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Cambridge International A Level Biology Revision Guide

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Chapter P1: Practical skills for AS<br />

The reliability of a measurement is the degree of trust<br />

that you can have in it. If your measurements are reliable,<br />

then you would expect to get the same ones if you repeated<br />

them on other occasions.<br />

Reliability is affected by both the accuracy and<br />

precision of your measuring instruments, and also by the<br />

kind of measurements that you are making. For example,<br />

in the rennin experiment, you have to make a decision<br />

about exactly when the end-point is reached. This is really<br />

difficult: there is no precise moment at which you can say<br />

the clots definitely form, so your measurement of the time<br />

at which this happens will be very unreliable.<br />

One of the best ways of dealing with poor reliability is<br />

to repeat the readings several times. For example, with the<br />

rennin experiment, you could set up three tubes of each<br />

concentration of the enzyme and measure the time to the<br />

end-point for each of them. You would then have three<br />

results for this particular enzyme concentration, which<br />

you could use to calculate a mean.<br />

Estimating uncertainty in<br />

measurement<br />

When you use a measuring instrument with a scale, such<br />

as a thermometer or syringe, you will often find that your<br />

reading doesn’t lie exactly on one of the lines on the scale.<br />

For example, if a thermometer has a scale marked off in<br />

intervals of 1 °C, you could probably read it to the nearest<br />

0.5 °C. We can assume that each reading you make could<br />

be inaccurate by 0.5 °C. This is the possible error in your<br />

measurement. So, if your temperature reading was 31.5 °C,<br />

we would show this as 31.5 °C ± 0.5 °C. In general, the<br />

potential error in a measurement is half of the value of<br />

the smallest division on the scale you are reading from.<br />

Figure P1.11 explains why this is so.<br />

In some situations, you might make two<br />

measurements, and your result is the difference between<br />

them. For example, you might want to measure a change<br />

in temperature. You measure the temperature at the<br />

start as 28.5 °C (error = ±0.5 °C) and at the end as 39.0 °C<br />

(error once again = ±0.5 °C). You then calculate the rise<br />

in temperature as 39.0 – 28.5 °C = 10.5 °C. But you have<br />

to assume that both of the measurements could be out by<br />

0.5 °C in the same direction. Your total error is therefore<br />

1 °C, and you should write the answer as 10.5 °C ±1 °C.<br />

You might be asked to express the size of the error as<br />

a percentage. To do this, you divide the error in the<br />

measurement by the measurement itself, and multiply<br />

by 100.<br />

Here, the temperature rise was measured as 10.5 °C,<br />

and the error was ±1 °C. Therefore:<br />

percentage error = (1 ÷ 10.5) × 100<br />

= 9.5%<br />

Errors in measurement are not the only important sources<br />

of error in biology experiments, and you can read more<br />

about this in the section on identifying significant sources<br />

of error on page 261.<br />

7<br />

6<br />

5<br />

The meniscus is at, or very<br />

nearly at, half way<br />

4<br />

between graduation lines,<br />

so you can read the<br />

volume as 5.55 cm 3 . But it<br />

could possibly be closer to<br />

5.50 or 5.60.<br />

3<br />

cm 3<br />

10<br />

Figure P1.11 Estimating uncertainty in measurement when<br />

using a scale.<br />

9<br />

8<br />

7<br />

6<br />

5<br />

4<br />

3<br />

2<br />

1<br />

Your reading error can therefore be up to half the smallest<br />

division on the 2 scale. The error here is therefore 2 ± 0.05 cm 3 .<br />

7<br />

6<br />

5<br />

Here the meniscus is just<br />

above the 5.5 line, but you<br />

4<br />

cannot read how much<br />

above. In this case you<br />

read to the nearest ruling,<br />

which is 5.50 cm 3 .<br />

Recording quantitative results<br />

Most of the experiments that you will do, either during<br />

your course or in the practical examination, will involve<br />

the collection and display of quantitative (numerical)<br />

results. You may be given a results table to complete, but<br />

often you will have to design and draw your own<br />

results table.<br />

Table P1.1 shows a results table that you could use<br />

for your results from the experiment investigating the<br />

effect of enzyme concentration on the rate of activity of<br />

rennin. Three repeat readings were made for each enzyme<br />

concentration, and a mean has been calculated.<br />

There are several important points to note about<br />

this results table, which you should always bear in mind<br />

whenever you construct and complete one.<br />

3<br />

255

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