Cambridge International A Level Biology Revision Guide

Chapter P1: Practical skills for AS
Explaining your results
You may be asked to explain your results. This requires
you to use your scientific knowledge to explain why the
relationship you have found between your independent
variable and your dependent variable exists.
QUESTION
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Difficulties in measuring the dependent variable,
due to human limitations. For example, in the rennin
experiment, you needed to judge the end-point, which
is impossible to do precisely using just the human eye.
You may sometimes have chosen a moment for the endpoint
that was relatively early, and sometimes chosen a
moment that was too late. So these types of errors are
also random errors.
P1.6 Use your knowledge and understanding of enzyme
activity to explain the results shown in Figure P1.12
(page 257).
Identifying sources of error and
suggesting improvements
You will often be asked to identify important sources
of error in your experiment. It is very important to
realise that you are not being asked about mistakes that
you might have made – for example, not reading the
thermometer correctly, or not measuring out the right
volume of a solution, or taking a reading at the wrong
time. These are all avoidable human mistakes and you
should not be making them!
Sources of error are unavoidable limitations of
your apparatus, measuring instruments, experimental
technique or experimental design that prevent your results
from being totally reliable (page 255). They generally fall
into the three major categories given below.
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Uncertainty in measurements resulting from lack of
accuracy or precision in the measuring instruments
that you were using, and from the limitations in
reading the scale. These are described on page 255.
These errors are likely to be the same all through your
experiment. They will be about the same size, and
act in the same direction, on all of your readings and
results. They are systematic errors.
Difficulties in controlling the standardised variables.
For example, if you were using a water bath to maintain
a constant temperature in the rennin experiment, it
may have been impossible to keep the temperature
absolutely constant. Variations in temperature could
have affected the rate of activity of the rennin, making
it impossible to be sure that all changes in rate of
activity were due to differences in your independent
variable – the concentration of the rennin. These errors
are likely to be different for different stages of your
investigation. They are random errors.
It is very important to learn to spot the really important
sources of error when you are doing an experiment.
Be aware of when you are having difficulties, and don’t
assume that this is just because you are not very good
at doing practical work! If you carry out the rennin
experiment, you will quickly realise how difficult it is to
keep your water bath at exactly the correct temperature
and to judge the end-point of the reaction precisely. These
are the really important errors in this experiment, and
they outweigh any others such as the error in measuring a
volume or in measuring temperature.
If you are asked to suggest improvements to an
experiment, your suggestions should be focused on reducing
these sources of error. Improvements could include:
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using measuring instruments that are likely to be more
precise, accurate or reliable – for example, measuring
volumes with a graduated pipette rather than a syringe
using techniques for measuring the dependent variable
that are likely to be more reliable – for example, using a
colorimeter to measure colour changes, rather than the
naked eye
using techniques or apparatus that are better able to
keep standardised variables constant, such as using a
thermostatically controlled water bath rather than a
beaker of water
controlling important variables that were not controlled in
the original experiment (note that it is also important to
say how you would control these variables)
doing repeats so that you have several readings of your
dependent variable for each value of your independent
variable, and then calculating a mean value of the
dependent variable.
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