A/B testing offers you a straightforward scientific way to design government programs, collect data, and decide what works best.
An A/B test measures and compares the effectiveness of different versions of a program feature, service, or communication.
An A/B test compares different versions of a service or communication against each other. Recipients are randomly selected to receive a version, allowing you to understand which works best.
Government agencies have run A/B tests to optimize:
– Outreach materials
– Communications and engagement
– Program features
– Processes (e.g., applications, payments)
A/B testing helps people figure out what changes are working using a scientific method.
Put your resources into what will be most impactful.
Know exactly how much your changes are improving program performance.
Find out if a new program or design is hurting performance before rolling it out widely.
Fortune 500 companies like Amazon and Google run thousands of A/B tests every year. More and more state and federal agencies are testing to get feedback from people on what makes their programs work better.
A/B testing works by using an experimental procedure that provides different versions of parts of a program – such as a letter, a web site, or step in the process – to people at random. Statistical analysis can confirm which version is working better and by how much.
Good preparation ensures a good test. Preparing for an A/B test means answering questions like what about the program will change? What will be in version A? What will be in version B? Who will receive each version, and how many people? What will you measure to determine which one works better?
Randomization is an important step to ensuring valid, scientific results. The people who are part of the A/B test are assigned randomly to one of the two groups. Randomization is a scientific procedure and the Gov42 A/B testing tool helps you do it simply and quickly.
The Gov42 A/B testing tool helps you compare the groups by quantifying the performance and telling you if the differences are statistically significant. Analyzing results begins by collecting data for the A and B groups based on what you decided to measure in Step 1. Input your metrics and results into the A/B testing tool and you’ll get a test summary statistics and graphics to share with your team.
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