This document provides information about science skills needed for Preliminary and HSC science courses. It discusses key science skills like planning investigations, communicating information, scientific thinking, and teamwork. It also describes the process of the scientific method which involves identifying a problem, developing a hypothesis, designing and conducting an experiment, collecting and analyzing data, and drawing conclusions. The document outlines the importance of variables, and defines independent, dependent, and controlled variables. It also explains the purpose and role of a control group in experimental design.

1. Stage
6
Science
Skills
Preliminary
&
HSC
Science
By
S.
Choi

2. Science
skills
• Science
skills
can
be
used
to
solve
everyday
problems.
• Science
skills
in
Preliminary
and
HSC
course
include:
– Planning
and
conduc<ng
inves<ga<ons
– Communica<ng
informa<on
and
understanding
– Scien<fic
thinking
and
problem
solving
– Working
individually
and
in
teams
• This
involves
gathering
and
processing
data
and
informa<on
from
primary
or
secondary
sources.
IMPORTANT!
Data
is
the
mass
of
disordered,
raw
material
from
which
informa@on
(knowledge)
is
abstracted
to
provide
evidence
to
support
argument
and
conclusions.

3. Primary
informa<on
and
data
• Primary
informa<on
and
data
is
original,
unedited
and
‘first-­‐
hand’.
• In
Preliminary
and
HSC
Science,
primary
data
is
collected
usually
through
conduc<ng
first-­‐hand
inves@ga@ons
i.e.
experiments
• Science
experiments
always
use
the
scien@fic
method
to
inves<gate
and
solve
a
problem.
• The
scien<fic
method
are
the
thinking
steps
taken
by
the
scien<sts
when
comple<ng
an
experiment.

4. Scien<fic
method
The
Scien<fic
method
involves
the
following
steps:
1. Iden<fy
a
problem
and
ask
a
ques@on
2. Make
a
hypothesis
–
an
educated
guess
or
possible
answer
3. Test
the
hypothesis
by
designing
&
conduc@ng
experiments
4. Collect
data
5. Analyse
the
data
6. Draw
conclusions

5. Scien<fic
method
flow
chart
Iden<fy
a
problem
Develop
a
hypothesis
Design
&
conduct
the
experiment
Collect
data
Analyse
the
data
Formulate
conclusions
AIM
HYPOTHESIS
EQUIPMENT,
RISK
ASSESSMENT
&
METHOD
RESULTS
DISCUSSION
CONCLUSION
Theory!
or
or

6. Aim
• Aim
is
statement
of
what
is
the
inten@on/
purpose
of
the
experiment
• It
outlines
what
is
being
inves<gated
and/or
what
is
hoped
to
achieve.
• Aim
almost
always
starts
with
the
word
“To”.

7. Aim
–
Examples
Sample
experiment
1. Sarah
wants
to
see
if
the
colour
of
the
light
help
plants
grow
taller.
2. James
wants
to
test
which
surface
is
best
for
bouncing
a
ball.
Aim
1. To
determine
which
light
colour
increases
a
plant’s
height.
2. To
determine
which
surface
increases
the
bounce
height
of
a
ball.

8. Variables
• Variables
are
the
factors
that
can
change
in
an
experiment.
• Changing
variables
can
change
the
results
of
an
experiment.
• Variables
can
be
classified
into
three
groups
– Independent
variable
– Dependent
variable
– Controlled
variables

9. Independent
&
dependent
variable
• Independent
variable
is
the
variable
that
is
purposely
changed
by
the
inves<gator
• Dependent
variable
is
the
variable
which
is
measured
for
each
change
in
the
independent
variable.
• When
designing
an
experiment,
careful
planning
is
required
so
that
only
ONE
independent
variable
and
ONE
dependent
variable
changes.
• All
other
variables
must
be
kept
constant,
otherwise
you
will
not
know
which
variable
is
causing
the
result.

10. Controlled
variable
• Controlled
variables
(a.k.a.
confounding
variables)
are
variables
when
changed
affect
the
outcome
of
the
experiment.
• Controlled
variables
MUST
be
kept
constant
(same)
throughout
the
experiment
or
it
will
not
be
a
fair
test
(valid).
• In
some
inves<ga<ons,
it
is
not
always
possible
to
keep
controlling
variables
constant.
• In
such
cases,
these
variables
should
be
monitored
to
decide
whether
or
not
the
factor
concerned
affects
the
outcome
of
the
experiment.
You
can’t
control
me!

11. Variables
–
Example
Sample
experiment
1. Sarah
wants
to
see
if
the
colour
of
the
light
help
plants
grow
taller.
2. James
wants
to
test
which
surface
is
best
for
bouncing
a
ball.
Variables
1. Independent
variable:
light
colour
Dependent
variable:
height
of
the
plant
Possible
controlled
variables:
amount
of
water,
<me
when
height
is
measured,
humidity
&
temperature
of
the
room,
light
intensity
2. Independent
variable:
surface
of
the
floor
Dependent
variable:
bounce
height
Possible
controlled
variables:
type
of
ball,
size
of
the
surface,
dropping
height
of
the
ball,
force
ac<ng
on
the
ball

12. Control
group
• An
control
group/experimental
control
is
one
that
is
treated
in
exactly
the
same
way
as
the
experimental
group
WITHOUT
the
factor
that
is
being
inves<gated.
• It
allows
proper
comparison
to
be
made,
where
any
differences
between
the
results
for
the
experimental
group
and
for
the
control
group
is
caused
by
a
single
independent
variable.
• Control
groups
are
generally
used
in
an
experiment
that
introduces
a
new
addi@onal
factor
instead
of
changing
a
pre-­‐exis<ng
factor.

13. Control
group
–
Example
Sample
experiment
1. Sarah
wants
to
see
if
the
colour
of
the
light
help
plants
grow
taller.
2. James
wants
to
test
which
surface
is
best
for
bouncing
a
ball.
Control
group
1. Control
group
will
have
to
be
the
plant
under
the
white
light
(or
natural
sunlight)
since
you
are
introducing
a
new
addi<onal
factor
by
replacing
pre-­‐exis<ng
factor
i.e.
natural
light/white
light
(original
factor)
is
replaced
by
different
coloured
lights
2. There
is
NO
control
group
for
this
experiment,
since
the
surface
of
the
floor
is
a
necessary
factor
for
the
ball
to
bounce,
hence
NO
new
addi<onal
factor
is
introduced.

14. Hypothesis
• Hypothesis
is
a
predic<on
or
an
‘educated
guess’
of
what
will
happen
in
an
experiment.
• It
can
be
tested
experimentally,
hence
it
should
be
related
to
the
aim.
• EVERY
inves<ga<on
must
have
a
hypothesis,
and
it
is
based
on:
– Background
informa<on
– Previous
observa<on
– Content/theory
knowledge
from
the
syllabus
– Experimental
method
• The
hypothesis
does
NOT
have
to
be
proved
correct!

15. If-­‐then
hypothesis
• The
most
useful
hypothesis
is
the
‘if-­‐then’
hypothesis.
• It
is
wrieen
as:
“If
something
happens
(independent
variable),
then
this
changes
(depended
variable)”.
• You
must
be
specific
about
WHAT
happens
and
WHAT
changes
occur
in
the
hypothesis.
• This
type
of
hypothesis
focuses
on
independent
and
dependent
variables
and
it
helps
you
to
plan
your
experiment.

16. Hypothesis
–
Example
Sample
experiments
1. Sarah
wants
to
see
if
the
colour
of
the
light
help
plants
grow
taller.
2. James
wants
to
test
which
surface
is
best
for
bouncing
a
ball.
Hypothesis
1. Possible
hypothesis:
a) If
the
light
is
red
colour,
then
the
plant
will
grow
higher.
b) The
plant
under
a
blue
light
will
grow
the
tallest.
c) Different
coloured
lights
will
not
affect
the
plant’s
height.
2. Possible
hypothesis:
a) If
the
surface
is
hard,
then
the
ball
is
bounce
higher.
b) The
ball
will
bounce
highest
on
the
concrete
floor.
c) The
harder
the
surface,
higher
the
ball
will
bounce.

17. Equipment
• Equipment
is
a
list
of
all
materials
required
for
the
experiment.
• Equipment
should
be
wrieen
in
a
list
with
dot-­‐
points!
• The
number/amount/size
of
the
materials
MUST
be
included.
• A
diagram
of
the
experiment
–
with
all
the
equipment
connected,
not
separate
–
can
be
very
useful.

18. Equipment
diagram
• When
drawing
an
equipment
diagram,
use
the
following
rules
for
scien<fic
diagrams:
1. Always
use
a
sharp
pencil
2. Draw
ALL
straight
lines
using
a
ruler
3. Draw
using
single
firm
lines
NOT
jagged
sketchy
lines
4. Diagrams
should
be
simple
2-­‐D
representa<ons
5. Do
NOT
close
off
openings
of
containers
6. Do
NOT
use
shading
or
colouring
7. All
equipment
should
be
labelled
with
straight
lines
8. Each
equipment
should
be
drawn
in
correct
propor<ons
9. Equipment
that
touch
each
other
should
be
touching
in
diagram
10. Use
at
least
¾
of
the
page
or
space
provided
for
drawing.
For
chemistry,
use
chemical
formulae
instead
of
the
names
of
chemical
substances.

19. Equipment
–
Example

20. Risk
assessment
• Risk
assessment
considers
the
nature
of
the
poten<al
hazards.
• It
looks
at:
– Risk:
descrip<on
of
possible
danger/hazard
– Injury:
descrip<on
of
specific
injury
caused
by
the
risk
– Preven@on:
elimina<on
of
hazard
or
precau<ons
taken
to
minimise
harm.
• Risk
assessment
could
be
wrieen
as
a
list
or
in
a
risk
assessment
table.

21. Risk
assessment
–
example
Risk
Injury
Preven@on

22. Method
• Method
or
an
experimental
procedure
is
a
detailed,
step-­‐by-­‐
step
list
of
what
is
done
in
the
experiment.
• It
is
a
set
of
ordered
instruc<ons
that
allow
another
scien<st
to
be
able
to
repeat
the
experiment.
• A
method
must
consist
of:
– Numbered
list
– Starts
with
a
verb
– Must
NOT
be
personal
– Use
scien<fic
language
• It
must
be
wrieen
in
exact
order
in
which
the
experiment
is
performed.

23. Results
• Aoer
conduc<ng
an
experiment,
it
is
important
to
record
any
observa<ons
and
data
collected.
• Observa<ons
should
be
wrieen
in
complete
sentences.
• Data
should
always
be
recorded
and
organised
in
a
table.
• If
suitable,
data
should
be
presented
in
a
graph.
• Tables
and
graphs
allow
the
connec<ons
between
data
(rela@onships)
to
be
determined
easily.

24. Table
of
results
Independent
variable
(unit)
Dependent
Variable
(unit)
Average
(unit)
Trial
1
Trial
2
Trial
3
Title
Independent
variable
should
ALWAYS
be
in
the
FIRST
column
Whenever
you
REPEAT
the
experiment,
you
should
average
the
data.
Each
column
should
have
a
relevant
heading
and
units
shown.
Data
from
your
REPEATED
experiment
should
be
organised
as
Trials
1,
Trials
2…
etc.
The
@tle
should
tell
the
reader
what
data
is
in
the
table.

25. Graphing
results
• Graphs
are
a
visual
way
of
displaying
the
data,
making
it
easier
to
iden<fy
paeerns
or
trends.
• Following
rules
should
apply
when
graphing
data:
– Use
ruler
&
pencil
(go
over
in
pen
later)
– Write
the
<tle
and
label
the
axes
including
units
– Independent
variable
goes
along
the
horizontal
axis
– Dependent
variable
goes
along
the
ver<cal
axis
– Use
at
least
¾
of
the
page
or
space
provided
for
graph.
• If
suitable,
always
draw
a
line
or
a
curve
of
best
fit.
IMPORTANT!
Line/curve
of
best
fit
is
a
con<nuous
line/curve
drawn
to
pass
close
to
the
points
on
a
graph.

26. Graphing
results
• Different
types
of
graphs
are
used
for
different
types
of
data.
• Line
or
sca^er
graphs
are
used
for
con<nuous
(measured)
data
–
both
independent
and
dependent
variables
should
be
con<nuous.
• Column
graphs
are
used
for
discrete
(counted)
data
–
at
least
ONE
variable
is
discrete.
• Some<mes,
pie
or
bar
graphs
are
used
to
display
po<ons
of
a
whole.

27. Graphing
results
–
example
(column
graph)
0
2
4
6
8
10
12
2
4
6
8
10
12
Number
of
students
Number
of
hours
per
week
Number
of
hours
students
spend
on
a
weekend
Independent
variable
should
ALWAYS
be
on
the
horizontal
axis
Dependent
variable
should
ALWAYS
be
on
the
ver<cal
axis
Leave
a
gap
before
1st
column
Columns
have
same
width
and
are
NOT
joined
Spaces
between
the
columns
are
equal

28. Graphing
results
–
example
(line
graph)
0
10
20
30
40
50
60
70
80
90
100
0
5
10
15
20
25
30
35
Temperature
(°C)
Time
(min)
Temperature
changes
of
water
over
@me
Dependent
variable
should
ALWAYS
be
on
the
ver<cal
axis
Independent
variable
should
ALWAYS
be
on
the
horizontal
axis
Visible
data
points
A
line
or
a
curve
connects
the
data
points

29. Making
predic<ons
using
graphs
• Graphs
can
be
used
to
make
predic<ons.
• Making
a
predic<on
between
two
measurements
is
called
interpola@ng.
– E.g.
• Making
a
predic<on
beyond
the
measured
values
is
called
extrapola@ng.
– E.g.
0
20
40
60
80
100
0
10
20
30
40
Temperature
(°C)
Time
(min)
Temperature
changes
of
water
over
@me

30. Determining
rela<onship
using
line
graphs
• Line
graphs
are
used
for
looking
at
a
cause
and
effect
rela<onship.
• A
graph
with
a
straight
line
shows
a
linear
rela@onship
i.e.
an
increase/decrease
in
one
variable
is
directly
propor<onal
to
the
increase/decrease
of
the
other
variable.
• A
linear
rela<onship
is
easier
to
extrapolate
from.
• A
graph
with
a
curve
could
show
a
more
complex
rela<onship,
which
can
be
determined
by
manipula@ng
the
data
(e.g.
“inversing”,“squaring”,
“cubing”,
“roo<ng”
or
“logging”
one
of
the
variables)
to
get
a
straight
line.

31. Determining
rela<onship
with
line
graphs
–
example
BEFORE
manipula@on
AFTER
manipula@on

32. Determining
rela<onship
using
scaeer
graphs

33. Discussion

34. Validity
• Validity
is
derived
correctly
from
premises
already
accepted,
sound,
supported
by
actual
fact

35. Validity
Valid data is evidence that is reliable
and which is relevant to the question
being investigated.
Just being reliable evidence is not enough.
The evidence has to be relevant as well.
For example…

36. Validity
Depends on
• the control of variables
• appropriate method
• Correct technique
• A valid investigation MUST be reliable.

37. Accuracy
• Exactness
or
conformity
to
truth

39. Errors

40. Reliability
• Trustworthy,
dependable

41. Reliability
Reliable data is evidence you can trust.
If someone else did the same experiment,
they would get the same result.
Your evidence will be more reliable
if you repeat your readings.
For example…

42. Reliability For example:
3 students measure the time for
1 swing of a pendulum:
Discuss which method is the
most reliable, and why.
• Jo measures 1 swing.
• Emma measures 1 swing,
but 20 times, and calculates
the average (mean) time.
• Jack measures 20 swings
and divides the time by 20. Physics for You page 359

43. Reliability
Is indicated by
• Consistent results
Over
• a (large) number of trials or replicates

44. Conclusion
Relates to three things:
• HYPOTHESIS; rejects OR supports it
• AIM; ‘answers’ it
• RESULTS; refers to them

45. Conclusions

46. Secondary
informa<on
and
data
• Secondary
data
is
“second-­‐
hand”,
edited
and
interpreted
material.
• Secondary
data
can
be
collected
from:
– Books
– Journals
– News
paper
or
magazine
ar<cles
– Posters
or
infographics
– Brochures
– Tables
or
graphs
– Videos
– Informa<on
from
a
website
– Blogs

47. Reliability
of
secondary
sources

48. Validity
of
secondary
sources

49. Secondary evidence is data
collected by someone else.
Secondary evidence
You may find it in a book or on the internet
BUT
You should always check to see if it is
reliable and valid.
For example…

50. Secondary evidence is data
collected by someone else.
Secondary evidence
Example 1
Some data on the pollution from a car
is published by the car manufacturer.
Would you trust this evidence,
without further data?

51. Secondary evidence is data
collected by someone else.
Secondary evidence
Example 2
Some data on the radiation emitted
from a mobile phone is published
by the phone company.
Would you trust this evidence,
without further data?

52. Scien<fic
wri<ng