Skills in HSC Biology 2024 Introductory Powerpoint.pptx

Detailed powerpoint of the various skills detaile din the NSW HSCbiology syllabus.

1. Skills in HSC Biology

2. Skills Questioning and predicting – develops and evaluates questions and hypotheses for scientific investigation Planning investigations – designs and evaluates investigations in order to obtain primary and secondary data and information Conducting investigations – conducts investigations to collect valid and reliable primary and secondary data and information Processing data and information – selects and processes appropriate qualitative and quantitative data and information using a range of appropriate media Analysing data and information – analyses and evaluates primary and secondary data and information Problem solving – solves scientific problems using primary and secondary data, critical thinking skills and scientific processes Communicating – communicates scientific understanding using suitable language and terminology for a specific audience or purpose

3. Primary vs secondary data Definitions of what is primary or secondary data differs across the disciplines. For the sciences, this is how we would define them: Primary scientific data - measurements collected by the experimenter during a laboratory practical – i.e. number and types of microbial colonies grown from different water sources in the school. Secondary scientific data - accessed by a student from websites, textbooks and other reliable sources – i.e. numerical data on the incidence of diabetes/melanoma in Australian population in a defined time period.

4. Qualitative vs quantitative data Quantitative data can be counted, measured, and expressed using numbers. Examples include - volume of gas produced (catalase enzyme expt ) number of bacterial colonies (microbial growth from water sources) Qualitative data is descriptive and conceptual. Examples include the colour and shape of bacterial colonies (microbial growth from water sources) whether gas is produced or not (catalase enzyme expt )

5. Scientific theories Scientists never talk about proving a theory, but rather about providing evidence to support a theory . When a large enough amount of evidence has been gathered that supports a theory, then that theory is accepted by the scientific community. Examples of theories in biology that have so much evidence supporting them that they are accepted are the Cell Theory and the Theory of Evolution by Natural Selection. There are many examples of theories and hypotheses in biology that were proposed and later rejected or changed when new evidence came to light. For example, the theory of spontaneous generation is now obsolete. Other theories studied are the Germ Theory and the Chromosomal Theory. You should know the work of scientists involved in the development of these theories.

6. Models in biology Biology uses models to describe biological systems and to make and test predictions. Models are constantly being refined as we learn more. For example, in the Mendelian genetics model, all genes are assumed to be inherited independently of each other and are either dominant or recessive. However, it has been found that some genes occur on the same chromosome or are located on sex chromosomes, and breeding experiments that involve these genes do not give the expected ratios typical of the Mendelian model. In these cases, non-Mendelian models such as sex-linkage, co-dominance or incomplete dominance are used to analyse patterns of inheritance, taking the additional complexities into account. You should be able to explain a “model” you used to help understand processes in Biology e. g. modelling polypeptide synthesis, modelling the immune system. You should be able to discuss the advantages and disadvantages of using these ‘models’ in understanding these complex processess .

7. The scientific method The scientific method is the process of systematically gathering information and data by observation and measurement, and using the information and data to formulate and test hypotheses. It is from such investigations that the body of scientific knowledge that we accept today has been accumulated.

8. Formulating a hypotheses The scientific method begins with asking questions (sometimes called research questions). Based on these questions, you formulate a hypothesis, which is a tentative answer to your question. This also involves reading the literature to research your question. Look at the following two examples: Poor research question: ‘How can we make a seedling grow the best?’ ‘Best’ is a vague term. What you mean by ‘best’ may not be what someone else means. Good research question : ‘Which one of two fertilisers gives the maximum growth of roots and stem in a seedling?’ This question is not vague. It tells you what you will be varying and what you will be measuring. It also gives a criterion for judging whether you have answered the question.

9. Formulating a hypothesis A hypothesis is a predictive statement about the relationship between the variables and is an ‘expected’ answer to your question. It is often written as an ‘If … then …’ statement, to explain an expected relationship, such as: ‘If x is introduced/increased/decreased, then y will increase/decrease/stay the same.’ An example of a hypothesis is: If the amount of nitrogen in the fertiliser provided to a seedling in the soil is increased, then the height of the stem and/or length of the roots of the seedling will increase. Your hypothesis should give a prediction that you can test, ideally quantitatively (that is, by taking measurements).

10. Experiment design and validity of results An experiment is designed and performed to test a prediction, and the results are then analysed. If the results of the experiment agree with the prediction, then the hypothesis is supported (never proved). Experiments are considered valid when scientists test the hypothesis that they intended to test and get consistent and accurate results when repeated. A valid experiment involves setting up controls and making sure that the only thing that changes in the experiment is the variable being tested. All other conditions must be controlled to remain the same. Experiments are considered reliable when they can be repeated to give the same results and random error is eliminated or minimised. An experiment is considered accurate when its measurements are close to the true value – for this to be achieved.

11. Variables When doing experiments, you need to decide which variable you will change, what you will measure and which variables you will control. Consider which variables you can control, and which you cannot. Typically, an experiment will have three types of variable: - one independent variable, which we are testing and we therefore purposefully change, some experiments can have two independent. variables - one dependent variable, which is the result that we measure – this changes as a result of changing the independent variable. We assume that the dependent variable is in some way dependent on the independent variable, - many controlled variables , which are kept constant so that they do not interfere with our results.

12. reliability Whenever possible you should make repeat measurements. This allows you to check that your measurements are reliable. Your results are reliable if repeat observations and/or measurements taken under exactly the same circumstances give the same results within experimental uncertainty. A reliable experiment is one which, if repeated multiple times, gives the same result (within an acceptable margin of error).

13. Accuracy Accuracy may refer to a result or to an experimental procedure. Accuracy of a result (data) is a measure of how close it is to an expected value given in scientific literature. To improve accuracy in experiments, we use the most precise measuring instruments available, avoid human error (for example, measuring errors), carry out repeat trials, and find an average to smooth out random errors. Accuracy is also linked to any uncertainty in measurement. For example, we can determine the size of red blood cells by estimating their number in a field of view and dividing by the size of that field of view. Alternatively, we can measure their size with more accuracy using a mini grid slide .

14. Validity To ensure that results are valid, in a primary investigation you must carry out a fair test. Identify variables that need to be kept constant. Develop and use strategies to ensure these variables are kept constant. Demonstrate the use of a control. Use appropriate data collection techniques. In a control, you remove the factor being tested in the experiment to see whether, without that factor, a different result is obtained. These steps ensure that the process used and the resultant data measure what was intended. Results need to be valid if you are going to be able to draw a conclusion from them.

15. summary

16. PRESENTING YOUR DATA – types of graphs

17. Analysing data You have developed many skills in analysing data when carrying out your depth study on diseases. They involve – - explaining results – graphs - making conclusions from data - using data to solve problems - doing further research to help support conclusions

18. Communication and peer review Scientists communicate their work to each other to share new ideas and information and as a way of contributing to the ongoing development of science. Before a scientific paper is published, it is reviewed by other scientists – experts in the particular area – who evaluate it. You should be able communicate your understanding of biology by using suitable language and terminology. NOW TRY THE HSC QUESTIONS ON WORKING SCIENTIFICALLY