Cambridge International A Level Biology Revision Guide

Cambridge International A Level Biology
Conclusions and discussion
The construction of a simple conclusion was described in
Chapter P1 on page 259. In a written practical examination
at A level, you will often be making conclusions from
data that you have not collected yourself, which is a bit
more difficult. The more experience you have of doing
real practical work, the better equipped you will be to
understand how to make conclusions from data provided
to you.
Your conclusion should begin with a simple statement
about whether or not the hypothesis that was being tested
is supported. This would also be the point at which you
could mention the results of any statistical tests, and how
they have helped you to make your conclusion.
You may also be asked to discuss the data and your
conclusion in more depth. You should be prepared to give
a description of the data, pointing out key features. This
might involve looking for trends or patterns in the data,
and identifying points on a graph where there is a marked
change in gradient (Chapter P1, page 260).
You could be asked to use the data to make further
predictions, perhaps suggesting another hypothesis
that could be tested. For example, for the petal length
investigation (page 497), we could start to think about why
the petal length in the woodland is greater than in the
garden. A new hypothesis could be: petals grow longer in
lower light intensity.
As well as describing the data, you could be asked to
use your scientific knowledge to attempt to explain them.
It is important to remember that the data in an A level
question could relate to anything from either the first
or second year of your course, so you need to revise all
of your work from both years in preparation for these
examination papers.
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Summary
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See also the summary for Chapter P1.
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A hypothesis about the relationship between two
variables predicts how one variable affects the other. It
should be testable and falsifiable by experiment.
To make up a 1% (mass/volume) solution, dissolve 1 g of
the solute in a small amount of water, then make up to a
total volume of 1 dm 3 . To make up a 1 mol dm −3 solution,
dissolve 1 mole of the solute in a small amount of water,
then make up to a total volume of 1 dm 3 .
The mean of a set of data is calculated by adding up all
the individual values and dividing by the total number
of readings. The median is the middle value in the set of
results. The mode is the most common value in the set
of results. The interquartile range is the range into which
the middle 50% of the data fall. Standard deviation is a
measure of how much the data are spread on either side
of the mean.
Standard error is a measure of the likelihood of the
mean of your sample being the true mean of the whole
population. There is a 95% probability that the true
mean lies within ±2 standard errors of the mean you
have calculated. This can be shown by drawing error
bars on a bar chart, where the error bar extends 2
standard errors above and below the
plotted value.
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If the error bars for two sets of data overlap, then there
is no significant difference between the two sets of data.
If the error bars do not overlap, it is possible that there
is a significant difference between them, but this is not
necessarily so.
The t-test is used to determine whether or not two sets
of quantitative data, each with an approximately normal
distribution, are significantly different from one another.
The χ 2 test is used to determine whether or not
observed results differ significantly from expected
results.
The Pearson linear correlation test is used to determine
whether or nor there is a linear correlation between two
sets of quantitative data.
Spearman’s rank correlation test is used to determine
whether or nor there is a correlation between two paired
sets of data that can be ranked.
When discussing an experiment you need also to
consider possible sources of error and validity of the
experiment, and be able to suggest ways in which the
experiment’s reliability could be improved. You should
be able to reach a conclusion about the data and use
of statistical tests, and perhaps make suggestions for
further experimental work.