Introduction to Power Analysis and Impact Sizing

Power analysis and impact sizing, or opportunity sizing, help inform decision-making and ensure resources are prioritized correctly and allocated to high return-on-investment projects. Using impact sizing and power analysis together allows you to determine if you have a large enough sample to conduct your experiment with sufficient statistical power.

Impact sizing is typically performed before investing in a product or launching an experiment. Comparing the impact size to the cost of running the experiment helps inform experiment design. In general, once you know the sample size, you can estimate the duration of the experiment, i.e. how long it would need to be run to get the required sample size.

Power analysis is a crucial step in experimental design to determine the sample size (and hence cost) needed to detect a certain effect size with a given level of confidence. It helps data scientists make informed decisions about experiment design and resource allocation, ensuring that experiments have a high likelihood of detecting meaningful effects if they exist.

Impact sizing questions are usually presented as applied questions during the experiment design (A/B testing) round or as case studies. Power analysis questions are usually presented as conceptual questions.

How to prepare

• Practice case studies. Practice solving case studies and hypothetical scenarios related to impact sizing and power analysis. These could involve real-world business problems where you're asked to estimate the potential impact of a proposed change or intervention. Focus on structuring your approach, identifying relevant data sources, selecting appropriate statistical methodologies, and communicating your findings effectively.
• Understand power analysis methodology and calculations. While online calculators are usually used to perform power analysis, you should understand the methodology and how to run a power analysis using statistical software.