7 Burning A/B Testing Questions & Answers for Data Science Interviews

A/B testing is a crucial skill for data scientists working on product development, marketing campaigns, website optimization, and more. Companies want to ensure they hire data professionals who truly understand how to design, run, and analyze A/B tests.

In this article, we’ll cover 7 common A/B testing questions that often come up in data science interviews and provide clear, simple answers drawing from trusted industry resources. Let’s dive right in!

What is A/B testing?

A/B testing (sometimes called split testing) is a method of comparing two versions of something to determine which performs better. You split users or customers into two groups:

• Group A sees the current/old version (the control)
• Group B sees the modified/new version (the treatment)

You then analyze metrics for each group over a set period of time to see if the treatment version provided a statistically significant improvement over the control.

For example, an e-commerce company may A/B test a new checkout flow process to see if it increases conversion rates compared to their current process.

1. How do you decide which ideas to A/B test?

Companies usually have many potential ideas for product updates, new features, new marketing campaigns, etc. It’s not feasible to A/B test every single idea. As a data scientist, you need to evaluate which ideas are worth testing based on:

• Quantitative analysis using historical data – Estimate the potential impact and “opportunity size” for each idea based on current user behavior and metrics
• Qualitative analysis from user research – Gather feedback through surveys, interviews, etc. to understand user pain points and preferences

Using a combination of quantitative and qualitative analysis, you can prioritize which ideas have the most potential upside to justify the time/cost of running an A/B test.

2. How do you determine the sample size and test duration?

To calculate the required sample size for an A/B test, you need:

• Desired statistical significance level (α) – Common value is 0.05
• Desired statistical power (1 – β) – Common value is 0.8
• The minimum detectable effect size (δ) that would be practically significant
• Estimate of sample variance

There are statistical formulas you can use that take these parameters as inputs to output the required sample size for the test.

Once you have the sample size, you can determine the test duration based on the number of users, visitors, etc. you’ll include each day. Tests often run for 1-2 weeks to account for cyclical patterns.

3. How do you handle potential interference between test groups?

The ideal setup involves completely separating the control and treatment groups into isolated samples with no communication between them. However, in some cases – especially social networks, two-sided marketplaces, etc. – this independence assumption breaks down.

Users may be influenced by friends/contacts in the opposite group, or both control/treatment users may compete for the same limited resources.

To mitigate this, you can try cluster-based randomization (grouping highly-connected users into the same test group), geographic splits, or time-based splits. Each approach has tradeoffs.

4. How do you analyze results if you see contradictory metrics?

In some cases, your A/B test may show an improvement in your primary metric (e.g. revenue) but a decline in secondary metrics (e.g. engagement). You need to quantify the negative impact and make a judgment based on your test’s goals.

Does the revenue increase outweigh lower engagement if that’s not your core focus? If they are equally important, you may need to re-evaluate or potentially cancel the test. Provide your rationale.

5. How do you adjust for novelty effects?

When users first experience a major product update, they may temporarily increase or decrease usage just due to the novelty of the change – not sustainable long-term behavior. This is called a novelty effect (or its opposite, a primacy effect).

If your test period is long enough, you can compare early user behavior to later behavior to identify and adjust for any novelty effects. You can also test only with new users not impacted by an existing experience.

6. How do you handle the multiple testing problem?

Sometimes you’ll want to A/B test multiple treatment variations against the control group. However, using the standard significance threshold (e.g. 0.05) increases your chance of false positives with every additional variation.

To compensate, you can apply techniques like the Bonferroni correction (dividing α by the number of tests) or controlling the false discovery rate. These adjustments reduce your false positive risk.

7. How would you communicate results and make a product decision?

Assuming your A/B test achieved statistical significance, the final steps are:

1. Clearly communicate the test methodology, results, and quantified impact to all stakeholders
2. Make a data-informed decision to either:
   • Launch the treatment to all users
   • Iterate with additional testing
   • Cancel the product change

Factor in qualitative feedback, implementation costs/complexity, timing considerations, and how well the change achieved the original test goals.

Those are 7 of the most common A/B testing questions you may face in data science interviews! Practice explaining your thought process and being comfortable with statistics.