Special Topics 504: Practical Methods in Analyzing Animal Science Experiments
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Special Topics 504: Practical Methods in Analyzing Animal Science Experiments

The course is:

Designed to help familiarize you with the most common methods used in Animal Science to set-up and analyze experimental data. Hopefully you will become more “procedurally aware”. This is to say that you will be able to recognize the conditions necessary to affect scientifically sound experiments and carry out a valid analyses of those experimental data.

The course is not:

Intended to be an exhaustive overview of all possible methods and we will not derive all of the variables in each method with mathematical proofs. The course will also not delve into the philosophy of scientific inference and procedure and we’ll not talk about such things as inductive vs. deductive reasoning etc.


Special Topics 504: Practical Methods in Analyzing Animal Science Experiments

You should already be familiar with:

Types of data: nominal, ordinal, interval and ratio

Frequency counts vs. Scores

Continuous vs. Discrete data

Descriptive statistics - means, modes, medians, variance, standard deviation, standard error, etc.

Independence of variables, repeated and independent measures

Parametric vs. Non-parametric data


Special Topics 504: Practical Methods in Analyzing Animal Science Experiments

Differences between Parametric and Non-parametric Data

Parametric Non-parametric

Typical Data Ratio or Interval Ordinal or Nominal

Assumed Distribution Normal Any

Assumed Variance Homogenous Any

Data Relationships Independent Any

Central Measures Mean Median

Usefulness Varied conclusions Simple; Less affected by outliers


Special Topics 504: Practical Methods in Analyzing Animal Science Experiments

Testing Parametric vs. Non-parametric Data

Parametric Non-parametric

Independent Measures t-test Mann-Whitney test

(of 2 groups)

Independent Measures 1-way ANOVA Kruskal-Wallis test

(of > 2 groups)

Repeated Measures Matched-pair , t-test Wilcoxon test

(with 2 conditions)

Repeated Measures 1-way, repeated measures Friedman’s test

(>2 conditions) ANOVA

Correlation Pearson Correlation Spearman Rank test


Special Topics 504: Practical Methods in Analyzing Animal Science Experiments

Testing Two Means

Student’s t-Test

This parametric test indicates the separation of two sets of measurements. It is a test to determine if two sets of measures are actually different and an experimental effect has been demonstrated. Typically this test is set up with a null hypothesis that indicates two measures (such as population or sample means) are the same.

Ho: μ1 = μ2 or x̄1 = x̄2

“Two groups of dairy cows produce the same amount of milk on two different diets”


Special Topics 504: Practical Methods in Analyzing Animal Science Experiments

Student’s t-Test

The test assumes a normal distribution of the data, that the underlying variances are equal and there is random assignment of the measures.

Actually, the t-distribution is the same as the normal (Z) distribution when n = ∞

With small sample sizes, the t distribution is “leptokurtic” (which is often the case with

biological samples).


Special Topics 504: Practical Methods in Analyzing Animal Science Experiments

Student’s t-Test

The value of “t” or the “t statistic” is calculated :

t = experimental effect / variability

or

t = the difference in group means / SE of difference between group means

t = x̄ - μ

S

Looks a lot like a “Z” score!


Special Topics 504: Practical Methods in Analyzing Animal Science Experiments

Student’s t-Test

There are two basic types of t-tests: Independent measures – unmatched samples

Matched-pair measures – samples are in pairs

Independent Measures: two – sample test

Tan k 1: fish grown without probiotic treatment on a normal diet.

Tan k 2: fish grown with probiotic treatment on a normal diet.

Ho: μ1 = μ2 or x̄1 = x̄2

All we need to generate a tscore and test whether fish in Tank 2 have grown differently than Tank 1 is an average of their weights (a mean) and the spread around that average (SD).


Special Topics 504: Practical Methods in Analyzing Animal Science Experiments

Homogeneity of Variances

An underlying assumption of the t-test and ANOVAs is that,

σ21 = σ22

This is also an assumption for tests that examine more than two variances. So given that , we can test for the “Homogeneity of Variances”

Ho: σ21 = σ22 = … = σ2k (where k is the number of samples)

Ha: σ21 ≠ σ22≠ …

When variances are equal they are said to be “Homoscedastic”.


Special Topics 504: Practical Methods in Analyzing Animal Science Experiments

A Simplified Test for Homogeneity of Variances

Divide the largest variance by the smallest variance to obtain an F-ratio.

If the F-ratio is less than the value in the appropriate cell, you can assume the variances are homogeneous.


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