Designing Experiments

The assumptions of ANOVA and 1-factor GLM, focusing on random sampling and independence. Learn about assigning samples to treatments through random and grouped approaches, and the importance of randomness in experimental design. Discover how to spot pseudo-replication in ANOVA and the significance of sample selection in ensuring generalizability.

• Designing Experiments
• Assumptions
• Sampling Approaches
• Experimental Design
• ANOVA

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Uploaded on Feb 24, 2025 | 0 Views

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1. Designing Experiments 1-Factor, 2 or more treatments 1

2. Assumptions of ANOVA / 1-factor GLM 1. Random sampling (allocation to treatments) 2. Independence 2

3. Assumptions of ANOVA / 1-factor GLM 1. Random sampling (allocation to treatments) 3

4. Assign samples to treatments: Approach 1 Randomly assign samples to treatments Treatment 1 Treatment 2 Treatment 3 4 Harvest mouse by Harlequeen CC BY 2.0

5. Assign samples to treatments: Approach 1 Randomly assign samples to treatments Treatment 1 Treatment 2 Treatment 3 Why is random important? THINK 5

6. Assign samples to treatments: Approach 2 Group samples by character Randomly assign samples within group to treatments Treatment 2 Treatment 1 Treatment 3 6

7. Assign samples to treatments: Approach 2 Group samples by character Randomly assign samples within group to treatments 7 http://365caws.blogspot.com/2016/07/anticlea-occidentalis-mountainbells.html

8. Select samples Randomly sample population of interest. (Helps ensure generality, but not an assumption of the test.) 8

9. Assumptions of ANOVA / 1-factor GLM 2. Independence 9

10. Spotting pseudo-replication in ANOVA Typical NOT pseudo-replication A B C D E F G A B C D E F Treatment 1 Treatment 2 H I J K L M N A B C D E F Treatment 3 O P Q R S T A B C D E F Non-independent measurements share a letter 10

11. Spotting pseudo-replication in ANOVA Pseudo-replication NOT pseudo-replication A A B B C C A B C D E F Treatment 1 Treatment 2 D D E E F F A B C D E F Treatment 3 G G H H I I A B C D E F Non-independent measurements share a letter 11

12. Sample size 12

13. Minimal requirement 2 or more independent samples per treatment Treatment 1 How many samples? Treatment 2 Use power analysis Treatment 3 13

14. Sample size Use power analysis 14

15. How many treatments? 15

16. How many treatments? Depends on what you want to test Which, of the two experimental designs, below, will have greater power? THINK. Treatment 1 Treatment 2 Treatment 3 vs. Treatment 1 Treatment 2 Treatment 3 Treatment 4 16

17. How many treatments? Depends on what you want to test For a given amount of resources, power decreases with treatment number. Treatment 1 Treatment 2 Treatment 3 vs. Treatment 1 Treatment 2 Treatment 3 Treatment 4 17

18. Basic experimental design 1. Random sampling (allocation to treatments) 2. Independence 1. Sample size 1. Number of treatments 18

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