What is the novelty effect in experimentation?

What's really being asked

This question tests your understanding of temporal biases in experimentation. The interviewer is checking if you recognize that user behavior is not static and that initial reactions to a change can be misleading. It's a test of maturity in experimental analysis, moving beyond simple p-values to assess the real, long-term impact of a feature.

The full answer

First, define the novelty effect as a temporary change in user behavior (positive or negative) resulting from the newness of a feature, not its intrinsic value. Users might click a new button simply because it's there and they're curious.

Second, explain how it biases results. It typically causes an initial spike in engagement metrics, which can lead to prematurely declaring a winner. If a decision is made based on this spike, the company might ship a feature whose true impact is flat or even negative once the novelty wears off.

Third, provide mitigation strategies. The primary method is to run the experiment for a longer duration, such as 2-4 weeks instead of just one. This allows you to observe whether the initial lift is sustained or decays over time. Another key strategy is to segment your analysis. By comparing the behavior of new users (for whom everything is new) versus tenured users, you can often isolate and quantify the novelty effect, as it's most pronounced in the latter group.

The mistakes people make

A common red flag is defining the term correctly but failing to offer any concrete mitigation strategies. This suggests academic knowledge without practical application. Another mistake is confusing the novelty effect with 'change aversion'. Change aversion is an initial negative reaction to a change that may improve as users adapt, whereas the novelty effect is typically an initial positive spike that fades. Suggesting that nothing can be done about it is also a major red flag.

What usually comes next

Expect follow-ups like: "How can you tell the difference between a true improvement and a novelty effect in a time-series plot of your primary metric?" or "Tell me about a time you had to account for this in an experiment you ran." They might also ask about the opposite effect: "What if you see an initial dip in metrics? How do you diagnose that?"

A concrete example

Imagine a redesign of a homepage where a key call-to-action (CTA) button is made larger and a different color. In the first 3 days of an A/B test, the treatment group shows a 20% lift in clicks on that CTA. However, by running the test for 14 days, you observe that the daily lift, which was 40% on Day 1, has steadily declined and stabilized at around 3% by the second week. The initial 20% lift was a mirage caused by novelty. The true, sustainable impact of the change is only 3%. Making a decision based on the first few days would have grossly overestimated the feature's value.