Cambridge International A Level Biology Revision Guide

Chapter P2: Planning, analysis and evaluation
A key feature to consider is whether you really were
measuring the dependent variable that you intended to
measure. For example, if you were doing a transpiration
experiment using a potometer, were you really measuring
the rate of loss of water vapour from the shoot’s leaves?
The measurement you were making was actually the rate
of uptake of water by the shoot from the potometer. You
should be able to explain why you think (or do not think!)
that this measurement can be relied upon to give you valid
data about the rate of transpiration.
It is important to be able to bring all of this information
together, and to be able to make an informed judgement
about the overall validity of the investigation and how
much it can be trusted for testing the hypothesis. As at
AS level, you should be able to suggest improvements to
the experiment that would increase its reliability.
Statistical
test
When to use it Criteria for using the test Examples of use
How to interpret the
value you calculate
t-test
You want to know
if two sets of
continuous data are
significantly different
from one another.
• You have two sets of continuous,
quantitative data (page 494).
• You have more than 10 but less
than 30 readings for each set of data.
• Both sets of data come from
populations that have normal
distributions.
• The standard deviations for the two
sets of data are very similar.
Are the surface areas of
the leaves on the northfacing
side of a tree
significantly different
from the surface areas on
the south-facing side?
Are the reaction times of
students who have drunk
a caffeine-containing
drink significantly
different from students
who have drunk water?
Use a t-test table to look
up your value of t. If this
value is greater than the
t value for a probability
of 0.05 (the critical value),
then you can say that
your two populations are
significantly different.
χ 2 test
You want to know
if your observed
results differ
significantly from
your expected
results.
• You have two or more sets of
quantitative data, which belong
to two or more discontinuous
categories (i.e. they are nominal
data – page 494)
Are the numbers of
offspring of different
phenotypes obtained
in a genetic cross
significantly different
from the expected
numbers?
Use a χ 2 table to look up
your value of χ 2 . If this
value is greater than the
χ 2 value for a probability
of 0.05, then you can say
that your observed results
differ significantly from
your expected results.
505
Pearsonʼs
linear
correlation
You want to know
if there is a linear
correlation between
two paired sets of
data.
• You have two sets of interval data.
• You have at least 5 pairs of data,
but preferably 10 or more.
• A scatter graph suggests there
might be a linear relationship
between them.
• Both sets of data have an
approximately normal distribution.
Is there a linear
correlation between
the rate of an enzymic
reaction and the
concentration of an
inhibitor?
Is there a linear
correlation between
the numbers of limpets
and the numbers of dog
whelks on a sea shore?
A value close to +1
indicates a positive linear
correlation. A value close
to –1 indicates a negative
linear correlation.
A value close to 0
indicates no correlation.
Spearmanʼs
rank
correlation
You want to know if
there is a correlation
(not necessarily
linear) between two
paired sets of data.
• You have quantitative data that can
be ranked.
• The samples for each set of data
have been made randomly.
• You have at least 5 pairs of data,
but preferably between 10 and 30.
• A scatter graph suggests there
might be a relationship between
the two sets of data (not necessarily
linear). Note: you cannot use this
test if the scatter graph is U-shaped,
i.e. the correlation is positive for
some values and negative for others.
Is there correlation
between the surface
area of a fruit and the
time it takes to fall to the
ground?
Is there a correlation
between the numbers of
limpets and the numbers
of dog whelks on a sea
shore?
Use a correlation
coefficient table to look
up your value of r s
.
If your value of r s
is
greater than the r s
value
for a probability of 0.05,
you can say there is a
significant correlation
between your two values.
Table P2.9 Summary of four statistical tests.