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EXPERIMENTAL
SCIENCE
Glossary of Terms
Variables
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Variables can be described in a number of ways:
1) Categoric – categoric variables have
values that are labels, e.g. eye colour
2) Continuous – continuous variables have a
numerical value, e.g. the number of pets you
have or your height
3) Control – a control variable is one that could
affect the outcome of the experiment but you
keep it the same
4) Independent – the independent variable is the one that
you will change during your experiment
5) Dependent – the dependent variable is the one that will be
measured after changing the independent variable
A Hypothesis and a Prediction
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Put simply, a prediction “predicts” the future
whereas a hypothesis is a proposal designed to
explain some observations. For example, consider an
experiment on acid reacting with marble:
I think that a stronger acid will cause the
marble to react quicker.
Here’s my data. I think that this data shows
that there is a relationship between the
strength of the acid and the rate of reaction.
Which one is a prediction and
which one is a hypothesis?
Validity, Fair Tests and Evidence
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For an experiment to be “valid” it must be a fair test, e.g:
I’m investigating how quickly acid reacts
with marble chips. I want to know if the
concentration of the acid affects the
reaction rate. Here are my results:
Clearly, the
stronger the
acid, the
quicker the
reaction.
Concentration of
acid
Temperature
reaction
happened at/OC
Time taken for
reaction to
finish/s
Low
20
30
High
50
100
Was this experiment valid? Was it a fair test? Can
this data be classed as “evidence”?
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Identify the Hazards of your
procedure
Data
Here are my results. I think I’ll draw
a graph of them as this is
“quantitative data”, e.g. the number of
people who have different hair colour.
I’ve also got some results
but mine are descriptive or
written in words – this is
“qualitative data”, e.g. my
hair colour.
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Calibration
I want to use this big tube as a
thermometer. How could I do it?
Step 1: Mark where a liquid
would be at a known value, e.g.
100OC
Step 2: Mark where the liquid
would be at a second known
value, e.g. 0OC
Step 3: Mark on a scale by
dividing the length by 100
sections – each section
represents 1OC. Now it is
calibrated!
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Calibration
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Here’s another example of using a known
quantity to calibrate something:
Resolution
You have a choice of two thermometers:
What is the resolution of each thermometer?
What is the resolution of the stopwatch used
in the experiment below?
Length of
pendulum/cm
Time taken to
complete 1 swing/s
10
0.63
20
0.90
30
1.10
40
1.27
50
1.42
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True Value and Accuracy
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The “true value” is the value that would be obtained
with ideal measurements. A measurement is judged
to be “accurate” if it close to that value. For
example, what does this thermometer read?
I reckon it’s 22OC
I reckon it’s 24OC
My measurement was the
most accurate as it was
closest
to the
true
I reckon
it’s
26Ovalue!
C
Uncertainty
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Consider the same thermometer readings again:
Is it 22OC, 24OC or 26OC?
“Uncertainty” means “the interval within
which the true value would lie” with a
given level of probability, e.g. “the
temperature is 24OC ± 2OC, to within a
probability of 95%”.
Measurement Error
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A “measurement error” is defined as “the difference between
a measured value and the true value”, e.g:
Here’s my friend. I’ve just
measured him with a metre ruler
and I think he’s 170cm tall.
Unlucky. I’m actually
175cm tall.
What is the difference between these results and what
is the percentage difference between them?
Precision
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“Precise measurements” are measurements that show very
little spread around the mean average value.
Which of the following two sets of data are the most
precise?
Conc. of
acid
Time taken for magnesium to react/s
Average time/s
Attempt 1
Attempt 2
Attempt 3
Low
50
52
54
52
High
20
24
22
22
Notice that precision depends only on random
errors – it gives no indication of how close
results are to the true value!
Anomalous Results
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Here’s a graph that shows us how the temperature of hot
water varies when it is left in a cold room. What would you do
with these results?
Temperature
of water/OC
x
x
x
x
x
x
x
x
0
Time/mins
Random errors
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Random errors can occur with any experiment but some
experiments can have more random errors than others. For
example, here are two experiments:
Hooke’s law,
where different
forces are hung
on a spring and
the extension is
measured.
Choice chambers, where
woodlice are “invited” to
choose their living conditions.
Which one of these experiments would probably have
the most random errors and what would do about it?
Zero Errors
What’s wrong with this balance reading?
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Systematic Error
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A systematic error is one where the measurement differs
from the true value by a consistent amount each time, e.g:
Notice that a zero error usually results in a
systematic uncertainty.
Systematic Errors on a Graph
x
x
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x
x
x
x
x
x
Resistance of
wire/Ω
0
According to this graph, a
wire that’s zero cm long has
some resistance, which can’t
be right. What went wrong?
Length of
wire/cm
Ranges and Intervals
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“Range” represents the range from the lowest and highest
values for a variable. “Interval” represents the quantity
between readings. For example, consider the following
experiment:
Length of
pendulum/cm
Time taken to
complete 1 swing/s
10
0.63
20
0.90
30
1.10
40
1.27
50
1.42
What is the range and interval of
these lengths?
“Repeatable” and “Reproducible”
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Here are my results sir. I did the experiment
3 times using the same method and equipment
and got the same results each time.
Here are my results sir. I
followed the same method as my
friend and got the same results.
This data is judged to be “reliable” as it stayed
the same after several different measurements.
Q. Which experiment shows “repeatable” data
and which one shows “reproducible” data?
Drawing a conclusion
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Here’s a simple experiment where the rate of cooling is
investigated when hot water is covered in different numbers
of layers:
Writing a valid conclusion
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A bad conclusion:
A simple statement saying what your results show.
A good conclusion:
A detailed statement showing what your results show AND a
description of other patterns, e.g. what happens between 4
and 5 layers of insulation? Why did this happen? Is your
graph a straight line or a curve? Do your results show
proportionality?
If the experiment was designed well, the data is valid (i.e.
from a fair test) and the conclusion accurately explains
what the data shows then we call the conclusion “valid”
Drawing a conclusion
Temperature
of water/OC
x
x
x
x
x
x
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3 layers
x
x
x
x
x
x
x
x
x
No layers
x
0
Q. What would
be a good conclusion for these
Time/mins
results?
Anomalous Results
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Where are the two anomalous results and what caused them?
Initial
No.
temp/
of
OC
layers
Temperature after ___ mins/OC
Temp
change/
1
2
3
4
5
6
7
8
9
10
OC
0
70
68
65
62
59
57
55
53
50
47
44
26
1
70
68
66
63
75
60
57
55
53
51
50
20
2
69
67
65
63
61
60
58
56
54
53
52
28
3
68
67
66
65
63
62
61
59
57
56
55
13
4
71
70
69
68
66
65
63
62
61
60
59
12
Calculating mean values
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To calculate the mean value of a range of results, follow the
following steps:
1) Add the numbers up
2) Press the equals button
3) Divide by how many numbers you added
For example, calculate the mean average of the following:
1) 8, 10, 12
2) 104, 106, 88
3) 34, 56, 40
Sketch Graphs
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A sketch graph is one that “sketches” the relationship between two
different variables without plotting any points. For example:
Sketch a graph to show how
the temperature of a hot cup
of coffee will change over
time:
Sketch a graph to show how
the rate of CO2 produced
during photosynthesis varies
with temperature:
Temperature of
water/OC
0
Time/mins
0
Which graph?
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Line graphs
Bar charts
Used for “continuous”
variables, e.g. height,
weight, length,
temperature, time etc
Used for “discrete” or
“categoric” variables,
e.g. number of people,
eye colour etc
Gradient / Slope
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Research Notes
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Submission of
RESEARCH NOTES
is on
MAY 9, 2016