1. Module 0 revision  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 organism’s cells  Mycology - the study of fungi  Genetics - the study of hereditary information

2. 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

3. 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.

4. 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).

5. 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 can’t make any accurate conclusions

6. 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!!

7. 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

8. 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 wouldn’t know whether the fertiliser has had an impact on their growth

9. 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.

10. 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.

11. Spot the dodgy hypothesis What’s wrong with these hypotheses? (see next slide for answers) i.Plants need light and water to grow ii.There’s no such place as heaven iii.I love my boyfriend twice as much as he loves me iv.Dogs fed on “Smartbix” are more intelligent than other dogs

12. Dodgy hypotheses uncut i.This has several problems. It has more than one variable (light and water) and is too vague. ii.This is not a subject science can address, since it relies on belief and not knowledge. It is not testable. iii.Same problem – we can’t quantify love, therefore we can’t test this iv.Another one that can’t be tested, because we don’t have a way of measuring the intelligence of dogs

13. 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 you’re 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

14. 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

15. 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!

16. Drawing graphs  Follow these steps: i.Decide what type of graph you need: discrete data uses a bar graph, continuous data uses a line graph ii.The independent variable is plotted on the x-axis iii.The scales on the axes must have equal intervals even if the data doesn’t iv.Remember to include a meaningful title

17. An example of discrete data

18. An example of continuous data

19. 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)