Practical Data Analysis Student Booklet
One eighth of your Science GCSE
Name
Title
You will be issued with the Information for Candidates sheet which will give background information
and the hypothesis you will plan and carry out experiments to test. An example hypothesis is: Different
fuels transfer different amounts of energy when they burn because of the different numbers of carbon
atoms in the fuel molecules.
You make choices about the methods, techniques and equipment used to collect high quality data. You
show awareness of safe working practices and the hazards associated with materials. At the highest
level, a full risk assessment is included.
The primary data collected by you is analysed to reveal any patterns or relationships.
You should show awareness of any limitations imposed by the apparatus or techniques used and
suggest improvements to the method. Recognition and management of risk will also be taken into
account when assessing this strand.
You assess how well the available data supports the hypothesis and explain its scientific basis. Quality of
written communication will also be taken into account when assessing this aspect of the work.
Strategy: research and planning 1 hour
In the research and planning stage, a limited level of control is required. This means that you can do this
part of the investigation without direct teacher supervision and at home, as required. This may include
collection of secondary data to use in planning your work. You can work with other students at this
stage. During the research phase you can be given support and guidance. Teachers can explain the task,
advise on how the task could be approached, advise on resources and alert the you to key things that
must be included in your final piece of work. However, you must develop your own individual work.
Collecting data 1 hour
In the data collection stage, again a limited level of control is required. You will carry out practical work
under direct teacher supervision to collect your primary data. You may work with other students during
this stage but you must be actively involved and develop your own, individual work in determining how
best to collect and record primary data. Secondary data should also be collected during this stage to
support or extend the conclusions to your investigation. It is not permitted to base the investigation
only on secondary data or computer simulations, you must do some experiments.
Analysis, evaluation and review 1 hour
The work for this stage is done in school under conditions of high control, which means
that you work on your own under direct teacher supervision. Teachers must be sure that what you hand
in is your own work. Your work has to be collected in between each lesson, including any USB memory
sticks and CDs. Tables, graphs and spread sheets may be produced using appropriate ICT.
Outline of your Practical Data Analysis
Aspect /8
Notes
Strand D: choice of
methods, techniques
and equipment
Strand E: revealing
patterns in data
Strand F: evaluation
of data
You make choices about the methods, techniques and equipment used to collect high
quality data. You show awareness of safe working practices and the hazards
associated with materials. At the highest level, a full risk assessment is included.
The primary experimental data you have collected is analysed to reveal any patterns or
relationships.
You should show awareness of any limitations imposed by the apparatus or techniques
used and suggest improvements to the method. Recognition and management of risk
should also be taken into account when assessing this strand.
You assess how well the available data supports the hypothesis and explain its
scientific basis. Quality of written communication will also be taken into account when
assessing this aspect of the work.
Strand G: reviewing
confidence in the
hypothesis
Review
Evaluation
Analysis
Method
Practical Data Analysis Check List
Method
Equipment
Range
Repeats
Describe the method you will use collect your data. Explain why it is suitable.
D
Write about and justify your choice of measuring equipment.
D
Write about and justify the range of values you will test.
D
Write about why you need to repeat measurements.
D
Risks
Identify any risks and suggest some precautions to minimise them.
A full risk assessment is needed for full marks.
D
Experiment
Carry out your experimental work and record your results. Make sure all tables and charts have
headings and units.
D
Process
Process your raw results. e.g. calculate averages or use a scientific equation.
You might calculate speeds for example from measurements of distance and time.
E
Comparison
As an alternative to plotting graphs you may carry out a mathematical comparison to show the
patterns in your data. For example you could calculate standard deviations.
E
Choose what you need to plot and select suitable scales. Add a key for multiple datasets.
E
Plot your points (means) onto the graph.
E
Add range bars to show the upper and lower values of any repeats.
E
Add a straight line or smooth curve of best fit through your data points.
E
If you used a straight line of best fit you could calculate the gradient of the line.
E
Describe what you graph tells you about how the input factor affects output.
E
Outliers
Identify your outliers. e.g. highlight them and add a key. Then suggest reasons for these outliers.
If there are no outliers explain how you know.
F
Repeatability
Comment on the accuracy of your measurements as indicated by the scatter in your repeats.
Write about the quality of your data based on this scatter.
F
Conclusion
Comment on whether your data and the trends you identified matches the prediction or
hypothesis.
G
Suggest why you found this match or mismatch using scientific ideas.
G
Graph
Plot
Bars
Line
Gradient
Pattern
Explanation
Hypothesis
Extension
Suggest how the hypothesis could be modified to account for the data more completely.
Describe in detail what extra data could be collected to increase confidence in the hypothesis
G
Glossary of Terms for Practical Investigations
accurate
conclusion
confidence
correlation
data
factor
fair test
good estimate
gradient
hypothesis
line of best fit
outcome
outlier
precise
prediction
proportional
range (input)
range (output)
range bars
repeatability
repeats
risk
true value
variable
This refers to a measurement that is close to the true value. A thermometer that always reads
5C too high is not accurate. (It may be precise though)
A statement of what you have found out. It will usually refer to your hypothesis.
A measure of how sure you are of a set of data. Primary data backed up by secondary data
leads to a high level of confidence.
If an outcome occurs when a specific factor is present, but does not when it is absent, or if an
outcome variable increases (or decreases) steadily as an input variable increases, we say that
there is a correlation between the two.
Primary data comes from observations or experiments that you carry out. Secondary data
comes from other peoples work. In science the search for explanations starts with data.
Factors can be changed. e.g. temperature, volume, voltage etc. Relevant factors are ones that
affect the outcome of an experiment.
To investigate the relationship between a factor and an outcome, it is important to control all
the other factors which we think might affect the outcome (a so-called ‘fair test’).
The mean of several repeat measurements is a good estimate of the true value of the quantity
being measured.
A quantitative measure of direct proportionality. It is calculated from a line of best fit on a
graph. Gradient = change in output value / change in input value
A hypothesis is a provisional statement that proposes a possible explanation to some fact or
event. e.g. If skin cancer is related to ultraviolet light , then people with a high exposure to UV
light will have a higher frequency of skin cancer.
A straight line or smooth curve that best fits a set of points on a graph.
This the output of an experiment. What you measure. The dependent variable.
If a measurement lies well outside the range within which the others in a set of repeats lie, or is
off a graph line, this is a sign that it may be incorrect. It is called an outlier.
Repeated measurements are likely to vary. The more precise a measurement the smaller this
variation is. The word reliable is often used in place of precise.
A statement of what will happen in an experiment that is based on some science. If a
prediction is found to be correct it leads to greater confidence in the science.
This is when a factor is mathematically linked to an outcome. Direct proportionality mean
doubling a factor will double an outcome and a graph of the factor against the outcome will be
a straight line. Inverse proportionality means that doubling a factor will half an outcome.
This refers to the ranges of the input variable tested during an experiment. A suitable input
range can be determined from test experiments. Generally the range should be as wide as the
equipment or methods will allow.
The span of a set of repeated measurements not including outliers. The more precise the
measurement the smaller the range will be.
A line on a graph to show the range of a set of repeated measurements.
This is a measure of how scattered a set of measurements is. If repeated measurements are
close to each other we say the repeatability is high and you can be more confident in your
estimate of the true value. It is closely linked to the output range.
This refers to making more than one measurement of the same quantity. The less precise the
repeats are the more will be required to give a reliable best estimate. Test experiments are
carried out to establish the precision of repeats and hence a suitable number of repeats.
To make a decision about a particular risk, we need to take account both of the chance of it
happening and the consequences if it did. To make a decision about a course of action, we
need to take account of both its risks and benefits.
We can never be sure a measurement gives us the true value of the quantity being measured.
But with a set of repeated measurements it is possible to estimate a range within which the
true value probably lies.
A factor that is taken account of in an experiment. An input or independent variable is the one
changed by the experimenter, the dependent variable is the one measured and control
variables are the ones kept the same to give a fair test.
GCSE Science Controlled Assessment Practical Data Analysis Mark scheme
2 marks
4 marks
6 marks
8 marks
D
Describe the method
and apparatus
selected to collect
data. Make an
appropriate comment
about safe working.
Display limited
numbers of results in
tables, charts or
graphs, using given
axes and scales.
E
F
G
Select individual
results as a basis for
conclusions.
Make a claim for
accuracy or
repeatability, but
without appropriate
reference to the data.
Correctly state
whether or not the
original prediction or
hypothesis is
supported, with
reference only to
common sense or
previous experience.
The response is
simplistic, with
frequent errors in
spelling, punctuation
or grammar and has
little or no use of
scientific vocabulary.
Comment on the
techniques and
equipment selected to
collect data, showing
some understanding
of the need for
repeatability. Correctly
identify hazards
associated with the
procedures used.
Construct simple
charts or graphs to
display data in an
appropriate way,
allowing some errors
in scaling or plotting.
Carry out simple
calculations eg correct
calculation of
averages from
repeated readings.
Describe the techniques and
equipment selected to collect
an appropriate range of data of
generally good quality,
including regular repeats or
checks for repeatability. Identify
any significant risks and
suggest some precautions.
Justify the method, range of
values, equipment and
techniques selected to collect
data of high quality. Complete a
full and appropriate risk
assessment identifying ways of
minimising risks associated
with the work.
Correctly select scales and
axes and plot data for a graph,
including an appropriate line of
best fit, or construct complex
charts or diagrams eg species
distribution maps.
Use mathematical comparisons
between results to support a
conclusion.
Indicate the spread of data (eg
through scatter graphs or range
bars) or give clear keys for
displays involving multiple
datasets.
Correctly identify
individual results
which are beyond the
range of experimental
error (are outliers), or
justify a claim that
there are no outliers.
Comment on whether
trends or correlations
in the data support the
prediction or
hypothesis and
suggest why by
reference to
appropriate science.
Some relevant
scientific terms are
used correctly, but
spelling, punctuation
and grammar are of
variable quality.
Use the general pattern of
results or degree of scatter
between repeats as a basis for
assessing accuracy and
repeatability and explain how
this assessment is made.
Explain the extent to which the
hypothesis can account for the
pattern(s) shown in the data.
Use relevant science
knowledge to conclude whether
the hypothesis has been
supported or to suggest how it
should be modified to account
for the data more completely.
Information is organised
effectively with generally sound
spelling, punctuation and
grammar. Specialist terms are
used appropriately.
Use complex processing to
reveal patterns in the data eg
statistical methods, use of
inverse relationships, or
calculation of gradient of
graphs.
Consider critically the
repeatability of the evidence,
accounting for any outliers.
Give a detailed account of what
extra data could be collected to
increase confidence in the
hypothesis. The report is
comprehensive, relevant and
logically sequenced, with full
and effective use of relevant
scientific terminology. There
are few, if any, grammatical
errors.