Experimental Design and Data Analysis Workshop

Presenters:

Scot Nelson

Jim Silva

Halina Zaleski

Materials Developed By:

Jim Silva

James Brewbaker

Halina Zaleski

Scot Nelson

- Objectives of Workshop

a. Scientific Method, Steps in Conducting a Research Experiment

a. Distributions

b. Descriptive statistics

c. T-test and confidence limits

- Basic Experimental Design Concepts

a. Basic Experimental Designs

2. Completely randomized design

4. Randomized complete block design

5. Blocking

6. Latin square

7. Exercise

b. Hypothesis Testing

1. Defining the question

2. Defining the objective(s)

3. Choosing a treatment design, Factorials

1. Number of samples

a. Data collection, entry and management

b. Avoiding bias, double-blind studies

c. Checking for normality, scatter plots, outliers

- Basic Statistical Analysis Methods

a. F test, F Table, F Calculator

d. Replication

e. Correlation

g. Linear regression example: Part 1, Part 2

- Data Presentation and Interpretation

a. Mean __+__ standard error

b. Mean comparisons

c. Charts and graphs

Learning Outcomes

- Objectives

Able to list the steps in conducting an experiment

- Review of Basic Statistical Concepts

Able to sketch a normal distribution

Able to calculate descriptive statistics

Able to perform t-tests and calculate confidence limits

Able to correctly use basic statistical terminology

- Basic Experimental Design Concepts

Able to correctly select/identify an experimental unit

Able to design and install a CRD

Able to design and install an RCBD

Aware of other designs (LS, SP)

Able to formulate an hypothesis

Able to design treatments to test:

- discrete variables (eg varieties)

- dose response

- factorials and interactions

Able to calculate the number of replicates needed

Able to select and follow a sampling design

Able to determine number of samples needed

Installing experiments

Able to install an experiment in the field

Able to install an experiment in animal pens

- Basic Data Management Concepts

Aware of bias and ways to limit it

Able to test data for normality and outliers

Able to prepare a data collection form (including environmental measurements)

- Basic Statistical Analysis Methods

Know how F test relates to t-test

Able to write out sources of variation and formula for degrees of freedom for CRD, RCBD and LS

Able to divide treatment SS into single degree of freedom comparisons

Able to use a multiple range test

Aware of assumptions in ANOVA and regression

Know difference between correlation and regression

- Data Presentation and Interpretation

Able to check for normality of data

Able to assign superscripts to means

Able to present data means, SEs and tests of significance in tables and graphs