Data integrity

Keeping data correct

• Integrity means data stays accurate. Two techniques protect it: validation and verification.
• They sound similar but do different jobs — a classic exam distinction.

Validation vs verification

• Validation checks data is reasonable as it is entered. Examples:
  • Range check (age 0–120), length check (a phone number), format check (an email has an @), presence check (not left blank).
• Verification checks data was copied correctly. Examples:
  • Double entry (type a password twice), or a visual check (read it back).

Checking data in transit

• When data travels, bits can flip. A checksum or check digit catches that.
• The sender calculates a small number from the data; the receiver recalculates it. If they differ, the data was corrupted.

number = "1389"
print(sum(int(d) for d in number) % 10)   # a one-digit checksum

Your turn

• Compute a simple check digit by summing the digits modulo 10. Real systems (like barcodes and ISBNs) use cleverer versions of this idea.

Covers: A-Level 6.2 (validation/verification), IGCSE 2.2 (error detection).

Common mistakes

• A checksum or check digit spots accidental changes to data.
• Validation checks data is sensible; verification checks it was entered correctly.