[Clearly describe the goal of the A/B test, including what you aim to measure and improve.]
[List the A/B test variants, specifying what differs between them (e.g., Variant A: current design, Variant B: new layout).]
[Define the success criteria for the test, such as key performance indicators (KPIs) or user engagement metrics.]
• [Identify and list the first actionable task for setting up the A/B test.]
• [Specify the second actionable task related to the test setup or monitoring.]
• [...continue listing all relevant tasks...]
• [Clearly define the first condition that must be met for the test to be considered valid.]
• [Specify the second success condition or expected outcome.]
• [...continue listing all acceptance criteria...]
We are conducting an A/B test on the Hogwarts Sorting Hat Algorithm to evaluate whether a new, data-driven approach improves sorting accuracy and student satisfaction. The goal is to measure if a machine learning-enhanced Sorting Hat provides better house assignments compared to the traditional method.
• Implement data-driven Sorting Hat questionnaire.
• Deploy A/B test to first-year students upon arrival.
• Monitor sorting results and collect feedback through Owl Post surveys.
• Analyze house activity engagement over the first semester.
• At least 80% of students report satisfaction with their house placement.
• No single house is overpopulated by more than 15% compared to others.
• Test runs successfully for one full academic year without causing inter-house conflict.