A/B testing is crucial to most companies’ user experience research. Let’s take a look at some A/B Testing Interview Question to prepare you for your big interview. As it is used as a data-driven decider of what ideas to move forward during product change experiments. As such, hiring managers are likely to ask you several questions on the subject if you’re pursuing a career in product management, data science, or related fields. An A/B testing interview will usually involve questions about deciding which ideas are worth testing, designing an A/B test, analyzing the results of your test, and making decisions based on those results.
A/B Testing Interview Question
- You Want to Run an A/B Test, But You’re Not Sure What You’re Testing For. Do You Proceed With the Test and Choose Your Variables Based on the Results?
A/B testing is usually a good idea when you know what you’re testing. For example, do you want to know if people are more likely to click a red button or a blue button or if adding a picture to a landing page will increase conversions? Many tests fail because the tests’ goals are unclear or multiple variants are tested at a time. While the latter can be done, simultaneously testing multiple variables can lead to uncertainty about the actual cause of the increase in conversions unless there is a winner by a considerable margin.
- You Have Four Ideas For a Landing Page Design. Do You Test All Four Simultaneously?
Running all four landing page designs against one another might seem like a great idea. However, it increases the number of factors from each design that can affect the test’s outcome and ultimately lead to unclear results. An A/B test is meant to be straightforward and concrete. Your best bet in this scenario is to run two tests, with two versions of the landing page each, and then run a final test to compare the winners.
- You Want to Run an A/B Test on a Landing Page. What Factors Would You Consider During Testing?
While the variables you’ll test depend on the objectives of your test, here are a few common ones:
- Calls-To-Action: There are several different elements you can test on your call-to-action, like how its location, page position, shape, or style affect your bottom line. You have to be sure of the aspect of the CTA you’re testing.
- Headline: A headline can have a significant impact as it’s usually the first thing a viewer sees on your site. You must be clear on whether you’re testing the effect of different headline styles or positioning in your A/B tests so that you can be sure of what caused the change.
- Images: How you design and use the images on your site could also be a factor to be tested. For example, is an image of a person holding your product generating more conversions, or an image of the product on its own?
- Copy Length: Does the conciseness of shorter texts yield better results, or are longer texts and more explanations of your offer better? Bear in mind that you want to keep the texts similar and tweak the volume.
- You Ran an A/B Test on a New Feature, and it Won, So You Pushed the Change to All Users. However, After a Week of Releasing the Feature, the Effect Quickly Declined. What is Happening?
It is the novelty effect. The novelty of the change wears off with repeated usage leading to a gradual decline in the effect. A typical follow-up question during interviews is, “how do you address the potential issues?” A good way to deal with the novelty effect is to completely rule out the possibility of it occurring in the first place by running the test only on first-time users. If there’s already a running test, you could find out if there’s a novelty effect by comparing the results of first-time users with those of existing users. This will give you an actual estimate of the extent of the novelty effect.
- After a Test, Your Desired Metric (Click-Through Rate) is Improving While the Number of Impressions is Declining. How Would You Make a Decision?
In reality, various factors are taken into consideration to make product launch decisions. However, for an interview question such as this, you could provide a simplified solution by focusing on that particular experiment’s objective. Is it to maximize engagement or something else? You also want to evaluate the impact of a negative shift in a non-goal metric. These will help you make a decision. If the goal is engagement, you can decide to progress with an improved CTR if the negative impact on impressions is acceptable.
- A Company Wants to Increase Conversion on Their E-Commerce Website by Either Enabling Multiple-Items Checkout (Previously, Users Can Check Out One Item at a Time), Allowing Unregistered Users to Checkout, or Changing the Colour and Size of the Purchase Button. How Would You Select Which Idea to Invest In?
An excellent way to evaluate the effect of different ideas is to conduct quantitative analysis using historical data. For example, before promoting multiple items checkout, you should analyze each user’s consecutive purchases. If only a small percentage of users purchased multiple items, you probably shouldn’t invest in this feature. A more effective approach would be to understand why users do not purchase multiple items. Can they only buy one product at a time because of the prices? Or do they find the checkout process too complicated and not want to go through it again?
The only problem with historical data is that it only tells you how you’ve done in the past. For a more comprehensive evaluation of each idea, you could conduct qualitative analysis with surveys and focus groups. This gives you valuable feedback and provides more insights into users’ pain points. A combination of qualitative and qualitative research can help provide directional insights on what ideas to A/B test.
- A Company Tested a New Feature on Their Social Network to Increase the Number of Posts Each User Creates. The Test Won by 1%. After the Feature is Launched to All Users, Do You Expect it to be the same as 1% or More or Less, Assuming There’s no Novelty Effect?
In social networks, you should expect to see a value larger than 1% because users’ behavior is affected by people in their social circles. In other words, a user will be more inclined to use a feature or if people in their network use it. This is called the network effect, and it holds for one-sided markets like social networks, like Facebook. However, the opposite is the case for two-sided markets like Uber.
- Why is A/B Testing Important to Businesses?
A/B testing allows businesses to learn how to improve their operations, products, and bottom lines. It provides businesses actionable insights to help them improve customer satisfaction and increase sales. A/B tests show companies exactly how they can efficiently use their resources and, consequently, enhance their ROI.
- You Want to Test Different Versions of Your Landing Page. You Run a Test With 10 Variants. One Solution Wins With a P-Value Less Than .05. Would You Affect the Change?
The simple answer is no, mainly because of the multiple variants in the test. However, using the standard Bonferroni correction, you can divide the significance level (0.05) by the number of tests (10 in this case). This would provide an answer of 0.005. A p-value of less than 0.005 would mean the test is significant. The Bonferroni correction does have some drawbacks.
- How Long Would You Run an A/B Test Before You Consider it Successful?
Once you have a clear goal for your test, you must let it run until you have a clear winner. Otherwise, your test is inconclusive. That said, here are some factors you should consider before deciding to end a test and declare a winner:
- Statistical Significance: The statistical significance of your test should be 95% or higher. So, if you have a statistical significance of 98%, the probability that the data is wrong is 2%. This means that it is improbable that the test’s results are due to chance instead of the change you introduced.
- Standard Deviation: The standard deviation of the conversion rate measures the amount of variation from the average. You want to make sure that the conversion rate ranges of the two pages do not overlap. In cases of overlap, you want the test to continue running until there’s a clear distinction between the two conversion ranges.
- Sample Size: Sample size represents the number of people who partook in the experiment. For statistical significance, the larger this number, the better.
Frequently Asked Questions
- What are the Questions Asked in an A/B Testing Interview?
There’s no way to precisely tell what questions you’d be asked in an A/B testing interview, not that you need to anyway. However, broadly speaking, you can expect questions about:
- Deciding which ideas should be tested
- Designing the A/B test
- Analyzing the results of your test
- Making decisions based on the results
- How Do I Learn For an A/B Testing Interview?
While there are many questions you might be asked in an A/B testing interview, the best way to prepare for it is to understand the entirety of A/B testing and related statistical concepts.
Preparing For Your Interview
The above A/B Testing Interview Question are meant to give you an idea of what you can expect at your A/B testing interview. However, the best preparation is to master the subject and related statistical concepts.