What is second order effect? Impact on experiment results

Have you ever made a change, thinking you knew exactly what would happen, only to be caught off guard by unexpected consequences? In the world of product development and experimentation, these surprises are known as second order effects. They're the unintended ripple effects that show up after we implement a new feature or strategy.

At Statsig, we understand how these hidden consequences can impact your products and experiments. Ignoring them isn't just risky—it can lead to serious missteps down the road. So let's chat about what second order effects are, how they can skew your experiment results, and what you can do to stay ahead of them.

Understanding second order effects in experiments

Second order effects are those sneaky, unintended consequences that pop up after we take an action. While first order effects are immediate and easy to spot, second order effects are like the echoes—they emerge over time and can be pretty unpredictable. They can really shake things up in product development and experimentation.

Take, for example, a resume optimizer designed to help users spruce up their resumes (that's the first order effect). Sounds great, right? But here's the kicker: it might also lead to people overstating their skills, which can erode trust with employers—the second order effect. Or think about the well-intentioned initiative in Hanoi to control rats that ended up causing people to breed rats for profit, leading to more tail-less rats running around—another classic second order effect.

So, how do we get ahead of these unintended outcomes? It's all about anticipating second order effects. This means diving deep into user motivations, understanding how different parts of your system interact, and being on the lookout for behaviors you didn't expect. Techniques like laddering questions during user research can be a game-changer in uncovering these hidden consequences.

But if we ignore these second order effects, we might find ourselves in a bit of a pickle—like the infamous Cobra Effect where attempts to fix a problem only make it worse. That's why embracing second-order thinking is so crucial. By asking the right questions, doing thorough research, and tapping into data analytics, we can build adaptable systems that help us measure the real impact of our product changes.

The impact of second order effects on experiment results

Second order effects don't just cause unexpected outcomes—they can really mess with your experiment data, leading to false conclusions and decisions that might steer you in the wrong direction. Because these effects often show up over time, they can be tricky to catch and interpret correctly.

If we overestimate second order effects, we might become overly cautious, potentially missing out on great opportunities. On the flip side, underestimating them can land us in hot water with unforeseen pitfalls. So what's the solution? We need to embrace second-order thinking when we're designing and interpreting experiments.

This means asking probing questions, diving deep with user research, and using data analytics to uncover those unintended consequences. Techniques like laddering questions can help us get to the bottom of how users might interact with new features in ways we didn't anticipate.

Plus, we can't forget about the interaction effects between our experiments and other factors in our product ecosystem. Detecting these interactions isn't always straightforward—it requires solid statistical methods and a careful eye when interpreting results. Tools like CUPED can come in handy here, enhancing the sensitivity of our experiments by using pre-experiment data to cut through the noise.

And let's not overlook novelty effects. These are those temporary blips in our metrics caused by introducing something new. If we're not careful, they can skew our results and lead us down the wrong path. By using time series analysis and being mindful of initial data driven by novelty, we can tell the difference between what's just a temporary buzz and what's a lasting effect.

Strategies to identify and anticipate second order effects

So how do we stay ahead of these second order effects? One great strategy is to use laddering questions during user research. This technique helps us peel back the layers of user motivations and behaviors that might lead to unintended consequences. By asking the right questions, we can spot red flags early and tackle risks before they become big problems.

Another key approach is designing adaptable systems. This means building flexibility right into your product architecture so you can make real-time adjustments and course corrections. When unexpected things happen (and they will), you'll be ready to pivot quickly, minimizing any negative impacts on your product's performance and user experience.

Don't forget about continuous data monitoring. Keeping a close eye on your data helps you detect emerging patterns and interaction effects. By leveraging data analytics tools to track key metrics and spot trends, you can catch potential issues early and take action before they escalate.

Here are a couple more tips:

• Regularly conduct sensitivity analyses to see how robust your product decisions are.

• Embrace second-order thinking by constantly asking yourself, "And then what happens?"

At the end of the day, tackling second order effects isn't about a single strategy—it's about combining user research, adaptable design, and data-driven insights. That's how you navigate the complexities of product development and deliver outcomes that really shine.

Statistical methods to manage second order effects in experiments

When it comes to experiments, managing second order effects means getting your stats game on point. Modeling interaction effects and including lower-order terms in your analysis is crucial. This helps ensure your results are accurate and you don't get misled by inflated false positives. Ignoring these effects can really throw a wrench in your conclusions.

Techniques like CUPED (Controlled-experiment Using Pre-Experiment Data) are super helpful. By leveraging data you already have, you can reduce variance and make your experiments more sensitive. It's like getting a clearer lens to see what's really happening.

Data-driven tools are your friends here. They help you analyze complex relationships and feedback loops within your experiments. At Statsig, we offer tools that enable real-time adjustments and controlled feature rollouts, which are key for navigating the intricacies of second order effects.

Also, keep an eye out for novelty effects—those temporary shifts in metrics when you roll out something new. By examining the time series of treatment effects, you can tell what's just a temporary blip and what's a sustained impact. This way, you're making decisions based on the long game, not just initial excitement.

Closing thoughts

Second order effects might be the hidden hurdles in product development and experimentation, but with the right strategies, you can turn them into opportunities. By embracing second-order thinking, designing adaptable systems, and leveraging robust statistical methods, you're setting yourself up for success. And remember, tools like those offered by Statsig can provide the real-time insights you need to stay ahead of the curve.

Curious to learn more? Check out our resources on understanding second order effects and novelty effects in experiments. Hope you find this useful!