Mastering Experimentation: Avoiding Pitfalls in High-Stakes Testing
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Understanding Experimentation and Causal Inference
In today's tech landscape, experimentation has become essential for companies like Netflix and Airbnb before launching new products. The prevailing wisdom is clear: no significant business decision should be made without conducting A/B tests on customer responses.
Indeed, business experiments yield valuable insights into consumer behavior, illuminating the often stark contrast between what people say and how they actually act. Without experimentation, predicting how customers will respond to new offerings is akin to blind men trying to assess an elephant's size; we simply miss the complete picture.
While the experimental method, like any research approach, has its limitations and assumptions, practitioners frequently proceed even when these assumptions are not fully met. Unfortunately, this can lead to the worst-case scenario: making decisions based on misleading evidence. False information is far more detrimental than having no data at all.
In this post, I will outline five prevalent mistakes that can derail your million-dollar experiments, along with strategies to address each issue.
Causal Inference and Its Importance
Causal inference involves tracing back to the fundamental causes of observed outcomes. In our daily routines, we often ponder questions such as: "Does the new user interface boost daily active users?" or "What factors contribute to a surge in signups?"
Through experimentation, we can identify causal relationships by controlling other variables and altering just one at a time. If random assignment isn’t feasible, data scientists can employ quasi-experimental techniques (such as the Five Tricks) and observational methods (like pairing or propensity score matching) to infer causality.
To summarize, causal inference begins with comparable experimental groups and concludes with distinct outcomes. If the groups—treatment and control—lack equivalence from the outset, the research design is inherently flawed.
1. Careful Metric Selection
Data scientists must establish the right metrics to evaluate the performance of their experiments, as poor metric choices can compromise the results.
For instance, consider a retail company experiencing declining sales. To meet targets, the management might propose significant promotions. If the data team suggests a 50% discount as a solution, should the promotion be implemented as planned?
Probably not! While the promotion could temporarily boost sales, it might also harm long-term profits, as customers might stock up and delay future purchases.
Selecting appropriate metrics is as crucial—if not more so—than the research design itself. The last thing we want is for various sectors to compete against each other destructively.
What to Do?
- Collaborate with key business stakeholders to determine primary and secondary metrics of interest.
- Understand the trade-offs involved in metric selection.
- Revisit metrics throughout the experiment as needed.
As a general guideline, experiments often tell compelling stories regarding short-term metrics but may fall short in indicating long-term performance.
2. Flawed Experimental Designs
As mentioned, data scientists need to ensure the experimental groups are comparable concerning key variables, often referred to as an "apples-to-apples comparison." If not, the results may be questionable.
Some commonly used designs in the industry, like One Group Before-After Comparison and Treated vs. Non-Treated Groups, can lead to misleading conclusions. For example, if we notice an increase in daily active users after a new UI rollout, we might conclude the update was effective. However, such designs often lack proper comparisons and may fall prey to selection bias.
Additionally, we cannot rule out alternative explanations, such as other significant changes occurring simultaneously with the UI update.
What to Do?
- Consult your engineering team regarding data availability.
- Assess whether the treatment and control groups are comparable. If not, determine the measures taken to rectify this.
3. Premature Experiment Termination
After selecting relevant metrics and ensuring comparability among experimental groups, it can be tempting to conclude the experiment early upon observing positive results. This tendency reflects a common cognitive bias—humans often seek confirmation of their expectations.
However, it's wise to remain patient and monitor whether results stabilize or regress toward the mean. The initial positive spike might simply be an anomaly.
What to Do?
- Continue the experiment beyond initial findings.
- If data is limited, create additional data points by alternating the treatment assignment.
- Observe the evolution of treatment and control groups over time.
4. Spillover Effects and Contamination
A critical question arises: do members of the treatment group interact with those in the control group? If they do, the results may be skewed due to spillover effects.
For instance, if treatment is randomly assigned to users in close proximity, interactions can lead to unintended sharing of treatment effects with the control group.
What to Do?
- Consult your engineering team to determine the level at which treatment can be randomized (e.g., user-level, city-wide).
- Identify whether experimental groups interact; if they do, consider adjusting the randomization level.
5. Questionable Randomization
In A/B tests and other experimental designs utilizing randomization, it’s crucial to ask: is the assignment truly random?
This may seem paradoxical, but random processes do not inherently guarantee random assignments. External factors can cause certain groups to be more likely to end up in the treatment group than others.
In cases where individual URL randomization is impractical due to cross-contamination, opting for city-level randomization may be necessary.
What to Do?
- This question requires deep understanding of the experimentation frameworks and the ability to discern subtle differences.
- Evaluate the lowest feasible level of random assignment with your engineering team.
Conclusion
This discussion has highlighted five common pitfalls that can undermine your experiments: metric selection, research design, timing, spillover effects, and randomization. Remember, the details can make a significant difference! Data scientists must remain vigilant to these common mistakes when executing experiments.
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