i have started my assignment but cant finish it . you dont have to do the proffesional context
SBS310 projects 1 SLE251 Project Assignment 2019 – 40% of final mark Data collection and analysis, and professional context Due date: Submission of final report: 5pm Friday 24th May Submit via Dropbox on Cloud Deakin - hardcopy or emailed reports will not be accepted. NOTE: Although this project allows for the data collection to be done in groups of 1,2 or 3 – ALL WRITE-UPS MUST BE INDIVIDUAL REPORTS. Objective: 1) Data collection, analysis and write-up (20% of final unit mark) You will collect and analyse some biological data in order to answer a biological question. These data may be in the form of observations or experimental measurements that you yourself have collected, or else taken from internet resources. DO NOT simply copy an analysis from another source, or from a previous assignment you have done for another unit. We strongly encourage you to formulate your own question that you wish to test and the practicals from Week 5 onwards are there to help you achieve this and provide feedback for you. However, on the SLE251 unit site we have suggested some experiments or online data sets, and relevant questions, that you may wish to test if you cannot think of an appropriate thing to measure and test yourself. 2) Professional context (20% of final unit mark) As the SLE251 unit also forms part of your career education, we require you to identify the professional context in which your work and the skills learnt are relevant and valued, and articulate how the project has helped provide you with evidence of some fundamental transferable skills: critical thinking, problem solving, communication, research skills and discipline specific knowledge; for inclusion in your resumé or in job applications and interviews. You will also find 2 jobs currently being advertised (one science-based, one non-science) and get you to address 4 2 relevant selection criteria from the job ads (2 for each job) using the STAR technique you will learn about in classes. Approach: For the data collection you can work in groups of up to three students - so either by yourself or with one or two other students. When you’ve collected and analysed the data you will need to write a report about what you have done. NOTE: Even if you have been doing this as a group project, you should each write a report in your own words – DO NOT COPY EACH OTHER’S REPORTS Either by yourself, or with your group, plan a simple experiment or set of observations. Your study will take one of two forms: either a) examine the difference between the mean measurements taken from two or more groups/samples. b) examine whether two sets of continuous (scale) measurements are related to each other In the type a) study ideally you should collect at least 20 measurements from each group in your study, and that those measurements should be continuous, or approximately continuous (for more information, see below). You need to follow the “scientific method”, recording your thoughts/methods/results at each step: In the type b) study you should collect at least 20 measurements for each of the two measurements you are making – each of these two types measurements should be taken from the same individuals/samples (e.g. measuring leaf litter cover and invertebrate abundance, or measures of salinity and vegetation quality in different streams). Here’s how you should you approach the task if you are planning to test your own question: 1. Make an observation about the world – ideally this should be something vaguely biological. 2. Your observation should raise a question. 3 3. Formulate a hypothesis which helps explain your observation. Your hypothesis may, or may not, be part of a larger theory that you have about the world. Be careful to make the hypothesis clear, specific and plausible, and if it does not already read like a prediction, it should readily generate predictions. 4. Your hypothesis may already be in the form of a prediction. If it is not, make a specific prediction about an experiment you could perform or a set of observations you could make. In the type a) study, your predictions should take the form: “group A, B and C will be different”, “A will be bigger than B or C” or “A will be smaller than B or C”. In the type b) study your predictions should take the form “measurements X and Y are related to each other”, “X is positively related to Y” or “X is negatively related to Y”. 5. Use your prediction and/or hypothesis to create a specific null hypothesis which your experiment/observations will test. The null hypothesis should be paired with the alternative hypothesis – the two should be mutually exclusive. 6. Plan an experiment or a use a set of observations to test the prediction you have made. The study should be as simple as possible, and should easily yield at least 20 data points in each category. Think carefully about discriminating between your null and alternative hypotheses: How accurate will your measurements need to be? How can you avoid measurement bias? What confounding variables will there be? Obviously, you needn’t worry too much about how generally informative your study will be – it is necessarily going to be rather narrow in scope, but do consider biological realism, especially if you’re planning an experiment. A note about ethics Deakin University, as with all institutions in Australia, has very strict protocols about conducting studies on humans or vertebrate animals. Consequently, unfortunately, we cannot allow you to carry out experimental investigative studies on people or vertebrate animals. There are no exceptions to this. Even apparently innocuous studies (such as seeing how long it takes for people to complete a crossword puzzle and relating this to age), fall under the Ethics guidelines. Note that using already collected data (say from an internet resource) is fine, because these data will have been collected under appropriate regulations and are in the public domain. 4 Observational studies that involve no interference with the persons or animals being observed (such as bird watching, or observing people’s behaviour in supermarkets) may be acceptable. To know where the line is drawn consider whether your study actively involves you addressing/interacting with a person (e.g. to ask a question), or handling an animal. If so, then that is not acceptable Experimental work, however, is fine for plants and invertebrate animals (with the exception of cephalopods (octopus and squid) and decapod crustaceans (crabs, yabbies, lobster) – although we assume it is unlikely that any of you are planning experiments on these animals). If you have any doubts about whether what you are planning is appropriate, please ask your demonstrators or Matt (Burwood) or Pete (Waurn Ponds). THERE will be severe penalties if you fail to comply with these ethics requirements. Two examples Here is an example of how your project might evolve (note that when you come to write this up, you should write in continuous prose in the past tense): A type a) study (a categorical predictor of continuous response variable) Observation: There seem to be a lot of magpies in suburban parks. Question: Are there magpies congregating where humans are more likely to be present? Theory: Magpies benefit from living in the city perhaps through the presence of food and water made available by humans Prediction: There are more magpies in parks within the city boundary than areas of parkland (bushland) outside of the city. Hypothesis: Same as prediction. Null hypothesis: Magpies in suburban parks and bushland areas exist at the same densities. Experiment/Observations: Pick a suburban park and walk around it for 20 minutes and count the number of magpies that you see. Repeat this for other areas of suburban parkland, and then compare with areas of the bush outside the city. Examples of things to consider: measurement precision (e.g. are you counting the 5 same magpies twice, are you walking a continuous route, rather than doubling back on yourself) - confounding variables/effects (e.g. could weather conditions influence how many magpies you see on a particular day? Perhaps bushland areas have thicker vegetation making it more difficult to see magpies. Any difference you find in such circumstances may be the result of the type of habitat you sample (open versus wooded), not the identity of the site). Compare average magpie count (continuous response variable) for suburban vs. bushland areas (categorical predictor variable). A type b) study (a continuous predictor of a continuous response variable) Observation: Tall people seems to be better at sprinting than short people Question: Is it an advantage to be tall if you are a sprinter? Theory: Tall people have long legs, which means they have longer stride length and so can cover ground more quickly. Prediction: Taller people will run 100m in a faster time than shorter people Hypothesis: There is a negative relationship between human height and time taken to run 100m (taller people take less time) Null hypothesis: There is no relationship between human height and time taken to run 100m Experiment/Observations: Analyse the results of all the Men’s 100m races at the London Olympics (http://www.london2012.com/athletics/event/men-100m/). For each heat click on the runners to get information on their height (in cm) and then also note their time they took to run 100m. Examples of things to consider: repeat- sampling: some individuals will have run more than one heat (final, semi-final etc.) so you may need to use a single measure (mean time), some individuals from well-funded countries are likely to have had access to better training than others – how could you control for this? Doing the analysis The data that you collect should be of a form that can be analysed using either be a t-test, or analysis of variance (ANOVA) for a type a) study or correlation or linear regression for the type b) study. These topics are covered in Lectures in weeks 4-8 and in Practicals 2-4. Don’t forget that when the predictor, explanatory variables are 6 categorical or have been chosen to be treated as categorical variables that ANOVA is used. A t-test is a particular form of ANOVA that compares two group