---
title: Filtering Default Testing | RGM®
url: https://realgrowthmatters.com/learn/experimentation/filtering-default-testing/
updated: 2026-06-10
source_html: https://realgrowthmatters.com/learn/experimentation/filtering-default-testing/
---

# Filtering Default Testing

Filtering Default Testing, explained for people who have to act on it. Covers the mechanism, the steps, and the failure modes, for experimentation leads, analysts, and growth teams.

By **David Schaefer** · [LinkedIn](https://www.linkedin.com/in/daschaefer/) · Updated May 2026 · 9 min read · [3 sources cited](#sources)

## Key takeaways

- Filtering Default Testing is a topic within Experimentation — a concrete choice, not a vague best practice.
- Define the term in one sentence everyone agrees with before you measure anything.
- Change one variable at a time so results are causal, not coincidental.
- A good tool on a fuzzy definition still produces a misleading dashboard.
- Review on a fixed cadence and write down what you changed and what moved.

## What Filtering Default Testing covers

Filtering Default Testing is a topic within Experimentation, the discipline of running controlled tests to find causal impact, from A/B and multivariate tests to geo experiments and lift studies, and this page gives you a working handle on it. Hold that thought.

The label hides the part that matters. Filtering Default Testing belongs to Experimentation — the discipline of running controlled tests to find causal impact, from A/B and multivariate tests to geo experiments and lift studies. The point is a shared handle the whole team can hold. Where teams slip is treating it as a buzzword instead of a choice. Turn it into a choice with an owner, a number, and a review date.

Experimentation is the discipline of running controlled tests to determine causal impact — including A/B tests, multivariate tests, geo experiments, and platform-native lift tests.

Apply this whenever you need to know if a change causally improves outcomes versus selection effects, seasonality, or coincidence.

The reference points worth knowing alongside it include Optimizely, GeoLift from Meta, Evan Miller's calculators, and the CXL Institute. A shared set of references is what makes a fast meeting possible. Keep that in view as the specifics pile up.

## How Filtering Default Testing works in practice

Filtering Default Testing is best understood as a chain: inputs, a signal, a lag, then a decision, then improve them one at a time. Keep that distinction.

Under the surface it is mostly bookkeeping and honest comparison. Divide the objective into levers, attach an owner to each, and monitor them. A good setup means each teammate can name their own lever without thinking.

Filtering Default Testing — the working components

| Element | What it is |
| --- | --- |
| **Inputs** | What you actually control week to week. |
| **Lag** | How long before the effect is visible. |
| **Baseline** | The pre-change level you compare against. |
| **Guardrail** | The limit that stops a local win from causing a global loss. |

Set a weekly check for anomalies and a monthly session for the harder questions. It is the kind of thing that looks obvious in hindsight and gets skipped in practice.

## How to apply Filtering Default Testing

Keep the sequence honest: define, measure, test one thing, record what you learned. Worth saying plainly.

1. **Define the term out loud.** State it once, clearly, and check that the room agrees. A split definition is the first thing to repair.
2. **Instrument before you optimize.** Make sure the number is measured cleanly. A change you cannot trust to your tracking is a change you cannot learn from.
3. **Change one thing and test it.** Test one change against a real control. Hold everything else steady so the outcome is cause, not season or mix.
4. **Review on a cadence and write it down.** Log the decision and the outcome on a fixed cadence. A written record is the memory the team actually keeps.

The order matters. Skipping the definition step is why dashboards get built and ignored. Hold onto that and the rest of the page is detail.

## Grounding Filtering Default Testing in real numbers

Anchor the figures here to published sources, not to numbers that get repeated in meetings. That part is non-negotiable.

Use external numbers to sanity-check direction, then measure your baseline. What is normal in one market can be misleading in the next. Use the one below to check direction, then measure your own baseline.

**Claim:** Email marketing returns are often cited near a 36:1 average across the industry. **Source:** [[Litmus]](https://www.litmus.com/blog/). **Context:** Treat any blended average as a starting reference, not a target for your account.

Any figure here without a source link is RGM analysis, drawn from reviewing real accounts. Use it as a prompt to measure, never as a quotable statistic.

## Common mistakes with Filtering Default Testing

Things go wrong when the term is undefined, the work is siloed, or no counter-metric is watched. Here is the short version.

The mistakes that quietly cost the most

- Reviewing only when something looks wrong, so slow declines go unseen.
- Letting one team own the metric while another owns the lever.
- Treating an industry benchmark as a personal target.

Watch for these. They rarely announce themselves. Putting them on a checklist costs minutes and prevents months of drift.

## Quick answers

How should a team treat Filtering Default Testing day to day?
:   As a recurring decision, not a one-time setting. Name it, measure it, and revisit it on a cadence so the choice stays matched to the current goal.

Can small teams use Filtering Default Testing?
:   Yes. Smaller teams often apply it better because fewer handoffs mean the person who owns the lever also owns the number.

Where do RGM observations fit here?
:   Any pattern labelled RGM analysis comes from reviewing real accounts. It is offered as a tested hypothesis, never as a substitute for measuring your own data.

## Frequently asked

What is Filtering Default Testing in simple terms?

Filtering Default Testing is a topic within Experimentation, the discipline of running controlled tests to find causal impact, from A/B and multivariate tests to geo experiments and lift studies. In plain terms, this page treats it as a recurring decision your team can make with a shared definition instead of restarting the debate each time.

Why does Filtering Default Testing matter?

It matters because it shapes how budget, effort, and attention get allocated. When filtering default testing is defined and measured well, spend follows what works; when it is fuzzy, spend follows whoever argues hardest.

How do you measure Filtering Default Testing?

Pick one primary number, instrument it cleanly, and pair it with a counter-metric so you are not gaming the goal. Then compare against a pre-change baseline rather than an industry average.

What references help with Filtering Default Testing?

Useful reference points include Optimizely, GeoLift from Meta, Evan Miller's calculators, and the CXL Institute. Tools matter less than a clean definition and trustworthy measurement; a good tool on a bad definition still produces a misleading dashboard.

What is the most common mistake with Filtering Default Testing?

Optimizing it in isolation. A local improvement that ignores the downstream business effect can look like a win on the dashboard while costing money elsewhere.

How often should you review Filtering Default Testing?

Set a weekly check for anomalies and a monthly session for the harder questions. The point is a fixed rhythm, so slow drift gets caught before it becomes a quarter-sized problem.

### Sources cited on this page

1. CXL Experimentation — [cxl.com/blog](https://cxl.com/blog/)
2. Evan Miller — [www.evanmiller.org](https://www.evanmiller.org/)
3. Meta GeoLift — [facebookincubator.github.io/GeoLift](https://facebookincubator.github.io/GeoLift/)