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Stage 6 science skills
 

Stage 6 science skills

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    Stage 6 science skills Stage 6 science skills Presentation Transcript

    • Stage  6  Science  Skills   Preliminary  &  HSC  Science   By  S.  Choi  
    • 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.  
    • 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.  
    • 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  
    • 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  
    • 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”.  
    • 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.  
    • 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  
    • 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.  
    • 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!  
    • 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      
    • 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.  
    • 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.  
    • 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!  
    • 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.  
    • 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.  
    • 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.  
    • 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.  
    • Equipment  –  Example  
    • 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.  
    • Risk  assessment  –  example   Risk   Injury   Preven@on                  
    • 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.  
    • 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.    
    • 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.  
    • 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.    
    • 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.  
    • 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  
    • 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  
    • 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  
    • 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.  
    • Determining  rela<onship  with  line   graphs  –  example   BEFORE  manipula@on   AFTER  manipula@on  
    • Determining  rela<onship  using  scaeer   graphs  
    • Discussion  
    • Validity   •  Validity  is  derived   correctly  from  premises   already  accepted,   sound,  supported  by   actual  fact  
    • 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…
    • Validity Depends on •  the control of variables •  appropriate method •  Correct technique •  A valid investigation MUST be reliable.
    • Accuracy   •  Exactness  or  conformity   to  truth  
    • Errors  
    • Reliability   •  Trustworthy,   dependable  
    • 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…
    • 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
    • Reliability Is indicated by •  Consistent results Over •  a (large) number of trials or replicates
    • Conclusion Relates to three things: • HYPOTHESIS; rejects OR supports it • AIM; ‘answers’ it • RESULTS; refers to them
    • Conclusions
    • 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  
    • Reliability  of  secondary  sources  
    • Validity  of  secondary  sources  
    • 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…
    • 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?
    • 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?
    • Scien<fic  wri<ng