2019 Year 11 EXPERIMENTAL SKILLS

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EXPERIMENTAL SKILLS AND SCIENTIFIC METHOD
2019
A Introduction: SCIENTIFIC METHOD
Believe it or not performing scientific research is a fairly basic human activity which nearly every
one performs as part of their daily life. Consider picking up a carton of milk. You observe that it
is very light and you immediately make a logical guess that it is empty or almost empty. You then
test this by looking inside the carton or swirling it around to see if this is true.
Scientific method involves the same basic four steps.
1.
Making Observations
2.
Forming an Explanation called a Hypothesis
3.
Testing your Hypothesis with an experiment.
4. Your hypothesis is Confirmed
5. Design more experiments to
further support your hypothesis
Your hypothesis is Refuted
Write a new hypothesis
HYPOTHESIS WRITING:

A hypothesis is a statement that predicts the outcome of your experiment by stating
the effect of the independent variable on the dependent variable.

In Year 11 and 12 Biology a hypothesis is usually in the following form
"If_______then________" .
“If the independent variable is altered in such and such a way then the dependent
variable will affected in such and such a way”.
Eg. If the temperature is increased, the fish velocity will increase.
The temperature is the independent variable and the fish velocity is the
dependent variable.
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
This is an unacceptable hypothesis. “If the temperature is increased, the fish velocity will
be affected.” “affected” is not specific enough. You need to predict the way that the
dependent variable will be affected.





A hypothesis must be actually testable. eg “the frog mated with the other frog because
he thought she was attractive” is not actually testable because how can we know what
a frog thinks.
Hypotheses (p) can not be proved by an experiment.
The experiment either supports the hypothesis (provides evidence for) or refutes the
hypothesis (provides evidence against).
An experiment can disprove a hypothesis.
If a hypothesis is disproved by an experiment, a new hypothesis is made, taking into
account all new evidence.
Question
Write a hypothesis for an experiment that is aiming to see the relationship between light intensity
and photosynthesis rate.
Hypothesis:
Dep. Variable =
Indep. Variable=
Theory
Variables are the factors which will affect the results of an experiment.
The Independent Variable
is the one variable that is being varied in different samples to see how it affects the
results.
The Dependent Variable
is the factor that is affected by (or depends) on the independent variable and is
measured as the results.
Ie the results depend on what you vary in the experiment.
Ie results = dependent variable
Constants or Controlled Variables
Are the variables kept constant in every sample to ensure they do not affect the
results.
All variables must be controlled to ensure that changes in the results can only be due
to the changes in the independent variable and not other variables.
Nb. The measuring instrument such as using the same ruler is not a controlled
variable because it isn’t a variable in the first place (ie. It shouldn’t matter which ruler
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you use, it won’t affect the results). Only if the choice of measuring instrument is
likely to affect the results should that be included.
A Fair Experiment must have
1 dependent variable
1 independent variable
all other variables controlled.
There must be only one independent variable so that you know that the results must
be as a result of that one variable. If there were two independent variables you would
not know which one is causing the results.
If your experiment is not fair, it is not a valid experiment and the results mean
nothing.
Control
A control sample or group is used to compare all other groups to. The control sample
often does not have an independent variable or would represent what happens in the
normal situation.
Sample Groups - An experiment usually has a number of sample groups where each
sample group tests a different value of the independent variable. Each sample group
may have a number of samples that are treated in the same way.
Sample size - big sample size for accuracy
The sample size is the number of samples in a sample group. The results from all the
samples in the sample group can be averaged together to get a more accurate average
and increase the reliability of the results by averaging out the effects of random
errors. Also having a large sample size allows for the identification and elimination
of anomalous results (outliers).
Example ; An experiment is testing the effect of colour on the absorption of heat by a painted
glass beaker left in the sun for 10 minutes.
There were 4 colours tested;
6 red beakers, 6 blue beakers, 6 yellow beakers, 6 white beakers and 6 unpainted beakers.
What is the sample size?
How many sample groups were there?
What was the control group?
What is an appropriate hypothesis for this experiment?
List two advantages of using 6 beakers of each colour.
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MEASURING RESULTS
1.Accuracy
Accuracy is a measure of how true the measurements are to the real values. Accuracy is only
looking at the measurement of the results rather than mistakes made in the design of the
experiment.
eg. a 2.20m pole may be measured at 2.0875m.
This measurement has a high resolution ( many decimal places) but it is not accurate.
Resolution and accuracy are entirely different. 2.21m is more accurate than 2.0875m.
Errors reduce accuracy - Errors include random errors and systematic errors-
2. Random Errors – create
scatter
3. Systematic
Random errors result in the measurement
sometimes being too low and sometimes too
high in a random way. However, the average
of a no. of measurements are fairly accurate.
Systematic errors make the readings all too high
by the same amount or all too low.
It is usually the fault of the measuring
equipment and the results can be adjusted after
wards.
Systematic errors are overcome by
recalibrating the equipment with known
standards and adjusting the measurements by
the required amount. Eg. check a
thermometer’s calibration in boiling water to
see if it measures 100C. If it reads 99C all
readings will be too low by 1C.
The greater the sample size the more accurate
the average. And therefore the more reliable
the result.
Please note; genetic variations in living
members of a sample group cause scatter
which are not strictly speaking random errors
(it is more due to uncontrolled genetic
variables). However, this scatter can also be
averaged out using a large sample size.
Errors – gives a
consistent shift in results
To check for systematic errors repeat the
experiment.
It is always useful to repeat an experiment with
eg.1 Errors of Parallax
exactly the same method but using different
measuring apparatus. If the second set of
When reading a thermometer, ruler,
results are out by a set amount, then a
measuring cylinder etc. your eyes must be at
systematic error exists in either or both
same level. If not the reading may be too high measuring devices used in the two
or too low depending on your position. Even experiments. They will need to be calibrated
if care is taken there is still some error of
and the results will need to be adjusted.
parallax.
Errors of Calibration
eg.2 Reaction times
eg. When a thermometer has the numbers
These effect your results often in a random
printed on the side sometimes they are too high
way. ie sometimes you are slower than other
or too low. Therefore all measurements taken
times.
with that thermometer will be out by the same
amount.
eg. 3 air currents affect the mass reading on
eg. If a student incorrectly zeros a balance then
an electronic balance in a random way
all the reading will be out by the same amount.
eg. tape-measures can get stretched so all
measurements are too low even though they
may get proportionately lower as the
measurement gets longer. Ie a 10cm length
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may be out by 1mm but a 50 cm length will be
out by 5mm.
3. Resolution
A measuring cylinder.
The resolution is the smallest difference
possible between two measurements. Ie a
measurement taken with a 100ml measuring
cylinder is taken to the closest 1ml. Possible
measurements are 1ml, 2ml, 3ml, 4ml etc.
Therefore the resolution of these
measurements is 1ml.
The resolution of your measurements should
not exceed the resolution of the measuring
device, ie. the smallest increment that the
measuring device can measure.
Eg. a school ruler has a resolution of 1mm
because this is the distance between the
smallest lines. Therefore it is not possible to
make a measurement of 33.5mm on a school
ruler. It should be rounded up to 34mm.
Resolution =
Measurement =
An ammeter.
4. Precision
The precision of results is a measure of how
scattered the results are due to random errors.
The more scattered, the less precise
Resolution =
Measurement =
High Precision
4.32
4.33
4.35
4.33
Low Precision =scatter
4.25
2.88
4.02
5.33
5. How many decimal places to take
your measurements to.
Resolution
Your measurements cannot be taken to a
greater resolution than the resolution of your
measuring equipment.
Reproduce-ability
Eg. the height of a person should be measured
to the closest cm eg. 156 cm = 1.56m
And not to the closest mm eg. 1568mm
=1.568m because you will always get the same
measurement in cm but not in mm.
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Your measurements should offer the greatest Changes in posture and error of parallax will
resolution possible but not so much that
affect the measurement in mm so much that it
errors cause significant scatter. If there is too will not be reproducible if measured again.
much scatter from errors, the measurement
will not be able to be accurately reproduced if
measured a second time. There is no point in
measuring to a resolution which doesn’t give
accuracy.
6. Significant Figures in Calculations
If you are multiplying or dividing measurements
you took in an experiment, your answer must
have the smallest number of significant numbers
of any of the measurements used in the
calculation.
7.
Eg.
25.6 /2.0 = 12.8 rounded off to 13
(2SF because 2.0 has 2 SF
Eg2. 14.34 x 212 = 2868 rounded off to
_________
Calculating averages – the no. of decimal
places of any average (mean) is the same as
the minimum number of decimal places of any
of the values used to make the average.
Eg. 7.5, 5.0, 3.3 3.45 are averaged to be
4.8125 cancelled down to 4.8
Calculate the average of
17.2 and 13.3 ……………….
calculate the average of 0.2 &0.6 & 0.5
&0.4 ……………..
Reliability
Reliability is the consistency of your measurement, or the degree to which an instrument measures
the same way each time it is used under the same condition with the same subjects. In short, it is
the reproducibility of your measurement.
sample size 3 with random errors
Sample size 10 with random errors
Expt 1
Expt1
17,25,11
ave 17.7
Expt 2
14,22,29
17, 25,11,23,29,13,15,19,15,22 ave 17.4
Expt 2
ave 21.7
With a sample size of three, the two averages
are not similar; Therefore the scatter due to
random errors are still affecting the average
and the results (averages) are not
reproduceable ie they are not reliable.
14,22,29,12,14,16,20,19,14,13 ave 17.3
With a sample size of ten, the two averages
are almost the same. This is because a sample
size of ten is large enough to average out the
effects of the scatter due to random errors.
There fore the results (averages) are
reproduceable ie they are reliable.
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The larger the sample size, the more reliable the average because a large
sample averages out the effects (ie. scatter) caused by random errors.
Please learn this off by heart.
Repeating Experiments
Repetition is when a whole experiment is repeated following the exact method at a different
time but using different equipment.
The aim is to see if the results are the same and therefore reproducible. If the results are
reproducible it indicates that the results are reliable. (see below)
If the results of a repeated experiment are the same, this indicates two things
1. your results are reproducible meaning that there are no systematic errors causing a shift
in results.
2. you have controlled the variables well ie it is a fair experiment.
3. Your sample size is large enough to remove the scatter caused by random errors.
These three things help to demonstrate that the results are RELIABLE.
It also partly indicates that the design of the experiment is valid as it indicates that there are no
uncontrolled variables influencing the results.
If the repeated results show the same trend but there is a consistent shift of the trend there is
probably a systematic error.
If the repeated results show a completely different trend, the original experiment is made invalid
and disregarded.
This may have been because
1. the original results were a “Fluke” caused by a freak of chance with extreme random
errors.
2. the results could have been fraudulent to support the ego of the experimenter.
3. There is an uncontrolled variable affecting the results that you are not aware of.
The role of repeating the experiment is not to get more accurate results. You can not average the
results from two different experiments together because the conditions would have been slightly
different and the equipment was probably different.
The role of repeating the experiment with different equipment is to
1. check for systematic errors ie if there is a systematic error , the same trend and
pattern will be detected , but the actual measurements will be shifted by a consistent
amount. This could mean that there was a calibration error in the equipment. It could
also mean that the slightly different conditions or biological sample was causing the
difference.
2. verify the results to see if they have a level of accuracy, truth and reproducibility
(verify means to find out if something is true by authenticating it from other sources)
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TABLES: You need to be able to design a table with
1. an explanatory title -" the Effect of the Indep. V. on the Dep.V"
2. labelled column headings
(Indep.V in the left column and Dep. V in the right column)
3. units in the column headings
4. all data with the same no. of decimal places.
5. Averages rounded off to the same no. of decimal places as the data.
6. any anomalous results can be identified with an asterisk or highlighter and made note
of. Anomolous results are not included in averages.
Table Title : The Effect of Age on Height of Boys
Units in headings(even %)
Indep.V
All
Ave
AGE
(years)
10
11
12
13
Dep V.
Ave. Dep V
Height of boys (m)
1.11
1.25
1.09
1.56
1.21
1.22
1.35
1.60
1.05
1.45
1.46
1.40
1.01
1.31
1.39
1.35
Average
Height(m)
1.10
1.31
1.32
1.48
Data has the same no. of dec. pl
has same dec.pl as data
Please don’t label things like
sample 1 sample 2 etc.
Quiz
1. What is wrong with the Table below?
A
B
C
Highest Speeds
17km
13
11.56
19km/hr
14
10.93
Average
18
13.5
11.245
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2. Draw a table below to show the following information.
A student looked at the affect of temperature on the number of eggs produced by a number of fish.
At an average temperature of 15ºC five fish produce 1010, 1300, 1044, 1066, 1219 eggs.
At 4ºC the fish produced 1987, 2061, 1832,1435, and 5 eggs. At 20ºC the fish produced 25, 103,
84, 34 and 59 eggs.
GRAPHS
You need to be able to draw a graph with
1. an explanatory title, “The Effect of the Indep.V on the Dep.V”
2. x axis (or horizontal axis) with the independent variable,
3. y axis (or the vertical axis) with the dependent variable.
D
I
4. axes labelled
5. units stated in axis labels
6. correct form of graph
eg a column graph- discontinuous x axis eg apples and pairs
eg. A line graph - continuous x and y axis eg. time, mass, volume etc.
7. the scales increase evenly on the axes (a break in scale can only be used if it is not
within the plotted region of the graph- otherwise it affects the shape of the graph)
8. use a line of best fit or a curve of best fit if random errors, genetic variation etc. in
the specimens have caused scatter in the results.
The line of best fit averages out the scatter so that a trend can be more easily seen.
When drawing a line/curve of best fit there should be almost even numbers of
points on either side of the line/curve and the line/curve should be as close to the
points as possible while maintaining a smooth shape.
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Extension : a good line of best fit will have the sum of the perpendicular distances of the points
from the line on one side equal to the sum on the other side.
9. Interpolate (inside graph) and exptrapolate (outside graph) points ie use a graph to
predict the value of unknown values using the line/curve of best fit.
10. The graph should be large and must not be less than half the size of the graph paper
provided.
Describing trends in graphs. You need to say whether the graph is increasing or decreasing and
then also whether it is changing steadily (straightline) or exponentially (at an increasing rate).
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Drawing lines of Best fit
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Activity
The effect of the blood alcohol concentration on reaction time.
Blood alcohol concentration (g/100ml)
Reaction Time (sec)
0.04
0.256
0.06
0.265
0.08
0.312
0.09
0.364
0.10
0.422
Plot the data on a graph Make the alcohol conc.axis extend to 0.11g/100mL
2. Predict the reaction time at O.07 and 0.11g/100ml of alcohol.
3. Describe the trend of the graph.
4. Comment on the number of random errors observed.
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MAKING CONCLUSIONS
Conclusions
1. need to be statements that are definitely true according to the results.
2. need to be as specific as possible.
3. are not generalisations or assumptions.
4. must not include any inferences. Inferences are attempts to explain why the results turned out
as they did.
Possible conclusions include
1. The radish plants have an optimum growth rate in height between the temperatures of 25C and 30C.
2. Radish plants do not grow in height at temperatures lower than 5C or higher than 55C.
You can not say "Radish plants grow faster at 30C because their enzymes are working faster". You didn't
test their enzymes so you can't conclude that was the reason why. This is an inference.
You can not say that "plants grow fastest between the temperatures of 25C and 30C". You only tested
radishes , not other plants and it probably wouldn't be true for other species. Additionally , you only tested
height not growth in the no. of leaves and other growth aspects. Be careful not to over generalise.
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Question. The graph below which shows the concentrations of 3 different sugars in whisky mash as it
ferments. List 2 appropriate conclusions for this graph.
3.
List two conclusions about the hormone CRH.
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EVALUATING AN EXPERIMENT
Evaluating anything is determining a “value” for it. Evaluating an experiment means that
you have to use arguments to decide whether the experiment is of value – ie how good or bad
it is? This is done be weighing up its relative strengths and weaknesses.
In our work this year you will need to be able to access whether an experiment was designed well
or if the data was reliable.
Strengths in an experiment that can be
included are
 state the conditions that were well
controlled eg. the temp. was kept constant
by ………
 the measurements could be taken
reasonably accurately. Eg. the distance
was taken with a mm ruler lying right
next to the object to avoid the error of
parallax.
 the measurements had a high resolution.



there was a lot of replication (a large
sample size of more than 5) so that the
effects of random errors and individual
variation could be averaged out. This
would make your results more
RELIABLE.
The results showed a consistency
suggesting that there were not many
uncontrolled factors affecting the results.
There may have been many sample
groups so that many points on the graph
could be drawn.
Weaknesses in an experiment that can be
considered. Please only discuss
weaknesses that have a significant impact
on the results.
 Consider which variables were not
controlled adequately even if an
attempt was made to control it. Could
they be affecting your results
significantly?
 Consider whether the design is fair or
whether it is not really testing the
hypothesis.
 Consider the sources of error when
taking measurements and how much of
an impact these errors are making on
the results.
 Consider whether the sample size was
too small (0 to 5) to make a reliable
average.
 Consider whether there were enough
points on the graph to see a clear trend.
If not which additional sample groups
should be tested to see a better trend.
 Any other design faults which are
peculiar to the experiment
Was your experiment valid? Ie Are your experimental results and therefore your conclusions true?
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Writing a Practical Report in the Correct Format
You will need to write your practical reports in the correct format.
The method we will use to write practical reports is a copy of the method that scientists write up
their research in journals for other researchers to read.
Scientific reports are written in past, impersonal tense.
These scientific reports always have the following sections in this order.
1. Abstract:
This is a short, paragraph summary of the entire practical including the aim,
method, results and conclusions. The abstract is designed for scientists to read the
experimental findings in brief to see if they want to read the whole report in detail.
2. Introduction This section is designed to give any reader the background information which
lead the scientist into deciding to do this experiment. You will need to write the
scientific background of what this experiment is about. It is usually about 100 to
200 words long. This should be in-text referenced where relevant.
3. Aim
A sentence to explain the purpose of the experiment.
4. Hypothesis A prediction for the results of the experiment that relates the dependent variable and
independent variable.
5. Variables A list of the independent, dependent and at least 4 controlled variables.
6. Apparatus This is list of special equipment and materials needed for the experiment. You
must include appropriate particulars about the materials which may effect the
results.eg. size of the equipment and the exact concentrations of solutions etc.
7. Method
You must give a list of what was performed in the practical in enough detail for
another student to follow your method and be able to perform an identical
experiment and get the same results! This is best written as a series of numbered
steps. A diagram drawn in pencil in the correct scientific format may need to be
added to add clarity. Do not forget to make this in past tense. Be careful to include
details like the number of samples tested and the exact way the results were
measured. Remember if you repeat something twice, it is done 3 times.
Ensure that at the end of the method that you assess the safety implications of this
experiment. Ie what precautions did you need to make during the experiment and
possible hazards.
8. Results
This section should display your observations and measurements in a form that the
reader can easily make their own conclusions.
The best way is to devise simple tables and graphs which are labelled and titled.
Other significant notes and qualitative observations can be written down. You may
need to manipulate the data such as finding averages or reciprocals or calculate
rates. Anything to make the trends in the data clear so the aim of the experiment can
be achieved. Ensure that you have the correct no. of Sig. Fig. and the averages are
calculated with the correct no. of decimal places.
9. Discussion This section needs to analyse the results and say what the results mean. It also
evaluates the experiment to see whether the results were actually valid.
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ANALYSIS
Analyse your data. Go through each table and its accompanying graph one by one.
For each graph/table
1. Describe the trend shown by the graph.
2. Describe how reliable this data is according to a. how much scatter is within each
sample group, and b. how well the average fit the line of best fit. This will tell you how much
scatter is left in the average. Decide whether the sample size was large enough to average out the
scatter effect of random errors.
3. Make your conclusions about what this experiment shows you. Be precise and
exact- no inferences and over generalisations.
4. Explain why you think this trend exists in terms of the science and biology you
know. Use terms such as “this is probably because” or “ maybe this is because” because you are
guessing why. The explanation may need to be lengthy and it should cover all parts of the trend
you see. Diagrams can be included here.
5. State whether the data confirms or refutes the hypothesis.
EVALUATION
The second part of the discussion is your evaluation of the experimental design and
procedure and how you would improve the design to overcome these design flaws.
1. Critically and logically evaluate the experimental design by briefly considering
the strengths and fully analysing the weaknesses. Focus on the weaknesses.
The weaknesses could be the following.
a. Look at each measurement you took through out the method and decide if there
were there problems in the way the measurement was taken that lead to a lot of
errors? Could these measurement methods be improved?
b. Was the sample size large enough?- how do you know? If not how big should it
be.
c. Were there enough sample points on the graph? If not which ones would you
include next time.?
d. Were there some controlled variables that were not controlled adequately? If so
how would you control them adequately next time?
e. Was the test fair?
f. Was the test actually designed to test what we were looking for?
g. Were there any systematic errors that would effect the conclusion of your
results? Most will not make much difference.
Do not discuss every little problem that would not significantly affect the results.
In light of the above discussion of the method, determine whether you still consider
the experiment and results valid.
2. Suggest additional improvements to the ones you have already discussed
especially ones that would improve the major design flaws that you identified.
These improvements should be clear, practical, new ways of doing the experiment
using equipment that already exists.
10. Conclusion:
A list of conclusions you can make. You may be repeating the conclusions you
have talked about in the discussion. Usually there is more than one conclusion that
you can make from the data. They should be highly refined and specific. Ensure
that these statements are about what the data is indicating not your possible
explanations of the data ie inferences. Do not comment on the success, enjoyment
or failure of the experiment.
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:
11. Further Enquiry: comment on the significance of your findings and any further
experiments that could be conducted to find out something new which
comes out of this investigation.
Experiments may be used to test hypotheses.
State a testable hypothesis, where appropriate.
Designing Investigations and Experiments
Design
Scientific inquiry involves designing procedures,
including practical investigations based on the
scientific method or observations made in the
field, to investigate questions. Designing an
investigation involves identifying:
 what needs to be observed
 the measurements that need to be taken
 the techniques that need to be used
 the apparatus or measuring instruments
needed.
Design and carry out investigations to explore
posed questions or hypotheses using the
scientific method.
Every step in a practical or issues investigation
serves a purpose.
Describe the steps of an investigation.
Design and carry out experiments to investigate
a biological issue.
Record and analyse observations.
Draw or interpret diagrams of the apparatus
used in an experiment.
Variables
Many practical investigations involve
deliberately changing one quantity and
determining the effect on another quantity.
These quantities are referred to as ‘variables’.
Identify the variables in a practical investigation.
The quantity being deliberately changed is
called the ‘independent variable’. The quantity
that changes as a result is called the ‘dependent
variable’.
Classify the variables in a practical investigation
as independent or dependent.
Other factors are held constant, if possible,
throughout a practical investigation.
Identify any factors that are deliberately held
constant throughout a practical investigation.
Conducting Investigations
Procedures
Practical investigations require a particular set
of actions to be carried out in a well-defined
order.
Follow instructions accurately and safely.
Safety and Ethics
Ethical practices must be followed when
conducting practical and issues investigations.
Work ethically with animals.
Safety must be considered when conducting
investigations.
Recognise hazards and work safely during an
investigation.
Many investigations involve the collaborative
efforts of a team.
Negotiate procedures with the other members of
the team. Define the role of each member.
Members of a team work together.
Perform the role of a team member.
Maintain confidentiality, report accurately, and
acknowledge the work of other people.
Errors in Measurements
Measurements are affected by random and/or
systematic errors.
Identify sources of errors and uncertainty that
may occur in an investigation.
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Random errors are present when there is
scatter in the measured values. Systematic
errors are present when measured values differ
consistently from the true value.
Distinguish between random and systematic
errors.
Where applicable, increasing the number of
samples minimises the effects of random errors
and improves the reliability of the data.
Explain the importance of increasing the number
of samples in a practical investigation.
Systematic errors can be identified and results
verified by repeating an experiment using an
alternative source of equipment and materials.
Explain the importance of repeating a practical
investigation where possible.
Precision, Reliability, and Accuracy
The reliability/precision of data collection is
related to the reproducibility of the
measurements.
Where possible, collect data using
measurements that can be reproduced
consistently.
Measurements are more reliable when there is
less scatter in the results.
Determine which of two or more sets of
measurements is most reliable.
Reliability depends on the extent to which
random errors are minimised.
Use averages or graphing as a means of
detecting or minimising random errors.
The accuracy of an experimental value indicates
how close the result is to the true value and
depends on the extent to which systematic
errors are minimised.
State which result of two or more experiments is
most accurate, given the true value.
The resolution of a measuring instrument is the
smallest increment measurable by the
measuring instrument.
Select an instrument of appropriate resolution
for a measurement.
The number of significant figures for a
measurement is determined by the
reproducibility of the measurement and the
resolution of the measuring instrument.
Record and use measurements to an
appropriate number of significant figures.
Information and Data
Investigations involve evidence, which may be
quantitative or qualitative.
Distinguish between quantitative and qualitative
evidence.
Valid conclusions depend on gathering
appropriate evidence.
In investigations, make and record careful and
honest observations and measurements.
Data can be more easily interpreted if presented
in a well-structured table.
Present data in an appropriate tabular form.
Include a title, column headings showing the
quantities measured and units used, and the
values observed or researched.
Graphs are a useful way of displaying data.
When a graph is plotted, the independent
variable (or a quantity derived from it) is plotted
horizontally and the dependent variable (or a
quantity derived from it) is plotted vertically.
Plot a graph of dependent variable versus
independent variable. Include a title, labelled
axes, and appropriate scales and units.
A line of best fit can show relationships between
variables in an experiment.
Draw a line of best fit through a series of points
on a graph such that the plotted points are
scattered evenly above and below the line of
best fit.
Understanding of a topic, issue, or question is
enhanced using information from different
sources.
Obtain information from different sources.
Information obtained must be critically examined
for accuracy and suitability for the purpose for
which it was sought.
Evaluate for bias, credibility, accuracy, and
suitability the information obtained from a
source.
The source of information must be recorded so
that the information is accessible to others.
List the sources of information using an
appropriate format.
Interpretation and Evaluation
Careful observation in a practical investigation is
essential for analysis and for comparison with
other investigations.
Describe a pattern observed in the results of an
investigation.
19
The scatter of data points above and below the
line of best fit is probably due to random errors.
Using the scatter in the graphs of data from
similar investigations, compare the random
errors.
Subsequent investigations can be improved by
the critical evaluation of the procedure and
results.
Analyse and evaluate information from a series
of observations or an investigation, and suggest
improvements or indicate the additional
information needed.
A conclusion should be written at the end of
each investigation.
Write a conclusion that is based on the results
of an investigation and related to the question
posed and the purpose of, or the hypothesis for,
the investigation.
20
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