Cambridge International A Level Biology Revision Guide

Cambridge International A Level Biology
494
QUESTION
P2.3 The molecular formula of glucose is C 6
H 12
O 6
.
a Describe how you would make up 100 cm 3 of a
1% solution of glucose.
b Describe how you would make up 250 cm 3 of a
1% solution of glucose.
c Describe how you would make up a 1 mol dm −3
solution of glucose.
d Describe how you would use the 1 mol dm −3
solution to make up a 0.5 mol dm −3 solution.
Measuring the dependent variable
Now let us go back to the yeast respiration rate
experiment, and the apparatus shown in Figure P2.2.
The dependent variable is the rate of respiration of
the yeast. This can be measured by recording the rate
of movement of the meniscus. Remember that, if you
are investigating rate, then time must come into your
measurements. You would need to record the position
of the meniscus at time 0, and then perhaps continue
to do this at regular time intervals for, say, ten minutes.
Alternatively, you could just record the position at time
0 and again at ten minutes. You would need to do this at
each temperature that you have decided to test.
Whatever the experiment that you are describing,
take care to describe exactly how you would measure the
dependent variable. Say what measuring instruments you
would use, and what you would do to make sure that your
measurements are made accurately. Say exactly what you
would measure and when you would measure it.
Identifying different types of variable
Often, the data about the dependent variable that you
collect in your experiment (your results) are numerical.
These are called quantitative data.
These quantitative data may be continuous or discrete.
If the variable is continuous, then each measurement,
count or reading can be any value between two extremes.
Your results will not necessarily be whole numbers. The
results for the yeast respiration rate experiment will be a
quantitative and continuous variable.
If the variable is discrete, then each measurement,
count or reading can only be one of a set number of
discrete values. For example, you might be asked to count
the number of prickles on each leaf in a sample of holly
leaves. The number of prickles will always be a whole
number – you cannot have half a prickle on a leaf.
Sometimes, the data you collect about the
dependent variable are not numerical. These are called
qualitative data.
Qualitative data can be ordered or categoric. Ordered
(ordinal) variables are those that – although you do not
have actual numerical values for them – can be organised
into an order or sequence. For example, you might do a
series of Benedict’s tests on a set of glucose solutions of
unknown concentration, and decide on the relative depth
of colour of each one. You can sort these into an order
– from the one that is least brick-red to the one that is
darkest brick-red – but you can’t assign an actual numeric
value for the colour of any of them.
Categoric variables are completely discrete, and you
can’t put them into order. Each observation fits into a
particular, clearly defined category. For example, in a
sample of dead leaves taken from a forest floor, you might
record the species of tree from which each leaf comes.
Each leaf comes from one species – there are no ‘overlaps’
between categories, and there is no way of ordering or
ranking them.
Controlling the controlled variables
You will be expected to describe how to control each of
the key variables in your plan. This is described on pages
253–254 in Chapter P1.
You might also need to think about doing a control
experiment as part of your investigation. The purpose
of a control is to check that it is the factor you are
investigating that is affecting the dependent variable, and
not some other factor. For example, in the yeast respiration
experiment, it would be a good idea to set up at least one
syringe with sugar solution but no yeast. This would be
a check that it is something that the yeast is doing that is
causing the change in position of the meniscus.