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Type-2 error

A Type-2 error occurs when you fail to detect a real effect or improvement. In other words, you accept the null hypothesis when it’s false (false negative). Example: missing the fact that a new layout actually improves sign-ups. This often leads to lost opportunities because useful changes go unnoticed.

Type-2 errors often happen when sample sizes are too small or test duration is too short. The risk of such errors is linked to the test’s power (probability of detecting a true effect).

To reduce them, teams should run tests longer, increase traffic, or refine hypotheses.

While less damaging than Type-1 errors, Type-2 errors can slow progress by hiding improvements. In competitive markets, missing out on optimizations due to false negatives can mean falling behind rivals.

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Type-1 Error

A Type-1 error happens when you wrongly conclude that a change made an impact when it didn’t. In testing, this means rejecting a true null hypothesis (false positive). Example: thinking a new button increased sales when, in reality, it was just random chance.

Trust Badges

Trust badges are small icons or symbols displayed on websites to build credibility and reassure users about safety, authenticity, or quality. Examples include SSL certificates, payment security icons, and more. They reduce hesitation during checkout by showing that the site is safe and reliable. The right trust badge placed at the right time can improve conversions.

Title Tag

A title tag is an HTML element that defines the clickable headline shown in search engine results and browser tabs. It describes the page content in about 50–60 characters.

Title tags play a key role in SEO because they help search engines understand the topic and attract users to click.

A good title tag is clear, descriptive, and includes the main keyword naturally.

Testing in Production

Testing in production means running experiments or deploying features directly on the live environment where real users interact. Instead of using a staging setup, teams test in the actual system to see how features behave under real-world conditions. While it provides accurate insights, it also carries risks like bugs or downtime affecting customers.

Test Hypothesis

A test hypothesis is a clear statement predicting what you expect to happen in an experiment. In CRO or usability testing, it outlines the change being tested, the expected impact, and the reason behind it.

A good hypothesis is measurable, specific, and based on user research or past data, not just guesswork.