What are the best ways to control for confounding variables in an A/B test?

1 Randomize the assignment

One of the most effective ways to control for confounding variables is to randomly assign the participants to the different variations. This way, you can reduce the bias and ensure that the groups are comparable in terms of their characteristics and behaviors. Randomization also helps to balance out the effects of any unknown or unmeasured confounding variables that might exist. To randomize the assignment, you can use various techniques, such as hashing, random number generation, or stratified sampling.

2 Use a control group

Another way to control for confounding variables is to use a control group that receives the baseline or original version of the product, service, or campaign. The control group serves as a reference point and a benchmark for comparing the performance of the other variations. By using a control group, you can isolate the effects of the variation from any external or internal factors that might affect the outcome, such as seasonality, trends, or user preferences. To use a control group, you need to ensure that it is large enough and representative of the population of interest.

3 Match the groups

A third way to control for confounding variables is to match the groups based on one or more relevant criteria. Matching is a technique that aims to create groups that are similar in terms of their confounding variables, so that they differ only in the variation they receive. Matching can help to reduce the variance and increase the precision of the estimates. However, matching can also introduce some challenges, such as reducing the sample size, increasing the complexity, or creating new confounding variables. To match the groups, you can use various methods, such as propensity score matching, exact matching, or coarsened exact matching.

4 Adjust the analysis

A fourth way to control for confounding variables is to adjust the analysis using statistical methods. Adjusting the analysis is a technique that attempts to account for the effects of the confounding variables on the outcome, and to estimate the causal effect of the variation. Adjusting the analysis can help to improve the accuracy and validity of the results, especially when randomization or matching are not feasible or sufficient. However, adjusting the analysis can also introduce some assumptions, limitations, or biases, depending on the method used. To adjust the analysis, you can use various methods, such as regression, ANOVA, or stratification.

5 Monitor the results

A fifth way to control for confounding variables is to monitor the results throughout the duration of the A/B test. Monitoring the results is a technique that involves checking the quality and consistency of the data, and detecting any anomalies, errors, or changes that might affect the outcome. Monitoring the results can help to identify and correct any issues that might compromise the validity and reliability of the results, such as data leakage, outliers, or interference. To monitor the results, you can use various tools, such as dashboards, charts, or alerts.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?