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Basic Statistical Guidelines for OSQR Project Plans Deb Palmquist USDA-ARS-MWA Statistician

Feeling in over your head with statistics reporting in the OSQR process project plans?

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Basic Statistical Guidelines for OSQR Project Plans Deb Palmquist USDA-ARS-MWA Statistician

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    2. Feeling in over your head with statistics reporting in the OSQR process project plans?

    4. The MWA statistician will be reviewing the statistical aspects of your OSQR project plans. The intent is to ensure that current analysis techniques are appropriately applied, comprehensible, and relevant to proposed research.

    5. Objectives and Sub-Objectives - Hypothesis section - Experimental Design section

    6. How do I write Hypotheses for my OSQR Project Plan? Basic, concise statement of what you expect to find from experimentation in your objective or sub-objective (Pg. 43 in OSQR handbook) Emphasis is on writing a testable hypothesis Proposed research should directly address hypotheses

    7. OSQR Hypotheses Use of will not can Not how (belongs in Experimental Design) Not why (dont put background information into a hypothesis, include it before or after) May have more than 1 hypothesis in one section so make sure to label them according to the Objective and Sub-objective numbering scheme

    8. Confirmatory experiments are hypothesis driven Comparisons among treatments are important Means and std devs are statistics of interest Pre-planned treatments with proper replication scheme Exploratory experimentation is data driven Relationships among variables are important Functional relationships and graphs are worth 103 means Observational studies may or may not include replication

    9. Confirmatory Comparisons of two or more categorical treatments Comparisons of a baseline or control to a set of treatments Comparison of new technology to current standard practice A statistical test is performed on collected data Exploratory Functional relationship of quantitative treatments to dependent Y variables Follow changes in treatment effects through time or temp Comparison of treatment functional responses The research may be non-hypothesis driven and no statistical testing will be done

    10. Hypotheses for Confirmatory Experimentation OSQR Specification Examples Hypothesis 1.1: A new pasture rotation schedule is more effective in increasing late weight gain of market bound cattle than two other industry standard rotation practices. (Hypothesis 1.1 refers to Objective 1, Subobjective 1)

    11. Hypotheses for Confirmatory Experimentation OSQR Specification Examples Hypothesis 2: New diet formulation SuperSteer will out perform current formulations by providing superior nutrition, eliminating BGH additives, and reducing animal waste. (Hypothesis 2 refers to Objective 2)

    12. Specific Goal for Exploratory Experimentation OSQR Specification Examples Specific Goal 3.a: To model a functional relationship between storage time and silage nutrition as measured by vitamin concentration. (Specific Goal 3.a refers to one of many hypotheses or goals under Objective 3)

    13. Specific Goal for Exploratory Experimentation OSQR Specification Examples Specific Goal 3.b: Models will be developed that provide optimum temperature and storage time ranges for various dairy forages. (Specific Goal 3.b refers to a second hypothesis or specific goal under Objective 3)

    14. Experimental Design Description of the research methodology Statistical experimental design structure Statistical treatment structure and analysis

    15. Experimental Design Research methodology - Self explanatory - Details of how the research will be conducted

    16. Experimental Design Statistical experimental design structure - RCB, CRD, GLM, Nonlinear estimation - Replication randomization scheme (how observations assigned to treatments) - Power analysis: adequate number of obs? - Identify experimental unit (where treatments will be applied)

    17. Experimental Design Statistical treatment structure - Identify treatments and what you are measuring (dependent Y-variable) - Analyses: ANOVA, regression, and even more specific if known: - Split-plots, repeated measures, factorial, nonlinear regression

    18. ARS scientists after a successful Project Plan completion!

    19. Me You

    20. Contact Information Debra Palmquist (Deb) MWA Biometrician 1815 N. University Street Peoria, IL 61604 Phone: (309) 681-6587 Email: deb.palmquist@ars.usda.gov

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