---
title: "Hire A/B Testing Specialists, Managed in Thailand | Azendo"
description: "Hire A/B testing specialists who get sample size right, stop peeking at results and flag what invalidates a test. Dedicated to your product, managed by us."
url: "https://azendo.co/skills/a-b-testing/"
---

1. [Home](https://azendo.co/)
2. [Skills](https://azendo.co/skills/)
3. A/B testing

# A/B testing specialists for your dedicated team.

A/B testing splits users between variants and measures the difference in a predefined metric. Done properly, it establishes causation as well as correlation, which makes it the most reliable way to know whether a change helped.

## Where A/B testing fits in a long-term product team.

The statistics are less forgiving than the tooling suggests. Sample size must be calculated before the test starts, from the smallest effect that matters; stopping when the result looks significant, known as peeking, inflates the false positive rate substantially, because a random walk crosses a threshold eventually. A test stopped early on a promising number is closer to a coin flip than to evidence.

Most product changes produce no detectable effect, and teams are consistently unprepared for that. A realistic programme expects a majority of tests to be inconclusive, which is itself valuable information: it says the change did not matter enough to build on.

## What your dedicated team does with A/B testing.

Experimentation is a capability, and the tool is the smallest part of it. It needs a truly random assignment process, metrics defined before the test, a decision rule agreed in advance, and someone who will hold the line when a stakeholder wants to stop early.

Building that habit takes time on the same product, which is what a [dedicated development team](https://azendo.co/services/dedicated-software-development-team/) provides.

## Common A/B testing use cases.

* Changes evaluated on evidence A measured causal effect, free of the confounds in a before-and-after comparison.
* Sample size fixed before the start A stopping rule set in advance, so the result is not manufactured by peeking.
* Inconclusive treated as an answer Accepting that most changes do not move the metric, and acting on that.

## Adding A/B testing skills to your team.

Experimentation sits with a [dedicated data engineering team](https://azendo.co/services/dedicated-data-engineering-team/) when the measurement pipeline is the constraint, and alongside product engineering when the assignment mechanism is.

You can add A/B testing capacity as one dedicated specialist or as part of a wider remote team, with the people managed by us from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.

## Roles that use A/B testing

* [Data Scientist Data science](https://azendo.co/services/dedicated-data-science-team/data-scientist/)

## Service areas that use A/B testing

* [Data science](https://azendo.co/services/dedicated-data-science-team/)

Capacity is agreed across your whole team, with one monthly fee, not per skill.

## Related in analytics and bi

* [Looker, a skill in our teams](https://azendo.co/skills/looker/)
* [Power BI, a skill in our teams](https://azendo.co/skills/power-bi/)
* [Tableau, a skill in our teams](https://azendo.co/skills/tableau/)
* [semantic layers, a skill in our teams](https://azendo.co/skills/semantic-layers/)
* [experimentation design, a skill in our teams](https://azendo.co/skills/experimentation-design/)
* [causal inference, a skill in our teams](https://azendo.co/skills/causal-inference/)
* [cohort analysis, a skill in our teams](https://azendo.co/skills/cohort-analysis/)
* [segmentation, a skill in our teams](https://azendo.co/skills/segmentation/)

## Tell us what you need A/B testing for.

A service delivery manager will reply with the specialists we'd suggest, the monthly capacity and what the first month looks like.

[Browse all skills](https://azendo.co/skills/)