likely real or just chance.

Why it matters

Opinions about design and copy are often wrong. Experiments replace guesses with evidence and measure causal impact, not just correlation. They need enough traffic to detect meaningful differences.

Example

A (control): 4,000 visitors, 200 signups -> 5.0%
B (variant): 4,000 visitors, 240 signups -> 6.0%

Whether that 1-point gap is statistically significant depends on sample size and the test used; a significance calculator or statistical test answers it.

Common mistakes

• Stopping early the moment the result looks good ("peeking"), which inflates false positives.
• Changing many things at once, so you cannot tell what worked.
• Ignoring side effects, such as a variant that raises clicks but slows page load.

Related

• Data analytics
• Data science
• Dashboard
• Latency

यह पेज सामान्य जानकारी है, जिसे हमारी संपादकीय टीम ने लिखा है और दस्तावेज़ीकरण बदलने पर अपडेट किया जाता है। सर्वर और प्रदाता अलग-अलग होते हैं, इसलिए बदलाव पहले सुरक्षित तरीके से जाँचें। देखें: संपादकीय नीति और अस्वीकरण.