Validation rules are constraints that help ensure entered data are plausible, correctly formatted, and suitable for analysis. They are one of the most important benefits of electronic data capture.
In paper-based systems, errors may only be discovered after forms are reviewed or entered.

In REDCap, many errors can be detected at the point of entry. Validation can be applied to dates, times, numbers, email addresses, phone numbers, and
other formats. For numeric fields, validation may include minimum and maximum values. For example, age may be restricted to 0-120 years, temperature to 30-45 degrees Celsius, and weight to 1-250 kilograms depending on the study population. For date fields, validation ensures that users enter dates in a consistent format. Additional logic checks may be needed to ensure that visit dates occur after enrollment dates or that date of birth occurs before consent date.

Validation rules should be based on protocol requirements, clinical plausibility, and operational context. If ranges are too narrow, users may be blocked or warned when entering true but unusual values. If ranges are too broad, invalid values may pass unnoticed. The data manager should consult investigators and clinicians when defining ranges for vital signs, laboratory results, age groups, gestational age, dosing, and other clinical measures.
It is important to distinguish hard validation from soft validation. A hard validation rule prevents the user from saving an invalid value. A soft validation rule warns the user but allows the value to be saved if confirmed. In many clinical research settings, soft validation is safer for biologically plausible but unusual values. For example, a very high temperature may be unusual but clinically possible. The system should warn the user and prompt verification rather than automatically reject the value. Truly impossible values, such as negative age, should be prevented.
Validation also supports standardization across sites. In a multisite study, one site should not enter temperature in Fahrenheit while another enters Celsius unless the database explicitly captures units and converts them. Field labels, units, and validation rules work together to reduce such errors. A field labeled “Temperature (degrees Celsius)” with a plausible Celsius range is less likely to accept a Fahrenheit value.
Validation rules are not enough by themselves. Users must still understand the forms, source documents must be accurate, and workflows must support timely entry. A user may enter a plausible but incorrect value if they read the wrong source record. Validation reduces errors; it does not eliminate the need for training, monitoring, and source verification.