Fundamentals of Scientific Research. Jason Downing & Emily Warschefsky

Transcription

1 Fundamentals of Scientific Research Jason Downing & Emily Warschefsky

2 Science Experiments MUST Involve mixing colored liquids in: test tubes beakers flasks Require the use of goggles (but not gloves because those don t look as cool) Best if: They produce smoke or save the whales

3 Science is Curiosity: question everything! Innovation: creativity is not just for the artsy folks! Exploration: the discovery of the unknown

4 What is research? Research butterfly pollination Non Scientific definition: Gather information and compile it in an essay style paper Scientific definition: Design a study that investigates one aspect of butterfly pollination

5 Where to start? Scientific method: Observation Hypothesis Experiment Conclusion Knowledge Science is not a recipe!

6 Where to start?

7 Expanding upon Observation Literature review Age/ability appropriate Find out what s out there Ask new questions based on what you find Websites Wikipedia is not always terrible Springboard to other resources

8 Resources Less technical: Scitable Scientific American Science Daily Good old fashioned textbooks!

9 Resources Advanced: Journal Articles Google Scholar Directory of open access journals ( Databases Many articles include complicated statistics and jargon Try to sort through the deep stuff How to read an article (coming up!)

10 Formulating a Hypothesis What is your question? Hypothesis Must be testable Must be able to prove it wrong Can never prove it right Two main types Null Alternative

11 Scientific research Two main forms: 1)Experimental 2)Observational Studies *Meta-analyses; synthesis of existing knowledge/literature

12 Experimentation An experiment deliberately imposes a treatment on a group of objects or subjects in the interest of observing the response. Treatments Experimental units Subject Responses Measurable values

13 Three Steps of Experimental Design 1) Define the problem and question to be addressed; develop a hypothesis 2) Define the research subjects of interest and need for sampling 3) Define the experimental design

14 1) Exploring and the research question Get outside and make observations! Before data collection, specific questions and goals must be clearly defined; try to think in testable hypotheses.

15 Types of Hypotheses A hypothesis is not a question, it s phrased as a prediction based on known parameters. Null hypothesis (H o ) : default hypothesis Alternate hypothesis (H a ): asserts a particular relationship between the phenomena (positive or negative) Examples: (H o ) : There is no difference in the germination and growth rates of passion vine (Passiflora suberosa) plants when planted with organic compost. (H a ): Germination and growth rates will be higher in passion vine (Passiflora suberosa) plants that are planted in organic compost.

16 2) Define subject population Subject population: All of the animals, plants, or other organisms to be included in experiment Data collected on the population will be used to draw conclusions

17 3) Define need for sampling Sample: one of many subsets that are selected from a population of interest Much small than the actual population (considered infinite) Results of the sample can be used to draw inference in the entire population

18 How large should my sample size be? Not so simple Random samples/non random Requires knowledge of the observed or expected variance among sample. How large is the difference between treatments Power analysis: determines minimal sample size needed to reduce residual variation and enhance statistics Generally N > 30, but the more the better!

19 Importance of Replication Replication: repetition on a large group of subjects reduces variability in experimental results Increases statistical significance and confidence level with which a researcher can draw conclusions about an experimental factor

20 4) Designing the experiment Three main parts: a.experimental Factor b.treatment (Independent Variable) c.outcome (Dependent Variable)

21 Experimental Factor Factor: a variable whose levels are set by the experimenter General type or category of treatments Treatments = different levels of a factor Example: Seeds are subjected to different three germination methods. The plants seeds are the experimental units, the germination methods are the factor, and the three types of germination methods constitute three levels of the factor 'type of germination.

22 Treatment Also known as: independent variable researchers administer to the experimental units Divided into levels of different amount or magnitude. Example: If each of three species of plant seedlings are given 5g, 10g, and 15g of a fertilizer, those amounts are three levels of the treatment.

23 Dependent Variable Dependent variable: the output or measured response to the treatment Example: The resulting height (cm) of the plants is recorded.

24 Importance of Controls Example: Suppose we wish to evaluate the effect of fertilizer on growth. We give each plant different levels of the treatment. All of the plants appear to have grown the same amount, and we conclude that fertilizer increases growth. The problem with this experiment is that we have neglected to include a control group, and therefore, we do not know how much the plant would grow without any fertilizer. Control group sets a baseline for comparison

25 Observational Studies Involves collecting and analyzing data without changing existing conditions.

26 Exercise in the garden We want to know what species of plant will attract the most pollinators to our school garden? Basic research question: What are the pollinator visitation rates of three common garden species? Null Hypothesis: H o :There are no differences in visitation rates among the three common garden species.

27 Experimental Units Three common garden plant species and associated pollinators (butterflies, bees, other visitors). Salvia coccinea Senna mexicana Pantas lanceolata

28 What to measure? Independent variable : Plant species Dependent variable : number of pollinator visits Parameters : No. of flowers per plant & watch duration Summary Statistics: Calculate visitation rate & mean visitation rate for each species for all replications

29 Not so SCARY Statistics Applying statistics involves three basic tasks: 1) collecting data 2) summarizing and exploring data 3) drawing conclusions an making decisions

30 Summarizing and Exploring Data Descriptive statistics Involves tabulating overall summaries Making graphs of data Deductive Probability Population Sample Statistics Inductive

31 Sample Means Most familiar way to summarize data by a central value sample mean, denoted by x = x 1 + x x n n Range = difference between the high and low score

32 Graphical Interpretations Histogram of 31 black cherry trees Box plot For observational data pie charts are useful

33 Normal distribution Often assumed that data comes from a population with a normal distribution Mean = mode

34 Data doesn't always form a normal distribution

35 Simple t test Also called Student s t test assessing the statistical significance of the difference between two sample means Different model for paired vs. unpaired groups Good for: small sample size, unknown standard deviation

36 Analysis of Variance (ANOVA) ANOVA: Statistical test of whether or not the means of several groups are all equal Essentially a t test for more than 2 groups

37 The p value A result is called statistically significant if it is unlikely to have occurred by chance alone, according to a pre determined threshold probability, the significance level. One "rejects the null hypothesis" when the p value is less than the significance level α (Greek alpha), which is often 0.05 or 0.01.

38 Statistics Software Packages available; MINITAB, SAS, SPSS, and Excel.

39 Dissemination/Communication Very, very important Modes of dissemination Oral presentations Posters Written Journal Articles Pop science articles Magazines Different modes for different audiences

40 Basics of a Journal Article 1. Abstract Read to get a general overview 2. Introduction A good source for other related studies 3. Materials and Methods Important if you want to do a similar experiment 4. Results Important, but technical 5. Discussion Review of results & the big picture

41 Journal Article Exercise As a group, read the article in your folder and prepare to present the article to the rest of the group. Try to identify the question, hypothesis, prediction, and significant results.

42 Dissemination/Communication Non traditional Dance your PhD

43 Additional Resources resources.com/ ScienceShot Glogster.edu

44 Statistics support The Research Assistant ch/link.asp

45 References (Definition taken from Valerie J. Easton and John H. McColl's Statistics Glossary v1.1)