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Best practices

Good tests rarely fail on technology — they fail on a fuzzy hypothesis, too short a runtime, or too many parallel changes. The patterns below keep planning and implementation sharp.

  1. Hypothesis

    Write a testable statement: “If we change the CTA copy to ‘Start free trial’, signup rate will increase because the entry barrier is clearer.”

  2. KPI / primary goal

    One primary event (e.g. signup). Secondary metrics (bounce, revenue) are context only — not a post-hoc “winner hunt.”

  3. Runtime & sample size

    Plan at least 1–2 weeks or until minimum sample size per variant is reached (default: 100 sessions). Account for seasonal swings (weekdays!).

  4. One change per test

    Otherwise you will not know what caused the effect. Split large redesigns into sequential tests.

  5. Decision

    Roll out only with significance and adequate sample size — see Analytics & evaluation.


  1. Clear hypothesis: Define what you want to test
  2. Measurable goal: Determine how you will measure success
  3. Sufficient duration: Plan for at least 1–2 weeks
  4. Statistical significance: Wait for enough data

  • Keep both variants meaningful in content (no keyword-spam version “just for the test”).
  • Avoid search engines indexing conflicting hero copy long-term: keep tests short, then ship the winner in markup.
  • Purely visual CSS changes (color, spacing) have low SEO risk; for headline/content swaps, prefer HTML variants with a clear control as fallback.
  • Always set a fallback element.
  • Optionally hide the body briefly until the variant is applied — recipe and debugging: Troubleshooting → Flicker effect.
  • Load the script as early as practical in <head> (defer is fine; blocking only if you must hard-avoid flicker).