Experiment types & delivery — found from sitemap — Plerdy

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Facts read from this source

• Ab significance calculator Report an error

  “A/B Testing Calculator for Statistical Significance”

• Calculator inputs Report an error

  “Visitors for A and visitors for B — total people who saw each version in the test, so we know sample size and not guess.”

• Calculator outputs Report an error

  “You get the conversion rate for each version (for example 1.00% vs 1.14%), the lift %, and a p-value that tells you how strong the test result is”

• Confidence levels Report an error

  “Confidence level — you choose 90 / 95 / 99% depending on risk tolerance in your team.”

• Statistical approaches Report an error

  “Frequentist A/B view: “Is this conversion difference in my test too big to be random noise?” You get a p-value and check if it is under 0.05 (5%).”

• Example result Report an error

  “Test "B" converted 34% better than Test "A". I am 99% certain that the changes in Test "B" will improve your conversion rate.”

• Test types Report an error

  “One-sided or two-sided — one-sided says “B will improve conversion”, two-sided also checks if B can hurt.”

• Alpha default Report an error

  “Alpha 0.05 (5%) is common. If p-value is under 0.05, most teams will say “B wins” and move rollout.”

• Free statistical significance calculator Report an error

  “It also works as a free statistical significance calculator you can open live in a meeting and say, “here’s the real result, we’re not guessing.””

• Chrome extensions free Report an error

  “Get free Plerdy Chrome extensions: SEO insights, heatmaps & UX tips.”

• Shopify app store Report an error

  “FIND IT ON THE Shopify App Store”

• GDPR Report an error

  “GDPR”

• Statistical power reported Report an error

  “Statistical power. Power is the chance your test can actually detect a real effect if the effect exists.”

• Rope mentioned Report an error

  “Some A/B platforms, for example VWO, also use an idea called ROPE (Region of Practical Equivalence).”

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