This document discusses experimental design and statistical power considerations for swine experimentation. It begins by explaining the importance of valid and precise animal experiments for developing new feeds and feeding standards. It then describes common experimental designs like completely randomized design and randomized complete block design. The document emphasizes that sufficient replication is needed within experiments to maximize statistical power. It also stresses the importance of performing a power analysis to determine adequate sample sizes and avoid type II errors. The document concludes by noting that power analyses are vital for efficient experimental planning and limiting unnecessary replications in swine research.

1. EXPERIMENTAL DESIGN AND STATISTICAL POWER IN SWINE
EXPERIMENTATION
BY
KAREEM, DAMILOLA UTHMAN
DEPARTMENTOF ANIMALNUTRITION
COLLEGEOF ANIMALSCIENCEANDLIVESTOCKPRODUCTION
FEDERALUNIVERSITYOF AGRICULTUREABEOKUTA.
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2. INTRODUCTION
 Experimental researches are the key studies for the development of new
feeds, feeding regimes and feeding standards that brings about improvement
in animal nutrition
 Animal experiments should inform decisions about what treatments should be
taken forward in trials only if their results are valid and precise. Biased or
imprecise results from animal experiments may result in the testing of
biologically inert or other substances in animal trials, thus wasting time and
the limited available resources without obtaining favourable results (Roberts
et al., 2002).
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3. TYPES OF EXPERIMENT
 Confirmatory experiments: This experiment is aimed at testing one
or more hypothesis. For example, an experiment may be set up to
investigate whether diet A is associated with greater performance
than diet B.
 Exploratory experiments: This experiment looks at producing data
that may be important for the generation of hypothesis to be
tested.
However, in many times both confirmatory and exploratory
experiments are overlapped in the same study (Johnson and
Besselsen, 2002).
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4. PREREQUISITES TO CARRYING OUT AN ANIMAL EXPERIMENTATION
 All experiments should be presented in a way that allows other researchers to
repeat it elsewhere.
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5. EXPERIMENTAL DESIGNS
Primary designs in swine experimentation are;
 CRD
 RCBD
 Factorial designs
 Latin square
Other designs include;
 Graeco-latin square
 Cross-over design etc
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6. NB: No matter what design is used, it is important to balance studies by having equal repli-
cation of each treatment factor to maximize the power available to detect treatment
differences.
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7. FACTORS TO CONSIDER IN DESIGNING AN ANIMAL EXPERIMENT
Number of animals
Pilot studies
Randomization
Blinding
Control groups
Type of variables
Statistical methods
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8. REPLICATION
Replication according to Aaron and Hays (2004) refers to the assignment of more
than one experimental unit to the same treatment. Each replication is said to be an
independent observation; thus, each replication involves a different experimental
unit
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9. REPLICATION
Replication refers to the assignment of more than one experimental unit to the
same treatment. Each replication is said to be an independent observation; thus,
each replication involves a different experimental unit.
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10. REPLICATION VS REPEATED MEASURES
If a treatment is assigned at random to a particular entity or
experimental unit of the same moment and location, then it is
considered a genuine replication. However, if the same animal is
measured several times, either at different locations or at different
moments in time and a treatment is assigned to the animal as a whole,
then these measurements are repeated measures and not replications.10

11. EXPERIMENTAL/STATISTICAL POWER IN SWINE EXPERIMENTATION
Most researchers are primarily concerned with Type I error (α), the probability that they will declare
a significant difference when none really exists (reject the null hypothesis when it is true). However,
researchers should more often be concerned with another type of error; Type II error (β), which
occurs when something is not declared different when it really is (fail to reject the null hypothesis
when it is false). Answers to typical questions that swine nutritionists ask are more dependent on
Type II error than Type I (Bedford et al., 2016). In order to avoiding this, there is a need to carry out
a power analysis as it reduces error and avoid under or overestimation of experimental animals.
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12. EXPERIMENTAL/STATISTICAL POWER IN SWINE EXPERIMENTATION
Statistical power can be defined as the probability of rejecting the null hypothesis while the alternative
hypothesis is true. If a researcher knows that the statistics in the study follow a Z or standard normal
distribution, there are two parameters that he/she needs to estimate, the population mean (μ) and the
population variance (σ2).
Z=
Χ−𝝁
𝝈
Power = 1-β
Statistical power is positively correlated with the sample size, which means that given the level of the other
factors, a larger sample size gives greater power
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13. PURPOSES OF STATISTICAL POWER
In research, statistical power is generally calculated for two purposes:
 It can be calculated before data collection based on information from
previous research to decide the sample size needed for the study.
 It can also be calculated after data analysis. It usually happens when
the result turns out to be non-significant. In this case, statistical
power is calculated to verify whether the non-significant result is due
to really no relation in the sample or due to a lack of statistical power.
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14. STATISTICAL POWER CALCULATION IN SWINE EXPERIMENT
Power calculations can be made during either the planning or the analysis stage of an experiment. In either
stage, essential information includes;
 significant level,
 size of the difference or effect to be detected,
 power to detect the effect (most researchers use 80%)
 variation in response, and
 number of replications or sample size
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15. METHODS EMPLOYED IN PERFORMING POWER ANALYSIS
Manual method
Illustrating a researcher that wants to calculate the sample size needed for a study.
Given that the researcher has the null hypothesis that μ=μ0 and alternative
hypothesis that μ=μ1≠ μ0, and that the population variance is known as σ2. Also,
(s)he knows that (s)he wants to reject the null hypothesis at a significant level of α
which gives a corresponding Z score, called it Zα/2. Therefore, the power function
will be;
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16. METHODS EMPLOYED IN PERFORMING POWER ANALYSIS
𝐏{𝐙 > 𝐙𝛂/ 𝟐 𝐨𝐫𝐙 < −𝐙 𝛂/ 𝟐|
𝛍 𝟏} = 𝟏 − 𝚽[𝐙𝛂/ 𝟐 − (𝛍 𝟏 − 𝛍 𝟎)/(𝛔/𝐧)] + 𝚽[−𝐙 𝛂/ 𝟐 − (𝛍 𝟏 − 𝛍 𝟎)/(𝛔/𝐧)]
i.e. 𝐏{𝐙𝛂/ 𝟐 < 𝐙 < −𝐙 𝛂/ 𝟐|
𝛍 𝟏} = 𝟏 − 𝚽[𝐙𝛂/ 𝟐 − (𝛍 𝟏 − 𝛍 𝟎)/(𝛔/𝐧)] + 𝚽[−𝐙 𝛂/ 𝟐 − (𝛍 𝟏 − 𝛍 𝟎)/(𝛔/𝐧)]
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17. METHODS EMPLOYED IN PERFORMING POWER ANALYSIS
Computer method
 Statistical packages (SAS, Genstat etc.)
 PowerAndSampleSize.com (tests1 mean, comparing 2 or more means, testing 1
proportion, comparing 2 or more proportions, testing odds ratios, and two 1-
sample tests (normal and binomial-based))
 power/sample-size calculator by Russel Lenth (tests of means (one or two
samples), tests of proportions (one or two samples), linear regression, generic
chi-square and Poisson tests, and an amazing variety of ANOVAs -- 1-, 2-, and 3-
way; randomized complete-block; Latin and Greco-Latin squares; factorial
designs; crossover design; split-plot; split-split etc)
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18. CONCLUSION
There are many possible interpretations of experimental designs, but it is the
inference from statistical analyses that is really important for researchers. The
researcher’s goals, and especially the degree of precision deemed necessary, are
particularly important when choosing how many animals should be used; should
more than one be put into each treatment?, how many replicates should be used
for each treatment,? etc. So, in this context, the planning of the experimental
design via power analysis calculation is thereby vital, as this will help in curbing
unnecessary replications and resource wastages in swine experimentation.
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19. SELECTED REFERENCES
 Aaron D. K. and Hays V. W. 2004. How many pigs? Statistical power considerations in swine
nutrition experiments. J. Anim. Sci. 2004. 82(E. Suppl.): E245–E254
 Aguilar-Nascimento J.E. 2005. Fundamental steps in experimental design for animal studies. Acta
Cirúrgica Brasileira - Vol 20 (1)
 Lenth, R. V. 2001. “Some Practical Guidelines for Effective Sample Size Determination,” The
American Statistician, 55, 187-193.
 Lenth, R. V. 2006. Java Applets for Power and Sample Size [Computer software]. Retrieved on 29th
April, 2019 from http://www.stat.uiowa.edu/~rlenth/Power.
 Pound P, Ebrahim S, Sandercock P, Bracken MB, Roberts I. 2004. Reviewing Animal Trials
Systematically (RATS) Group. Where is the evidence that animal research benefits humans? Br Med
J.. 328:514-7.
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20. THANKS FOR
YOUR ATTENTION
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