1. Experimental Simulating Program
ESP
Experimental Simulating Program
USER GUIDE
Prepared by:
Dr. Ricaro V. Nunes, Universidade Estadual do Oeste do Parana
Dr. Gene M. Pesti, University of Georgia
Msc. Jomara Broch, Universidade Estadual dp Oeste do Parana
Version 1.0

2. Determining experimental power for planning purposes:
Introduction
Determining experimental power for planning purposes:
By it's very nature is a risky business;
Experiments are planned because we don't know what the results will be!
In planning, we hope that the means and variances of control and treated groups will be similar to previous experiments;
There is always the possibility that any imposed treatments will affect individuals differently, changing the variation between treatment groups.

3. Experimental errors often occur even before field trials:
Introduction
Experimental errors often occur even before field trials:
One of the main mistakes made may be related to the number of repetitions that we will be used;
To assist in the proper choice of the minimum number of replicates per treatment in a given assay:
- a spreadsheet has been developed ;
Remember this spreadsheet is a tool to assist in choosing the ideal number of replicates;
The results will not always be satisfactory because of variation between experiments

4. In order to use this spreadsheet:
We must carry out a literature review regarding the purpose of the work being proposed;
Identify the following points of each parameter that will be measured:
- Mean and Standard deviation of the Treatment Control (TC)
- Difference expected between the means of the CT and the Group to be studied.

5. For Example the Mean and Standard Deviation for Broiler Growth Experiment
Average weight at 42 days of age: 2950 grams
Standard deviation: ± 115
Expected mean difference: 5%

6. IT IS UNDERSTOOD - Reference or Control treatment as: - Standard
It is important to emphasize that the conditions in the experiments predicted from are similar to the conditions in the facilities to be used;
- After simulating some experiments, we will perform some tests.

7. Home page
Introduction the E S P
Instructions to User
Simulations:
6 replicates
10 replicates
20 replicates

9. Instructions for the User: For example the data

10. Body Weight Simulation
You can simulate using 6, 10 or 20 replicates

11. Only enter the values in the cells in black
Body Weight Simulation

12. Maximum desired difference between means Simulated values appear
Data to Be Entered
Average (grams)
Standard deviation
Simulated values appear
Tests are performed

13. Output Data CV of the Average and SD Mean Difference
Mean, SD and CV 5% different from Control
20 Simulated data Treatment (Control)
20 Simulated data Treatment (Test)
Actual Mean Difference about 5%

14. Output Data Each row is an experiment simulation (20)
Randomly generated data for each replicate (six)
Mean and SEM of each simulated treatment

15. Output Data
Difference (P) observed between the treatments by the t-test, with 6 replicates
Probability <0.05 and <0.01
Total Number of significant simulations
Percentage of finding difference between two treatments according to the input data

16. With 6 replicates With 10 replicates
We would have 35% different at P<0.05 and 5% at P<0.01
With 20 simulated experiments we could find:
With 10 replicates
With P<0.05 ~ 65% different
With P<0.01 about 40% different

17. With 20 replicates
In this example, we can find almost 100% difference between two treatments with 20 replicates

18. Important points ❶ With ESP students can see how changing Means
Standard Deviation
Number of Replicates
Influence Experimental outcomes and Statistical Inference