1) Why experiments need to be designed
- Very simple, need common sense, biological insight and careful planning
- Myth 1: It does not matter how you collect your data, there will always be a statistical way that will allow you to analyze them (Independent data points and other assumptions)
- Myth 2: If you collect lots of data something interesting will come out, and you will be able to detect even very subtle effects (quantity of data is no substitute for quality)
2) The cost of poor design
- Time and money (limited return if any on the efforts and resources invested)
- Ethical issues (I can not emphasize too strongly that while wasting time and energy on badly designed experiments is foolish, causing more human or animal suffering or more disturbance to an ecosystem than is absolutely necessary is inexcusable. They can be avoided by careful consideration of how your experiment is designed)
3) Relationship between experimental design and statistics
- It is essential that you think about statistics that you will use to analyze your data before collecting them
- Every statistical test will have slightly different assumptions about the sort of data they require or the sort of hypothesis that they can test.
- Designing experiments is as much about learning to think scientifically as it is about the mechanics of the statistics that are used to analyze the data. It is about having confidence in your data
- Experimental design is about the biology of the system, and that is why the best people to devise biological experiments are biologists themselves
4) Why correct experimental design is important to biologists
- The two major goals of designing experiments are to minimize random variation and account for confounding (extrinsic) factors.
- Random variation (Between-individual variation, within-treatment variation or noise) quantifies the extent to which individuals in our sample differ from each other.
- Random variation is everywhere in biology but things are different in other branches of science (physics and weight of electrons).
- Confounding (extrinsic) factors: If we want to understand the effect of one factor (A) on another factor (B), however, our ability to do this can be undermined if B is also influenced by another factor (C) which is the confounding factor
- Example: Effect of sunlight on the weight of a certain fish when temperature is closely linked to sunlight and also affect the weight of the fish.
1) We cannot be a good biologists (biotechnologists) without understanding the basics of experimental design
2) The basics of experimental design amount to a small number of simple rules, without the need for complex mathematics
3) If you design poor experiment, then you will pay in time and resources wasted
4) More important than time and resources is the need to reduce suffering to animals or humans or disturbance to an ecosystem