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• Biology is the study of life
• There are different branches of biology,
  including
• Botany - the study of plants
• Zoology - the study of animals
• Ecology - the study of the inter-relationships
  between plants, animals and the environment
• Biochemistry - the study of the reactions that
  take place within an organisms cells
• Mycology - the study of fungi
• Genetics - the study of hereditary information

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The Scientific Method

• This is the basis for all science, not just
  biology
• Science proceeds in an orderly fashion using a
  series of well defined steps
• Good science is based on accurate observations,
  which may be quantitative or qualitative

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Quantitative vs Qualitative

• Quantitative involves measurement and results in
  numerical data
• Qualitative involves observations that are
  descriptive rather than numerical
• Both are valid, but are used under different
  circumstances
• Examine Case study 1.1 on p. 2 of text and
  identify the two types of data used in it.
  Compare to Case study 12.1 p 191.

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Case studies

• Case study 1.1 is primarily descriptive, with
  many details on the life of the feathertail
  glider. There is little numerical data presented,
  apart from the size of the organism
• Case study 12.1 describes a study of the effect
  of habitat size on populations and contains
  significant numerical detail (the actual numbers
  are not presented but the data that was collected
  was numerical).

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Variables

• Good experimental design requires you test only
  one thing at a time (do you know why?)
• If you test more than one thing at a time, you
  will never know which one has produced the
  results, so you cant make any accurate
  conclusions

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Variables

• Independent variable this is the one you change
  on purpose. Also called the manipulated variable,
  or the experimental variable
• Dependent variable this is the one that responds
  to the independent variable it is the one we
  monitor in the experiment. Also called the
  responding variable
• Controlled variables are those we keep the same
  for the duration of the experiment.
• Confused? See the next slide!!

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A little example

• An experiment was done to test the effect of
  concentration of a particular fertiliser on
  tomato growth.
• Independent variable amount of fertiliser (this
  is what we changed)
• Dependent variable growth rate (this is what we
  measured)
• Controlled variables (these might affect the
  results, so we keep them the same for all plants)
• pot size, water, light, temperature, type of
  tomato, time interval between measurements

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More about variables

• Given an experimental design, can you pick the
  type of variables, and work out what is being
  tested, and why each variable must be controlled?
  Try Q.1, p.14 of text
• Do you know the difference between controlled
  variables, and the control group in an
  experiment? The latter is used as a comparison.
  So in our tomato experiment, we would need a set
  of tomatoes that were grown with no fertiliser
  this is the control group, and tells us what
  normal tomato growth rates are. Without this, we
  wouldnt know whether the fertiliser has had an
  impact on their growth

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Experiments the good, the bad and the ugly

• Well designed experiments test only one variable
  at a time, so that any observed responses can be
  attributed to the factor being tested
• A good experiment is one that can be repeated by
  another researcher, and give the same results.
  This is called reliability
• It has replicates ie more than one of each
  treatment, to reduce the effect of variability
  between individual organisms, and allow for
  averaging.

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Hypotheses

• An hypothesis is a testable statement
• Every experiment needs one
• An hypothesis can be framed as an if then
  statement
• It should set limits on the problem see the list
  on p. 15 of the text for how to do this
• When an hypothesis is worded in the negative, it
  is called the null hypothesis. For example The
  addition of nitrogenous fertiliser will have no
  impact on plant growth is a null hypothesis.
  These are used because it is easier to disprove
  something than it is to prove it.

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Spot the dodgy hypothesis

• Whats wrong with these hypotheses? (see next
  slide for answers)
• Plants need light and water to grow
• Theres no such place as heaven
• I love my boyfriend twice as much as he loves me
• Dogs fed on Smartbix are more intelligent than
  other dogs

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Dodgy hypotheses uncut

1. This has several problems. It has more than one
   variable (light and water) and is too vague.
2. This is not a subject science can address, since
   it relies on belief and not knowledge. It is not
   testable.
3. Same problem we cant quantify love, therefore
   we cant test this
4. Another one that cant be tested, because we
   dont have a way of measuring the intelligence of
   dogs

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Sampling in the field

• For practical reasons, biologists take samples
  rather than attempting to deal with whole
  populations or communities
• What factors may cause bias in a sample? See text
  p. 164, Section 10.6
• What is the importance of sample size? If it is
  too small, it may not be representative of the
  population. If it is too large, it defeats the
  purpose of sampling (ie, if youre sampling 95
  of the population, you might as well put in a
  small extra effort and measure the whole
  population). Ten percent is a good figure to work
  on

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Graphs and graphing

• These questions are a free gift in the WACE exam!
• If you have to interpret a graph, look carefully
  at the axes note scale and units
• If you have to interpolate (read between data
  points) use a ruler to read off the axes

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Graphs and Graphing

• Remember these terms?
• Extrapolate means to read beyond the values of
  the graph, ie to estimate the behaviour of the
  curve above and below known values. It is less
  accurate than
• Interpolate which means to read between known
  values.
• Examiners like to ask you to do these!

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Drawing graphs

• Follow these steps
• Decide what type of graph you need discrete data
  uses a bar graph, continuous data uses a line
  graph
• The independent variable is plotted on the x-axis
• The scales on the axes must have equal intervals
  even if the data doesnt
• Remember to include a meaningful title

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An example of discrete data
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An example of continuous data
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Special graphs

• Check the scales on the axes!
• Are they linear, or logarithmic?
• Why would you use a logarithmic or semi-log (ie
  one axis linear, the other log) scale?
• Log scales are used when the data covers a very
  large range of values. It allows you to plot them
  accurately (eg data like 0.01, 6, 190, 5640, 25
  700 can be plotted on the same graph-this would
  be very difficult on normal paper)