Errors and Uncertainties

Overview

This lesson introduces one of the most important differences between pre-AS and AS practical work: you are now expected to talk about the quality of data precisely. The goal is to describe errors and uncertainties in a way that is scientific rather than vague.

What You Need to Know

• Systematic errors shift measurements in one direction, while random errors cause scatter.
• Zero error is a common example of a systematic problem.
• Precision and accuracy are related but not the same thing.
• Repeated measurements help you understand the spread of data and reduce the effect of random error.
• In derived quantities, simple absolute or percentage uncertainty methods are often enough at this stage.
• Clear uncertainty work improves the quality of both practical conclusions and final answers.

How to Work Through It

1. Start by separating examples of random and systematic error.
2. Compare accuracy and precision using simple measurement sets or target diagrams.
3. Practise reading or estimating measurement uncertainty from instruments.
4. Finish with short calculations where uncertainties are combined in derived quantities.

Check Your Understanding

• Is the error you are describing random or systematic, and why?
• Can a set of readings be precise but inaccurate?
• What does a zero error do to the final result?
• How do you decide whether to combine absolute or percentage uncertainties in a simple case?

Common Mistakes

• Using accuracy and precision as synonyms.
• Calling every uncertain measurement a systematic error.
• Quoting an uncertainty without linking it to the instrument or method.
• Combining uncertainties mechanically without thinking about the quantity being calculated.

Next Steps

• Keep a small set of model uncertainty statements in your notes for practical write-ups.
• Use this language in the topic practice work, not just in uncertainty questions.