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Sequential testing beliefs update#
Activation of stopping rule algorithm based on real data + Knowledge update.Warm-up stage (250 unique visitors per variation) + Knowledge update.Test setup: utilizing assumptions based on prior test history.Leverage the Bayesian tests for growth hacking and iterative A/B testing if you need quick results with less traffic required. As the test progresses and more visitors participate, the impact of the prior fades. The prior may fix the noise at the beginning of the test when the sample size is small. The Bayesian approachĪ Bayesian test starts from a weak prior assumption about the expected conversion (prior distribution). The stopping rule – stops the test and reduces budgets once the winner or underperformer is obvious. The benefits of the Bayesian approach in SplitMetrics are that users can find indirect control over the risk because they can control the expected loss stopping rule (threshold of caring). Hence, users can make faster decisions with lower costs of experiments by incorporating beliefs or knowledge as part of the experiment, compared with the Frequentist or Sequential methods. The Bayesian approach can be helpful in cases where marketers have some beliefs and knowledge to use as a primary assumption (in our case, it is informed prior in the default settings) that helps algorithms calculate the probability of related events to the likelihood of a specific outcome. The Bayesian testing is a new enhanced approach in experiments provided by SplitMetrics. You can change the Significance Level and MDE that affect the required traffic to end the experiment and the probability of getting the wrong results. This algorithm allows you to take complete control over the experiment results. Determine the exact significance level and analyze the performance of all variations after an experiment is finished. Use the Sequential method to check global ideas and hypotheses, and more complex experiments with more traffic are required.It helps maximize conversions when there’s no time for gathering statistically significant results. Bandit algorithms allow you to adjust in real-time and quickly send more traffic to better variation. Use Multi-armed bandit by SplitMetrics for testing up to 8 variations and reduce the budget by automatically excluding bad-performers.The SplitMetrics platform offers an early stopping experiment by toc (threshold of caring) with the expected loss tolerance if there’s an overperformer, underperformer, or approximately equal with the opportunity to spend less on a test.
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Marketers and growth managers can check the effect daily and don’t overspend by finishing tests earlier with enough data for conclusions. Enhance your A/B testing with the Bayesian approach as an industry gold standard for iterative A/B testing and growth hacking with less traffic required.– Ken Rice, Department of Biostatistics of University of Washington