Blocking: Controlling known variations

Ever run an A/B test and gotten wildly different results when you repeated it? You're not alone - and there's a good chance you're dealing with confounding variables that are messing with your data.

The fix isn't complicated. It's a technique called blocking that's been around since the 1920s, and it's probably the most underused tool in the modern experimenter's toolkit. Let me show you how it works and why you should care.

The importance of blocking in experimental design

Think of blocking like this: you're trying to test whether a new checkout flow increases conversions, but you know that mobile users behave differently from desktop users. Instead of letting that device difference muddy your results, you create separate "blocks" for mobile and desktop users. Now you can see the true effect of your checkout changes without device type throwing everything off.

This simple move can double or triple your ability to detect real differences in your experiments. When you reduce the noise in your data, suddenly those 2-3% improvements that matter to your business become visible instead of getting lost in the static.

The idea started with farmers trying to test fertilizers. They noticed that one side of their field always grew better crops because it got more sun. So they divided their fields into blocks with similar sun exposure and tested within each block. The hardness testing example shows this perfectly - researchers used metal coupons as blocks because they knew different metal specimens would behave differently. Same principle applies whether you're testing vascular grafts or conversion rates.

What's brilliant about blocking is that it doesn't just work for physical differences. Time-based variations are fair game too. The manufacturing process example blocked by day of the week because Mondays are always weird and Fridays... well, you know how Fridays go. By accounting for these natural rhythms, they got cleaner data about what actually mattered.

How blocking works: Concepts and methodologies

Here's the basic recipe: identify what's causing unwanted variation in your data, group similar units together based on those factors, then run your experiment within each group. The formal name for this is Randomized Complete Block Design (RCBD), but don't let the jargon scare you off.

Let's say you're testing three different email subject lines. You suspect that:

• Time of day affects open rates

• User engagement level matters

• Geographic location plays a role

Instead of hoping these factors balance out randomly, you create blocks. Morning emails to highly engaged East Coast users go in one block. Evening emails to casual West Coast users go in another. You test all three subject lines within each block.

When you analyze the results, something magical happens. Your statistical software (or just a good old ANOVA) can separate out the variation into three buckets:

• Variation from your actual treatments (the subject lines)

• Variation from your blocks (time, engagement, location)

• Everything else (random error)

By pulling out that block variation, you're left with a much clearer picture of whether those subject lines actually made a difference. The error variance shrinks, and suddenly you can detect smaller effects that would have been invisible before.

Practical applications of blocking in various fields

Manufacturing teams have this down to a science. They know that material from different batches behaves differently, so they block by batch number. This way, when they're testing a new production temperature, they're not fooled by Batch 47 being naturally superior to Batch 46. It's about reducing unexplained variation to see what really drives quality.

Product testing gets interesting when you add human factors. Different operators might have slightly different techniques. Morning shifts might be more careful than night shifts. Smart teams block for these factors instead of pretending they don't exist. By assigning all treatments to each operator and each shift, you get reliable and precise data about what actually works.

Software experiments are where blocking really shines in the modern world. You've got natural blocks everywhere:

• Device types (iOS vs Android, mobile vs desktop)

• User segments (new vs returning, free vs paid)

• Geographic regions

• Time zones

• Even browser versions if you're that kind of thorough

Instead of running one massive A/B test and hoping for the best, you can block by these factors and get much cleaner insights. The team at Statsig sees this constantly - experiments that looked like failures actually had strong positive effects in specific blocks that were getting averaged out in the overall results.

Implementing blocking to control confounding variables

First step: figure out what's messing with your data. Identifying potential nuisance factors isn't rocket science - it's usually the stuff everyone already complains about. "Oh, our weekend traffic is totally different." Great, block by weekday vs weekend. "Power users skew everything." Perfect, block by usage level.

The key is being honest about what varies in your system. In traditional experiments, it might be:

• Equipment differences

• Operator skill levels

• Environmental conditions

• Raw material batches

In digital experiments, think about:

• Traffic sources (organic vs paid)

• User demographics

• Session timing

• Previous experiment exposure

Once you've identified your blocks, the implementation is straightforward. Design your experiment so each treatment appears in each block. This isn't about perfect balance - it's about acknowledgment. You're saying "yes, these factors matter, and we're going to account for them instead of crossing our fingers."

Modern tools make this easier than ever. No-code experimentation platforms let you define blocking variables with a few clicks. You can segment by user properties, randomize within segments, and analyze results without writing a line of code. This democratizes good experimental design - you don't need a statistics PhD to run clean experiments anymore.

The payoff is huge. Blocking can turn a "no significant difference" result into clear winners and losers. You uncover effects that were hiding in the noise. Your statistical significance improves not because you ran more users, but because you ran a smarter experiment.

Closing thoughts

Blocking is one of those techniques that seems obvious once you get it, but most teams still don't use it. They run massive A/B tests, get muddy results, and either make decisions based on noise or conclude that nothing works. Meanwhile, teams using blocking are finding 5-10% improvements that others miss entirely.

The best part? You can start small. Pick one obvious source of variation in your next experiment and block for it. See how much cleaner your results get. Once you experience that clarity, you'll never go back to hoping randomization saves you.

Want to dive deeper? Check out:

• Montgomery's "Design and Analysis of Experiments" for the full statistical treatment

• Statsig's guide on controlling for confounding variables

• Any agricultural field trial paper from the 1930s (seriously, those farmers knew their stuff)

Hope you find this useful!