Biology

9.3 - Evaluating & Improving a Method

3 min

note 1 of 2

Sources of error and improvements

A source of error is something in the method that makes a reading less accurate.
The marking rule: every error must be paired with an improvement that reduces it - an error on its own scores nothing.
So always name the error, then say what you would change to fix that specific error.

Common errors and their fixes

The same handful of errors turn up dressed in different experiments. Each improvement must target its own error:

Source of error	Improvement that fixes it
A liquid added drop by drop from a pipette, so the volume varies	Measure the volume with a syringe or measuring cylinder
An end-point (a colour change) judged by eye	Compare against a colour standard or use a colorimeter
A water bath cooling during the run	Use a thermostatically controlled water bath

A syringe fixes an inconsistent volume; a thermostatic bath fixes a drifting temperature.
The wrong pair - such as "use a syringe" for a temperature that fell - does not score.

note 2 of 2

Anomalies and repeats

An anomaly is a result that does not fit the pattern of the others.
When you find one, identify it and leave it out of the mean, so one stray reading does not drag the average off.

You repeat readings and take a mean

A single reading might be an anomaly you wouldn't otherwise spot.
The mean of several repeats sits closer to the true value.

This reduces the effect of random error, so the mean is a better estimate of the true value.

Representative samples are a separate idea

A representative sample applies when you measure a number of living things, rather than repeating one measurement.
Measuring one leaf tells you little - it might be unusual. Instead you measure ten and take a mean, so the figure stands for the group.
Choose the individuals without bias.

Don't muddle the two

Repeating one measurement and taking a mean - This is about reliability.
Measuring many individuals - This is about representativeness.
Both take a mean, but they answer different questions.

key terms

TermDefinition

Source of errorSomething in the method that makes a reading less accurate.

Anomalous resultA result that does not fit the pattern of the other results.

Representative sampleA sample of individuals chosen without bias that stands for the whole group.

end of notes

Key points to remember

Sources of error and improvements

A source of error is something in the method that makes a reading less accurate.

The marking rule: every error must be paired with an improvement that reduces it - an error on its own scores nothing.

So always name the error, then say what you would change to fix that specific error.

Common errors and their fixes

The same handful of errors turn up dressed in different experiments. Each improvement must target its own error:

Source of error	Improvement that fixes it
A liquid added drop by drop from a pipette, so the volume varies	Measure the volume with a syringe or measuring cylinder
An end-point (a colour change) judged by eye	Compare against a colour standard or use a colorimeter
A water bath cooling during the run	Use a thermostatically controlled water bath

A syringe fixes an inconsistent volume; a thermostatic bath fixes a drifting temperature.

The wrong pair - such as "use a syringe" for a temperature that fell - does not score.

Anomalies and repeats

An anomaly is a result that does not fit the pattern of the others.

When you find one, identify it and leave it out of the mean, so one stray reading does not drag the average off.

You repeat readings and take a mean

A single reading might be an anomaly you wouldn't otherwise spot.

The mean of several repeats sits closer to the true value.

This reduces the effect of random error, so the mean is a better estimate of the true value.

Representative samples are a separate idea

A representative sample applies when you measure a number of living things, rather than repeating one measurement.

Measuring one leaf tells you little - it might be unusual. Instead you measure ten and take a mean, so the figure stands for the group.

Choose the individuals without bias.

Don't muddle the two

Repeating one measurement and taking a mean - This is about reliability.

Measuring many individuals - This is about representativeness.

Both take a mean, but they answer different questions.