What is the best way to compare versions of 2D graphics prototypes using A/B testing?

2 How to set up an A/B test?

Before you start an A/B test, you need to have a clear hypothesis and a measurable outcome. A hypothesis is a statement that predicts how the change in your graphic will affect the outcome. For example, "Changing the background color from blue to green will increase the click-through rate by 10%". A measurable outcome is a metric that you can track and quantify, such as the number of clicks, views, or purchases. You also need to decide how long you want to run the test and how many people you want to include in each group. Ideally, you want to have a large and representative sample size that can give you statistically significant results.

3 How to run an A/B test?

To run an A/B test, you need to use a tool or a platform that can randomly assign your audience to either the control or the variant group and show them the corresponding graphic. You can use online tools such as Google Optimize, Optimizely, or Visual Website Optimizer, or you can create your own tool using code. For example, you can use HTML, CSS, and JavaScript to create two versions of your graphic and use a random number generator to determine which one to display. You also need to integrate your tool with an analytics service, such as Google Analytics, that can track and measure the outcome of your test.

4 How to analyze and interpret the results?

After you run your A/B test for a sufficient period of time and collect enough data, you need to analyze and interpret the results. You can use statistical methods, such as t-tests, z-tests, or chi-squared tests, to compare the mean or the proportion of the outcome between the control and the variant groups and determine if the difference is statistically significant. You can also use graphical methods, such as bar charts, pie charts, or histograms, to visualize the distribution and the variation of the outcome across the groups. The results of your A/B test will either confirm or reject your hypothesis and help you decide which version of your graphic is more effective.

5 How to apply and improve your A/B test?

Once you have the results of your A/B test, you can apply the winning version of your graphic to your project and monitor its performance. However, A/B testing is not a one-time process, but a continuous cycle of improvement. You can always test new variants of your graphic or test different elements of your graphic, such as the font, the size, the shape, or the position. You can also run multiple A/B tests simultaneously or sequentially, as long as you control for the confounding factors that might affect the outcome. A/B testing is a powerful way to optimize your 2D graphics design and enhance your creativity and innovation.