Pre-Test Calculator

A video streaming service has updated its recommendation algorithm to boost user engagement and retention, it holds no strong preconceived expectations about the improvement of performance. In this scenario, a two-tailed test is the optimal choice. This method is ideal because it allows the streaming service to determine if the algorithm update has led to any significant changes in watch time, whether those changes are increases or decreases. A two-tailed test enables the company to remain unbiased and open to both positive and negative outcomes, effectively capturing any significant variations in user behavior related to the algorithm change. The reason a two-tailed test is appropriate is the absence of a definitive directional hypothesis in advance.

A B2B software company, specializing in project management solutions for small engineering firms, aimed to increase its subscription sign-up rates. To achieve this, the company had redesigned its website’s user interface and content layout. Operating in a niche market resulted in low website traffic, which posed a significant challenge for gathering enough data for traditional A/B testing.

To overcome this, the company opted for Bayesian A/B testing as its preferred method. This approach was particularly advantageous because it allowed the integration of the company’s extensive industry knowledge into the testing process. This expertise was invaluable in a low-traffic environment, providing critical insights into user behavior and preferences. Bayesian A/B testing enabled the company to make data-driven decisions, despite the limited data, utilizing their deep industry expertise to optimize subscription sign-up rates.

An e-commerce company planned to launch a new layout for their product pages to enhance conversion rates. Before implementing the change, the experimentation team conducted a pre-test analysis, projecting around 50,000 visitors and a 5% conversion rate. They used a pre-test A/B test calculator to estimate the Minimum Detectable Effect (MDE) over time.

This analysis tracked the impact of the new layout across several weeks, aiding the team in determining the most effective duration for the test. These insights helped in strategic planning and resource allocation by indicating when the MDE would be achievable. The results suggested that extending the test beyond this point might not justify the additional traffic and time. Clear timelines were set for stakeholders, guiding crucial business decisions and marketing strategies dependent on the test outcomes. This proactive approach maximized resource efficiency, prevented excessive testing durations, and enabled prompt, data-driven actions during the optimal testing period.

An online retailer with a robust experimentation program typically conducts A/B tests on their upper funnel landing pages using a 90% statistical confidence level, which is appropriate for the lower risks associated with the early stages of the customer journey. However, when they launched a new checkout experience designed to streamline the purchasing process and integrated a recommendation engine to potentially increase the average order value, they recognized the need for greater precision. Given the critical nature of the checkout phase and the high cost of errors at this stage, they increased their statistical confidence level to 99% for this specific test. This strategic adjustment aims to minimize risk and ensure that any observed increases in revenue from the recommendation engine are statistically significant, not just due to chance. It also highlights the retailer’s commitment to maintaining the integrity of customer decision-making at this crucial point in the sales funnel, demonstrating the importance of tailoring confidence levels to the specific context and risk of each testing scenario.