什么是A/A测试，您真的需要使用它吗？

What's worse than working with no data?

使用“不良”数据。

As marketers, we love to test headlines, call-to-actions, and keywords (to name a few). One of the ways we do this is by running A/B tests.

As a refresher,A/B testing是分裂观众测试广告系列的许多变体的过程，并确定哪些性能更​​好。

But A/B testing isn't foolproof.

In fact, it's a complicated process. You often have to rely on testing software to pull the data, and there's a high probability of receiving a false positive. If you're not careful, you could make incorrect assumptions about what makes people click.

So how can you ensure your A/B test is operating correctly? This is where A/A testing comes in. Think of it as a test to the test.

The idea behind an A/A test is that the experience is the same for each group, therefore the expected KPI (Key Performance Indicator) will also be the same for each group.

例如，如果A组中有20％在着陆页上填写表格，则预期的结果是，B组中有20％（与相同版本的着陆页互动）将做到这一点。

Differences Between an A/A Test and an A/B Test

进行A/A测试类似于A/B测试；每个观众分为两个类似大小的组，但是每个组都不会将每个组的内容变体变体，而是与同一内容的相同版本进行互动。

这是思考它的另一种方法：您是否曾经听过“将苹果与橘子进行比较”？A/B测试完全可以做到这一点 - 比较一个内容的两个不同变体，以查看哪些性能更​​好。A/A测试将苹果与一个相同的苹果进行了比较。

When running an A/B test, you program a testing tool to change or hide some part of the content. This is not necessary for an A/A test.

A/A测试还需要比A/B测试更大的样本量，以证明存在明显的偏差。而且，由于样本量如此大，这些测试需要更长的时间才能完成。

How to Do A/A Testing

确切的操作方式，根据testing tool你用。例如，如果您是HubSpot Enterprise客户在电子邮件中进行A/A或A/B测试，例如，HubSpot将自动将流量分为您的变体，以便每个变化都会接收访问者的随机抽样。

让我们介绍运行A/A测试的步骤。

1. Create two identical versions of a piece of content — the control and the variant.

创建内容后，确定您希望与测试进行测试相同样本量的两组。

2.确定您的KPI。

A KPI is a measure of performance over a period of time. For example, your KPI could be the number of visitors who click on a call-to-action.

3. Using your testing tool, split your audience equally and randomly, and send one group to the control and the other group to the variant.

运行测试，直到控制和变化达到确定的访问者数量。

4. Track the KPI for both groups.

Because both groups are sent to identical pieces of content, they should behave the same. Therefore, the expected result will be inconclusive.

A/A Test Uses

A/A testing is primarily used when an organization implements a new A/B testing software or reconfigures a current one.

您可以运行A/A测试以完成以下操作：

1.检查A/B测试软件的准确性。bob电竞官方下载

A/A测试的预期结果是，观众对同一内容的反应类似。

But what if they don't?

这是一个示例：XYZ公司正在新的着陆页bob全站app上运行A/A测试。将两个组发送到着陆页的两个相同版本（控件和变体）。A组的转化率为8％，而B组的转化率为2％。

In theory, the conversion rate should be identical. When there is no difference between the control and the variant, the expected result will be inconclusive. Yet, sometimes a "winner" is declared on two identical versions.

When this happens, it is essential to evaluate the testing platform. The tool may have been misconfigured, or it could be ineffective.

2.为将来的A/B测试设置基线转换率。

想象一下，XYZ公司在着陆页面上进行了bob全站app另一个A/A测试。这次，A组和B组的结果相同 - 两组都达到了8％的转化率。

Therefore, 8% is the baseline conversion rate. With this in mind, the company can run future A/B tests with the goal of exceeding this rate.

例如，如果公司在登录页面的新版本上运行A/bob全站appB测试，并且获得了8.02％的转换率，则结果在统计上不显着。

A/A测试：您真的需要使用它吗？

To run an A/A test, or not — that is the question. And the answer will depend on who you ask. There is no denying that A/A testing is a hotly debated topic.

反对A/A测试的最普遍的论点可能归结为一个因素：时间。

A/A测试需要大量时间运行。实际上，A/A测试通常需要比A/B测试更大的样本量。在测试两个相同的版本时，您需要大量样本量才能证明存在明显的偏见。因此，该测试将需要更多的时间才能完成，这可能会花费在运行其他有价值测试的时间中。

However, it makes sense to run an A/A test in some cases, especially if you are uncertain about a new A/B testing software and want additional proof that it's both functional and accurate. A/A tests are a low-risk method to ensure your tests are set up properly.

A/A testing can help you prepare for a successful AB testing program, provide data benchmarks, and identify any discrepancies in your data.

尽管A/A测试具有实用性，但进行此类测试应该是相对罕见的。虽然A/A测试可以在新的A/B工具或软件上运行“健康检查”，但由于运行的大量时间，可能不值得优化网站或营销活动的每个小改动bob电竞官方下载。