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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
Throwing darts at a board while someone jogs your elbow. The darts land scattered around the bullseye —
but the average position is still roughly the bullseye. Throw enough darts and the average
homes in on it. That is random error.
On a graph: random error shows as points scattered above and below the line of best
fit, and as long error bars. The line still passes through the true relationship — the points just sit
loosely around it.
3. Systematic Error
Key Term
Systematic error shifts every reading by the same amount, or in
the same direction. The results are consistently too high or consistently too low.
Common sources:
Zero error — a balance reading 0.02 g with nothing on it, so every mass is
0.02 g too high
A mis-calibrated colorimeter that was not zeroed against a blank
Reading a burette or measuring cylinder consistently from above or below the meniscus (parallax)
A water bath that reads 40 °C but actually sits at 38 °C throughout
Heat losses from an unlagged vessel — always losing, never gaining
The point examiners test
Repeating does not reduce systematic error. Every repeat is wrong by the same amount, so
the mean is wrong by that amount too. This is the single most examined distinction in the whole topic —
if a question asks why repeats did not help, systematic error is almost always the answer.
How to reduce it
Check and reset the zero before use. Calibrate
instruments against a known standard. Read at eye level to avoid parallax. Use a control or blank to
correct the baseline.
On a graph: systematic error shows as points lying close to a line of best fit that is
shifted — a line that should pass through the origin but has an unexpected intercept is the classic
signature of a zero error.
Analogy
A bathroom scale that reads 2 kg with nobody on it. Weigh yourself a hundred times and average the
result — you are still 2 kg heavier than the truth. No amount of repeating rescues you; you have to
fix the scale.
4. Accuracy vs Precision
These are not synonyms, and questions exploit that.
Key Terms
Accurate — close to the true value. Precise — repeats are close to each other, regardless of whether they are
close to the truth.
ACCURATE + PRECISE PRECISE, NOT ACCURATE ACCURATE, NOT PRECISE
20.1 20.2 20.0 24.1 24.2 24.0 18.0 22.0 20.1
true value = 20.1 true value = 20.1 true value = 20.1
tight AND on target tight, but shifted scattered, but averages right
↑ systematic error ↑ random error
Common mistake
Calling a set of results "accurate" because the repeats agree. Agreement between repeats is
precision. A zero error gives beautifully precise, consistently wrong results.
For a single reading, the uncertainty is half the smallest division. For a measurement
needing two readings — a burette, or a thermometer read at start and end — it is
one whole division, because you take the uncertainty twice.
Worked example. A measuring cylinder with 1 cm³ divisions is used to measure
25 cm³.
Measure only 5 cm³ in the same cylinder and the uncertainty is still ±0.5 cm³ — but that is
now 10 %. This is why measuring a small volume in a large cylinder is poor
technique, and why the fix is to use a smaller cylinder or a syringe with finer divisions.
Combining uncertainties
When quantities are multiplied or divided, add
their percentage uncertainties. A rate found from a volume measured to ±2 % and a time measured to
±1 % carries an overall uncertainty of ±3 %.
What mark schemes look for
Show the substitution, not just the answer. Write (0.5 ÷ 25) × 100 = 2.0 % — the
working carries a mark of its own, and a bare number cannot earn it.
6. Writing Evaluation Points That Score
"Human error" earns nothing. It names no source, no direction and no fix. Every evaluation point should
do three things: name the source, say which type of error it is, and
give a specific improvement.
✗ "There may have been human error."
✗ "The results could have been more accurate."
✓ "The colour change endpoint was judged by eye, which is subjective and
introduces random error. Using a colorimeter to measure absorbance would
give an objective, repeatable endpoint."
✓ "The water bath was set to 40 °C but the thermometer read 38 °C, so every
rate was systematically too low. Calibrating the thermometer against a
known standard would remove this."
Common mistake
Suggesting "repeat more times" as the improvement for a systematic error. It is the correct fix for
random error and the wrong one here — and examiners use exactly this to separate students who have
understood the distinction from those who have memorised a phrase.
Practice Questions
Practice Question 1
A student measures the mass of five potato cylinders on a balance that reads
0.03 g when empty. State the type of error this introduces and explain why repeating the measurements
would not improve the results.
Practice Question 2
A student measures 10.0 cm³ of solution using a measuring cylinder graduated in
0.2 cm³ divisions. Calculate the percentage uncertainty, and suggest one change that would reduce
it.
Practice Question 3
Four repeats of an enzyme rate at 35 °C give 1.8, 1.9, 1.7 and 3.4 cm³
min⁻¹. Explain how the student should treat the fourth value and what they should write in their
evaluation.
Practice Question 4
A set of results is described as "precise but not accurate." Explain what this means
and give one likely cause.
What to Memorise
Random → scatter → repeats helpSystematic → shift → repeats don'tAccurate = close to truePrecise = close to each otherSingle reading → ± half a divisionTwo readings → ± one whole division% uncertainty = (unc ÷ value) × 100Multiply or divide → add the % uncertainties
Concepts Checklist
Exam Tips
What mark schemes look for
Name the error type explicitly — write the words "random error" or "systematic error." A
description of the problem without the label usually scores half of what it could.
The trap
"Repeat and take a mean" is the right improvement for random error and the wrong one for systematic
error. Read which one the question is describing before you reach for it.
Easy marks
Uncertainty questions are pure method: half a division for one reading, a whole division for two, then
divide and multiply by 100. Show the substitution and the marks follow.
Worth remembering
If a question tells you the repeats agreed closely but the answer was still wrong, it is handing you
"precise but not accurate" — and the cause it wants is systematic error.