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Experimentation communities: Internal networks
Ever tried running an experiment in your company only to realize nobody else knew about it? Or worse, discovered someone else was running a conflicting test at the same time? You're not alone.
The best experimentation programs aren't built on tools or processes - they're built on people talking to each other. When teams share what they're testing, what went wrong, and what actually moved the needle, that's when real progress happens.
The role of internal networks in experimentation communities
Think of internal networks as the nervous system of your experimentation practice. They're not formal org charts or Slack channels - they're the actual connections between people who care about testing and learning. These communities of practice spring up naturally when you get experimenters together, whether that's data scientists, product managers, or engineers.
The magic happens when these social and interaction networks start influencing how you design experiments. You get better tests when different perspectives collide. That backend engineer might spot a technical constraint you missed. The marketing analyst might know about a campaign that could skew your results. This cross-pollination helps you catch biases and confounding factors before they tank your experiment.
But here's the thing - insights trapped in one team's retrospective doc might as well not exist. The most effective experimentation communities actively push findings across the organization. They hold regular share-outs, maintain wikis of past experiments, and create spaces where people can ask "has anyone tested this before?" This isn't just about avoiding duplicate work (though that's nice). It's about building institutional memory that makes every subsequent experiment smarter.
Building your own network of peers isn't optional if you want to level up as an experimenter. Find the people who've been burned by the same issues you're facing. Connect with folks who can look at your experiment design and immediately spot the flaws. These relationships become your secret weapon for navigating the messiness of real-world testing.
Want to really accelerate your community's growth? Start sharing publicly. Writing about your experiments - both the wins and the spectacular failures - does two things. First, it forces you to think critically about what you learned. Second, it attracts other experimenters who want to swap war stories. Before you know it, you've got a thriving internal network of people pushing each other to run better tests.
Designing experiments that account for network effects
Running experiments in networked environments is like playing chess while someone keeps moving the board. Your users don't exist in isolation - they talk to each other, influence each other, and sometimes actively work against your nice clean test/control split.
The biggest trap? Assuming your treatment and control groups are independent when they're not. Picture this: you're testing a new social feature, and users in your treatment group start using it with their friends in the control group. Suddenly your "control" isn't controlling for anything. Companies like Meta learned this the hard way and developed cluster experiments specifically to handle these spillover effects.
Exposure maps have become the go-to solution for understanding these interdependencies before you launch. Here's how they work:
Map out how users actually interact with each other
Identify clusters of highly connected users
Assign entire clusters to treatment or control (not individuals)
Account for varying exposure levels in your analysis
The trade-offs get interesting when you dig into network density thresholds. Too sparse, and you miss network effects entirely. Too dense, and contamination makes measurement impossible. Research on experimental networks suggests there's often a sweet spot where you can still learn while effects propagate naturally.
One practical tip from teams who've been burned: start documenting network structures before you need them. When conducting any kind of network testing, having baseline data about connections and influence patterns saves massive headaches later. Build these practices into your standard operating procedures so they're not an afterthought when you're rushing to launch an experiment.
Building a culture where experimentation thrives
Tools and processes don't create experimentation culture - people do. The best experimentation cultures feel more like communities than corporate initiatives.
Start with psychological safety. People need to know that a failed experiment won't torpedo their performance review. In fact, the most valuable experiments often come from spectacular failures that teach you something fundamental about your users or product. Create spaces where people can share these failures without judgment - whether that's a monthly "experiments that surprised us" meeting or a dedicated Slack channel for experiment post-mortems.
David Robinson's advice about blogging applies perfectly to internal knowledge sharing. When you write up your analysis - even the messy, inconclusive ones - you create opportunities for serendipitous connections. That random analyst from another team might have exactly the insight you need to crack your problem.
Your internal networks become powerful debugging tools when you use them right. Before pushing anything to production, tap into your community:
Share your experiment design for feedback
Run it by someone who's tested similar features
Check if other teams have conflicting tests planned
Document your learnings for the next person
Following testing best practices means leveraging these networks at every stage. The goal isn't perfection - it's catching the obvious mistakes before they cost you.
Remember that building these peer networks takes intentional effort. You can't just wait for them to form organically. Host brown bags, create experimentation guilds, or start a book club around testing methodologies. The specific format matters less than creating regular touchpoints where experimenters can connect and learn from each other.
Making experimentation scalable with the right tools
Once your experimentation community hits critical mass, you'll hit scaling challenges. Manual processes that worked for 10 experiments a month fall apart at 100. This is where platforms like Statsig come in - they handle the infrastructure so your team can focus on asking the right questions.
The key features that actually matter for internal experimentation:
Override capabilities: Test your experiments internally without polluting production data
Environment configurations: Run the same experiment across dev, staging, and production
Trustworthy statistics: Know your results are calculated correctly (because who has time to validate p-values manually?)
Support for advanced techniques: Sequential testing, multi-armed bandits, and other methods that go beyond basic A/B tests
But tools alone won't save you. The most successful teams combine powerful platforms with strong internal practices. They use overrides to let team members preview experiments before launch. They maintain separate testing environments to validate experiment logic. They document not just what they tested, but why they made specific design choices.
Your network of peers becomes even more valuable as you scale. These are the people who can share:
Which tools actually deliver on their promises
Workarounds for common platform limitations
Best practices for organizing experiments at scale
War stories about what happens when experiments go wrong
Want to accelerate adoption? Start documenting and sharing everything. Create templates for common experiment types. Build a library of past experiments that others can reference. Share post-mortems openly so everyone learns from mistakes. Transparency isn't just nice to have - it's what transforms a bunch of individual experimenters into a true community of practice.
Closing thoughts
Building strong internal networks isn't a nice-to-have for experimentation programs - it's the foundation everything else rests on. When experimenters across your organization share knowledge, catch each other's blind spots, and learn from collective failures, you create something more powerful than any individual tool or process.
Start small. Find one other person who cares about experimentation and grab coffee. Share what you're working on. Ask what challenges they're facing. Build from there.
For more on building experimentation practices, check out Statsig's guides, dive into community discussions on Reddit, or explore how to build your professional network more broadly.
Hope you find this useful!