# Testing Short Helper Text Under Each Form Field

Page: https://alidemirbas.com.tr/en/lab/ab-test-library/tests/testing-short-helper-text-under-each-form-field

## Hypothesis

If a short, always-visible explanation under the selected field says what is expected, visitors may enter the value correctly on the first try. The validation error rate could drop as a result, although the extra copy may add a little to completion time.

## When to run this test

When one field keeps producing errors, whether form analytics shows validation errors concentrated there, support requests come in about it, or session recordings show visitors retrying the value in different formats, that field is a good candidate for helper text. If the helper text works, errors and abandonment at the field with added guidance should fall, and questions on the same topic should come in less often. If the error comes from the field itself, for example an unnecessarily strict format or a misleading label, fix the field first; do not use helper text to patch a design problem.

## Primary metric

**Validation error rate**: Define the same eligible input attempts in both arms and compare validation errors.

## Other metrics

- **Form completion rate**: Make sure completion does not fall.
- **Field-level abandonment**: Check whether visitors are less likely to leave the field with added guidance.
- **Average completion time**: Track material increases in completion time and review reading burden separately.
- **Support contacts**: Check whether questions about the same field decrease.

## What to check before and during the test

- **Helper text**: Add one predefined, always-visible short explanation below the selected field.
- **Keep constant**: Keep field design, label, validation and error messages unchanged.
- **Scope**: Choose a field based on observed errors instead of adding copy to every field.
- **Audience**: Review new and returning users separately.
- **Separate tests**: Test examples, on-focus help or additional fields separately.

## What to watch when reading the result

- Read the drop in validation error rate together with the form completion rate and field-level abandonment. If errors fall but completion drops, the explanation may have created a new obstacle, and that is no reason to ship it.
- This result covers only the field and helper text you tested; before adding similar copy to other fields, check whether their errors come from the same uncertainty. Review new and returning users separately, and keep watching support contacts after the test ends, since they arrive later.
- Before launch, make sure the helper text has readable contrast. Faint gray copy would measure whether visitors notice the text, not whether it helps.
- Validation errors on a single field can be a rare event, so a small difference may stay unclear for a long time. Enter the current error rate in the [sample size calculator](https://alidemirbas.com.tr/en/calculators/sample-size-calculator) to see how large a sample per variant a rare event demands. Spread that over each arm's daily traffic to plan the run length up front, and do not stop early just because the numbers look favorable.

## Frequently Asked Questions

### How do field instructions differ from error messages?

Instructions explain what is expected before input. Error messages explain why an entry failed and how to fix it. Neither replaces the other. Associate guidance with the relevant field.

### Why use validation errors rather than completion as the primary outcome?

The hypothesis concerns preventing input errors. Define comparable entry attempts in both arms. Use validation-error rate as the primary outcome and completion as a guardrail.

### Is putting the explanation in a tooltip the same test?

No. This variant adds continuously visible guidance. A tooltip changes access and visibility. Preserve the error message and keep the guidance readable.

### How should additional reading time be interpreted?

Longer time can be acceptable if errors fall without unacceptable abandonment. Treat time as diagnostic and examine predefined new and returning-user groups where relevant.