Anomalies, Random & Systematic Error

Revise Anomalies, Random & Systematic Error for Biology 3 (IAL) WBI13 (AS Level) — revision notes and instant AI marking.

📖 Revision notes · preview

Edexcel IAL Biology • Unit 3: Practical Skills in Biology I

Anomalies, Random & Systematic Error

🎯 Big idea: Every measurement you take is slightly wrong. The skill Unit 3 actually tests is knowing which kind of wrong it is — because random error scatters your points and can be beaten by repeating, while systematic error shifts every point the same way and repeating will never touch it. Naming the right one, and knowing what it does to your graph, is where the marks sit.

Summary — What This Topic Covers

What an anomaly is, how to identify one honestly, and what to do with it
Random error — where it comes from, what it looks like, and why repeats fix it
Systematic error — including zero error — and why repeats never fix it
The difference between accuracy and precision, and why an experiment can be one without the other
Calculating percentage uncertainty and combining it across a calculation
Writing evaluation points that earn marks instead of repeating "human error"

1. Anomalies — Spotting Them Honestly

Key Term

An anomalous result (outlier) is a value that does not fit the pattern shown by the rest of the data — it sits well outside the spread of its repeats, or well off the line of best fit drawn through the other points.

An anomaly is defined by the pattern, not by being a number you dislike. If your repeats at 40 °C are 21, 22, 20 and 45 mm³, the 45 is anomalous — it lies far outside the spread of the other three. If they are 21, 22, 20 and 25, the 25 is not anomalous; it is just the scatter you would expect from random error.

What to do with one

Identify it, exclude it from the mean, and say that you did. Circle it on the graph, leave it out of the calculated mean, and state in your evaluation that it was excluded and why. Never delete it silently, and never quietly "adjust" it.

Common mistake

Including an anomaly in the mean and then blaming the odd-looking mean on "human error." If a value is anomalous enough to mention, it is anomalous enough to leave out of the mean — and you must say so.

An anomaly is a signal, not just a nuisance. A single wild value usually means something happened on that one run — a bubble in the syringe, a mis-read scale, a piece of tissue that was not the same size. Saying what you think happened is worth more than saying it was anomalous.

2. Random Error

Key Term

Random error causes readings to scatter unpredictably either side of the true value. It has no fixed direction — sometimes too high, sometimes too low.

Random error is the reason repeats of the same measurement never come out identical. Typical sources in a biology practical:

Judging when a colour change is "complete" in Benedict's test — slightly different each time
Reading a meniscus or a scale at a slightly different eye position
Natural biological variation — no two potato cylinders, leaves or yeast cultures are identical
Small timing differences when starting and stopping a stopwatch

How to reduce it

Repeat and take a mean. Random errors cancel out over repeats because they fall either side of the true value. More repeats, and using apparatus with finer resolution, both reduce the scatter.

Analogy

average

On a graph: