1. Experimental Design

2. Design must be set prior to investigation Any changes to protocol can/will negate previous data collection efforts Design phase should be when you ask the “tough questions” to make sure that your design “holds water”

3. Experimental Design 1.QUEST design 2.Limitations w/ QUEST design 3.Factors to consider when designing your own study

4. Experimental Design 101 Limit yourself to useful data What are useless data: - unreliable or unrepeatable - irrelevant to problem - inappropriate spatial & temporal Scales - hopeless experimental design cannot be statistically tested

5. Rules of the Road 1. Not everything that can be measured should be Do you have an ecological or biological reason for hypothesizing x, y, or z

6. Rules of the Road 2. Find a problem and state your objectives clearly “The purpose (or objective) of the present study was to…”

7. Rules of the Road 3. Collect data that will achieve your objectives and make “your statistician” happy Sound design should include: - large sample size - replicate sample design - random, independent samples - take statistical test into account

8. Rules of the Road Replicate samples should be collected from the smallest division of your dataset If you want to compare # fish among locations (RF, 30’, 40’) you will need multiple fish surveys conducted at each location How many??? – minimum of 3 for parametric testing

9. Rules of the Road 4. Some ecological questions are impossible to answer at the present time Know when to say when… Impacts of Global warming upon semester long data…

10. Sampling, Statistics and Ecology Oh My! Sample Measured Study Population of Interest StatisticsEcology

11. Designing Field Studies Descriptive Statistics – used to summarize data; explain, describe complex systems mean, st.dev, cluster Analytical Statistics – used to test hypotheses t-test, ANOVA Experimental Design for each type of statistics very different

12. Designing Field Studies Need to know something about what you plan to examine to correctly design a study Pilot studies are an important aspect Allows you determine feasibility of: - techniques - sampling sites - sample size - statistics

13. Spatial and Temporal Scales

14. Scales of Measurement Correct data scale – nominal, ranking, ratio Discrete or continuous Significant figures

15. 5. Decide on the number of significant figures before you begin the experiment Rules of the Road

16. 6. Never report an ecological estimate without some measure of its error Problems with ecological studies - biological problems are not statistical problems - temporal-spatial variability in ecological systems - stats only cope with random errors Rules of the Road

17. 7. Be skeptical about stats results Just because it is statistically significant does not mean it is important Rules of the Road

18. 8. Never confuse statistical significance with biological significance Questions must have theoretical concept to be ecologically significant Rules of the Road

19. 9.Record data in computer speadsheets soon after data is collected Rules of the Road

20. 10. Garbage in Garbage out The conclusions of your study are only as good as your data The more energy you put into the design of the study – the better the payoff at the end Rules of the Road

21. Design + Data + Statistics = Conclusions However – how do we translate Questions into a solid design? Hypotheses - a statement that something is true Integrating Concepts

22. Q: Does fish abundance differ with depth? H: Mean Fish abundance is equal among sites at Ke’ei (RF, 30ft, 40ft)‏ Q: Is there a relationship between sea urchin abundance and rugocity H: There is no linear relationship (Corrleation) between sea urchins and rugocity (r = 0)‏ Questions into Hypotheses

23. 1. Define Questions 2. Translate Questions into Hypotheses 3. Use hypotheses to define Design 4. Use design to define sampling protocol 5. Collect Samples MOP Experimental Design

24. Introduction to Data Entry

27. Data Entry Intro 1. Enter and manage data in a spreadsheet, not a statistical program 2. Keep consistent datasheets 3.Check & recheck data entry 4. Maintain a “raw data” archive

28. Data Entry Intro 5. Keep track of changes to Master Datasheets 6. Process “new” data in a timely fashion 7.Start “exploring” data immediately 8. Recognize how stats programs view data 9. Consistency, Consistency, Consistency

31. Data Correctly Entered?

32. We have a problem!

33. How About Now?

35. Statistical Programs Very limited view of data Variables – columns Data - rows Variable of interest – Response (#Acanthaster sp.) Categorical group – Factor (Isobath, Group, Year)

36. ! MINITAB X Invalid response variable. Too few items. Une a single numeric column. OK

37. QUEST 2006 Master Data File Compiled by Brian Tissot & Jen Smith, May 23, 2006 Mobile Invertebrates DepthLocationQuadspongesflatwormsConus sp.C. caputserpentisCypraea tigrisH. sanguinaeus Morula/DrupaOctopus Team 3RFI100000000 200000000 300400000 400800000 500400000 600840000 700000000 800000000 900000000 Team 6RFO100000000 200000040 300400000 400000040 5000 ! MINITAB X Invalid response variable. Too few items. Une a single numeric column. OK

38. Data Correctly Entered? ! MINITAB X Invalid response variable. Too few items. Une a single numeric column. OK

39. At the end of each day: 1. Check your field data sheets – incomplete, errors 2.Enter your data into electronic data sheets 3. Summarize and explore data as soon as possible Data Entry Tips