Experimental Design

1. Experimental Design

2. Experimental Design 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 QUEST design Limitations w/ QUEST design 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. Statistics Ecology Sampling, Statistics and Ecology Oh My! Sample Measured Study Population Population of Interest

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

16. Rules of the Road 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

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

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

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

20. Rules of the Road 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

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

22. Questions into Hypotheses 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)‏

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

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

37. X MINITAB Invalid response variable. Too few items. Une a single numeric column. ! OK QUEST 2006 Master Data File Compiled by Brian Tissot & Jen Smith, May 23, 2006 Mobile Invertebrates Depth Location Quad sponges flatworms Conus sp. C. caputserpentis Cypraea tigris H. sanguinaeus Morula/Drupa Octopus Team 3 RF I 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 3 0 0 4 0 0 0 0 0 4 0 0 8 0 0 0 0 0 5 0 0 4 0 0 0 0 0 6 0 0 8 4 0 0 0 0 7 0 0 0 0 0 0 0 0 8 0 0 0 0 0 0 0 0 9 0 0 0 0 0 0 0 0 Team 6 RF O 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 4 0 3 0 0 4 0 0 0 0 0 4 0 0 0 0 0 0 4 0 5 0 0 0

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

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